developing transport performance measures for construction
TRANSCRIPT
Developing transport
performance measures
for Construction Logistic
Solutions
Master Thesis
Author: Farah Naz
Supervisor: Anna Fredriksson
Examiner: Helena Forslund
Term: VT19
Subject: Degree Project in Logistics
Level: Master (2nd Level)
Course code: 5FE04E
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Abstract
Purpose
The purpose of the study is to identify transport related performance measure
within construction logistics in order to evaluate construction logistics
solution. The aim is also to verify identified transport performance measures
by available empirical data from both cases i.e. Case 1 and Case 2.
Methodology
This study is exploratory case study with qualitative research method. The
research approach of this study is both deductive as well as inductive. Data has
been collected from literature review, semi structured interview, focus group
discussion and empirical data.
Research question (RQ)
RQ1 aims at identifying transport related performance measures and then
classifying them according to terminal, checkpoint and their respective
construction sites? The answer to this question lies in Figure 17 and 18.
RQ2 refers to what kind of data is needed to measure identified transport
performance measures. The answer to this question lies in the analysis of RQ2.
RQ3 is related to what empirical data is available at construction logistics end.
The answer to this RQ3 is that mostly the” duration of activities” has been
found within both cases empirical data which seems to be insufficient to
calculate identified performance measures in RQ3.
RQ4 the aim of RQ4 is to find the gap between needed and available data. And
the answer to this RQ can be found in Table 35.
Conclusion
Theoretical and practical case discussion of Case 1and Case 2 has given an in
-depth view regarding the phenomena of construction logistics solutions. This
paper will help in creating awareness among developer and main contractors
regarding the benefit of construction logistics solution.
Key words
Construction Industry, Construction Logistics, Construction Logistics
Solution, Transport Performance Measures
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Acknowledgments
First of all, I want to express my profound gratitude to my supervisor, Anna
Fredriksson, for proposing this topic and for her continuous support and
guidance throughout the process of research and authoring this thesis. I am
also grateful to my examiner Helena Forslund for being very helpful and
providing valuable information and suggestions at each seminar. Besides this,
I also want to thank all the opponents in my class for their positive feedback
during the seminars. I also want to appreciate Linnaeus University for giving
me this opportunity to write this thesis. Finally, a quick thank you to my friend
Prasannjeet Singh for his constructive criticism and also for proofreading the
manuscript.
This research could not have been written without the generous assistance of
my friends and family who always encouraged and supported me throughout
my authorial journey. To all of you, I extend my deep appreciation.
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Table of contents
1 Introduction 1
1.1 Background 1
1.2 Problem discussion 3
1.3 Purpose of the study and research questions 5
1.4 Conceptual model for research question 1 6
1.5 Originality/Value of paper and Limitations 7
1.6 Thesis outline 8
2 Methodology 10
2.1 Methodology outline 10
2.2 Research purpose 11
2.3 Research design 13
2.4 Research Method 14
2.5 Research approach 15
2.6 Population and Sampling 18
2.7 Research process 20
2.8 Data collection methods 21
2.8.1 Literature review 21
2.8.2 Semi Structured Interviews 23
2.8.3 Focus group 24
2.8.4 Documentation and statistics 26
2.9 Data Analysis 26
2.10 Research Quality 27
2.10.1 Reliability 27
2.10.2 Validity 28
2.11 Ethical considerations 28
3 Contextual background of the study 29
3.1 Basics of construction industry 30
3.1.1 Construction Process 31
3.1.2 Construction Flows 32
3.1.3 Construction site organization 38
3.2 Significance of construction industry 38
3.3 Supply Chain Management in Construction Industry 40
4 Framework for RQ1 42
4.1 Theory 42
4.1.1 Construction logistics 42
4.1.2 Construction logistics solution 44
4.1.3 Construction site and construction site logistics 49
4.1.4 Performance measures and their importance 51
4.2 Frame of reference for semi structured interviews 60
4.3 Empirical findings from semi structured interviews 63
4.3.1 Case 1-Terminal 64
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4.3.2 Case 2-Checkpoint 65
4.4 Combining findings from theory and semi structured interviews 67
4.5 Frame of reference for focus group protocol 69
4.6 Empirical finding from focus group discussion 73
4.6.1 Case 1-Terminal 73
4.6.2 Case 2-Checkpoint 75
4.7 Conceptual model of the study 75
4.8 Analysis 77
4.8.1 Explanation of the consolidated table 78
4.8.2 Other Objectives 80
5 Framework for RQ2 83
5.1 Theory 83
5.1.1 Objective: Effective transport planning 83
5.1.2 Objective: Reduction in transportation time 87
5.1.3 Objective: Transport cost minimization 87
5.1.4 Objective: To achieve environmental sustainability 89
5.1.5 Objective: To ensure security 90
5.1.6 Objective: To ensure safety 90
5.2 Frame of reference 91
5.3 Empirical findings from both Case 1 and Case 2 91
5.4 Analysis 92
6 Framework for RQ3 98
6.1 Empirical data from Case 1 Terminal 98
6.1.1 Analysis 99
6.2 Empirical data from Case 2 Checkpoint 108
6.2.1 Analysis 110
7 Framework for RQ4 113
7.1 Analysis 114
8 Overall analysis 115
9 Conclusion 117
9.1 Further study 118
10 Reference list 119
11 Appendix 137
11.1 Semi structured interview guide for RQ1 137
11.2 Protocol for focus group 139
11.3 Interview guide for research question 2 140
11.4 Excerpts of semi structure interview for RQ2 141
11.5 Snapshot of original data by Case 1-Terminal 142
11.6 MATLAB code for the analysis of Case-1 Terminal Data 144
11.7 MATLAB code for the analysis of Case-2 Terminal Data 150
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Table of Figures
Figure 1: Conceptual model of research question 1 (Own illustration) ................................... 7
Figure 2: Dependencies among research questions (Own illustration) .................................... 9
Figure 3: Framework for research questions (Own illustration) ............................................ 10
Figure 4: Deductive Inductive approach (Adopted) .............................................................. 17
Figure 5: Abductive research approach (Adopted) ................................................................ 18
Figure 6: Research process of this study (Own illustration) .................................................. 20
Figure 7: Process for shortlisting publications (Own illustration) ......................................... 22
Figure 8: Contextual background of the study (Own illustration) ......................................... 30
Figure 9: Different phases of construction process (Own illustration) .................................. 32
Figure 10: Material flow in construction project (Adopted) .................................................. 35
Figure 11: Information flow in general construction project (Adopted) ................................ 37
Figure 12: Traditional construction supply chain (Adopted) ................................................. 41
Figure 13: Pictorial explanation for RQ1 theory (Own illustration) ...................................... 42
Figure 14: Shipment consolidation at Terminal (Adopted) ................................................... 47
Figure 15: Modelling of Checkpoint (Own illustration) ........................................................ 49
Figure 16: Activities occur at construction site (Own illustration) ........................................ 51
Figure 17: Conceptual model after theory and empiry (Own illustration) ............................. 76
Figure 18: Consolidated conceptual model after analysis (Own illustration) ........................ 82
Figure 19: A sample path of the vehicle (Own illustration) .................................................. 84
Figure 20: Saving in distance by terminal (Adopted) ............................................................ 85
Figure 21: Fast Companies .................................................................................................. 104
Figure 22: Slow Companies................................................................................................. 104
Figure 23: Most inefficient orders ....................................................................................... 105
Figure 24: Most efficient orders .......................................................................................... 105
Figure 25: Number of Deliveries and Time between Deliveries ......................................... 107
Figure 26: Lognet data sheet................................................................................................ 108
Figure 27: Case-2 Empirical Data ....................................................................................... 109
Figure 28: Standard Deviation Graph for Case-2 ................................................................ 111
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Table of Tables
Table 1: Methodology selection (Own illustration) ............................................................... 11
Table 2: Various research purposes (Adopted) ...................................................................... 13
Table 3: Deductive and Inductive approach (Adopted) ......................................................... 17
Table 4: Sampling technique (Adopted) ................................................................................ 19
Table 5: Key words used for searching articles (Own illustration)........................................ 22
Table 6: Semi structured interview cases (Own illustration) ................................................. 24
Table 7: Focus group details (Own illustration) .................................................................... 26
Table 8: Physical flows steps (Adopted) ............................................................................... 33
Table 9: Information flow steps in construction project (Adopted) ....................................... 37
Table 10: Four roles of supply chain management in construction (Adopted) ...................... 41
Table 11: Comparison between terminal and checkpoint (Adopted) ..................................... 49
Table 12: Performance measures related to effective transport planning (Adopted) ............. 56
Table 13: Performance measure for time minimization (Adopted) ....................................... 57
Table 14: Performance measures related to cost minimization (Adopted) ............................ 58
Table 15: Performance measures related to environmental sustainability (Adopted) ............ 59
Table 16: Security related performance measures (Adopted) ................................................ 59
Table 17: Safety performance measures (Adopted) ............................................................... 60
Table 18: Operationalization for semi structured interviews (Own illustration) ................... 63
Table 19: Performance measures highlighted by Case 1 during interview (Own illustration)
............................................................................................................................................... 65
Table 20: Performance measures highlighted by Case 2 during interview (Own illustration)
............................................................................................................................................... 67
Table 21: Focus group protocol (Own illustration) ............................................................... 68
Table 22: Operationalization of focus group protocol (Own illustration) ............................. 73
Table 23: Additional performance measures got highlighted during focus group discussion-
Case 1 (Own illustration) ....................................................................................................... 74
Table 24: Additional performance measures got highlighted during focus group discussion-
Case 2 (Own illustration) ....................................................................................................... 75
Table 25: Consolidated performance measures (Own illustration) ........................................ 78
Table 26: Performance measures to be studied under RQ2 (Own illustration)...................... 83
Table 27: Snapshot of Case 1 (Terminal data translated in English) ..................................... 99
Table 28: Processed table for Efficient/Inefficient Activities .............................................. 101
Table 29: Efficient Orders ................................................................................................... 102
Table 30: Inefficient Orders................................................................................................. 102
Table 31: Slow and Fast Companies.................................................................................... 103
Table 32: Time difference between deliveries ..................................................................... 106
Table 33: Delivery frequency .............................................................................................. 107
Table 34: Case-2 Sort By Company .................................................................................... 110
Table 35: Comparison between required and available data ............................................... 115
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1 Introduction
This chapter introduces the research area of this study and tries to define the
important constructs briefly. This is then followed by the problem discussion,
purpose of the study and four research questions. After this, the chapter
introduces the conceptual model of the study and originality/value of the paper
along with limitations. The study outline is also mentioned in this chapter end
to follow subsequent chapters of this thesis clearly.
1.1 Background
Construction industry not only plays an important role in economic
development but also in the lives of almost every individual (Sears et al.,
(2015). Razak Bin Ibrahim et al. (2010) define “construction” as a complex
production of physical infrastructure by the co-operation of temporary teams.
According to United Nations International Standard of Industrial
Classification (ISIC) (2008) “construction industry” is defined as an industry
consisting of firms responsible for building structures such as offices,
hospitals, airports, shopping centers, housing, factories etc. as well as civil
engineering such as infrastructure for water supply, irrigation, transportation,
power generation and the likes. As per Rangelova (2015), construction
industry is complex in nature due to the involvement of multiple stakeholders
and wide connections with other areas such as manufacturing, material
handling, energy, finance, labor, equipment etc. Razak Bin Ibrahim et al.
(2010) are of the view that construction industry owns fundamental position
in converting the aspirations and needs of people into reality by executing
various construction development projects.
In accordance with Andersson and Nilsson (2018), the Swedish construction
industry has turnover of 639 billion SEK, which can be linked to an increase
of 71% over the last decade. They further suggest that Sweden is the fastest
urbanizing country across all of Europe. According to Gothenburg Port
Authority (2016), the rate of construction in Gothenburg and other cities in
Sweden is higher than ever. Due to urbanization, there are many urban
construction projects going on in Sweden (Janné, 2018). “Urban construction
projects” are considered as short-term network of teams in a city premises.
These temporary organizations consist of many phases and in each phase many
participants take part resulting in complexity of construction projects (Janné,
2018; Janné and Fredriksson, 2018).
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Vrijhoef and Koskela (2000) explain that many supply chain management
initiatives have been taken within construction industry in the end of 1980’s
in order to enhance internal and external efficiency, minimizing waste and to
add value across entire construction supply chain. They further suggest that
the major initiative of Supply Chain Management (SCM) in construction has
been in the field of “logistics” which is defined as flow of materials, tools and
equipment from the point of release to the point of consumption. Fredriksson
(2018) defines “construction logistics” as providing construction site with
resources in the form of materials, machines and personnel in an efficient
manner along with managing resources efficiently on the construction site
itself as well as ensuring efficient recycling and waste management that
enables circular economy and durability. United Nation (ISIC) (2008) defines
“construction site” as a place where construction activities such as building,
repair, additions and alterations, erection of prefabricated structures etc. take
place.
According to Sullivan, Barthorpe and Robbins (2010), Matouzko and
Methanivesana (2012) and Janné (2018) the future of construction logistics
lies in construction logistics solutions. Janné (2018) defines “construction
logistics solution” as logistic solution applicable in construction projects in
order to coordinate material flows, thus resulting in less transport disruptions
and efficient construction. Sullivan et al. (2010) and Janné (2018) say that
dedicated construction logistic solutions are not widely adopted and is
considered as new phenomenon in the construction industry. Janné (2018)
suggests that there are two types of construction logistics solution i.e. terminal
and checkpoint. He further suggests that the main difference between the two
is that “terminal” co-ordinate deliveries to various construction sites thus
reducing the number of deliveries as well as number of times on-site personnel
has to receive and handle materials whereas “check point” focuses on just in
time deliveries as distinguished from following consolidation approach to
deliveries.
According to Ying, Tookey and Seadon (2018) “performance measures” are
widely used to evaluate the performance of any industry. Epstein and Rejc
(2005) suggest that “evaluation” is a systematic determination of merit, worth
and significance of something. They further suggest that it is an assessment to
determine the worth or fitness of any subject. According to Ying et al. (2018),
measurement of logistics is an important step to improve construction industry
performance. Velimirovic, Velimirovic and Stankovic (2011) suggest that
appropriate selection of metrics to be used for measuring is of great
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importance. They call these metrics as performance measures. They further
suggest that performance measures provide information about performance in
the past, performance as of now and the likely performance in the future.
Jahangirian et al. (2017) define “performance measure” as a quantifiable
metric that is used to gauge or compare performance in terms of achieving
strategic and operational goals. The terms “performance measures” and
“performance metrics” have been used synonymously in this study.
Minges (2017) suggests that transport management plays crucial role in
assuring construction logistics competitiveness. He further adds that
measurement and quantification of transport flows within construction
logistics is of prime importance for many researchers and practitioners. In
order to measure and quantify transport flows, Sutton and Austin (2015)
highlight the importance of data collection. According to them, “data
collection” is a process of gathering information empirically as well as
theoretically. By “empirically” they mean data collected by observation and
experience. They further suggest that in order to make informed decisions,
provide solutions to the complex problems, analyze new insights etc. it is
essential to play with the data. Surkis and Read (2015) defines “data” as facts
and statistics collected for the purpose of analysis and finding research results.
Jayasinghe, Sano and Nishiuchi (2015) refer “transport flow” as vehicle
movements or flow of vehicles as well as other activities such as loading,
unloading etc.
1.2 Problem discussion Due to fragmented nature and unwillingness to change, the construction
industry is often considered as backward, inefficient and poorly organized
(Janné, 2018). This has also been advocated by Fellow and Liu (2012),
Ekeskär and Rudberg (2016), Berden (2017), Dubois, Hulthén, Sundquist
(2019). Jensen (2017) suggests that construction industry has remained slow
in adopting the proven benefits of logistics and supply chain management.
Sullivan et al. (2010) suggest that every major industry such as manufacturing
(mainly automotive), retailing, shipping, third party logistics etc. is reaping
benefits from employing logistics whereas construction industry is incurring
heavy costs due to poor logistics management. They further suggest that
effective logistics management is critical for the success of current businesses,
primarily the ones that involve huge supplier networks and just in time
deliveries. According to Sullivan et al. (2010) market forces and labor relations
will ultimately have to trigger adoption of logistics by construction industry.
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Once this happen, the actors who are not able to adopt will not survive because
of high costs and waste levels and in this way even if they want to sustain, they
would not be able to continue their operations (Sullivan et al., 2010).
As per Ying et al. (2018) and Dubois et al. (2019) construction project involves
numerous transport related activities which account for 39-58% of total supply
chain costs. Sullivan et al. (2010) also suggest that transport is a major
component of supply chain which is responsible for delivering goods and
services to the construction site. Despite the fact that different industries (such
as manufacturing, retailing, shipping, third party logistics etc.) have achieved
out class performances, cost savings, efficiencies and that they have set various
examples of best practices by implementing logistics, a question still arises as
to why the construction industry is not widely adopting logistics and why is
this not becoming a reality? This reflects a research gap within the area of
construction logistics that besides knowing the benefits of implementing
logistics there is a lack of knowledge within the construction industry about
how these reductions, efficiencies, etc. can be achieved. For this, there is a
need for providing detailed advice to decision makers (such as developers,
contractors etc.) on how construction logistics can be controlled and
effectuated. As of now, decision makers do only have a vague idea of how
they can improve transport flows or how should they organize transport or
employ construction logistics, thus achieving maximum efficiency. This
implies that there is a need to really pinpoint and identify main performance
indicators of transport flows in order to control, manage and improve
construction logistics. This can be justified by Edwards Deming quote
mentioned by Lingard, Wakefield, Blismas (2013);
“If you can’t measure it, you can’t improve it”
(Edwards Deming, 1994)
As construction industry is not well organized in terms of data management
there is need to identify what data is available to evaluate performance and
where data is lacking. Lingard et al. (2013) suggest that Deming means that
one cannot know whether one is successful until success is defined and
tracked. They further suggest that with a clearly established measure for
success, one can quantify progress and adjust to achieve the desired outcomes.
This shows that construction industry is still at its initial stages regarding
transport flow and logistics performance measurements and reality is that very
little is known about construction specific logistics (Dubois, et al., 2019).
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1.3 Purpose of the study and research questions The purpose of the study is to identify transport related performance measures
within construction logistics and to see which performance measures are
relevant for terminal construction logistics solution, checkpoint construction
logistics solution and their respective construction site. As mentioned above,
terminal and checkpoint are two types of construction logistics solutions and
are also the focus of this study. These performance measures will be developed
while keeping in mind the common transport strategic objectives such as
effective transport planning, minimizing time for transportation, transportation
costs minimization, achieving environmental sustainability, ensuring safety
and security. Besides this, the study will also throw light upon the availability
of empirical data at construction logistics solutions end (i.e. terminal and
checkpoint) and also a reflection on what further data is needed in order to
calculate identified performance measures accurately. The aim of the study is
to create construction logistics awareness among multiple construction
stakeholders mainly developers and contractors who are the main decision-
making bodies in any construction project. The study will also put emphasis
on the need of organizing transport flows within the Swedish construction
industry.
Following research questions (RQs) have been formulated in order to fulfill
the purpose of the study;
Research Question 1
What performance measures can be used for the evaluation of construction
logistic solutions and their respective construction sites with respect to
transport flows?
Rationale behind RQ1: There are very few studies regarding the transport
related performance measures specifically within the context of construction
logistics. The focus of this research question is to identify transport related
performance measures within “construction logistics”. These identified
performance measures will then be used to evaluate the performance of
construction logistics solutions and their respective construction sites in terms
of transportation. Since construction logistic solution is a new phenomenon
within construction logistics so it is important to measure its performance in
terms of transport flows.
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Research Question 2
What kind of data is needed to measure identified transport related
performance measures in research question 1?
Rationale behind RQ2: The focus of this research question is to find out the
way how identified transport performance metrics in research question 1
within construction logistics can be measured quantitatively. The need of data
will be determined by identifying factors or variables that will collectively
form performance measures. Developing formulas and calculations will play
an important role in figuring out the data required to measure identified
transport performance metrics. In other words, RQ 2 is a detailed breakdown
of performance measures identified in RQ1 in the form of formulas and
calculations.
Research Question 3
What transport data is empirically available from construction logistic
solutions?
Rationale behind RQ3: The study aims at verifying the performance measures
identified in research question 1 in order to see that whether those
performance measures are applicable for construction logistic solution
evaluation or not. Therefore, the focus of this research question is to check
what data the cases under study are recording and is it available for cross
checking the identified transport related performance measures.
Research Question 4
What is the gap between required and available data to measure identified
transport related performance measures in research question 1?
Rationale behind RQ4: As the construction industry is not adequately
organized, the odds are high that there would be some missing data, in
addition to the fact that the data might not be present in a ready-to-use format.
The rationale behind this research question is to see what empirical data is
missing, what empirical data is further needed in addition to the available data
to calculate the identified performance measures accurately.
1.4 Conceptual model for research question 1
The conceptual model of research question 1, which is main and independent
research question, is shown in Figure 1 below, where two types of construction
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logistic solutions have been mentioned (terminal and checkpoint). The
transport flow has been shown with the help of line with arrows at both ends
representing transport going to construction logistics solution and construction
site and further coming back from construction site to construction logistics
solution. The focus of the paper is on measuring transport flows performance
(i.e. mainly vehicle movement, flow of vehicles, loading, unloading, material
handling activities etc.) only under the concept of construction logistics within
construction industry. The purpose of the RQ1 has been shown by mentioning
performance measures boxes on different construction logistics solution and
their respective construction sites. In checkpoint construction logistics
solution, transit (which is a route or a journey from one place to another) comes
before checkpoint whereas in terminal construction logistic solution, transit
starts when vehicle leaves the area around terminal and is referred as journey
between terminal and construction site.
Figure 1: Conceptual model of research question 1 (Own illustration)
1.5 Originality/Value of paper and Limitations The study will help in developing transport related performance measures
within construction logistics as very few studies have discussed transport
performance measures in the context of construction industry. This study will
Terminal
Construction Site
*Performance measures are related to transport flows only Transport Flows
Transport related performance measures to, from and within the construction site
Checkpoint
Performance Measures
Transit Performance
Measures
Transit Performance
Measures
Site Performance Measures
Site Performance Measures
Performance Measures
Construction Site
Construction Logistics Solution
Construction Logistics
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provide an overview that which performance measures belong to which
construction logistic solution. This will give further insight regarding
construction logistics solution evaluation (i.e. what performance measures are
similar among terminal, checkpoint and their respective construction site and
what varies). In this way, the study will contribute in improving the
performance of construction logistics solutions by letting developer and main
contractor know where to focus their efforts. The study will provide deeper
understanding to developers and main contractors regarding available versus
required data to quantify transport performance measures. This study will
guide the decision makers (i.e. developer and main contractor) that what
further data they should consider and enter into their database in order to
evaluate performance measure accurately.
Overall, the study will enhance knowledge for all the actors involved in the
construction project such as client/owner, sub-contractor, suppliers, designers
etc. but mainly developers and main contractor. In this way, the thesis will not
only contribute theoretically but practically as well by digging deep into the
data and acting as major step towards further establishment of logistics in the
future construction industry.
As very few studies have mentioned transport related performance measures
within construction logistics so for developing construction transport
performance measures other industries such as manufacturing, retailing, third
party logistics (TPL) have been consulted. Due to fragmented and unstructured
nature of construction industry, it is difficult to find relevant data for
calculating performance measures precisely. In addition to this, the data has
been stored in a very disorganized way thus requires so much time to make it
in a ready to use format. Besides this, the study has been conducted from the
perspective of construction industry so the findings of the study cannot be
generalized to other industries. Due to time constraint, the scope of the study
is limited to transportation performance measures only.
1.6 Thesis outline After introduction, the next chapter (chapter 2) will discuss methodology of
the thesis. This will include research purpose, research design, research
approach, data collection methods, research quality mainly. Then chapter 3
will discuss contextual background of the study. The aim of this chapter is to
develop basic knowledge about construction industry. In addition to this, the
chapter will throw light upon the significance of construction industry. The
role of supply chain management in the construction industry is also
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mentioned in this chapter. Following this chapter, all four research questions
formulated in introduction chapter (Chapter 1) will be discussed one by one
each having its own theory, frame of reference (operationalization), empiry
and analysis. Research question 1 will consider all sections i.e. theory, frame
of reference, empiry and analysis whereas research question 2 will use the
same frame of reference (operationalizations) as used by research question 1,
research questions 3 do not require theory and will only incorporate empiry
and analysis whereas research question 4 is all about analysis. The reason for
responding each research question separately is because of dependencies
among research questions. For example, research question 2 is dependent on
the findings of research question 1 which by itself is independent research
question. Likewise, research question 4 is dependent upon the findings of
research question 1, 2 and 3. Like research question 1, research question 3 is
also independent research question. The dependencies among research
questions can be understood by following Figure 2;
It can be seen from above diagram that RQ1 and RQ3 both are independent
research questions whereas RQ2 is dependent upon the findings of RQ1. RQ4
is also dependent upon the findings of RQ2, RQ3 and RQ1. After contextual
background chapter, all four research questions will be studied under their own
chapter and each chapter will have more or less all sections such as theory,
frame of reference (operationalization), empiry, analysis (where applicable).
The thesis structure regarding these research questions can be shown in the
following Figure 3. After this an overall analysis will be made following the
conclusion.
RQ1
RQ2
RQ3
RQ4
Figure 2: Dependencies among research questions (Own illustration)
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2 Methodology
This section discusses the methodological considerations that have been
employed in this dissertation. It begins with research purpose, design, method
and approach. After this population and sampling has been done. Then
research process for this study has been explained followed by data collection
methods and data analysis. The methodology chapter will be concluded with
research quality and ethical considerations.
2.1 Methodology outline The methodology outline is given in Table 1 below;
RQ1
Theory
Frame of reference
Empiry
Analysis
RQ2
Theory
Frame of reference
Empiry
Analysis
RQ3
Empiry
Analysis
RQ4
Analysis
Figure 3: Framework for research questions (Own illustration)
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Methodology Selected Methodology
Research purpose Exploratory
Research design Case study
Research method Qualitative
Research approach Deductive with traces of induction
Sampling Purposive
Research process Iterative
Data collection methods Literature review, individual and
group interview, focus group,
documents and statistics
Data analysis 4 types of analysis
1.Consolidation and grouping of
performance measures
2. Theory and personal knowledge
and experience
3. Data analysis by using MATLAB
and Excel
4. Based on the findings of RQ1,
RQ2 and RQ3
Research quality External validity, construct validity
and internal and external reliability
Ethical considerations Anonymous and informed
respondents
2.2 Research purpose
According to Yin (2018) research projects can be classified into three research
purposes i.e. exploratory, descriptive and explanatory. Saunders et al. (2016)
suggest that in addition to these three above mentioned research purposes there
are two more research purposes (i.e. evaluative and combined). Yin (2018) and
Saunders et al. (2016) suggest that “exploratory” research provides initial
groundwork for future research. They further suggest that in exploratory
studies the focus is on open questions starting with words such as “how” and
“what”. They further suggest that exploratory study makes researcher to
understand certain phenomena in detail, to seek new insights and to find out
what is happening by asking various questions. Yin (2018) suggests that
exploration can be done with literature research, a focus group discussion or
case studies. He further suggests that data from exploratory study is mostly
qualitative in nature.
“Descriptive” studies according to Saunders et al. (2016) describe people,
Table 1: Methodology selection (Own illustration)
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products and situations. They further suggest that descriptive studies are done
when there is need to describe characteristics and functions of an event. The
aim of descriptive study is to obtain complete and accurate information by
careful planning of research process (Saunders et al. 2016). According to them,
the descriptive study answer questions such as “how”, “what”, “when”,
“where” or “who”. As far as, “explanatory” research is concerned, Saunders
et al. (2016) suggest that it focuses on cause and effect relationship between
different variables. They further suggest that the main purpose of explanatory
research is to explain why certain phenomena takes place and how future
occurrences can be predicted. Yin (2018) suggests that explanatory research
answer questions such as “how” or “what”. Besides this, Saunders et al. (2016)
highlights that “evaluative” research focuses on the process of gathering and
analyzing information to provide feedback and improve effectiveness. They
suggest that evaluative research aims at collecting and analyzing information
regarding activities, characteristics and outcomes of a phenomenon. They
further suggest that evaluative research answer questions such as “when”,
“where”, “which” or “who”. Yin (2018) and Saunders et al. (2016) suggest
that one study can have two or more research purposes thus resulting in
“combined studies”.
The research purposes have been shown in Table 2 below;
13 | P a g e
Research Purposes Prime focus Answering questions
Exploratory Seeks to formulate new
problems for the
discovery of ideas and
thoughts as well as
more precise
investigation into the
subject matter
“how” and “what”
Descriptive Documents the
existence of certain
social conditions at a
given moment or over
time
“how”, “what”,
“when”, “where” or
“who”
Explanatory Emphasize the testing
of theoretically
significant hypothesis
“how” or “what”
Evaluative Seeks explanations of a
program´s success or
failure
“when”, “where”,
“which” or “who”.
Combined Combine above
mentioned research
purposes
Answers combination
of above-mentioned
research questions
As far as the research questions for this study are concerned, this study is
exploratory in nature because the purpose is to investigate the phenomena of
construction logistics solution.
2.3 Research design
According to Saunders et al. (2016) “research design” is a framework that
guides researcher about answering the research questions of the study. Bryman
(2012) suggests that a “research design” is a framework for the collection and
analysis of data. He further suggests that there are five prominent research
designs which are as follows;
- Experimental design (such as quasi experiment)
- Cross-sectional design (most common form is survey research)
- Longitudinal design (such as panel study and cohort study)
- Case study design
- Comparative design
Table 2: Various research purposes (Adopted)
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The research design used in this study is “case study design”. According to
Baxter and Jack (2008) and Bryman (2012) case study design aims at studying
complex phenomenon with detailed and intensive analysis. Saunders et al.
(2016) define “case study” as a research design which involves empirical
investigation of special existing phenomenon within its real-life context using
multiple sources of evidence. They further suggest that case study research
design best suits when the purpose of the study is to gain a rich understanding
of the research context and research processes. Saunders et al. (2016) explain
that as case study design answer “what” questions so it is more often used in
studies having exploratory research purpose.
Single versus multiple case study
Yin (2003) distinguishes between the cases based on single versus multiple
case dimension. According to him, a “single case” represents critical case or
an extreme and unique case. He further suggests that single case is selected
when it provides the researcher an opportunity to observe and analyze a
phenomenon rarely studied or observed before. He adds on that when there is
more than one case then the study is referred as multiple case study. He
explains that multiple case study is used when there is need to establish some
connection between the two cases i.e. whether the findings of the first case
also match with the findings in other case so that the results can be generalized.
As Yin (2003) suggests that multiple case study is preferable to a single case
study so in this study multiple case study has been chosen.
Unit of analysis (Holistic versus embedded)
Yin (2003) suggests that the other dimension on which cases can be
distinguished is “unit of analysis”. He suggests that the unit of analysis is
“holistic” when researcher study the case organization as unified whole
whereas if the researcher examines departments, workgroups, sub-units within
the organization rather than treating it as a unified whole then it would be
considered as “embedded” unit of analysis. In this study holistics unit of
analysis is considered because data has been collected only from one unit i.e.
transportation/vehicle flows.
2.4 Research Method
Saunders et al. (2016) suggest that there exist three research methods for
carrying out research (i.e. Qualitative, Quantitative and Mixed). Bryman
(2012) suggests that “quantitative research method” deals with the collection
and analysis of data in numeric form (quantitative data). They suggest that
quantitative method is objective, standardized, structured and emphasize
15 | P a g e
relatively large scale and representative sets of data. But due to variable control
and other restrictions quantitative method produce trivial findings of little
consequence (Bryman, 2012). It can be said according to Bryman (2012) that
quantitative research methods aim at testing the theory. While throwing light
upon “qualitative research method” Saunders et al. (2016) suggest that it is
more open and responsive to its subject. They suggest that qualitative research
strive to capture experience as close as possible to what participants feel or
live. They further suggest that it involves collection of non-numeric
information referred as qualitative data. They suggest that although qualitative
research method provides deeper understanding but at the same time it is hard,
complicated and more time consuming. It can also be said that qualitative
research methods play an important role in theory generation rather than
testing it (Bryman, 2012). Saunders et al. (2016) suggest that some studies
incorporate both qualitative and quantitative research methods in order to
obtain comprehensive and holistic view by filling the gaps offered by both
methods. They explain that the combination of both research methods i.e.
qualitative and quantitative then result in mixed method research.
As the focus of this study is on words so “qualitative research method” is
being used in this study.
2.5 Research approach
Bryman (2012) suggests that there are two main approaches towards research
i.e. deductive and inductive. He explains that these approaches are classified
based on relationship between theory and research. He further explains that
research approach directs the researcher that when and how theory is to be
collected. Saunders et al. (2016) suggest that a researcher follows deductive
approach when he/she first collects the theory and after finalizing it moves to
the next step of data collection. Bryman (2012) suggests that in case of
deduction, researcher test the developed theory with the help of collected data.
In other words, it can be said that deductive approach begins with theory
development and then theory guides the researcher what data is to be collected
in order to verify the theoretical findings (Yin, 2018). According to Saunders,
et al. (2016) deductive approach is quicker to complete because while
developing theory researcher gets the clear idea of the required data and
afterwards data is collected in one go. They also highlight that it is easy to
predict accurate time schedules in deductive approach. Whereas Saunders et
al. (2016) suggest that in case of inductive approach, researcher develop theory
after collecting data. They further suggest that based on data analysis
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theoretical data is collected (i.e. theory follows data analysis). They also
explain that inductive research approach is time consuming because it requires
longer period of data collection and analysis to extract the useful meanings
from the data. The deductive and inductive approach have been explained in
the Table 3 below;
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Deductive approach Inductive approach
Moving from theory to data Moving from data to theory
Collection of quantitative data Collection of qualitative data
Highly structured approach More flexible approach and allow
changes in research purpose as the
study progresses
Require representative samples of
larger size in order to generalize
results
Less concerned with the need to
generalize
Low risk strategy (no response risk
mainly)
High risk (chances of no useful data
patterns or theory emergence)
Explanation of causal relationships
between variables
Close understanding of the research
context
Operationalization of concepts to
have clear definitions
Researcher is part of the research
process
This can be shown by below Figure 4;
In addition to this, there is third type of research approach known as “abductive
approach” in which the researcher mixes both research approaches (i.e.
deductive and inductive). Saunders et al. (2016) suggest that abductive
approach overcome the weaknesses of both deductive and inductive
approaches. They explain that the main weakness of inductive approach is that
it is incomplete (i.e. it is hard to get 100% results) and can lead to false
conclusions even with accurate observations whereas the main weakness of
deductive approach lies in its assumption that initial premises are correct but
if one or more premise turn incorrect then whole argument becomes invalid
Theory
Observations/Findings
Observations/Findings
Theory
Deductive approach Inductive approach
Table 3: Deductive and Inductive approach (Adopted)
Figure 4: Deductive Inductive approach (Adopted)
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and unsound.
Saunders et al. (2016) suggest that unlike deductive and inductive research
approach, abductive research can explain, develop or change the theoretical
framework before, during or after the research process. They further suggest
that abductive research moves back and forth between inductive and open-end
research settings to more deductive attempts of hypothesis verification.
Abductive research approach has been used in this study because there is less
theory and the aim is to develop more theory with the help of empiry. This is
shown below in Figure 5;
2.6 Population and Sampling Saunders et al. (2016) suggest that it is important to define research population.
According to them, the full set of cases from which a sample is taken is
considered as “population”. They further suggest that it is impossible to collect
data from whole population due to certain constraints such as size,
accessibility, time, cost etc. Due to this fact, they emphasize the need of
targeted population which is population of construction logistic solution in
Sweden for this study. The target population for this study is small. Further
sampling has been done from this small targeted population. They highlight
the importance of sampling by mentioning that sampling saves time, data
collection becomes more manageable due to fewer subjects involved, analysis
gets quicker because of less amount of data and so on.
Bryman (2012) suggests that sampling can be done on the basis of two
techniques i.e. probability or representative sampling and non-probability or
judgmental sampling. He further suggests that when there is equal and known
chance for each case to get selected then this is referred as probability sampling
Figure 5: Abductive research approach (Adopted)
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whereas when the chance of case being selected is unknown then this can be
considered as non-probability sampling. Saunders et al. (2016) further
classifies probability and non-probability sampling techniques which are as
follows in Table 4;
Sampling
Probability Sampling techniques Non-Probability sampling
techniques
- Simple random - Quota
- Systematic - Purposive
- Stratified random - Snowball
- Multistage cluster - Convenience
Purposive sampling has been done in order to answer research objectives of
this study. Because cases have been selected with the purpose of studying
construction logistics solution phenomena. Saunders et al. (2016) suggest that
generally case studies use purposive sampling because of small sample size
and need of informative cases.
Selection of cases
As the purpose of the study is to explore construction logistic solution
phenomena within construction logistics, so very few companies are operating
in this field. For this study one company has been chosen from terminal and
one from checkpoint.
Saunders et al. (2016) suggest that cases are selected on the basis of following
criteria such as able to address the research questions raised in a study and case
should be real world phenomena that has some concrete manifestation/should
not be an abstraction.
Case 1-Terminal is a construction logistics solution located in Linköping for
a project called Urban Escape, which is at the heart of Stockholm, Sweden.
Urban Escape is a largest urban development project consisting of houses,
offices, hotels, shops, restaurants, cafes, bars, and meeting places
(Urbanescape, 2019). The case 1 is responsible for making deliveries from
suppliers to the construction site by using a principle of consolidation.
Case 2-Checkpoint is a construction logistics solution located in Linköping
for a project called Ebbepark, which is at the heart of Linköping, Sweden.
Table 4: Sampling technique (Adopted)
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Ebbepark is a construction project offering new opportunities and workplaces
such as schools, housing apartments, offices, leisure activities etc. to the
people of Linköping. The case 2 is also responsible for making deliveries from
suppliers to the construction site by using just in time delivery schedule.
2.7 Research process The steps involved in the research process for this study are shown in the
Figure 6 below;
This section will provide the general overview of the steps involved in the
research process, the details of which will be explained later in this chapter in
section 2.8 (data collection methods) and 2.9 (data analysis). In order to get
better understanding of the thesis topic, literature review was conducted as first
step. While doing literature review, it was found that very few articles discuss
transport related performance measures within construction industry. This
raised the need of interviews with selected cases in order to get better hold of
the subject matter and to develop good theory regarding transport performance
measures as far as construction logistics is concerned. Both individual and
group interviews were arranged depending upon the availability of intervieews
within each case. This is the second step in the research process. With the help
of theory and interviews, a protocol for focus group has been developed. The
purpose of focus group is to have an expert opinion regarding the identified
transport performance measures and their relevance. The focus group
discussion has also helped in developing understanding that how transport
related performance measures varies among terminal, checkpoint and
construction site. This can be called as step three in the research process. After
having focus group discussions, the step four and step five is to do analysis for
research question 1 and research question 2 respectively. After analyzing the
first two questions, data from cases were collected as step six in order to
empirically verify the performance measures identified in first two research
questions. After collecting and analyzing data, the answers to research
questions 3 and 4 were given. This is considered as the last steps i.e. step seven
Literature
review
Individual
&
Group
Interviews
Focus
Group
Analysis of
RQ1
Analysis of
RQ2
Data from
cases
Analysis
of
RQ3
Analysis
of
RQ4
Figure 6: Research process of this study (Own illustration)
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and eight in the research process.
2.8 Data collection methods The data collection method used in this study are literature review, interviews
both individual and group, focus group and case documents and statistics. The
detailed data collection methods will be discussed one by one below.
2.8.1 Literature review
Literature review is the secondary data used in this study. O´Reilly and
Kiyimba (2015) describe “secondary data” as data collected by someone else
other than the researcher him/herself. According to them, existing available
resources can be considered as secondary data (for example, research reports,
reference books, electronically published articles and so on. In order to answer
research questions 1 and 2 literature review has been conducted. Electronic
means have been used in order to gather secondary data for this study. Figure
7 shows the search technique applied to obtain the list of publications used in
this study. Database such as Google scholar has been used as the primary
search engine because it contains large number of quality sources including
the ones by the most reputable publishers such as Elsevier, ResearchGate,
Emerald insight etc. The other sources used are One Search, Diva and Scorpus.
All journals and research articles used in this study are related to construction
industry and transportation. Peer review articles are given preference. In order
to use valid and trustworthy source, publication institute and citations are
given much importance. Few articles are not available in full text so “Abstract”
has been used in order to understand the findings of those research articles.
HBR articles have also been studied in order to understand latest
advancements in this area. The initial aggregation resulted in approximately
146 publications which are then shortlisted to 90 publications. The process of
shortlisting publication is shown below in Figure 7;
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The initial list was manually investigated to select the most recent and relevant
publications. Publications in language different than English or not accessible
were discarded. In order to find relevant articles snowballing technique was
also used. This practice of back tracing remained quite effective and helped in
finding credible literature. Some secondary or tertiary studies such as survey
papers, dissertations, systematic literature reviews were also considered if they
appeared to be relevant. The key words used for searching relevant sources are
shown in the Table 5 below;
List of search key words
-Construction industry -Terminal
-Construction supply chain -Checkpoint
-Construction logistics -Construction Site
-Construction process -Performance measures
-Construction logistics solutions -Transport related performance
measures
-Construction performance
measures
-Calculations for transport
performance measures
Initial List
Other
Sources
One search
Scopus
Ecluding
Non-English
Papers
Excluding
Non-Accessible
Papers
Snowballing
Excluding
Old
Publications
Manual
Selection
Shortlisted
Publications
Google Scholar
Figure 7: Process for shortlisting publications (Own illustration)
Table 5: Key words used for searching articles (Own illustration)
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Harvard referencing style has been used throughout the thesis and sources of
the data can be viewed in the reference list.
2.8.2 Semi Structured Interviews
Semi structured interviews are the primary data used in this study. O´Reilly
and Kiyimba (2015) refer “primary data” as data specifically gathered by the
original researcher for his/her research purposes. In order to get good
understanding of transport performance measures within construction
logistics, interviews were conducted in order to get answers for research
question 1 and 2 due to insufficient amount of relevant theory.
According to Bryman (2012), semi structured in depth interviews are widely
used for case study research. In this study, semi structured interviews are
conducted in order to get deep understanding of transport performance
measures being used in construction logistics by selected cases. Saunders et
al. (2016) suggest that semi structured interviews are given preference in
qualitative study because it allows researcher to get rich and detailed answers
along with deep idea of intervieew´s point of view. Bryman (2012) suggests
that in order to have effective semi structured interviews, there is a need to
develop “interview guide”. According to Saunders et al. (2016), interview
guide for semi structured interviews can be formulated based on previous
literature, personal knowledge, field experience and information gathered
from preparatory work. The similar approach has been followed in preparing
interview guide for this study.
Two interviews have been conducted for getting deep understanding regarding
transport performance measures within construction logistics. The interview
conducted with Case 1-Terminal was group interview whereas the Case 2-
Checkpoint interview was individual. Both interviews were conducted face to
face for the period of two hours. According to Bryman (2012), it is good to
conduct face to face interviews because it allows researcher to collect more in-
depth data and comprehensive understanding due to clear body language and
facial expressions. He further suggests that face to face interview gives an
interviewer the opportunity to probe for more explanations regarding
interviewees responses. The interview details are mentioned in Table 6 below;
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2.8.3 Focus group
In order to have expert opinion and focused discussion on performance
measures identified via literature review and semi structured interviews, two
focus groups have been conducted i.e. One for the Case 1 and one for Case 2
consisting three to four participants excluding moderator/facilitator each. The
participants of the focus group are selected based on their expert knowledge
in the field. The purpose of the focus group was to see that whether the
identified performance measures are relevant to Case 1 and Case 2 along with
their respective construction sites.
Primary data has been collected by using focus group in order to answer RQ1
and RQ2. While explaining “focus group” Bryman (2012) suggests that it is a
form of group interview aims at focused discussions in which there are several
participants (usually at least 4) in addition to the moderator/facilitator.
According to him, the person who runs the focus group session is usually
called the moderator or facilitator and he or she is expected to guide each
session. The whole discussion revolves around this focus group protocol
which has resulted from literature review and semi structured interviews.
Interviewed cases
Case Industry
area of
interviewed
case
Number of
respondents
Position of
respondent
Date of
the
interview
(ddmyyyy)
Duration
of the
interview
in hours
Type of
interview
Case 1-
Terminal
Construction
logistic
solution
3 Founder of
case
company,
Sales
executive,
Business
unit
manager
14.1.2019 2 Group
interview
Case 2-
Checkpoint
Construction
logistic
solution
1 Logistic
consultant
at case
company
14.1.2019 2 Individual
interview
Table 6: Semi structured interview cases (Own illustration)
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The details about the focused group are presented in the following Table 7,
including the case company´s area of business, the position of the focus group
participants, the date and duration of focus group along with number of
participants involved.
Focused group Case Industry area
of case
Number of
participants
Position of
participants
Date of the
focus
group
(ddmyyyy)
Duration of
the focus
group in
hours
Case 1-
Terminal
Construction
logistic
solution
5
Business
unit
manager of
case
company,
founder of
case
company,
PhD
researcher at
Linköping
University,
Master´s
degree
student,
Associate
professor at
Linköping
University
(Moderator)
04.4.2019 2
Case 2
Checkpoint
Construction
logistic
solution
4 Logistics
consultant at
case
company,
PhD
researcher at
Linköping
University,
Master´s
degree
student,
Associate
professor at
15.4.2019 1
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Linköping
University
(Moderator)
2.8.4 Documentation and statistics
For research question 3 and 4, the use of primary data such as company
documents and statistics has been collected for both cases. Bryman (2012)
suggests that information through documents and data play very important role
for researchers who are conducting case studies. Cases documents are used to
get the overview and progress of the projects that these cases are involved
with. And the data has been used in order to see that whether the identified
performance measures can be measured with this data or not and if not then
what further data is needed. This also gave insight that how these two cases
are recording data at their end. The data provided by Case 1 consists of order
numbers, company codes, activity codes and their descriptions, date of
activities, time taken in performing activities, internal markings mainly. This
data has been provided for the period of 5 months. The case 2 data consists of
delivery ID, company name, vehicle arrival, vehicle departure, duration,
summary of activities performed, date, projects, unloading site, supplier,
number of vehicles and type of vehicles. This data provided by Case 2 is
through portal named as “Lognet”. The data in the “Lognet” is not exportable.
So, one-week data has been manually entered in Excel in order to answer
research questions 3.
This study incorporates an additional quality of “iterative data collection”. The
use of this data collection process increases the level of flexibility during the
research. Bryman (2012) describes iterative data collection process when a
researcher does a reflection about the theory and collected data. He further
suggests that it happens when the researcher weave back and forth between
data and theory to ensure that correct data is collected for the assumptions.
According to him, when the researcher wants or requires further information
to verify the strength of theory, he/she can go back and get more data.
2.9 Data Analysis Four analysis has been conducted in this study due to the presence of four
research questions. The analysis for first research question is made by
Table 7: Focus group details (Own illustration)
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consolidating performance measures. The consolidation has been done in
order to remove duplication and redundancy among identified performance
measures. The other reason for doing consolidation is to have few but
comprehensive performance measures. In this way it will become easy to
monitor performance measures. The selection of performance measures (for
consolidation) has been done based on ease of calculation.
For research question 2, the analysis has been done on the basis of theory and
personal knowledge and experience. Empirical findings remain unable to
provide deep insights into the subject at hand.
For research question 3, the analysis has been done using numerical computing
programs such as Matlab and Excel. Besides this, the findings such as efficient,
inefficient orders and fast and slow companies have been made by using data
provided by Case 1. Standard deviation and mean are calculated in order to
see the variation among data entries in the data provided by Case 2. Graphical
analysis has also been done to show the findings.
The analysis of research question 4 has been done on the basis of findings from
research question 2 and research question 3 mainly. As this research question
is to find a gap between needed and available data. So, reflection has been
made on this research question.
2.10 Research Quality According to Bryman (2012) the most important criteria for research quality
are reliability and validity. He further suggests that these criteria have always
remained a question mark for qualitative studies because of its subjective
nature.
2.10.1 Reliability
Bryman (2012) suggests that there are two types of reliability i.e. external
reliability and internal reliability. Saunders et al. (2016) suggest that external
reliability is concerned with the repeatability of the study results. In other
words, it can be said that what is the possibility to exactly replicate the research
findings. In qualitative research, to maintain external reliability is challenging
because the social environment and the conditions keep on changing. It is very
difficult to produce exact same results when using interviews and focus groups
as data collection method (Bryman, 2012). In this study, external reliability
has been assured by providing interview guide and protocol for focus group in
Appendix 1 and Appendix 2. If needed, all the notes taken during interview
and focus group session can also be provided.
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Bryman (2012) further discusses the second type of reliability which is
internal reliability. According to him, internal reliability deals with
consistency while conceptualizing measures. In other words, it can be said that
it deals with a question that whether the measures that are devised for concepts
in the study are consistent or not. Internal reliability in this study has been
assured by making use of different data collection techniques and by
discussing with supervisor that whether the results have been interpreted in a
correct way as they were supposed to be interpreted. In addition to this,
different researchers’ opinion who has similar background knowledge has
been considered several times to ensure consistency in defining the key terms.
2.10.2 Validity
According to Yin (2018) and Saunders et al. (2016) there are three types of
validity i.e. external validity, construct validity and internal validity. Bryman
(2012) suggests that in case of exploratory studies external validity is very
important to ensure whereas internal validity plays a crucial role for
explanatory research. As this study is exploratory study, so not much
consideration has been given to internal validity in this study.
Saunders et al. (2016) suggest that external validity deals with a question
whether the results of the study can be generalized beyond the specific
research context. Although the aim of the study is to develop performance
measures for construction logistics solution so this may lead to the formulation
of performance measures for other construction logistics solutions involving
transportation flows and vehicles such as trucks.
Yin (2018) suggests that construct validity refers to a degree that how well
researcher has measured the constructs that he/she claimed to measure. In this
study, construct validity has been ensured by identifying the right operational
measures for the terms used. The terms under study has been defined in a
measurable way and this has been done through operationalization of all-
important concepts. Besides this, same terminologies and wordings have been
used throughout the study in order to give a clear idea to the reader.
2.11 Ethical considerations According to Bryman (2012), Saunders et al. (2016) and Yin (2018) it is very
important to consider ethics while conducting research and gathering
empirical data. They further suggest that it is important to give respondents
and participants enough information about the significance of the study, how
this study will be conducted, how the answers will be handled and used and
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why is there need to conduct this study. In addition to this, it is essential to
take participant’s consent that whether they want to take part and contribute in
the research or not. In addition to this, it is important to ask if it is allowed to
record respondents’ responses or not. It is also necessary to know that
participants want to disclose information about themselves or prefer to remain
anonymous.
The purpose of this study is to create awareness and contribute theoretically
so this study can be used and transferred to other purposes. There are certain
ethical considerations made in this study such as company names have been
kept anonymous. Instead, case 1 terminal and Case 2 checkpoint has been used
in this study. Very less information has been disseminated while describing
the cases. In order to maintain anonymity, the company’s sources are not listed
in the text, appendix and reference list. Names of the participants will not be
mentioned throughout the study. In addition to this, all respondents were sent
interview questions and focus group protocol well before in time so that they
can prepare their responses and think about their responses. No recordings
have been done during the interview and focus group discussions whereas
detailed notes have been taken.
3 Contextual background of the study
In this chapter, the contextual background of the study will be discussed. The
study is being conducted from construction logistics perspective within the
construction industry. This gives rise to the need of understanding the basics
of construction industry (construction process, construction flows i.e. physical
and information flow and construction site organization) and its significance.
It is also important to know the role of supply chain management within
construction industry. The contextual background will set the stage for all
research questions being studied in this thesis.
The pictorial view of contextual background chapter has been shown below in
Figure 8.
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3.1 Basics of construction industry Razak Bin Ibrahim, et al. (2010) state that “construction” is the activity of
creating and repairing physical immobile structures and related facilities. They
suggest that schools, houses, hospitals, airports, railways, factories, shopping
malls are some of the examples of construction. Hughes, et al. (2015) advocate
above definition and consider “construction” as economic activity aimed
towards creation, renovation, repair or extension of fixed assets in the form of
buildings, land improvements, roads, bridges, dam etc.
According to Razak Bin Ibrahim, et al. (2010), “construction industry”
comprises of companies involved in the construction of buildings and other
structures, heavy construction, additions, alterations, reconstruction,
installation, maintenance and repairs. They also add that companies
responsible for destruction or flattening of buildings, clearing of building sites
and sale of wreckage also come under the definition of construction industry.
This can be summarized by United Nations (2013) definition of construction
industry that is a sector of national economy that handles land preparation and
development. It involves repair and remodel property too.
Contextual background of the study
Section 3.2
Significance of
construction industry
Section 3.1
Basics of
construction industry
Section 3.1.1
Construction
process
Section 3.1.2
Construction flows
Section 3.1.3
Construction
site organization
Section 3.1.2.1
Physical flow
Section 3.1.2.2
Information flow
Section 3.3
Supply chain management
in construction industry
Figure 8: Contextual background of the study (Own illustration)
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Sears, et al. (2015) suggest that construction team involves architects,
engineers, craft workers, specialty contractors, material suppliers, designers,
main contractor, project manager and client/owner mainly. They further
suggest that construction work is done by contractors which differ in size and
skill set. Some contractors are specialized in some tasks of construction project
and are known as specialty contractors whereas there are general contractors
who are responsible for broader construction tasks. General contractors
subcontract specialty contractors for specific tasks and in this way a network
of general and specialty contractors come into existence.
Razak Bin Ibrahim, et al. (2010) suggest that construction companies work in
an uncontrollable environment i.e. outside in the field and is susceptible to
number of various variables and unpredictable factors. Peiffer (2015) suggests
that there are three categories of construction industry i.e. building
construction industry, heavy construction industry, and special trade
construction industry. The focus of this study is mainly on building
construction category.
3.1.1 Construction Process
Sears, et al. (2015) state that “construction projects” are complicated and time
consuming. They suggest that construction project generally gets completed
in the form of several phases such as planning and definition, design,
procurement and construction and project completion. All these phases require
a lot of supervision, monitoring and diverse range of specialized services (such
as electrical, concrete, excavation, piping, roofing etc.).
According to Odeh (2019) construction projects/process starts with a
client/owner who then communicate his/her idea to consultant/project
manager or a designer. Sears, et al. (2015) suggest that client/owner is the one
who initiates the construction process by identifying the need for a new
facility. They further suggest that generally client sets the project boundaries
in terms of budget and other requirements. After “need identification and
sharing” “planning phase” starts in which broad project characteristics are
discussed such as location, performance goals, size, layout, configuration,
equipment, services and so on. Odeh (2019) suggests that after conceptual
planning, the work on preliminary “design phase” starts. Sears, et al. (2015)
suggest that design phase incorporates architecture and engineering design of
whole project such as working drawings and specifications. Odeh (2019)
suggests after client´s approval the design gets finalized. He further adds that
after design selection the “procurement and construction phase” begin.
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Sears, et al. (2015) suggest that procurement consists of ordering, execution
and delivering of required materials and equipment and with the help of this
material, equipment, labor, supervision and management construction takes
place. After construction is done the project is delivered to client for
occupation and utilization and all contracts are closed out. (Sears, et al. (2015).
The construction process is shown and summarized in Figure 9 below;
3.1.2 Construction Flows
Sacks (2016) suggests that due to complex and dynamic nature of construction
industry it is difficult to control and manage flows within the construction
projects. Kalsaas and Bolviken (2010) define “flow” as continuous stream of
something”. They also suggest that “flow” is a chain of events (sequence),
continuous movement, moving freely and value addition without interruption.
Sacks (2016) further suggests that flows in the construction industry comprises
of “physical flows” such as flows of materials and equipment’s and
“immaterial flows” such as flow of information, crew, space and external
conditions e.g. weather, authority’s approval and so on. He suggests that the
visible flow in the construction industry is primarily of workers and their
equipment because the final product in this case is immovable (i.e. building).
Alves and Formoso (2005) are of the view that by explicitly and systematically
Figure 9: Different phases of construction process (Own illustration)
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planning and controlling construction flows (such as flow of workers,
equipment, materials etc.) process transparency can be enhanced. In addition
to this, there would be reduction in cost, time, wastage and variability (Alves
and Formoso, 2005).
3.1.2.1 Physical Flows
Alves and Formoso (2005) suggest that physical flows consists of both
material and production unit flows. Sacks (2016) suggest that it is difficult to
measure physical flows because the flow perspective was missing in
traditional construction management. Patel and Vyas (2011) suggest that
efficient and effective physical flows are designed in order to ensure
construction material availability at right time and in right quantity. According
to them physical flow begins with on-site material need generation. In order to
fulfill this need material is ordered in the store from different suppliers and for
that indent is generated. According to Merriam-Webster (2019) “indent” is
referred as official order or requisition for goods. Patel and Vyas (2011) further
suggest that after indent generation in store material availability is checked
and in the absence of required material, vendors are selected from the client´s
approved vendor list. The inspection of received material stock is done and
unnecessary stock is returned to vendors. After completing all these steps,
material is issued to concerned department and with the help of right
equipment and transport the material is delivered to the point of use (Patel and
Vyas, 2011). The steps are summarized as follows in Table 8 (Patel and Vyas,
2011);
Steps involved in physical flows
• On-site need for material
• For ordering material in the store, indent is generated
• Checking of in-store material availability
• Vendor selection from client´s approved vendor list
• Inspection of received stock
• Return of unnecessary stock
• Material issued to concerned department
• Material delivered to right place by utilizing right equipment and
transport
Alves and Formoso (2005) suggest that uncertainty in the physical flow can
be reduced by better planning and control at different hierarchical levels in the
construction process. For example, a general site lay out should be developed
Table 8: Physical flows steps (Adopted)
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at master plan level (strategic), workflows of repetitive processes should be
prepared at operational level and cleanliness and workplace order should be
managed at tactical level (Alves and Formoso, 2005). Alves and Formoso
(2005) consider qualification and selection of suppliers, selection of transport
equipment’s (cranes, lifts, excavators, etc.) and their location, material storage
area, material consumption rates as important physical flow decisions.
Here it is important to mention type of construction material and their delivery
in order to know the nature of material vehicles carry in construction industry
or what type of material is being transported to the construction site.
Types of construction material
Li et al. (2016) suggest that there are four types of material used in construction
projects namely construction material, packaging material, extracted material
and target building material. They refer “construction material” to those
material which is used to form the target building element. According to
Napier (2016) examples of construction material can be cement, concrete,
wood, glass, steel, aluminum, copper, gravel, stone, rock, plastic, textile etc.
Li et al. (2016) suggest that large amount of construction material gets
consumed in forming target building material and as a result very few
construction materials gets wasted. They consider “packaging material” a
material that cover/package construction material. Tolstoy, Bjorklund and
Carlson (1998) consider mineral wool, plaster board, card board, plastic sheets
as packaging material. According to Pericot (2011) every construction
material is delivered in some type of packaging at construction site. Li et al.
(2016) suggest that packaging material does not play any role in forming target
building element and therefore this end up as construction waste. They define
“extracted material” as soil abstractions in the construction process. Tolstoy et
al. (1998) suggest clay, sand etc. as extracted material. They are of the view
that extracted material become waste if not used in target building elements or
in backfilling. They define last type of material “target building element” as
design elements of a building such as windows, doors, steel beams, bricks,
frames etc. Part or whole of a building element can be considered as wastage
due to change in design or poor construction quality.
Li et al. (2016) show material flow of typical building construction project in
the following Figure 10;
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Li, et al. (2016) demonstrate construction site as dotted rectangle. According
to them input to target building is various construction materials such concrete,
cement, glass, wood, rocks and gravel etc. as well as other construction
packaging materials such as plastic sheet, cardboard, wool etc. They further
suggest that the output of construction are different building elements formed
such as frames, doors, windows etc. and construction waste for example
broken tiles, bricks, cables, pipes, insulation material and so on. They add on
that the construction waste mainly include extracted materials involved in the
construction of target building. According to them, the sum of construction
materials, packaging materials and extracted material is equal to the target
building elements and construction waste.
Delivery of construction material at Site
Napier (2016) suggests that with the help of equipment and manual labor
construction material is delivered at desired location into industrial containers
of various types. Materials are picked and sorted with the help of specially
designed equipment and are then loaded into trucks and containers of various
types. According to Olsson (2000) the traditional and most common way is to
deliver construction material at site. In this way materials are sourced by the
contractor and then client does payment after on-site material delivery.
3.1.2.2 Information flow
Golyani and Yan Hon (2010) highlight that in construction industry there is
still existence of inaccurate and untimely information flow. According to
Phelps (2012) information flow incorporates a starting point known as source,
an ending point called as receiver, a path as interaction and driving force said
Figure 10: Material flow in construction project (Adopted)
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as mutual relevance. Otjacques, Post, Feltz (2005) suggest that due to large
number of stakeholders involved in construction projects, hundreds of
information flow occurs. Golyani and Yan Hon (2010) suggest that
construction information mainly consists of documents such as design
drawings, specifications, schedules, budget calculations, meeting´s minutes,
invoices, flow charts and so on. According to them, construction information
keeps on changing throughout the construction process due to continuous
improvements in the planning and design phase. They are of the view that as
multiple stakeholders are involved in the construction project so effective and
efficient information sharing plays an important role in better and informed
decision-making regarding construction projects. Otjacques, Post, Feltz (2005)
define “information sharing” as the set of all elements responsible for
providing some knowledge and evidence to concerned individuals. According
to them, information consists of changes, cost control, documents and reports,
risk analysis, programming and so on. They further state that both formal and
informal information sharing has great influence on construction projects
quality, cost and timely completion.
Forcada Matheu (2005) throws light upon information flow steps in general
construction project. He suggests that initially “client´s requirements” are
shared with project manager and designer. After finalization of client´s idea
“strategic brief” is prepared which consists of procedures, project
configuration, consultants and other people to be involved. The next step is to
develop “full brief” based on discussed strategic brief which focuses on cost
estimations and procurement aspects. After this, “production information” is
prepared in order to obtain tender (Forcada Matheu, 2005). Tribelsky and
Sacks (2010) are of the view that at tender stage information should be
complete and clear. According to Kenton (2019) “tender” is structured
invitation to suppliers to submit a bid for supplying products and services
whereas “bid” is an offer to set price. Bid determines the cost and value of
something (Kenton, 2019). Forcada Matheu (2005) suggest that as tender is
released potential contractors and sub-contractors are evaluated. Contractors
are then appointed, and construction starts. According to Ndekugri and
McCaffer (2006) during design and construction there is continuous need of
information to be transferred quickly and reliably between stakeholders. After
completion of construction final inspection and final settlement is done and is
known as “handing over”. Ndekurgi and McCaffer (2006) emphasized the
importance of reliable, standard and efficient flow of information.
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The summary of information flow steps in construction project is shown below
in Table 9 (Forcada Matheu, 2005);
Information flow steps in construction project
1- Sharing of client´s requirement with project manager and designer
2- Preparation of strategic brief (includes procedures, project
configuration, decisions regarding people to be involved)
3- Development of full brief (focusing on cost estimations and
procurement aspects)
4- Preparation of production information for obtaining tender
5- Evaluation of contractors and sub-contractors after the release of
tender
6- Appointment of contractors for construction project
7- After handing over of construction project, final inspection and final
settlement is done
Bröchner (2005) has demonstrated information flow in general construction
project below which is adopted by Bishop´s information flow model which he
presented in 1972 in Figure 11;
Table 9: Information flow steps in construction project (Adopted)
Figure 11: Information flow in general construction project (Adopted)
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3.1.3 Construction site organization
Merriam-Webster (2019) defines “construction site” as a space of ground
occupied or to be occupied by a building. According to Holland (2014)
construction site organization is critical, thorough and meticulous process
resulting in healthy and safe construction site throughout the building process.
As per Vidovszky (2015) construction sites are prepared by managing traffic,
protecting the public, storing material and managing waste, required paper
work and meeting legal requirements. Holland (2014) suggests that it is
important to keep pedestrians and vehicles separate on construction site. She
further adds on that there are separate entryways and exits for pedestrians and
vehicles. She is of the view that walkways and crossings are well drained in
order to avoid any slips. She considers obstructions, visibility and barriers very
critical for well managed construction site. She states that vehicle movements
on construction site should be minimized such as workers or visitors are not
allowed to park car near construction site, entry to work site should be
controlled and specific area should be allocated for delivery vehicles in order
to avoid unnecessary trips across construction sites.
Vidovszky (2015) suggests that it is critical to enhance visibility on
construction sites as maximum as possible and this can be done by placing
mirrors, reverse alarms, closed circuit television (CCTV) cameras, adequate
lighting and so on. She further suggests that it is of prime importance to define
boundaries of construction site. According to her, material storage and
stacking should be kept aside so that accidents can be avoided. Holland (2014)
suggests that manholes and pits should be covered with lids. According to her
flammable materials should be kept away from storage area to hinder
accidental ignition and contamination. In addition to this, she suggests that
other facilities such as toilet, washing, drying, cooking, eating area, rest area,
soundproof telephonic booth should be allotted to construction workers and
employees. She also states that materials should be kept safe from theft,
vandalism, and careless behavior.
3.2 Significance of construction industry Fira (2015) states that the idea of construction is as old as human history and
therefore evolution of construction and human lifestyle took place hand in
hand. This evolution took place from primitive age cave to a tent, a hut, an
igloo, a log cabin, a castle, a palace, a detached house and today`s modern red
brick town house, concrete apartment building and sustainable wooden
structure. Fira (2015) highlighting the importance of construction suggests that
modern humans spend on average one third of their income on construction in
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the form of housing. According to McKinsey& Company (2017) construction
matters because it accounts for 13% gross domestic product (GDP) globally
and it employs 7% of the world´s working population. Carlgren (2017) define
gross domestic product (GDP) as total value of all final goods and services
produced in a country during specific period. He states that increase in GDP
over time reflects country´s economic growth. Fira (2015) further suggests that
urbanization and modern living has spurred construction globally and further
make construction a more crucial sector for any economy.
According to Rose (2015) construction industry despite being one of the oldest
and crucial sectors in human history as well as in economic development
respectively has shown slow adoption of modern techniques and methods in
comparison to other sectors mainly manufacturing, retail, third party logistics
service providers and so on. For example, according to McKinsey& Company
(2017) the “retail industry” has adopted global supply chains, digitized
distribution systems, customer intelligence and transformed the small retail
outlets known as mom-and-pop stores into large scale modern retailers such
as Walmart and Aldi. Likewise, “manufacturing industry” has also
incorporated lean and extensive automation. O´Reilly (2015) adds on that
“third-party logistics” have also gone far in terms of implementing information
systems and supply chain planning. According to him, the entry of specialized
firms in express parcel deliveries such as DHL, FedEx, UPS has revolutionized
the third-party logistics industry. The underlying reasons according to her are
involvement of multiple independent stakeholders, the temporary nature of
work, continuously changing construction network in terms of teams and
involved parties i.e. every construction project is unique (no two jobs would
exactly be the same), conflicting interests of owner and project manager, lack
of knowledge transfer from one project to another due to involvement of
different stakeholders in different projects, non-technical education
background of contractors and other concerned parties and so on.
According to Burke (2018) construction industry needs to be revolutionized in
order to fulfill its role in future economic development. Brasington (2018)
advocates by saying that traditionally construction industry is very
competitive, dependent on public-sector demand, extensively regulated,
vulnerable to economic fluctuations, disorganized, risk averse and slow in
technology adoption but now time has come to consider new solutions in order
to cope with future demands. He further highlights that inefficient project
management and execution, insufficient skill base, inadequate design
processes and lack of research and development investments has made
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construction industry fragmented and backward in comparison to other
industries (i.e. manufacturing, retail, third party logistics service provider etc.)
3.3 Supply Chain Management in Construction Industry Lee (2002) suggests that in order to gain competitive advantage supply chain
management (SCM) has become one of the most important aspect. Dubios et
al., (2019) define” supply chain management (SCM)” within construction
industry as the planning and management of all activities along with
coordination and collaboration with channel partners (which can be suppliers,
intermediaries, third party service providers and customers. Andersson and
Nilsson (2018) and Dubios et al., (2019) suggest that supply chain
management plays four major roles in construction from the perspective of
supply chain, the construction site or both. They also suggest that these roles
have been identified by Koskela and Vrijhoef in 2000. These roles are
mentioned below in Table 10 (Andersson and Nilsson, 2018; Dubios et al.,
2019);
Roles of
SCM in
Construction
From which
perspective/focus
To achieve
Beneficial
for
First role Focus on impact
of supply chain
on site activities
- To reduce time
consumption from
activities and cutting
overall costs on site
- To ensure dependable
material and labor flows
to the site to avoid
disruption to the
workflow
Main
contractor
Second role Focus on impact
of supply chain
on itself
-To reduce costs not
only on-site as well as
along whole supply
chain such as logistics,
lead time and inventory
Material
and
component
supplier
Third role Focus on
transferring
activities from
site to early
-To avoid interference
between on-site
activities
Contractors
and
suppliers
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stages of supply
chain
-To achieve wider
concurrency between
activities
Fourth role Focus on
integrated
management and
entire supply
chain
-To improve supply
chain and sit production
Clients,
suppliers
or
contractors
Koskela and Vrijhoef (2000) suggest that construction supply chain (CSC)
possess certain characteristics which makes it unique and complex. They add
on that construction supply chain is converging supply chain which means that
it directs all materials to the construction site where structure is built from
incoming materials. They also say that it is temporary supply chain which
comes to end with the completion of construction project. According to them,
construction supply chain is characterized as unstable, fragmented and
complicated. They also highlight that construction supply chain is make-to-
order supply chain because every project creates a new product or prototype.
There is very less repetition because most of the times every project is different
and does not belong from same kind (without considering exceptions).
Koskela and Vrijhoef (2000) depicts traditional construction supply chain
below in Figure 12;
Table 10: Four roles of supply chain management in construction (Adopted)
Figure 12: Traditional construction supply chain (Adopted)
Initiative Tendering Design Procurement
Use Hand OverConstruction
on site
Operation
capacity
Material
production
Parts
manufacture
Fabrication
of elements
Resident PrincipalArchitect &
consultants
Direct
suppliers &
sub-contactors
Indirect
supplier
Main Contactor
Information flow (orders, schedules, forecasts, etc)
Material flow (supplies, production, deliveries, etc)
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4 Framework for RQ1
The framework for RQ1 consists of theory, frame of reference, empirical
findings and analysis.
4.1 Theory This chapter will discuss theory for research question 1. As the RQ1 is about
exploring transport related performance measures for construction logistics
solutions and their respective construction sites so this chapter will start by
brief overview of construction logistics which then set the stage for introducing
construction logistics solutions. After that a light will be thrown upon
construction site and site logistic. Then performance measures and their
importance, need of performance measures within construction industry and
link of performance measures with transport objectives will be discussed. In
the end transport related performance measures will be mentioned.
The theoretical framework for RQ1 is shown below pictorially in Figure 13;
4.1.1 Construction logistics
Council of supply chain management professionals (CSCMP) (2016) defines
“logistics” as “part of supply chain management that plans, implements and
controls the efficient, effective forward and reverse flow and storage of goods,
Section 4.1
Theory
Research Question 1
What performance measures can be used for the evaluation
of construction logistic solutions and their respective construction sites
with respect to transport flows?
Section 4.1.1
Construction
Logistics
Section 4.1.2
Construction
logistics solution
Section 4.1.2.1
Terminal
Section 4.1.4
Performance measures
and their importance
Section 4.1.2.2
Checkpoint
Section 4.1.3
Construction site
and construction site
logistics
Section 4.1.4.1
Transport performance measures
Figure 13: Pictorial explanation for RQ1 theory (Own illustration)
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services, and related information between the point of origin and the point of
consumption in order to meet customer’s requirement”. Sullivan et al., (2010)
define “logistics” as the process of designing, managing and improving supply
chains which involve purchasing, manufacturing, storing and transport. Appel
(2016) suggests that logistics have evolved over time i.e. from the management
of all activities facilitating movement and coordination of supply and demand
(with respect to time and place) to controlling the physical flow of materials
and goods as well as related information that a firm sends, transfers and
receives.
Ying, Tookey and Roberti (2014) suggest that construction logistics is a vital
part of construction supply chain management (CSCM) and can be defined in
various ways. According to them, “construction logistics” comprises of
planning, organizing, coordinating and controlling of material flows from
point of extraction to the point of incorporation into the finished building.
Janne and Fredriksson (2019) define “construction logistics” as all activities
related to supplying the right material and resources to the right customer and
construction site to meet customer´s requirements.
Tsaxiri (2018) suggests that the main aim of construction logistics is to manage
construction projects in an effective way. She further adds that construction
logistics includes numerous activities such as planning, purchase, control,
coordination, forecast, warehousing, transportation, inventory management
and customer service. Janne and Fredriksson (2019) suggest that construction
logistics encompass planning, supplying and maintaining loading and
unloading zones, on or off-site warehousing and on and off-site materials
handling.
Lundesjö (2015) suggests that no efficiency improvements such as cost
reductions and other certainties can be made in construction industry without
the application of professional logistics. Sullivan et al. (2010) suggest an
estimated 10-20% of all construction costs are transport related. Ying et al.
(2019) are also of the view that construction logistics is an important part of
construction supply chain management as far as project management and costs
are concerned. Sullivan et al. (2010) and Dubois et al. (2019) suggest that there
are four reasons construction industry should employ dedicated approach to
logistics. The first reason according to them is to maximize the productivity
and efficiency at the construction site, second is to maximize the quality of
logistics, third is to reduce environmental impact and fourth one is to maximize
safety and health on site. According to Sullivan et al. (2010) logistics is one of
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the most important elements of construction project and has the potential to
affect cost, speed of construction, planning reliability and so on. Andersson
and Nilsson (2018) suggest that as construction logistics incorporates several
processes within construction industry, it is important for parties involved in
construction project to practice supply chain management.
Sundquist, Gadde, Hulthen (2018) suggest that construction logistics can be
divided into two categories i.e. “supply logistics” and “site logistics”.
According to them, “supply logistics” is related to specification, acquisition,
transport and delivery of materials to the construction site whereas “site
logistics” is concerned with on-site physical flow planning and material
handling. Andersson and Nilsson (2018) suggest that there are two ways to
improve construction logistics. The first one is to reduce inventories of
resources and the second is by enhancing coordination between material flow
and information flow. The first one is referred as traditional approach whereas
the latter is considered as modern approach. Fadiya et al. (2015) suggest that
construction supply chain integration can be achieved by linking supply
logistics and site logistics.
4.1.2 Construction logistics solution
According to Andersson and Nilsson (2018) construction industry is facing
logistics challenges such as congestion at site, delayed deliveries, other supply
chain mismanagement and so on. They further add that due to lack of logistics
standardization, unplanned deliveries take place resulting in chaotic situations
both at roads and at construction sites. Considering this, “construction logistic
solution” is considered a mean to achieve logistics efficiency and effectiveness
both at construction site and on roads. The rationale behind this solution is to
overcome congestion, un-planned deliveries with very short notice to drivers
(Andersson and Nilsson, 2018). Ekeksär and Rudberg (2016) and Sundquist,
Gadde, Hulthén (2018) suggest that “construction logistics solution” can range
from just a small change in working practices, implementing planning systems
and information and communication tools, large scale terminal networks
structures or just in time solutions.
According to Le et al. (2019) the idea of construction logistics solution has yet
to gain wide acceptance within the construction industry. They further suggest
that due to involvement of multiple stakeholders in construction projects it
becomes unclear that who will get the ultimate benefit and who will incur costs
for bringing improvements in the construction projects. They suggest that this
makes participants less motivated to bring improvements in the construction
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projects. They also highlight that main contractors and sub-contractors in order
to achieve their own economic benefits just focus on increasing the efficiency
of their own tasks and responsibilities and do not bother about whole
construction project efficiency.
Need for construction logistics solution
Russo and Comi (2010) highlight that the increasing transport vehicles in
urban areas is leading to congestion, air pollution, noise and increased logistics
costs. They also suggest that different types of vehicles also enhance safety
risks. They further suggest that this raised the need of having effective logistics
systems in the form of construction logistic solution. They further suggest that
it is vital to consider logistics solutions where loads can be consolidated and
deconsolidated thus improving transport management. Sundquist et al. (2018)
suggest that due to poor management of materials, equipment and tools
construction industry is not performing on its optimum. Sobotka and
Czarnigowska (2005) suggest that logistics performance can be improved by
proper planning of delivery and storage and better organization of materials
handling and resource utilization. Janne and Fredriksson (2019) while
emphasizing the importance of construction logistics solution suggest that it
will fulfill the need of limited space at construction site, reduce environmental
impact, improve accessibility and other noise restrictions.
Civic Handbook (2018) highlights that construction logistics solution should
encompass three main areas i.e. transports, site and planning and organization.
It also suggests that a comprehensive construction logistic solution is the one
which incorporates more activities and more roles. Civic Handbook (2018)
also emphasize that construction logistic solutions are not universal rather they
should be adapted to unique settings and peculiarities. The two-construction
logistics solution “terminal” and “checkpoint” and “site logistics” are
discussed below;
4.1.2.1 Terminal
Yamada, Taniguchi and Noritake (1999) define “logistics terminals” as multi-
company distribution centers as well as complex facilities with multiple
functions that fulfills the requirements of supply chain management using
information and communication technologies (ICT). Russo and Comi (2010)
define “terminal” as a place where long distance transport is converted into
short distance transport and consignments are also sorted and packaged here.
They further suggest that in order to build logistics terminals, their function,
size, location, management as well as legal formalities should be considered.
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According to them, “logistics terminals” are connection points between line-
haul and local pick-up/local deliveries. They define “line-haul” as long-
distance transport by large trucks on expressways and local pick-up/delivery
which is short distance transport by small trucks on urban streets. Sullivan et
al. (2010) define “terminals” a storage facility that hold materials or equipment
for a limited time period before delivering them to the point of consumption
on the construction site.
Matouzko (2015) suggests that “terminal” is a distribution facility which
ensures material deliveries to construction site. He further suggests that it is a
temporary warehouse made for material handling and big enough where trucks
can be off-loaded and turned around. Janne and Fredriksson (2019) suggest
that terminal can be put forth by different bodies such as the developer or
municipality. They further suggest that the aim of construction logistics
solution is to coordinate and plan deliveries to multiple construction sites
within an urban area. In other words, it can be said that “shipment
consolidation” is the main aim of terminal (Civic Handbook, 2018).
Matouzko and Methanivesana (2012) suggests that terminals are construction
consolidation centers and are utilized in supplying and distributing materials
to several construction projects. They further suggest it is an effective supply
chain management solution because it ensure safe and efficient material flow
from supplier to the construction site. Matouzko and Methanivesana (2012)
highlight that terminals distribute and supply materials in a right time, to the
right place and in the right quantity.
According to Matouzko and Methanivesana (2012) the idea behind terminals
is to combine multiple part loads into one consolidated shipment. Ekeskär and
Rudberg (2016) suggest that aim of terminal is to establish efficient logistics
systems and to reduce total social and environmental costs of transporting
goods within urban areas. Matouzko and Methanivesana (2012) have depicted
this in Figure 14 below;
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According to Sullivan et al. (2010) terminals involve receipt, temporary
storage and distribution of construction material and equipment. They further
suggest that materials and equipment are delivered to terminals by the supplier
which is then stored for shorter period and then delivered to the point of use
by dedicated logistic team. Janne (2018) while explaining the functionality of
terminal suggests that after the placement of order by main contractor,
suppliers deliver material either directly or via terminal. He further suggests
that in case of terminal, materials from different suppliers are received,
controlled, registered and put away for storage. He adds on that materials are
picked on requirement, thereafter, packed and delivered to the construction
site.
Matouzko and Methanivesana (2012) highlight that with the help of using
terminals un-interrupted materials supply can be made to different
construction projects in an overloaded urban area. Lundesjö (2011) and
Sullivan et al. (2010) suggest that the use of terminals can reduce freight traffic
to site by up to 70% by consolidating number of individual deliveries of
material and equipment, provide the possibility of delivering material at night
when roads are not that busy, provide more control and accountability by
offering single point of contact, reduce harmful CO2 emissions by using
appropriate low emission, fuel efficient vehicles, deliver materials in exact
quantities as required by builders, increase time reliability by acting as
production buffer for materials extended lead times, reduce waste by using
reusable delivery cartons and packaging. Janné (2018) suggests that by having
terminals as a coordinating contact point, main contractors can focus on their
operations without being worried about maintain contacts and relationships
with multiple other contractors. Matouzko and Methanivesana (2012) suggest
Single
Consolidated
Shipment
at
Terminal
Shipment no. 1
Shipment no. 2
Shipment no. 3
Shipment no. 4
Shipment no. 5
Construction site 1
Construction site 2
Construction site 3
Construction site 4
Figure 14: Shipment consolidation at Terminal (Adopted)
48 | P a g e
that terminals result in better supply certainty, reduced number of deliveries to
site, reduced amount of stored materials and reduced waste.
4.1.2.2 Checkpoint
Checkpoint is another type of construction logistics solution with an aim of
providing controlled access to construction site by controlled queues and just
in time deliveries (Civic Handbook, 2018). Matouzko and Methanivesana
(2012) suggest that just in time deliveries refer to deliveries that are made in
time for usage. Landqvist and Rowland (2014) suggest that due to narrow
loading and unloading areas at construction site there is greater demand for
accurate and precise just in time deliveries. Matouzko and Methanivesana
(2012) suggest that just in time deliveries help in completing tasks without any
delay. Tsaxiri (2018) suggests that a checkpoint is established on site in order
to organize the trucks coming from different contractors for all project´s
stages. She further suggests that checkpoint organizes the trucks, sets up the
lanes and the paths on which truck will transfer the material and unload them
safely as per contractor requirements. She identifies that construction projects
where waiting time for the material is greater than just in time deliveries play
an important role in making material reach the site on time.
Janné (2018) suggests that the delivery process in checkpoint starts when
contractor places an order with a supplier. At the same time, contractor makes
delivery bookings in the checkpoint using information and communication
tools (ICT). In this type of system, time slots for deliveries are booked and
specified with information on sort of materials, types of vehicles, goods
volume etc. Once the supplier has shipment ready a delivery announcement is
sent from the supplier to the contractor and the checkpoint operator and the
shipment is delivered to the physical checkpoint. Here the delivery may have
to wait for its allotted time slot before the final delivery to the construction site
is carried out (Janné, 2018).
Matouzko and Methanivesana (2012) highlight that just in time deliveries
within Checkpoint help in reducing on-site material storage, which in turn
reduces the risk of material damage and other safety incidents. Tsaxiri (2018)
suggests that it helps in reducing waiting time for material to reach on site. She
highlights that checkpoint also reduce workers stress by handling material in
a better way. The checkpoint model can be understood by below Figure 15;
49 | P a g e
4.1.2.3 Comparison between Terminal and Checkpoint
Tsaxiri (2018) suggests a terminal is considered as a good solution where there
are more projects in the area because then multiple construction sites will be
served simultaneously and there would be a smaller number of trucks
involved. She further suggests that transport is more flexible in case of
terminal whereas in checkpoint it is difficult to make changes in the deliveries.
According to Janné (2018) the difference between terminal and checkpoint lies
in the way material deliveries are planned and carried out. The focus of
terminal is mainly on the consolidation of deliveries whereas the checkpoint
focuses on just in time deliveries. Tsaxiri (2018) compares the two solutions
in terms of their usage depending upon different situations and this is shown
in Table 11 below;
Situation Construction logistic solution
High number of vehicle on-site Use terminal
Workers waiting for materials Use checkpoint
Transportation problem and queues
in the gate
Use combination of terminal and
checkpoint
Delays with suppliers Use terminal
Many contractors in tight area Use checkpoint
Environment issue Use terminal (reduce no. of vehicles)
Damaged material and waste Use terminal
4.1.3 Construction site and construction site logistics
United Nation (ISIC) (2008) defines “construction site” as a place where
construction activities takes place. “Construction activities” as per Razak Bin
Ibrahim, et al. (2010) are referred to all types of activities related to erection
Construction
SiteCheck
point
Figure 15: Modelling of Checkpoint (Own illustration)
Table 11: Comparison between terminal and checkpoint (Adopted)
50 | P a g e
and repair of non-moveable structures and facilities. Olsson (2000) considers
construction sites a strong logistic spot due to number of materials, people,
equipment and vehicle movements and flows. From logistics point of view, he
highlights that usually very little effort has been made to manage and handle
on-site material efficiently and effectively. He found that a lot of construction
activities are culturally accepted and remain unquestioned and site logistics is
one among those culture driven activities. He states that this makes site
logistics difficult to manage and organize.
Matouzko and Methanivesana (2012) identify that construction workers utilize
less than 50% of their total time in productive and value adding activities on
site. The non-value adding tasks on which they spend most of their time are
moving products around the site and unloading trucks. They then emphasized
that on site construction logistics provide skilled workers ample time by
creating safe, clean and effective workplace so that they can focus more on
valuable work. According to Civic Handbook, (2018) the aim of site logistics
is to have fewer on-site transport, fewer unnecessary movements and greater
controlled and coordinated deliveries.
Sikka, Dawood and Marasini (2006) suggest that construction logistics system
consists of three phases i.e. routes and logistics schedules, site layout and site
resource planning. They further explain that “routes and logistic schedule”
focus on external transportation systems and strive towards optimal traffic
network to deliver material at site such as vehicle routes and route travel time.
“Site layout” as per Sikka, Dawood and Marasini (2006) consists of locations
such as off-loading, loading, storage etc. In addition to this, they suggest that
site layout also map how material movement will take place within
construction area. They further add on that construction site resource planning
include integration of work requirements (such as labor, cranes, machinery,
equipment etc.) with external logistics and site layout. According to them,
construction site logistics is very dynamic in nature because variety of
materials and assembly components are transported towards the site and
eventually the site turns into a finished building. The main activities that take
place at construction site are shown below in Figure 16;
51 | P a g e
4.1.4 Performance measures and their importance
Goshu and Kitaw (2017) suggest that “performance” is a construct around
which organizational competitiveness and excellence revolves because
according to them performance reflects organizational ability to manage
projects and other services. Hove and Banjo (2015) suggest that the term
“performance” due to multi-faceted and subjective nature is not easy to define,
describe and measure. They further suggest that traditionally “performance” is
described as efficiency, effectiveness, improvement, growth, and success.
According to Dubois, et al., (2019) “efficiency” is defined as “how well the
resources expended are utilized whereas “effectiveness” is defined as the
extent to which the established goals are accomplished.
Zetterberg and Minges (2017) define “performance measures” as a metric used
to quantify the efficiency or effectiveness of an action. They further suggest
that performance measures help decision makers and managers to compare
actual versus estimated performance as far as effectiveness, efficiency and
quality is concerned. They add on that if there is a gap between actual versus
desired performance then corrective actions can be taken based on information
gathered by performance measures. Goshu and Kitaw (2017) further suggest
that in order to remain competent in the market, organizations need to know
that how best they are running their activities, how well the resources are being
utilized, how to meet set goals and objectives, how to bring efficiency and
Construction
Site
StorageCrane
Waste Mgmt
Loading
Un-Loading
Delivery
precision
On site
inventory
level
Material
relocation
Figure 16: Activities occur at construction site (Own illustration)
52 | P a g e
effectiveness in current processes and so on. Cain (2004) suggests that
developing performance measures is the first step towards improvement that
brings benefits for all the involved parties. Takim, Akintoye, Kelly (2014)
suggest that organizations measure both financial and non-financial aspects as
well as compare performance measures with others in the industry in order to
improve results.
Yu, et al., (2007) suggest that performance measures are very important for
the success of almost every organization. Kulatunga, Amaratunga, Haigh
(2007) suggest that performance measures lead to profitability and sustainable
competitive advantage. They further suggest that performance measures act as
a monitoring tool and keep organizations on track in achieving their objective.
Theeranuphattana and Tang John (2008) suggest that in this complex and
continuously changing environment performance measures play critical role
in increasing profits, market penetration and market share. Lukviarman (2004)
suggests that performance measures describe how well work is being done
from cost, time and quality aspects.
Latiffi, et al. (2014) suggest that although it is important to have performance
measures but at the same time this is quite challenging. They further suggest
that it is hard to develop performance measures because obtaining correct
source of information is difficult especially when variables or constructs that
are supposed to be measured are changing constantly. They add on that it also
requires right knowledge of methods to measure performance and experience
of using the right tools. Balm et al. (2014) also advocates that lack of right data
makes it more complicated to developed performance measures. They further
suggest that to measure everything in practice is not that easy. Looy and
Shafagatova (2016) also highlight that it is difficult to replicate performance
measures from one organization to another because of their organization
dependent nature. They further suggest that it is not easy to select appropriate
performance measures because best practices vary from company to company.
Need for construction transport performance measures
Garcia-Arca, Prado-Prado, Fernandez-Gonzalez (2018) suggest that “transport
management” has become a serious concern for construction industry not only
from economic aspect of cost reduction and service improvement but also from
other aspects such as reduction in resource consumption, carbon emission and
traffic congestion. Valenciaport Foundation (VPF) (2014) considers
construction industry as one of the biggest producers as well as consumers of
transport in urban areas. It also highlights that due to fragmented nature of
53 | P a g e
construction industry and involvement of multiple stakeholder’s transport has
gained critical importance.
Andersson et al., (2019) advocate that due to complex nature of construction
supply chain it is very difficult to predict and simulate the impact of new
solutions on the transport systems. As a result, it becomes difficult to convince
stakeholders for bringing improvements within construction logistics (Goshu
and Kitaw, 2017). Due to this, Wegelius-Lehtonen (2001) emphasizes the need
of transport performance measures in construction industry. Latiffi, et al.,
(2014) highlight that in order to bring improvement and sustainability,
construction industry should recognize the importance of transport
performance measures.
Linking performance measures to strategic objectives
Marr (2015) suggests that in order to develop a performance measure care
should be taken that to which strategic objective the performance measures
relates to. Olsen (2016) suggests that performance measures are developed in
order to achieve certain goals and objectives. She further suggests that
performance measures are quantifiable expressions of pre-determined goals
and objectives. According to her, it is of considerable importance to identify
the right performance measures because they inform decision makers that
whether progress is taking place towards goal achievement or not. Goshu and
Kitaw (2017) define “goals” as desired result or outcome and “objectives” are
defined as a support to specific goals which provide additional details or
strategies on how the goal will be achieved.
Yan, Xu, Han (2015); Yan and Zhang (2015); Harish (2013) suggest that
construction industry faces various transportation challenges during
construction such as simultaneous pick-up and delivery, managing travel time
uncertainty, vehicle routing optimization, high transportation costs, air
pollution, traffic incidents and so on. In order to meet these challenges,
transportation objectives such as to make effective transport planning and
coordination (Yan, Xu, and Han, 2015); (Jia, Deschamps, Dupas, 2016); (Fu,
2017); Sichwardt (2011), Jonsson (2008) reduce transportation time
(Multanen, 2011), minimize transportation costs (Murray, 2018);
(Stringfellow, 2019); (Yin, Tookey, Seadon, 2018), ensure environment
sustainability (Sichwardt, 2011) and to have less safety and security incidents
(Polzin, 2002; Burdick, 2012; Jacob, 2017); (Tamuli, 2016; Botha, 2005) are
formulated. Baird (2017) highlights the difference between “security” and
“safety”. He suggests that security is the protection against deliberate threats
54 | P a g e
whereas safety is being secure against unintended threats. In order to achieve
these transport objectives, performance metrics are developed (Yin, Tookey,
Seadon, 2018) in the following section.
4.1.4.1 Transport performance measures
In order to develop transport related performance measures for construction
industry, other industries such as manufacturing, retail, third party logistics
(TPL) have been considered because very few studies have been conducted on
performance measures regarding construction transport. In addition to this,
these performance measures are general transport performance measures
without being classified as terminal, checkpoint and construction site because
no credible study has been found in this realm.
4.1.4.1a) Performance measures related to effective transport planning
Sichwardt (2011) suggests that transport should be planned in a way that it
allows suppliers to deliver the material efficiently i.e. to meet material
requirement in the shortest possible time with minimum number of kilometers
driven. Jonsson (2008) suggests that transport planning determines the
structure of transport networks and the flow of traffic. Performance measures
for effective transport planning along their definitions and reference is
mentioned in below Table 12;
Performance
measures
What it entails Reference
Reduced number of
miles driven outside of
pre-determined routes
This measure refers to
number of miles that
vehicle will travel
outside of already
determined route in
order to reach the
destination. Due to
this, vehicle may take
more time to reach the
destination. So, by
measuring this a time
buffer can be estimated
that will eventually
help in effective
transport planning.
Robinson (2019), Ryus
et al., (2013)
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Reduce total distance
driven
It refers to the total
distance that vehicle
has travelled. This
measure will give
information regarding
delivery distances and
helps in effective
transport planning.
Kaparias and Bell
(2011), Freight best
practice (2010)
Ensure trucks
availability (trucks
confirmed vs
requested)
This measure refers to
the truck’s availability
in need. Truck
unavailability will lead
to delays which is a
sign of ineffective
transport planning.
This indicator will help
in fleet management.
Brierley (2017), Key
performance indicator
survey (2013)
Reduced no. of
unplanned deliveries
This measure refers to
the presence of
contingency plan
within transport
system. Based on this
measure, the extra time
that gets consumed in
unplanned delivery
will be reduced.
Beetrack (2016),
Faschingbauer (2015)
Optimal no. of
deliveries arrives at
gate per day
This performance
measure leads to
efficient delivery
scheduling which
consequently results in
efficient transport
planning. The input for
this measure will be
effective material
requirement planning.
Thomas (2017), Ogden
and Turner (2015)
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Maximize vehicle
utilization
-travel capacity
-weight capacity
Vehicle utilization can
be measured in two
ways i.e. in terms of
travel and in terms of
weight. “Vehicle travel
utilization” will help in
calculating no. of trips
thus playing major role
in effective transport
planning whereas
“vehicle weight
utilization” will
determine the quantity
of material carried.
Malacarne (2018),
Hosseini and Shirani
(2011)
4.1.4.1b) Performance measures related to time minimization
Multanen (2011) suggests that one of the transport objectives is to reduce
delivery times as maximum as possible. He further suggests that like other
industries, construction industry also demand quick and fast material
deliveries. He adds on that due to fragmented nature of construction industry,
it is necessary to save time wherever possible. In order to achieve this
objective, suggested performance measures are as follows in Table 13,
Table 12: Performance measures related to effective transport planning
(Adopted)
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Performance measure What it entails Reference
Ensure on time delivery On time delivery is the
date and time of the day
promised by supplier to
make a delivery to its
client. It helps in
optimal utilization of
time.
Marion (2019)
Reduce truck
turnaround time
Truck turnaround time
is a major performance
measure including
loading and unloading
times as well as access
time to construction
facility
Van Der Spoel, Amrit,
Van Hillegersberg
(2015)
Minimize vehicle travel
time
It is defined as total
time that vehicle take in
reaching its destination
Yan, Xu, Han (2015),
Lomax, Schrank,
Lasley, Eisele (2013)
Reduce the time taken
in preparing shipping
documents
This refers to time
taken in preparing
shipping document. It
also indicates the time
difference between
order placed and
making shipping
documents ready
Naoh (2018),
Manaadiar (2017)
Minimize the time
taken in doing shipping
documents corrections
It refers to time taken in
doing corrections in
shipping documents
such as address, price,
quantity, product detail
and so on
Naoh (2018),
Manaadiar (2017)
4.1.4.1c) Performance measures related to cost minimization
Murray (2018) suggests that transportation costs are major portion of overall
logistics costs. Stringfellow (2019) suggests that reducing transportation cost
is top priority of many companies. Following are the performance metrics for
transportation costs in Table 14;
Table 13: Performance measure for time minimization (Adopted)
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Performance
measure
What it entails Reference
Reduce no. of trips
per vehicle
This measure refers that how
many trips vehicle must make
to meet the customer demand.
It also determines the no. of
vehicles that one should have.
Florida department
of transport (2014),
Freight best practice
(2010)
Ensure vehicle
fuel efficiency
including idle time
This measure refers to the
distance that a vehicle can
travel by using a particular
amount of fuel.
Waters (2019),
EECA business
(2017)
Reduce no. of
empty miles
Empty miles refer to the
distance travelled while
generating no income. Empty
miles results when truck travel
without load.
Freight best practice
(2010)
Minimizing
maintenance cost
There is critical need to
maintain vehicles in order to
avoid any kind vehicle failure.
It includes cost of cleaning and
costs of good quality tyres etc.
Dönmez and
Zemmouri (2016)
Reduce no. of
damages during
delivery
This measure refers to damages
that occur in transit or at the
time of loading and unloading.
Bodenheimer (2014)
Optimize no. of
vehicles
movement
This measure includes all the
vehicle movements from
delivering material to
removing waste at construction
site
Yin, Tookey,
Seadon (2018)
4.1.4.1d) Performance measures related to environmental sustainability
Sichwardt (2011) suggests that due to global warming and green-house effect
environment protection is common objective of many transport companies. He
further suggests that since CO2 and N2O emissions results from burning
vehicles fossil fuel, so the connection of environment and logistics is very
Table 14: Performance measures related to cost minimization (Adopted)
59 | P a g e
interesting. Due to globalization and increasing competition, transport
companies are adopting environment friendly processes and green logistics
Sichwardt (2011). Performance measures for this objective are mentioned
below in Table 15;
Performance measure What it entails Reference
Reduction in CO2
emission
This measure refers to
minimize air pollution
due to CO2 emission
via vehicles
Sichwardt (2011)
Minimize no. of
breaches in noise limits
This measure refers to
the number of times the
vehicle has violated the
noise limitations such
as horns, sound of
engines, material
loading and unloading
at night etc.
Sound noise limit
report (2005)
4.1.4.1e) Performance measures related to security assurance
Polzin (2002) suggests that transport security is logistically complex and
remains an important part of security business protocol. He further suggests
that a secure transportation system is critical because transportation facilities
may be target of terrorists intending to harm the economy. These performance
measures are shown in Table 16 below;
Performance
measure
What it entails Reference
Ensure good
quality
packaging of
hazardous
material
This measure refers to quality
criteria for material packaging. It
suggests that material should be
packaged in strong and leak tight
way while delivery
Burdick (2012)
Reduce on
road material
theft
On road material theft affect
productivity and drain profits. On
road material theft should be
avoided.
Berg and Hinze (2005)
Table 15: Performance measures related to environmental sustainability
(Adopted)
Table 16: Security related performance measures (Adopted)
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4.1.4.1f) Performance measures related to safety assurance
Tamuli (2016) suggests that vehicle transport safety is very important because
road traffic accidents contribute one third of the total accidents. He further
suggests that road accidents are one of the leading causes of preventable deaths
worldwide. Botha (2005) indicates that transport safety can be measured by
no. of transport incidents. These are mentioned inTable 17 here;
Performance measure What it entails Reference
Reduce no. of transport
incidents
This refers to number
of traffic incidents and
casualties resulting
from crashes per time
period
Tamuli (2016)
Ensure vehicle
insurance
In order to safeguard
vehicles against
accidents, damage and
theft it is important to
insure vehicles.
Jonck (2019)
4.2 Frame of reference for semi structured interviews The operationalization of key terms used in interview guide (attached in
Appendix 1) regarding semi structured interview is as follows in Table 18,
Key term Conceptual definition Operational
definition
Construction
industry
An industry consisting of
firms responsible for
building structures such as
offices, hospitals, airports,
shopping centers, housing,
factories etc. and civil
engineering such as
infrastructure for water
supply, irrigation,
transportation, power
generation and the likes
ISIC (2008)
Construction industry
is a building industry
which constructs
houses, hospitals,
schools, commercial
real estate and so on.
Construction
logistics
Construction logistics is
defined as providing
construction site with
resources in the form of
Construction logistics
is defined as the
planning, execution,
control of
Table 17: Safety performance measures (Adopted)
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materials, machines and
personnel in an efficient
manner along with
managing resources
efficiently on the
construction site itself as
well as ensuring efficient
recycling and waste
management that enables
circular economy and
durability Fredriksson
(2018)
procurement,
transport, stationing of
workers, materials and
other resources in
order to complete a
construction project
Construction
logistics solution
Construction logistics
solution” is defined as
logistic solution applicable
in construction projects in
order to coordinate
material flows, thus
resulting in less transport
disruptions and efficient
construction Janne (2018)
Construction logistic
solution is a solution
to coordinate material
flows to ensure
efficient construction
and reduce
disturbances on urban
transport system
Performance
measures
A quantifiable measure
that is used to gauge or
compare performance in
terms of achieving
strategic and operational
goals. Jahangirian, Taylor,
Young, Robinson (2017)
A quantifiable
indicator used to
assess how well an
organization or
business is achieving
its desired objectives
Efficiency How well the resources
expended are utilized.
Dubois, et al., (2019)
Ratio of useful output
to total input or doing
things in a right way.
Effectiveness The extent to which the
established goals are
accomplished. Dubois, et
al., (2019)
How much of a
product or service is
produced in a given
time frame? Doing the
right things.
Transport flows “Transport flow” is
referred as vehicle
movements/ flow of
vehicles (i.e. the number of
vehicles that pass through a
specific point during a
specific interval of time)
and material handling
Transport flows is a
movement of vehicles
such as trucks
62 | P a g e
activities such as vehicle
unload, dumping, material
carrying, vehicle reloading
etc. Jayasinghe, Sano and
Nishiuchi (2015)
Objectives “Objectives” are defined as
a support to specific goals
which provide additional
details or strategies on how
the goal will be achieved.
Goshu and Kitaw (2017)
Objective is a specific
step that company
takes to a achieve a
desired result
Effective transport
planning
To plan transport in a way
so that material
requirement can be met in
the shortest possible time
with minimum number of
kilometers driven
(Sichwardt, 2011).
To plan transport in a
way that vehicles can
visit maximum
number of touch
points, deliver largest
amounts of material,
minimize delivery
time and reduce
distance travelled etc.
Security Protection against
deliberate threats (Baird,
2017)
Potential harm caused
by others. For
example, if somebody
throws the brick or if
somebody starts a fire.
Safety Being secure against
unintended threats (Baird,
2017)
Protection against
things that happen by
chance/accident. For
example, a brick falls
on a builder head or if
fire starts by accident
etc.
Comprehensiveness It means including or
dealing with all or nearly
all elements or aspects of
something. Cambridge
dictionary (2019)
Comprehensiveness
means complete,
covering almost every
aspect.
Early delivery Early delivery is a delivery
that is made well before
agreed time. Northey
(2018)
Delivery that
happened before time.
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On time delivery On time delivery is the date
promised by supplier to
make a delivery to its
client. It helps in optimal
utilization of time.
Marion (2019)
Delivery that happen
exactly on agreed time
Late delivery Late delivery is a delivery
that take place after the
agreed upon time of
delivery. Darvik and
Larsson, (2010)
Delivery that happed
after agreed time
Vehicle utilization
(km)
Vehicle utilization will
help in calculating no. of
trips or kilometer travelled
by vehicle in a specific
period thus playing major
role in effective transport
planning.
Hosseini and Shirani
(2011), Malacarne (2018)
Mileage i.e. how many
kilometers a vehicle
can cover per hour
Vehicle weight
utilization
Vehicle weight utilization
will determine the quantity
of material carried.
Hosseini and Shirani
(2011), Malacarne (2018)
How much volume a
vehicle can carry
Shipping
documents
Shipping documents are
papers that accompany a
shipment listing the date
shipped, the customer, the
method of shipment and
the quantities and
specifications of goods
shipped. Collins English
dictionary (2019).
Documents also
known as pick list
containing
information such as
customer address,
shipment date,
delivered products
code and description,
product quantity,
value and so on.
4.3 Empirical findings from semi structured interviews The main aim of semi structured interviews was to identify transport related
performance measures within construction logistics solutions. These
interviews have highlighted those transport related performance measures
whose measurement is crucial for both logistics solutions. At this point in time,
no classification has been made that which transport performance measures
Table 18: Operationalization for semi structured interviews (Own illustration)
64 | P a g e
belongs to terminal, checkpoint and construction site. This empirical finding
reflects only transport related performance measures without being classified
into terminal, checkpoint and construction site.
4.3.1 Case 1-Terminal
According to business unit manager (14.1.2019) construction logistic solutions
can bring efficiency to the construction site. As he suggests that the benefits
of logistic solutions are obvious, but it is hard to verify these benefits. He
further adds on that in order to quantify construction logistics benefits, there
is a need to develop transport performance measures. On asking about the
transport performance measures being used by their company, sales executive
(14.1.2019) suggests that due to fragmented nature of construction projects it
is hard to develop good and comprehensive performance measures. In addition
to this, he suggests that the process of performance measure development is
also not so streamlined. Founder of case company (14.1.2019) suggest that it
is important to have few but comprehensive and holistic performance
measures. He is of the opinion that too many performance measures make it
difficult to monitor them thus resulting in no improvement. All the respondents
are of the view that it is important to have well defined performance measures,
and these should be developed from 360-degree perspective. In addition to the
performance measures discussed in theory, business unit manager (14.1.2019)
highlights some other performance measures which are as follows in Table 19;
65 | P a g e
Performance measures
highlighted by Case 1-terminal
during interview
Explanation
Reduction in average waiting time
at gate and stops etc.
According to business unit manager
(14.1.2019), 50% of the daytime gets
wasted in waiting. So, this is very
important to consider as a
performance measure.
Optimal route planning to and
from construction site
Case founder (14.1.2019) suggests
that it is important to optimally plan
delivery routes. He is of the view
that a lot of time and cost get wasted
if there is ineffective route planning.
Easy access to construction site Case founder (14.1.2019) suggest
that it is essential for construction
logistic solution to give vehicles
easy access to the construction site.
Because otherwise vehicles will not
be able to come to the destination
and unload the required material
which will eventually lead to delay
in construction project.
Assuring estimated time of arrival
(ETA)
Sales executive (14.1.2019) suggest
that it is important to measure
estimated time of arrival because it
will give a clear idea that when a
vehicle is expected to arrive at a
certain place.
They also suggest that it is good to develop performance measures aligning
with the strategic objectives.
4.3.2 Case 2-Checkpoint
Logistics consultant at case company 2 (14.1.2019) advocates Case 1
respondents and have similar views. According to her, following performance
measures are necessary to develop which are as follows in Table 20;
Table 19: Performance measures highlighted by Case 1 during interview (Own
illustration)
66 | P a g e
Performance measures
highlighted by Case 2-terminal
during interview
Explanation
Reduce cost of delivery Logistics consultant (14.1.2019)
suggests that cost of delivery is very
high. So, it is important to measure
the costs of delivery and reduce the
unnecessary steps to make it less
costly
Reduce cost of moving material According to her, in order to get
discounts generally more material is
ordered as compared to what is
needed which results in cost for
moving material. By measuring this,
wastage can also be reduced along
with the reduction in cost of moving
material
Optimal transport per day Logistics consultant (14.1.2019)
suggests that it is important to
determine what amount of transport
should be there and how this
transport will vary between different
days as well as between different
parts of the day.
No. of booked vs un-booked
deliveries
According to logistics consultants
(14.1.2019) in checkpoint for
delivery and pickup, a booking is
done in order to effectively manage
delivery. So, it is important to
measure no. of booked vs un-booked
deliveries in order to control
inefficiency that takes place due to
un-booked delivery.
Vehicles schedule reliability She suggests that it is important to
have reliable vehicle schedule so that
there should be no delays and no
waiting times.
Minimize delivery distances Logistic consultant (14.1.2019)
suggest that measuring delivery
distance is very important
performance measure because it then
helps in defining proper delivery
routes
67 | P a g e
Effective loading and unloading
(in terms of time)
She suggests that there should be
efficient loading and unloading and
no time should be wasted while
performing these activities. So, this
is an important performance
measure in order to bring
improvement in transport flows.
Reduce road damage due to
vibrations
According to her, transport vehicles
impact overall environment as well
such as road damage due to vehicle
movements. She suggests that it is
important to meet weight limits and
criteria for vehicles before going on
road. As construction material is
heavy so care should be taken in
deciding how much load a vehicle
should carry.
4.4 Combining findings from theory and semi structured
interviews Empirical findings from semi structured interviews along with theory have
resulted in identifying transport related performance measures which are
applicable to both logistics solution and their respective construction site. This
has helped in developing protocol for focus group discussion which will be
mentioned below in Table 21 and as well as attached in Appendix 2. The aim
of focus group was to identify which performance measure is relevant to what
logistic solution.
Table 20: Performance measures highlighted by Case 2 during interview (Own
illustration)
68 | P a g e
Objective:
Effective
transport
planning
Objective:
Reduce
transportation
time
Objective:
Transport
cost
minimization
Objective:
To achieve
environmental
sustainability
Objective:
To ensure
security
Objective: To
ensure safety
Reduced no. of
miles driven
outside of pre-
determined
routes
Ensure on time
delivery
Reduce no. of
trips per
vehicle
Reduction in
CO2 emission
Ensure good
quality
packaging
of
hazardous
material
Reduce no. of
transport
incidents
Reduce total
distance driven
Reduce truck
turnaround time
-Loading and
unloading time
- Easy access to
construction site
-Utilization rate
of loading and
unloading
equipment
Ensure
vehicle fuel
efficiency
including idle
time
Minimize no.
of breaches in
noise limits
Reduce on
road
material
theft
Ensure vehicle
insurance
Ensure trucks
availability
(trucks
confirmed vs
requested)
Minimize
vehicle travel
time
Reduce no. of
empty miles
Road damage
due to vehicle
vibrations or
vehicle
overload
Reduced no. of
unplanned
deliveries
Reduce the time
taken in
preparing
shipping
documents
Minimizing
maintenance
cost
Optimal no. of
deliveries
arrives at gate
per day
Minimize the
time taken in
doing shipping
documents
corrections
Reduce no. of
damages
during
delivery
Maximize
vehicle travel
capacity
Reduction in
average waiting
time at gate
Optimize no.
of vehicles
movement
Maximize
vehicle weight
utilization
Vehicles
schedule
reliability
Reduce cost
of delivery
Table 21: Focus group protocol (Own illustration)
69 | P a g e
4.5 Frame of reference for focus group protocol The operationalization of focus group protocol is mentioned below in Table
22;
Key terms Conceptual definition Operational definition
Reduced no. of
miles driven
outside of pre-
determined
routes
This measure refers to no. of miles
that vehicle will travel outside of
already determined route in order to
reach the destination.
Robinson (2019), Ryus et al.,
(2013)
This measure will ensure the effectiveness/
accuracy of planned routes. Are routes planned
well that vehicles do not face any problem in
following those routes or vehicles have to
follow another route to reach their destination?
The focus of this measure is to see how well
vehicle routes have been designed.
Reduce total
distance driven
It refers to the total distance that
vehicle has travelled. This measure
will give information regarding
delivery distances and helps in
effective transport planning.
Kaparias and Bell (2011), Freight
best practice (2010)
Total distance driven is measured to see how
many miles or kilometers vehicle has to travel.
Measuring this will help in planning deliveries
i.e. how much time will be needed to reach
vehicle to reach destination, what should be the
buffer time for vehicles and so on. This
measure will also help in determining the
optimal speed level that drivers can maintain to
reach to the destination on time.
Ensure trucks
availability
(trucks
confirmed vs
requested)
This measure refers to the truck’s
availability in need. Truck
unavailability will lead to delays
which is a sign of ineffective
transport planning. This indicator
will help in fleet management.
Brierley (2017), Key performance
indicator survey (2013)
The measure aims at measuring no. of vehicles
required for material supply deliveries. It will
help in identifying delays due to vehicles non
availability.
Reduced no. of
unplanned
deliveries
This measure refers to the presence
of contingency plan within
transport system. Based on this
measure, the extra time that gets
consumed in unplanned delivery
will be reduced.
Beetrack (2016), Faschingbauer
(2015)
This measure aims at measuring planning
effectiveness or how well delivery process has
been designed.
Optimal no. of
deliveries
This performance measure leads to
efficient delivery scheduling which
consequently results in efficient
The measure aims at measuring the total
material requirement at construction site on
daily basis. This also indicates that how much
70 | P a g e
arrives at gate
per day
transport planning. The input for
this measure will be effective
material requirement planning.
Thomas (2017), Ogden and Turner
(2015)
material should be made available at supplier’s
end.
Maximize
vehicle travel
capacity
Vehicle capacity is a measure for
vehicle utilization. Vehicle
utilization will help in calculating
no. of trips thus playing major role
in effective transport planning.
Malacarne (2018), Hosseini and
Shirani (2011)
It refers to the number of times trucks are in
use in a given period.
Maximize
vehicle weight
utilization
Vehicle weight utilization will
determine the quantity of material
carried. Malacarne (2018),
Hosseini and Shirani (2011)
Weight capacity utilization is defined as the
ratio of truck weight plus the material weight
that truck is carrying and total weight capacity
of the truck.
Ensure on time
delivery
On time delivery is the date
promised by supplier to make a
delivery to its client. It helps in
optimal utilization of time.
Marion (2019)
It mainly means “being on time”. On time
delivery (OTD) can be measured by
calculating the amount of shipments delivered
on time to the customer in relation to the total
number of orders shipped. It indicates
organization capability to achieve requested
delivery date (RDD)
Reduce truck
turnaround
time
-Loading and
unloading time
- Easy access to
construction site
Truck turnaround time is a major
performance measure including
loading and unloading times as
well as access time to construction
facility. Van Der Spoel, Amrit, Van
Hillegersberg (2015)
Truck turnaround time is defined as the
average time utilized between a truck’s arrival
at any plant/facility and its departure from the
same. In simple words it is in time and out time
of truck.
Minimize
vehicle travel
time
It is defined as total time that
vehicle take in reaching its
destination. Yan, Xu, Han (2015),
Lomax, Schrank, Lasley, Eisele
(2013)
It refers to reduction in total travel time of the
vehicle.
Reduce the time
taken in
preparing
This refers to time taken in
preparing shipping document. It
also indicates the time difference
between order placed and making
Shipping documents include bill of lading,
commercial invoice, certificate of origin,
insurance certificate, packing list and other
custom clearance documents.
71 | P a g e
shipping
documents
shipping documents ready. Naoh
(2018), Manaadiar (2017)
Minimize the
time taken in
doing shipping
documents
corrections
It refers to time taken in doing
corrections in shipping documents
such as address, price, quantity,
product detail and so on. Naoh
(2018), Manaadiar (2017)
It reflects the accuracy of shipping
documentation such as address, invoice
number, customer name, right material name,
quantity and price calculations
Reduction in
average waiting
time at gate and
stops
This measure refers to the vehicle
waiting time at gate and stops. 50%
of the daytime get wasted in
average waiting time. Business unit
manager (14.1.2019)
It refers to waiting time that truck has to pass
while being in queue at the entrance gate of
construction site
Vehicles
schedule
reliability
It refers to dependability on vehicle
time of arrival and departure.
Logistic consultant (14.1.2019)
It shows that how reliable or consistent vehicle
schedule is
Reduce no. of
trips per vehicle
This measure refers that how many
trips vehicle must make to meet the
customer demand. It also
determines the no. of vehicles that
one should have. Florida
department of transport (2014),
Freight best practice (2010)
It shows the number of trip that vehicle will
make from supplier to construction site.
Ensure vehicle
fuel efficiency
including idle
time
This measure refers to the distance
that a vehicle can travel by using a
particular amount of fuel. Waters
(2019), EECA business (2017)
It refers how efficiently vehicle fuel is getting
consumed in comparison to its
output/performance.
Reduce no. of
empty miles
Empty miles refer to the distance
travelled while generating no
income. Empty miles results when
truck travel without load. Freight
best practice (2010)
Empty miles refer to empty returns that truck
has to do in some cases.
Minimizing
maintenance
cost
There is critical need to maintain
vehicles in order to avoid any kind
vehicle failure. It includes cost of
cleaning and costs of good quality
tyres etc. Dönmez and Zemmouri
(2016)
The cost incurs in order to maintain vehicle
72 | P a g e
Reduce no. of
damages during
delivery
This measure refers to damages that
occur in transit or at the time of
loading and unloading.
Bodenheimer (2014)
Damages can happen during transit, loading
and unloading or at site.
Optimize no. of
vehicles
movement
This measure includes all the
vehicle movements from delivering
material to removing waste at
construction site. Yin, Tookey,
Seadon (2018)
Vehicle movements refer to all movement
from delivering material to carrying back the
waste as a return
Reduce cost of
delivery
Cost of delivery is hourly cost of
delivery divided by number of
deliveries made each hour. Logistic
consultant (14.1.2019)
Total delivered cost is the amount of cost that
is incurred by the company from point of
manufacturing a product to its delivery. It
includes all costs such as packing, loading,
unloading, transporting, etc
Reduction in
CO2 emission
This measure refers to minimize air
pollution due to CO2 emission via
vehicles. Sichwardt (2011)
To reduce air pollution
Minimize no. of
breaches in
noise limits
This measure refers to the number
of times the vehicle has violated the
noise limitations such as horns,
sound of engines, material loading
and unloading at night etc. Sound
noise limit report (2005)
To reduce air pollution
Road damage
due to vehicle
vibrations or
vehicle overload
Due to vehicle overload and
resonance, roads get damaged.
Logistic consultant (14.1.2019)
Roads getting torn due to heavy duty vehicles
Ensure good
quality
packaging of
hazardous
material
This measure refers to quality
criteria for material packaging. It
suggests that material should be
packaged in strong and leak tight
way while delivery. Burdick (2012)
Packaging should be leak tight and of good
strength
Reduce on road
material theft
On road material theft affect
productivity and drain profits. On
road material theft should be
avoided. Berg and Hinze (2005)
To reduce on road material thefts
73 | P a g e
Reduce no. of
transport
incidents
This refers to number of traffic
incidents and casualties resulting
from crashes per time period.
Tamuli (2016),
Botha (2005)
Reduce no. of vehicle accidents
Ensure vehicle
insurance
In order to safeguard vehicles
against accidents, damage and theft
it is important to insure vehicles.
Tamuli (2016),
Botha (2005)
In order to remain safe from any unforeseen
circumstance, it’s good to have vehicle
insurance
4.6 Empirical finding from focus group discussion The aim of focus group was to classify performance measures according to
terminal, checkpoint and construction site. So, the main theme under focus
group discussion was “what performance measures do you consider are the
most relevant”. The purpose was to verify and cross check identified transport
performance measures via theory and semi structured interviews and also to
make changes (add/remove) accordingly.
4.6.1 Case 1-Terminal
According to business unit manager (4.4.2019) the measure “reduced no. of
miles driven outside of pre-determined routes” is relevant to terminal because
terminal follows a milk run approach. He further suggests that it would be easy
to measure by selecting one regular supplier and then taking observations.
Founder, business unit manager (4.4.2019) further suggests that performance
measures such as reduced total distance driven, ensure on time delivery, reduce
truck turn-around time, vehicle schedule reliability, reduced no. of empty
miles, reduced no. of damages, reduction in CO2 emissions, minimizing
maintenance cost, reduced no. of unplanned deliveries, optimal no. of
shipment arrive at gate per day, maximize vehicle weight utilization, reduction
in average waiting time at gate, reduce the time taken in preparing shipping
documents, minimize the no. of errors in shipping documents, minimize
vehicle travel time, no. of vehicle movements, reduce no. of trips per vehicle,
reduce no. of transport incidents, ensure good quality packaging of hazardous
material, reduce on road material theft, road damage due to vibrations,
optimize vehicle movements are all relevant to terminal.
Table 22: Operationalization of focus group protocol (Own illustration)
74 | P a g e
They further add on that instead of truck availability they also require right
kind of fuel due to objective of achieving environmental sustainability. In
addition to this, they also emphasized the importance of not only vehicle
capacity in terms of kilometers and volume but also in terms of shape of
vehicle. They suggest that it is very important for them to consider shape of
vehicle to fit in the turning area and also they need to care about the height of
vehicles due to entrance into the basements. They add on that in winters they
ensure the use of winter tyres for having more grip in the snow.
Regarding vehicle insurance for safety they suggest that all their vehicles are
already insured. Regarding noise breaches, they suggest that it is good to have
but they have not faced any difficulty with this. Additional performance
measures which got highlighted during focus group are shown in Table 23;
Additional performance measures
got highlighted during focus
group discussion
Explanation
Reduced no. of unauthorized
entries
While talking about security,
business unit manager (4.4.2019)
suggest that reduced no. of
unauthorized entries to construction
site are also important to measure.
Reduced no. of interruptions to
ongoing construction site
Again, referring to security, business
unit manager (4.4.2019) suggest that
there is need to ensure reduced no. of
interruptions to ongoing
construction activities.
Certificate for good eco driving Founder (4.4.2019) suggest that it is
their aim to achieve certificate for
good eco driving in order to ensure
environmental sustainability
objective
Predictability of turnaround time Moderator (4.4.2019) suggest that it
is also important to have good
estimates regarding turnaround
times
Table 23: Additional performance measures got highlighted during focus group
discussion-Case 1 (Own illustration)
75 | P a g e
4.6.2 Case 2-Checkpoint
Logistic consultant (15.4.2019) suggests that for checkpoint the performance
measure “minimizing distance from checkpoint to unloading zones” is more
relevant rather than performance measure “reducing total distance driven”.
She further suggests that reduction of unplanned deliveries is very relevant for
checkpoint. According to her, optimal no. of shipments arrives per day,
reduction in truck around time, reduced no. of damages, reduction in material
theft, availability of no. of parking spaces for unloading available vehicles is
relevant. She further suggests that optimal no. of vehicle movements and
reducing cost of delivery also plays an important role in checkpoint
performance evaluation.
Other performance measures mentioned in focus group protocol are not very
relevant to checkpoint according to logistic consultant (15.4.2019). This is
because they do not own their own vehicles. Additional performance measures
got highlighted during focus group discussion are mentioned in Table 24;
Additional performance measures
got highlighted during focus
group discussion
Explanation
Ensure unloading available
vehicles
Logistics consultant (15.4.2019)
suggest that it is important to ensure
unloading of available vehicles
Maximum no. of available parking
spaces
Logistics consultant (15.4.2019) is
of view that it is important for
checkpoint to have a greater number
of parking spaces
4.7 Conceptual model of the study Figure 17 below shows conceptual model after theory and empiry. In this
model, all identified transport related performance measures (i.e. via theory,
semi structured interview and focus group have been arranged according to
terminal, checkpoint and their respective construction sites.
Table 24: Additional performance measures got highlighted during focus group
discussion- Case 2 (Own illustration)
76 | P a g e
Figure 17: Conceptual model after theory and empiry (Own illustration)
3
Con
stru
ctio
n Lo
gist
ic S
olut
ion
6 6
Ter
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al
Con
stru
ctio
n S
ite
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ctio
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ite
*Per
form
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sure
s ar
e re
late
d to
tran
spor
t flo
ws
only
Tra
nspo
rt F
low
s
Tra
nspo
rt r
elat
ed p
erfo
rman
ce m
easu
res
to, f
rom
and
with
in th
e co
nstr
uctio
n si
te
Per
form
ance
Mea
sure
s
- E
nsur
e on
tim
e
deliv
ery
-Ens
ure
relia
bilit
y
in tr
avel
tim
es
- E
nsur
e rig
ht k
ind
of tr
uck
and
fuel
avai
labi
lity
- O
ptim
al n
o. o
f
ship
men
ts a
rriv
e at
gate
per
day
-Max
imiz
e ve
hicl
e
utili
zatio
n in
km
(
mor
e sh
ape
and
heig
ht)
- M
axim
ize
vehi
cle
wei
ght u
tiliz
atio
n
- Red
uce
time
tak-
en in
pre
parin
g s-
hipp
ing
docu
men
ts
- Min
imiz
e th
e no
.
of s
hipp
ing
docs
erro
r
- Red
uce
C02
emis
sion
- Red
uce
num
ber
of a
ccid
ents
- Red
uce
num
ber
of d
amag
es d
ue
to v
ibra
tions
Che
ckpo
int
Per
form
ance
Mea
sure
s
- M
inim
izin
g di
stan
ce fr
om c
heck
poin
t
to u
nloa
ding
zon
e
- R
educ
e nu
mbe
r of
unp
lann
ed d
eliv
er-
ies
- M
axim
um n
umbe
r of
ava
ilabl
e pa
rkin
g
spac
es
- E
nsur
e un
load
ing
avai
labl
e ve
hicl
es
Tra
nsit
-Red
uced
num
ber
ofm
iles
dr-
iven
out
side
of p
re d
eter
min
-
ed r
oute
s
-Red
uce
tota
l dis
tanc
e dr
iven
-Red
uce
num
ber
of e
mpt
y
mile
s
-Min
imiz
e ve
hicl
e tra
vel t
ime
-Red
uce
num
ber
of d
amag
es
-Ens
ure
vehi
cle
fuel
effic
ienc
y
-Opt
imiz
e nu
mbe
r of v
ehi-
cle
mov
emen
ts
-Red
uce
vehi
cle
mai
nte-
nanc
e co
st
-Ens
ure
good
qua
lity
mat
-
eria
l pac
kage
Tra
nsit
-Red
uce
num
ber o
f dam
ages
-Opt
imiz
e nu
mbe
r of v
ehic
le m
ovem
ents
-Red
uce
cost
of d
eliv
ery
-Red
uctio
n in
mat
eria
l the
ft
Site
-Red
uce
num
ber o
f dam
ages
-Ens
ure
on ti
me
deliv
ery
-Num
ber o
f una
utho
rized
entr
ies
-Num
ber o
f int
erru
ptio
ns
to o
ngoi
ng c
onst
ruct
ion
oper
atio
ns
-Opt
imal
num
ber o
f shi
pmen
ts
arriv
e at
gat
e pe
r day
-Red
uce
time
take
n in
prep
arin
g sh
ippi
ng
docu
men
ts
-Eas
y ac
cess
to th
e co
ns-
truc
tion
site
-Red
uctio
n in
ave
rage
w-
aitin
g tim
e at
the
gate
-Red
uce
truc
k tu
rnar
ound
time
Site
-Opt
imal
num
ber
of d
eliv
erie
s ar
rive
at g
ate
per
day
-Red
uctio
n in
ave
rage
wai
ting
time
at g
ate
-Red
uce
num
ber
of d
amag
es
-Red
uce
truc
karo
und
time
Con
stru
ctio
n lo
gist
ics
77 | P a g e
4.8 Analysis The purpose of research question 1 was to identify transport related
performance measures for the evaluation of construction logistics solution and
their respective construction site. Business unit manager (14.1.2019)
expressed his opinion during semi structured interview that it is good to have
few but comprehensive performance measures so for this reason there is need
to consolidate performance measures. The other reason for consolidating
performance measure is to remove redundancy and duplication among
identified performance measures. For example, the performance measure
“optimize no. of vehicle movements” aims at measuring the same variables as
another performance measure “reduce cost of delivery”. These both
performance measures aim at reducing logistics cost. The criteria for choosing
performance measures in consolidation process is ease in calculation.
The consolidated table for transport related performance measures within
construction industry is shown below in Table 25;
Objective:
Effective
transport
planning
Objective: To
reduce
transportation
time
Objective:
To minimize
cost
Objective:
To achieve
environmental
sustainability
Objective
: To
ensure
security
Objective:
To ensure
safety
Performance
measures:
Vehicle route
optimization
• Reduce
total
distance
driven (no.
of stops,
distance
between
stops)
• Minimize
vehicle
travel time
(no. of
miles
Performance
measures:
Reduce truck
turnaround time
• loading/un
loading
activities
• vehicle
waiting
time
• access
time to
constructi
on site
• utilization
rate of
loading
Performance
measures:
Minimize no.
of in-transit
damages
Performance
measures:
Minimize road
damage
-Road
vibrations and
overload
Performan
ce
measures:
Ensure
good
quality
packaging
of
hazardous
material
Performance
measures:
Reduce no.
of transport
incidents
78 | P a g e
traveled
and speed)
and
unloading
equipment
Effective
contingency
planning
• Reduced
no. of un-
planned
deliveries
Minimize
vehicle
maintenance
costs
-cleaning
-oil and filter
change
-tire
replacement
-brake pad
and fluid
replacement
-coolant
replacement
-vehicle
washing
-wear and
tear etc.
Reduce CO2
emission
-Maximize
vehicle
utilization
-Optimize no.
of vehicle
movements
-Vehicle route
optimization
Reduce on
road
material
theft
Ensure
vehicle
insurance
Effective material
requirement
planning
• Optimal
no. of
shipments
arrives at
gate per
day
Maximize
vehicle
utilization
-time
-capacity
Optimize no.
of vehicles
movement
4.8.1 Explanation of the consolidated table
For effective transport planning, three major performance measures have been
identified i.e. vehicle route optimization, effective contingency planning,
Table 25: Consolidated performance measures (Own illustration)
79 | P a g e
effective material requirement planning. And then identified transport related
performance measures have been clubbed/merged into these major
performance measures which will be discussed in detail below;
4.8.1.1 Effective transport planning
4.8.1.1a) Vehicle route optimization
According to Yan, Xu, Han (2015) vehicles route optimization minimizes total
travelling time as well as reduce uncertainties in pickup and delivery timings.
He further suggests that effective transport planning includes route
determination i.e. to visit maximum number of touch points, to deliver largest
amounts of material, minimize delivery time and distance travelled etc.
Business unit manager (14.1.2019) explained during interview that the biggest
challenge they are facing with terminal construction logistic solution is “how
to route deliveries to and from construction site”. For this purpose, this
consolidated measure has been chosen. Sichwardt (2011) suggests that in case
of distribution via terminal it is good to choose shortest distance for respective
routing. He further suggests that shortest distance will not only save costs but
there would be less CO2 emissions as well. He adds on that vehicle route
optimization can be measured by “Reduced Total Distance Driven” metrics.
Eriksson (2015) suggests that in order to calculate this it is important to have
data on number of stops, distance between stops, time for hauling etc. In
addition to this, “Minimize Travel Time” is also considered as important for
vehicle route optimization Yan, Xu, Han (2015). Lomax et al. (2013) define
“Travel Time” as the door to door sum of all travel times. They further suggest
that factors such as bad weather, unexpected vehicle breakdown, congestion,
driver skill and experience will be considered in this.
4.8.1.1b) Effective contingency plan
Sanchez-Rodrigues, Potter, Naim (2010) suggest that uncertainty in supply
chain act as a hurdle in effective transport management and control. They
further suggest that uncertain situation can occur due to external factors (such
as road closures, accidents, terrorist attacks, etc.,) as well as internal factors
(such as poor planning and demand prediction). Logistic consultant
(15.4.2019) suggests that unplanned deliveries create inefficiency in whole
delivery process within checkpoint construction logistics. She further suggests
that transport planning should be so effective that there should not be any
unplanned deliveries. But she adds on that if due to unforeseen circumstances
any unplanned delivery takes place then the system should be so strong that
without any delay it can respond to unexpected situation without wasting any
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time. Anastasia (2017) suggests that it is important to have effective
contingency or business continuity plans because it reflects organization
capability to deliver its products or services even after any disruptive incident.
Faschingbauer (2015) suggest that contingency plan is the best customer
service that transportation company can provide by allowing freight to remain
operational. Beetrack (2016) suggests that there should be flexibility within
the delivery management process and transporters should respond and adjust
schedules as unplanned issues arise. The effectiveness of contingency plan can
be measured by responsiveness of unplanned deliveries metric.
4.8.1.1c) Effective material requirement planning
Sarkar et al. (2013) suggest that material requirement planning (MRP) helps
in calculating the material and components required to construct a building.
They further suggest that it includes three steps i.e. to know what material and
components are in hand, identifying which additional material is needed
followed by raising request for procurement or production. They further
suggest that material requirement planning ensure availability of required
materials in time of need while maintaining adequate inventory levels.
Thomas (2017) highlights that material requirement planning play crucial role
in effective shipment planning i.e. what, where and when the material is
required. He further suggests that effective material planning helps in
calculating “Optimal no. of shipments arrive at gate per day” performance
metric.
4.8.2 Other Objectives
In order to meet the objective of transportation time minimization, a
consolidated measure “reduced turnaround time” will be considered. This is a
very comprehensive measure because it includes loading/unloading activities,
access time to construction site and also utilization rate of loading and
unloading equipment. For meeting objective of cost minimization, the
performance measures such as reduced no. of damages (in-transit), reduced
vehicle maintenance cost, maximum vehicle utilization and optimal no. of
vehicle movements have been considered. These performance measures have
been selected because they are relevant to both construction logistics solutions
and will also give true picture of logistics cost. As far as environmental
sustainability objective is concerned, two performance measures have been
selected i.e. reducing the road damage incurred due to vehicle vibrations and
vehicle overload and reduction in CO2 emission. CO2 emission can be reduced
by maximizing vehicle utilization, optimizing number of vehicle movements,
81 | P a g e
optimizing vehicle route. Good quality packaging of hazardous material and
reducing material theft are two measures chosen to ensure security. And lastly
reduced no. of transport incidents and having vehicle insurance are necessary
measures for ensuring safety.
The conceptual model with consolidated transport performance measures
classified according to construction logistics solution (i.e. terminal and
checkpoint) and their respective sites are shown below in Figure 18.
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Figure 18: Consolidated conceptual model after analysis (Own illustration)
6 6
Term
ina
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Con
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n S
ite
Con
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Reduce tota
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-
Min
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e v
ehic
le tra
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Effective m
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-O
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Effective c
ontingency p
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-R
educed n
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-M
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ize v
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Effective c
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Effective m
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ize n
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of ship
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Tra
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-Reduced n
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-Min
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-Optim
ize n
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-Good q
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-Reduced C
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-Reduced n
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ansport
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Tra
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-Reduced n
um
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of dam
ages
-Optim
ize n
um
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of vehic
le m
ovem
ents
-Reduce m
ate
rial th
eft
Site
Reduced n
um
ber
of dam
ages a
t site
Effective m
ate
rial re
quirem
ent pla
nnin
g
-O
ptim
al num
ber
of shpm
ents
arr
ive a
t gate
per
day
Reduced n
um
ber
of tr
ansport
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Reduce tru
ck turn
aro
und tim
e Site
-Reduced m
ate
rial th
eft
-Reduced n
um
ber
of tr
ansport
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-Reduced tru
ck turn
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Co
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gis
tics
Co
nstr
uctio
n lo
gis
tic s
olu
tio
n
83 | P a g e
5 Framework for RQ2
The research question 2 is “what kind of data is needed to measure identified
transport related performance measures in research question 1. The purpose of
this research question will be satisfied by providing calculations to the
identified transport performance measures in RQ1. Here the prime focus is to
only show the way for calculating identified performance measures
irrespective of goal assigned to them i.e. maximize or minimize. The
operationalization for this research question will remain the same as used in
research question 1. Because this chapter shows calculations for same
performance measures that have been explained previously in section 4.2 and
4.5.
5.1 Theory This chapter will discuss theoretical formulas for the following identified
performance measures shown in Table 26;
Identified performance measures
from RQ1
Identified performance measures
from RQ1
Objective: Effective transport
planning
-Total distance driven
-Total travel time
-No. of unplanned deliveries
-No. of shipments arrives at gate per
day
Objective: To achieve
environmental sustainability
-Road damage through vibrations
-CO2 emission
Objective: Minimizing
transportation time
-Truck turnaround time
Objective: To ensure security
-Packaging of hazardous material
-On road material theft
Objective: Cost minimization
-No. of in-transit damages
-Vehicle maintenance costs
-Vehicle utilization (time, capacity)
-No. of vehicle movements
Objective: To ensure safety
-No. of transport incidents
-Vehicle insurance
After this empiry will be discussed and then analysis will be made on the basis
of theory and empiry.
5.1.1 Objective: Effective transport planning
The objective “effective transport planning” is majorly divided into vehicle
route optimization, effective contingency planning and effective material
Table 26: Performance measures to be studied under RQ2 (Own illustration)
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requirement planning which are further measured by following performance
measures.
5.1.1.1 Total distance driven
According to Network for transport calculations, NTMCalc (2010) the “total
distance driven” can be calculated as follows;
Let us consider a vehicle as shown in Figure 19 that is supposed to unload
goods to n different construction sites, each named 1, 2, 3, … , n, respectively.
The vehicle is assumed to start from a certain Starting Point, and it finishes its
route by coming back to that point.
In the forthcoming discussion, the distance between two sites would be
expressed in the form of:
𝑑(𝑋, 𝑌)
Figure 19: A sample path of the vehicle (Own illustration)
85 | P a g e
Where, X and Y represent two distinct sites, that could either be a construction
site, or the starting point.
Therefore, the total distance traveled (D) by the vehicle can be represented by
𝐷 = 𝑑(𝑆𝑃, 1) + 𝑑(𝑛, 𝑆𝑃) + ∑ 𝑑(𝑖, 𝑖 + 1)
𝑛−1
𝑖=1
Here, SP denotes the Starting Point.
In order to find the saving in distance by a terminal, the formula suggested by
Sichwardt (2011) can be considered which is as follows;
Sichwardt (2011) explains saving in distance formula with the help of Figure
20 that if each construction site is served by a single vehicle from the terminal,
then the total distance would be 2x15km and 2x12km=54 km. He highlights
that if only one vehicle is used in a single trip the total distance would be 15
km+5 km+12 km= 32 km. So according to Sichwardt (2011) the total distance
saved would be equal to 15km + 12 km -5 km = 22 km
Terminal
Construction
site A
Construction
site B
15 km
5 km
12 km
Figure 20: Saving in distance by terminal (Adopted)
Saving in distance by terminal= Distance (Terminal to Construction Site
A) + Distance (Terminal to Construction Site B) – Distance (Construction
Site A to Construction Site B)
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5.1.1.2 Travel Time
According to Lomax et al. (2013), the total travel time can be calculated as
follows;
They further suggest that the “delay” in travel time can be calculated as
follows;
5.1.1.3 No. of unplanned deliveries
According to Sanchez-Rodrigues et al. (2010) the formula for calculating “no.
of unplanned deliveries” is as follows,
5.1.1.4 No. of shipments arrive at gate per day
As per NTMCalc (2010) the “no. of shipments arrives per day” can be
calculated as;
Or it can also be calculated as (NTMCalc, 2010);
Travel Time= Vehicle miles of travel/Travel speed
Delay in travel time= [(Vehicle miles of travel/actual speed) – (Vehicle
miles of travel/free-flow speed)]
No. of unplanned deliveries= No. of unplanned deliveries/ Total no. of
deliveries
No. of shipments arrive per day = Daily material requirement/ total
material requirement (for a project)
No. of shipments arrive per day = Rate of material being used
per day
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Thomas (2017) has suggested a formula for calculating “optimal number of
shipments per day” which is as follows;
According to him, “average delivery lead time” is the amount of time taken
by shipment to arrive at final destination”.
Delivery lead time can be calculated as (Thomas, 2017);
For example;
Month Order Amount Delivery time
Jan 80 pieces 7 days
Feb 80 pieces 5 days
Mar 50 pieces 3 days
Average lead time = 15 days/ 3 (no. of orders) = 5 days
5.1.2 Objective: Reduction in transportation time
5.1.2.1 Truck turnaround time
The “truck turnaround time” can be calculated in minutes or hours as
suggested by (Hensel, 2014);
5.1.3 Objective: Transport cost minimization
5.1.3.1 No. of damages (in transit)
As per Sonoco (2017), “number of damages” can be calculated as;
Sonoco (2017) further calculated “cost of damage” as;
Optimal no. of shipments arrives at gate per day = (Average daily
material consumption x delivery lead time) + Safety stock
Delivery lead time =Delivery time in days/ Total no. of orders
Truck Turnaround time= (Vehicle Time out) – (Vehicle Time in)
No. of damages = Total loss and damage/total stock costs
Cost of damage per unit= Total sales + Total profit before cost of
damage + Cost of damaged unit to customer + Cost to replace the unit
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5.1.3.2 Vehicle maintenance cost
The formula for calculating “vehicle maintenance cost” suggested by Dönmez
and Zemmouri (2016) is as follows;
5.1.3.3 Vehicle utilization
The formula for calculating “vehicle utilization” by (Malacarne, 2018) is as
follows;
According to Hosseini and Shirani (2011), “vehicle utilization” is calculated
in terms of time and weight which is as follows;
5.1.3.4 Cost incurred by no. of vehicle movements
The cost incurred by number of vehicle movements can be calculated by
transportation cost as per Ying, Tookey and Seadon (2018) because according
to them vehicle movements incorporate transportation from start of delivery
to the point when material is returned. So, “cost of vehicle movements” can
be estimated by “transportation cost” which is mentioned as follows;
Where CO is vehicle operating cost and D is the distance between site and
depot of the ith trip.
Vehicle maintenance cost = Sum of all maintenance cost (oil and filter
change + tire replacement + brake pad and fluid replacement + coolant
replacement + vehicle washing + general wear and tear etc.)
Vehicle utilization= Kilometers per vehicle per period
Or
Vehicle utilization= Operational hours or days per vehicle per period
Vehicle utilization (time)= Utilized time/available time
Vehicle utilization (weight)= Utilized load capacity/Available load
capacity
Total vehicle utilization= capacity utilization x time utilization
Transportation cost = σ COi x Di𝑛𝑖=1
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5.1.4 Objective: To achieve environmental sustainability
5.1.4.1 Road damage
According to Bai et al. (2009) the “road damage” can be calculated with a
damage function which measures the decline of road quality to traffic or axle
passes. The general form of damage function is shown below;
Where g= damage index
N= no. of times an axle group of specified weight pass
τ = the no. of axle passes at which the pavement reaches failure
β= damage rate for a given axle
5.1.4.2 CO2 emission
NTMCalc (2012) suggests transport companies should use energy-based
approach to calculate CO2 emission because it is easy and accurate to record
energy and fuel use and also to convert energy or fuel values into CO2
emissions. According to NTMCalc (2012) every liter of fuel consumed results
into a certain amount of CO2 emissions.
The formula used by NTMCalc (2012) to calculate “CO2 emission” is as
follows;
Sichwardt (2011) suggests fuel-based method to calculate CO2 emission which
is as follows;
𝑔 = (𝑁/𝜏 )β
CO2 emissions= fuel consumption x fuel emission conversion factor
[Tonnes CO2-emissions = litres x kg CO2 per liter fuel/1.000]
∑ CO2 emissions = ∑t=1 T (AFCt,n,m x hv x EF)
Where,
AFC = absolute fuel consumption in [1/100km] for the number of trucks (t
= 1, …. T), sort of truck (n= Scania, Volvo), Euro class (m = 0, ….5)
Hv = heating value of fuel expressed in [GJ/L]
EF= emission factor expressed in [kgCO2 /GJ] or [kgCO2 /L]
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5.1.5 Objective: To ensure security
5.1.5.1 Quality of hazardous material packaging
Bodenheimer (2014) suggests that “material packaging quality” can be
calculated as;
5.1.5.2 Risk of on road material theft
Polzin (2002) suggest that “risk of material theft” can be calculated as
follows;
5.1.6 Objective: To ensure safety
5.1.6.1 No. of transport incidents
The “number of transport incidents” can be calculated by following formula
according to (Botha, 2005);
5.1.6.2 Vehicle insurance
Coverfox (2019) suggests that the major components in calculating “vehicle
insurance” are insured declared value (IDV), cubic capacity, manufacturing
year, geographical location and no claim bonus (NCB).
Cover fox (2019) highlights that insured declared value (IDV) decreases as the
age of the vehicle increases and as a result insurance premium decreases as the
vehicle gets older. According to Coverfox (2019) “insured declared value” of
vehicle is calculated as;
The formula for calculating “vehicle insurance” is (Coverfox, 2019);
Quality of hazardous material packaging = No. of client´s complaints
Risk of on road material theft= Probability of incident attempt x
vulnerability x damage
No. of transport incidents = Rate of fatalities per vehicle x kilometers
travelled
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5.2 Frame of reference The frame of reference for this research question will remain the same as used
for research question 1 because there is no new term introduced in here. This
will only reflect the calculations of already identified transport related
performance measures. The interview guide for this is attached in Appendix 3.
5.3 Empirical findings from both Case 1 and Case 2
According to Business unit manager at Case 1 (14.1.2019) and Logistics
consultant at Case 2 (14.1.2019), there is no proper record of data at their end.
This can be due to the fact that multiple stakeholders are involved in the
construction project and as construction industry is fragmented in nature, so
the sharing of data is not taking place. Logistics consultant suggests
(14.1.2019) that this thesis is their first step towards data collection. Founder
of case company 1 (14.1.2019) also share same view as logistics consultant at
case 2. Upon asking questions that how do you measure performance measures
such as total distance travelled, unplanned deliveries, no. of shipments per day,
estimations regarding material demands vehicle weight utilization, both
respondents explicitly said that they do not measure these performance
measurements. The excerpts of the questions and respondents answer are
attached in Appendix 5.
Regarding turnaround around time, business unit manager (4.4.2019) suggests
that they measure truck turnaround time only with the help of data regarding
loading and unloading time. He further suggests that truck turnaround time
depends upon the time of the day because no. of queues vary in whole day.
Regarding vehicle capacity utilization business unit manager (4.4.2019)
suggests that they do not measure it exactly, but they calculate it in the form
of fill rate, and in loading meters. In addition to this, he highlighted that as
Vehicle insurance premium = Own damage premium- (depreciation + no
claim bonus) + Liability premium
Where,
Own damage premium is measured by insured declared value (IDV) which
is calculated as;
Insured declared value = Age of the vehicle + ex-show room price
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they do not get any data from suppliers, so it becomes difficult for them to
foresee the amount of material coming and also amount of trucks outgoing.
5.4 Analysis
The purpose of this research question is to find what data is needed to measure
the identified transport performance measures. From empirical findings this
has become clear that both cases are not maintaining sufficient data to
calculate these performance measures. On the basis of theory, it can be said
that following data is needed;
Reduce Total Distance Driven
• Data Required:
1. The location of touchpoints
2. List of availability of direct routes between various
touchpoints.
• Description: With only the accessibility of the aforementioned data, it
could be easy to calculate the shortest route that should be opted by the
vehicle so that it completes its journey in the shortest possible time via
the classical traveling salesman problem (Lawler, 1985). Note that
other constraints such as geography of the road (uphill/downhill),
speed limits, etc. are not considered in this scenario. Moreover, it is
assumed that the vehicle travels at a constant speed while visiting all
these touch points. Furthermore, it is also presumed that there is no pre-
determined sequence that the truck has to follow for unloading the
materials.
Minimize Vehicle Travel Time
• Data Required
1. The location of touchpoints.
2. List of availability of direct routes between various touch
points.
3. The unloading schedule of various unloading sites.
• Description: As it can be observed, the first two data required to
minimize the travel time is similar to that of the previous performance
measures, i.e. Reduce total distance driven. This is because the time
taken to complete a journey by a vehicle is directly proportional to the
distance traveled. According to 𝑠 = 𝑣 ∗ 𝑡, where s signifies the
distance traveled and v is the velocity of the vehicle. Thus, a shorter
distance will automatically imply that the travel time is reduced.
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Additionally, the vehicle travel time can further be reduced if the
unloading schedule of various unloading sites are known, as in this
case, the vehicles can easily reduce the waiting time in these unloading
sites. Note that, in the previous performance measure, the shortest route
is calculated by disregarding the unloading schedule, however, in this
case, this schedule can be considered to further minimize the time.
Thus, a trade-off has to be established where the shortest route is
calculated by the aforementioned traveling salesman problem, and
then comparing it with the respective unloading schedule, and finally
a manual analysis can be done to find out the concluding shortest route.
Reduction in unplanned deliveries
• Data required:
1. List of all the previous unplanned deliveries.
• Description: To reduce the number of unplanned deliveries, a list of
all the previous such incidents is required. Hereupon, these incidents
can be individually analyzed as to what was the primary ground that
led to this unplanned delivery. Following which appropriate actions
can be taken so that these deliveries are minimized in the future.
Optimize the number of shipments arrived at gate per day
• Data required
1. Material required for consumption at a construction site
2. Materials that are going to be delivered by the supplier
3. The time of delivery
• Description: It is important for the construction sites to gather the
information beforehand about the amount of material required between
two consecutive shipment deliveries. This way, the aforesaid details
can be shared with the suppliers so that all the required materials are
timely received. This will also prevent any unplanned deliveries that
will result in an increased number of shipments that arrive per day.
Furthermore, if the construction sites are aware of the list and amount
of materials that are going to be delivered by the supplier, they can plan
their construction activities in such a way that all the materials are
consumed appropriately. However, at the moment the construction
sites are largely unaware of the amount of materials that are going to
be provided by the supplier in their trip (Business unit executive,
14.1.2019). Additionally, the awareness of delivery time will also help
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reduce the number of shipments, as tasks such as unloading the
shipment can be organized accordingly.
Reduce truck turnaround time
• Data required
1. The breakdown of time utilized in each activity that constitutes
one instance of loading or unloading.
2. Ease of access to the construction site by the vehicle
3. Utilization rate of loading and unloading equipment
• Description: An instance of loading or unloading activity can
comprise of many sub-activities, such as sorting, packaging, lifting,
filling, etc. These sub-activities can be individually assessed to find out
a particular quantum of activity that may be responsible for delaying
the complete process of loading or unloading. The sub-activities can
then be further analyzed and improved to decrease the total turnaround
times. Moreover, it is important that the vehicle takes minimum
amount of time to enter the construction site. Proper arrangements
should be done by the construction sites this can be minimized.
Similarly, utilization rate of loading and unloading equipment would
reveal how effectively are the set of equipment utilized. This
information can be used to reduce the total utilization time, thus also
plummeting the overall truck turnaround time.
Reduce number of damages
• Data required
1. The breakdown of each activity that constitutes one instance of
loading or unloading.
2. The packaging process
• Description: As discussed with the Business Unit Manager
(14.01.2019), it was found out that the most number of damages occur
while loading and unloading of shipments. Thus, a detailed analysis is
required about each activity that takes place while loading and
unloading so that the damages can be reduced.
Minimize vehicle maintenance cost
• Data required
1. The factors that lead to vehicle damage such as:
▪ Oil and filter exhaustion
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▪ Tire damage
▪ Brake pad and fluid damage
▪ Coolant exhaustion
▪ Untidiness of vehicles
▪ General wear and tear
▪ Other miscellaneous damages
2. The location of touchpoints
3. List of availability of direct routes between various
touchpoints.
• Description: The factors mentioned in the first point above are the
primary reasons that lead to the damage of vehicles, which in turn
increases the vehicle maintenance cost. Analyzing these factors so that
the vehicle damages can be reduced will eventually decrease the cost
of vehicle maintenance. Moreover, it can be observed that points 2 and
3 are similar to the ones required to reduce the total distance traveled,
as minimizing the total distance traveled will, in due course lead to the
reduction of maintenance cost, since any type of damage in the vehicle
is directly proportional to the distance traveled by the vehicle. Thus,
lesser the distance traveled, lesser is the vehicle damage, resulting to a
reduced vehicle maintenance cost.
Maximize vehicle utilization capacity
• Data required
1. Volume of the containers of the vehicle
2. Type of material
• Description: As it is known that the weight of any material is
equivalent to the density of the material multiplied by its volume, the
type of material will tell its density, and thus, the maximum amount of
material by weight that could be loaded in the truck could be calculated
by the above formula. Let´s consider that n different types materials
are being transported in a truck. Thus, the total volume of the materials
transported can be calculated as:
𝑣𝑡𝑜𝑡𝑎𝑙 = ∑ (ρ
𝑖
𝑚𝑖)
𝑛
𝑖=1
Where:
• ρ𝑖 signifies the density of ith material, and
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• mi signifies the mass of ith material
Thus, above formula will effectively formulate the maximum amount
of materials that can be loaded into a truck before actually loading the
materials, as vtotal should always be less than or equal to the total
volume of the truck. Thus, a systematic plan can be developed to load
the materials.
Optimize number of vehicle movements
• Data required
1. Data required to reduce total number of distance drive.
2. Data required to reduce the time traveled.
3. Data required to optimize the number of shipments arrived per
day.
4. Data required to reduce truck turnaround time.
5. Data required to reduce number of damages.
6. Data required to maximize the vehicle utilization capacity.
• Description: To optimize the number of vehicle movements, it is
imperative that the vehicle travels the least, completes its journey in
the minimum possible time, the number of shipments arrived per day
are optimized, the truck turnaround time is reduced, there are less
damages in the vehicle, and the vehicle is utilized to its maximum
capacity. Thus, all the data to achieve the above performance measures
are needed to accomplish the vehicle movements optimization. In other
words, achieving the above performance measure will automatically
optimize the number of vehicle movements.
Reduce road damage and CO2 emission
• Data required
1. Data required to optimize the vehicle utilization capacity
2. Data required to optimize the vehicle movements
3. Data required regarding fuel efficiency
• Description: In order to reduce road damage and CO2 emission
there is a need to optimize vehicle utilization capacity and vehicle
movements because in this way number of trips and shipments will
be planned exactly according to the need and therefore this will
lead to effective utilization of fuel. There is need to ensure that
efficient and effective utilization of vehicle takes place.
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Good quality packaging of hazardous material
• Data required
1. What type of material is being transported
2. How long the material will remain in transit
3. What harm it could generate if got mishandled
• Description
Burdick (2012) define “hazardous material” as substance or material
capable of posing an unreasonable risk to health, safety, environment
and property when transported in commerce. So, it is important to take
care of material packaging while transportation in order to avoid any
security threat.
Reduce on road material theft
• Data required
1. Real time vehicle location
2. What material the vehicle is carrying
• Description
It is important to track vehicles in order to ensure on road material
safety. By knowing what material is being transported extra care
should be taken and if needed security should also be provided.
Reduce number of transport incidents
• Data required
1. Knowledge of incidents that has taken place in the past
2. Based on this any future event can be predicted
• Description
In order to ensure safety, it is important to remain vigilant and
implement safety measures.
Ensure vehicle insurance
• Data required
1. Knowledge regarding premium, age of vehicle, depreciation,
insured declared value, liability premium etc.
• Description
In order to avoid any vehicle loss, it is good to ensure vehicle insurance
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6 Framework for RQ3
The purpose of research question 3 is to identify what data is empirically
available from construction logistic solution. No theoretical evidence is being
employed in this research question.
6.1 Empirical data from Case 1 Terminal The data from Case 1 Terminal has been received in excel file for the period
of six months ranging from November 2018 to March 2019. The data was
provided in Swedish language which was later translated into English. A
snapshot of the sample data in its original form is attached in Appendix 4
whereas the snapshot English is shown below in Table 27. Here only 22 rows
are shown but in actual there were 11, 080 rows in the data with various details.
The headers in this data are briefly explained here. “Order number” reflects
the order that is created in Case 1 systems and is handed to the customer to
order activities on and also to be used later for billing purposes. “Company
code” is a code for companies such as Skanska which then have several orders.
“Activity code” is a code for an activity such as activity T132 represents
activity in the warehouse when forklift is used to move material. Likewise,
T105 is transport with a truck. “Activity detail” is the description or summary
of the activity code. “Date” represents the date of activities. “Duration” is the
time it took to perform the activity. The duration is in hours. For example, it
took Case 1 0.25 hours (15 minutes) to label the material to Skanska.
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6.1.1 Analysis
As it can be seen from above snapshot, limited parameters have been provided
in this data. After extensively investigating the data, it has been discovered
that a lot of inferences can be made. However, the analysis was limited to
finding a certain amount of essential deductions, that are:
Finding out
1. The list of orders whose constituent activities holistically performed
relatively faster than the global average. (Efficient Orders)
2. The list of orders whose constituent activities holistically performed
relatively slower than the global average. (Inefficient Orders)
3. The list of companies towards which the activities done by the terminal
were quick. (Fast Companies)
4. The list of companies towards which the activities done by the terminal
were slow. (Slow Companies)
OrderNumber CompanyCode ActivityCode ActivityDetails Date Duration
B1501 2727 T131 Fråga 11/21/2018 0.17
B1501 2727 T131 Order 12/5/2018 0.33
B1501 2727 T132 Plock 12/5/2018 1
B1501 2727 T132 avfalls handtering lass 1 12/5/2018 0.5
B1501 2727 T132 avfallshandtering lass 2 12/5/2018 0.9
B1501 2727 T132 lastning avfall 12/6/2018 0.25
B1504 2791 T105 transport inkl chaufför 11/9/2018 3
B1504 2791 T105 transport inkl chaufför 11/13/2018 2
B1504 2791 T105 transport inkl chaufför 11/16/2018 3
B1504 2791 T105 transport inkl chaufför 12/18/2018 3
B1504 2791 T105 transport inkl chaufför 12/19/2018 2.5
B1504 2791 T105 transport inkl chaufför 12/20/2018 2
B1504 2791 T105 transport inkl chaufför 12/20/2018 3
B1504 2791 T105 transport inkl chaufför 1/17/2019 3
B1504 2791 T105 transport inkl chaufför 1/21/2019 2.5
B1504 2791 T105 transport inkl chaufför 2/13/2019 2
B1504 2791 T132 lastning div staket 2/13/2019 0.8
B1504 2791 T132 lagerarbete inkl truck 2/27/2019 0.5
B1504 2791 T105 transport inkl chaufför 2/27/2019 2
B1504 2791 T132 lastning 2/27/2019 0.5
B1725 2450 T131 inleverans 11/6/2018 0.17
Table 27: Snapshot of Case 1 (Terminal data translated in English)
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5. Number of deliveries done by the terminal per company.
6. The delivery freequency for a company by the terminal.
All the analysis has been done by using MATLAB R2019a and Excel 2010.
Comprehensive discussion regarding the analysis is described in the following
sections.
6.1.1.1 Efficient and inefficient orders
As mentioned above, since the duration was provided in hours with the data
values smaller than 10 or in fractions for the most part, it was decided to
convert them to minutes by multiplying the column with 60 for readability.
Thereafter by splitting the dataset using the column ActivityDetails, the
average duration for each individual activity was calculated. Thus, the average
duration for a particular activity will be the same over the complete dataset.
This duration was added to the table by appending a new column with the
name AverageTime (Table 28). It can be observed that the average time for the
activity plock remains constant throughout the table. Subsequently, the actual
duration of a particular activity was compared with the global average for that
activity, and the time-difference was then noted down in a separate column
named Efficiency. A positive value indicates that the activity performed faster
than the global average. Likewise, a negative value designates a slower
process. The numeric value tells the amount of time the activity is faster or
slower than the average.
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Since an OrderNumber consists of multiple ActivityDetails, the Efficiency of
one OrderNumber was calculated by adding the Efficiency of all the activities
in an order. Finally, if the resultant Efficiency for an order was a positive
number, it was inferred that that particular order performed faster than the
global average (efficient order). Similarly, a negative number deduced a
slower performance (inefficient order).
Although efficient and inefficient orders has been derived, it should be noted
that the orders can still not be compared with each other quantitatively as the
orders do not contain equal number of activities. Thus, an order with 10
activities, each a minute faster than the average will be equal to an order with
just an activity, but 10 minutes faster than the average. Thus, for comparing
different orders, the numeric value was further divided by the total number of
activities in to find efficiency of an order per activity. A snapshot of this table
can be seen at Table 29. It can be inferred intuitively from the table that
OrderNumber CompanyCode ActivityCode ActivityDetails Date Duration AverageTime Efficiency
B1501 2727 T132 plock 12/5/2018 60 27.6206 -32.3794
B1516 2009 T132 plock 12/3/2018 30 27.6206 -2.3794
B1519 2727 T132 plock 11/6/2018 15 27.6206 12.6206
B1519 2727 T132 plock 11/8/2018 30 27.6206 -2.3794
B1519 2727 T132 plock 11/8/2018 30 27.6206 -2.3794
B1519 2727 T132 plock 11/13/2018 15 27.6206 12.6206
B1519 2727 T132 plock 12/3/2018 60 27.6206 -32.3794
B1519 2727 T132 plock 12/4/2018 60 27.6206 -32.3794
B1519 2727 T132 plock 12/6/2018 30 27.6206 -2.3794
B1519 2727 T132 plock 12/12/2018 15 27.6206 12.6206
B1533 2009 T132 plock 12/10/2018 10.2 27.6206 17.4206
B1533 2009 T132 plock 2/15/2019 15 27.6206 12.6206
B1533 2009 T132 plock 3/8/2019 15 27.6206 12.6206
B1533 2009 T132 plock 3/21/2019 10.2 27.6206 17.4206
B1552 2727 T132 plock 11/20/2018 30 27.6206 -2.3794
B1552 2727 T132 plock 2/4/2019 30 27.6206 -2.3794
B1552 2727 T132 plock 2/7/2019 150 27.6206 -122.3794
B1552 2727 T132 plock 2/11/2019 30 27.6206 -2.3794
B1585 2727 T132 plock 11/28/2018 15 27.6206 12.6206
B1585 2727 T132 plock 12/17/2018 30 27.6206 -2.3794
B1585 2727 T132 plock 1/21/2019 30 27.6206 -2.3794
Table 28: Processed table for Efficient/Inefficient Activities
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efficiency has a high variance whereas efficiency per activity has a low
variance, and thus, it can be compared.
Order Number Efficiency Efficiency Per Activity
'B1599' 142.2453 35.561325
'B2242' 141.9013 35.475325
'B2164' 194.3151 32.38585
'B2226' 284.8346 31.64828889
'B1977' 187.9621 31.32701667
'B1928' 149.2803 29.85606
A similar structure can be seen for the inefficient orders (Table 30).
Order Number Inefficiency Inefficiency Per Activity
'B2301' -74.048 -10.57828571
'B2291' -462.9924 -10.06505217
'B2318' -19.6325 -9.81625
'B2279' -19.092 -9.546
'B1666' -181.0507 -8.621461905
'B2360' -1365.4038 -8.127403571
'B2223' -90.1356 -7.5113
Finally, all the efficient and inefficient orders have been graphically described
using a bar graph respectively in Figure 24 and Figure 23.
Implication:
The implication of finding efficient and inefficient order is this that the most
efficient order will be compared with the most inefficient order and then it can
be observed that why same activity such as plock is taking place quickly in
efficient order whereas the same activity i.e. plock is taking so much time.
From this, the process of efficient activity can be studied and in this way, short
coming of slow activities can be found out which can be improved further.
6.1.1.2 Slow and fast companies
Continuing the work from Table 28, the entire dataset was first separated by
the company. Furthermore, by the column ActivityDetails, it was realized that
the total unique activities in the complete dataset are 1032. However, not all
Table 29: Efficient Orders
Table 30: Inefficient Orders
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the activities are performed by the terminal for all the companies. Furthermore,
it was also observed that the exact same activity takes different to complete in
different instances. Thus, it was decided to list down for all the 1032 unique
activities, the company code for which the terminal performs that activity the
fastest and the slowest.
An excerpt of the final table containing 1032 rows for each unique activity can
be seen in in the Table 31. The first column names the activity, followed by
the second column that lists the name of the company for which the terminal
performed the activity with the longest duration, whereas the third column
enumerates the companies that performed the activity in the quickest possible
time.
Activity performed Slow Quick
3 tel. samtal. ingen info om vart godset ska. 2009 2450
4 samtal med frågan om leveransplats.2 till kunden och 2 till servisti
2822 2009
4 tel. samtal. ingen information om vart godset ska. 2009 2450
admin/order 2009 2450
armeringsnät lastning 2450 2009
Finally, to quantitatively represent the data, the number of time a company
appears in the Slow column as well as the Quick column was calculated. For
instance, in the sample Table 31, a total of 3 activities were performed the
slowest by the terminal for the company 2009, whereas 3 activities were
performed the quickest for the company 2450. The top 3 companies for which
the activities were performed the fastest are listed below.
Company Activity Count
2450 739
2009 266
2545 18
Similarly, the top 3 companies for which the activities were performed the
slowest are listed below.
Company Activity Count
2009 729
2450 68
2547 4
Table 31: Slow and Fast Companies
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This data has been pictorially represented in the form of pie-chart
Implication:
The implication of this finding is that why one task being performed by one
company is more effective whereas the same task performed by other company
is ineffective so by digging deep into this the task that is performing slow can
be improved.
Figure 21: Fast Companies
Figure 22: Slow Companies
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Figure 24: Most efficient orders Figure 23: Most inefficient orders
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6.1.1.3 Number of Deliveries and Frequency
In this section, the total number of deliveries performed by the terminal for a
company was calculated. This was done by analysing the ActivityDetails
column. The Swedish word used in the table for delivering in the goods is
inleverans. Thus, all the ActivityDetails that had the word inleverans were
considered to be a delivery. Thereupon, the number of deliveries per company
was calculated, following which an attempt was made to calculate the time
duration between two consecutive deliveries. Note that since the delivery time
is mentioned in Date, and not DateTime, the companies that had all their
deliveries in a single day were ignored while calculating the average duration
between two consecutive deliveries. Furthermore, those companies were
ignored as well, that had no deliveries, or just one delivery, as to calculate the
time between two deliveries in our case needs at least two deliveries in two
distinct dates. Thus for each company, a data similar to the one in Table 32 is
calculated, where total number of rows represent total number of deliveries,
and the difference between the time of a delivery and the time of immediately
next delivery will denote the time difference between two consecutive
deliveries.
Order Number
Company Code
Activity Code
Activity Details
Date Difference
'B2158' 2009 'T131' 'inleverans' '23-Nov-2018 00:00:00' 4
'B2158' 2009 'T131' 'inleverans' '23-Nov-2018 00:00:00' 0
'B1533' 2009 'T131' 'inleverans' '26-Nov-2018 00:00:00' 3
'B1533' 2009 'T132' 'inleverans' '26-Nov-2018 00:00:00' 0
'B1533' 2009 'T131' 'inleverans' '04-Dec-2018 00:00:00' 8
As it can be observed, the last row in the above table has a time difference of
8, as the time of delivery is 4th December, which is 8 days after 26th November.
Thenceforth, an aggregate table similar to Table 33 is created containing the
Company Name, the number of deliveries and average time between two
consecutive delivery for a company.
Company Name Delivery Count Time Between Deliveries
2009 71 1.871429
2450 359 0.405028
Table 32: Time difference between deliveries
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2591 34 3.787879
2709 25 4.291667
2727 226 0.657778
2811 26 4
Finally, the complete data, part of which is represented in Table 33 was
normalized between 0 and 1 separately for number of deliveries and time
between two consecutive deliveries, so that they can be pictorially analyzed.
Subsequently, these were represented in the form of a bar graph that can be
seen in the Figure 25.
Implication:
It can be observed that the companies that have high amount of deliveries have
very less time between two consecutive deliveries, whereas this time
difference is high in case of companies that have less deliveries.
Thus, the number of deliveries is roughly inversely proportional to the average
time between two consecutive deliveries.
Table 33: Delivery frequency
0
0.2
0.4
0.6
0.8
1
24
50
27
27
30
52
28
17
20
09
25
91
30
49
28
22
30
59
28
11
27
09
29
77
28
29
30
14
29
85
29
97
30
26
28
12
30
48
28
87
30
42
Number of Deliveries and Time Between Deliveries for Companies
Deliveries Time Between Deliveries
Figure 25: Number of Deliveries and Time between Deliveries
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6.2 Empirical data from Case 2 Checkpoint
Case 2 Checkpoint has given access to their Lognet for empirical data which
is shown below in the Figure 26;
The data entered in Lognet is on weekly basis and the data from this portal
cannot be exported into Excel spreasheet so only one week data was manually
entered in excel to do the analysis which is shown as follows in Figure 27;
Figure 26: Lognet data sheet
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Figure 27: Case-2 Empirical Data
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As it was not a large amount of data, so the findings are not that much
conclusive, however, many points of interests have been identified.
6.2.1 Analysis
In order to analyze the data, it was first sorted out by company, by project and
by unloading site. Mean and standard deviation is calculated for all. This can
be shown as follows;
6.2.1.1 By company
CompanyName MeanDuration StandardDeviation
Assemblin EL 1.5 0
Assemblin Vent 0.5 0
BM Byggställningar 1 0
Cramo 0.375 0.176776695
Dahl 2 0
ED-Bygg 6 0
STB Ställningsbyggarna Bestorp AB 0.5 0
Schenker 2 0
Strängbetong AB 1.178571429 0.668153105
Torpheimer Tubes 0.25 0
Unspecified Company 9.333333333 4.082482905
YIT 1 0
Åhlin & Ekeroth 5.388888889 3.887301263
In this snapshot, it can be seen that the standard deviation in the time duration
for companies (i.e. Assemblin, Dahl, Schenker) having more than one
entry/observation is zero implying that the total time taken to complete one
activity by that particular company never changes. This can happen because
of two reasons i.e. either they are too perfect or in Lognet activity time has
been entered upon estimation rather than actual time. According to Logistics
Consultant (15.4.2019) the time estimations has been made for mass transports
which keep on coming continuously.
There are two companies (Cramo and Stränbetong AB) whose standard
deviation related to duration to complete activity is in considerable range that
<1 hour (less than 1 hour). In addition to this, there are two other companies
Table 34: Case-2 Sort By Company
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(Unspecified and Åhlin & Ekeroth) with a very high standard deviation for the
time duration per activity which reflects that these companies perform
different nature of work which require different time to get completed. If more
data is available then further insights can be extracted. There is also a need to
know that what is the work that happen in one hour and what is the work that
happen in four hours because in Lognet the activity details/summaries are not
that clear.
Graphical representation:
It can be seen from this graph (Figure 28) that many companies have zero
standard deviation which means there is no variation in the duration of their
activities. But it has also been found that some companies have zero deviation
because they only have only one or two entry. For example, Assemblin EL.
But then there are companies such as Dahl which has more than one entry but
even then its standard deviation is zero. According to Logistics consultant
(15.4.2019) the reason for this is that these companies have standard delivery
time i.e. they have an agreement that deliveries should come at the same time
always. So, it shows there is no variation because what is happening today is
mirror image of tomorrow so no inference can be drawn from this.
As the total number of observations recorded for this week are only 49 and the
total no. of companies are 13, 6 companies had only one entry. Thus, not much
could be inferred there. In order to analyze the data and infer anything out of
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
StandardDeviation
Figure 28: Standard Deviation Graph for Case-2
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it, there must be some variance in the data. However, it was observed that all
the entries within a company are absolutely identical, barring a few. For
example, for the company Åhlin & Ekeroth for the project Dynamic, the truck
always arrives at 06:30 and departs at 4 pm throughout the week, hence, there
is a lack of variation. The reason for this is that this truck comes under mass
transport, i.e. they keep on driving throughout the day. As it can be seen, the
standard deviation for most of the companies is zero or very less.
This can be seen here;
6.2.1.2 By project
Project Mean Duration Standard Deviation
Calm 10.16666667 2.041241452
Checkpoint Depos
1.840909091 0.527644853
Dynamics 9.5 0
Workshop 1.758333333 2.201570103
When one-week data has been filtered "by project" then it is found that there
are only 4 projects. 3 projects (Calm, Checkpoint, Dynamics) have their
respective and exclusive unloading sites (Calm 1, Chp depo, Dune 1)
respectively. The 4th project i.e. Workshop has a total of 4 unloading sites
which implies that Project workshop is the bigger project as compared to other
projects.
As the 3 smaller projects (Calm, Checkpoint depos, Dynamics) take exactly
same time in completing their work in every instance (all in 11 hrs, 2 hrs, 9.5
hrs) except just two entries. Because of those two entries there is slight
difference in the standard deviation value of these projects. But overall the
same time taken by all activities show signs of approximation in the data
entry.
The bigger project i.e. Workshop can be further investigated once I have more
data. It is also needed to know more details that why some tasks take 1 hour
while others take 9.9 hours to get completed.
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6.2.1.3 By unloading site
Unloading site Mean Duration Standard Deviation
Calm 1 10.16666667 2.041241452
Chp depot 1.840909091 0.527644853
Dune 1 9.5 0
Pit 6.75 3.889087297
V1 1.40625 1.209615494
V2 2 2.853569194
V3 1 0
Observing the data set in perspective of unloading sites, it has been realized
that it is also quite similar to what has been observed in "data filtered by
project" as each project has their own exclusive unloading sites. Therefore,
similar conclusions can be inferred here.
Comments about Case 2 data:
It seems that quite a lot of approximation has been done while entering data
into the "Lognet" because a lot of activities over the span of one week start at
the same time and end at same time and also takes exactly the same time. As
there is no variation in the data thus little to no analysis can be done in those
entries.
As there are no unique IDs in this data so it become difficult to find chain of
events (dependencies that which activity occurred first and which after) for
one whole process/order. All events in Case 2 data appeared to be independent
as this point in time.
7 Framework for RQ4
The purpose of research question 4 is to find a gap between required and
available data to measure identified transport related performance measures in
research question 1? This research question does not require any theory and
empiry, so analysis is the main response to this research question.
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7.1 Analysis As it could be seen from research question 2 various data is required to
accomplish the performance measures proposed, however these data are
currently unavailable for the analysis. Furthermore, efficient orders, inefficient
orders, fast companies, slow companies were calculated above however to find
out how can these inefficient orders and slow companies be improved,
breakdown of each activity is required so that the sub activities from inefficient
orders and slow companies could be compared with efficient orders and fast
companies respectively. In this the factors leading to inefficiency could be
found out, analysed and thereafter rectified for the overall improvement.
The gap between required and available data can be found out by making
comparison between what data is needed and what data is available from Case
1 and Case 2. This can be done with the help of Table 35 below;
Data required (as
found in RQ2)
Data available from
Case 1
Data available from
Case 2
Location of touch
points
Duration of different
activities such as plock,
inleverans, transport
inkl chaufför, lossning
av slingbil mainly
Duration of activities
different activities such
as truck with gravel
transport, daily
delivery, emptying
container mainly
List of availability of
direct routes between
various touchpoints
Order number Delivery ID
Unloading schedule of
various unloading
sites
Company code Company name
List of previous un-
planned activities
Activity code and
activity details
Arrival and departure
Material required for
consumption at a
construction site
Date Duration
Time of delivery Amount/quantity of
material in tonnage
Summary of activities
Information
regarding what
material is going to be
delivered by a
supplier
Customer marking Date
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Breakdown of loading
and unloading
activities in terms of
time
Internal marking Projects
Utilization rate of
loading and unloading
equipment
Signature Unloading site
No. of damages - Supplier
Details regarding
product packaging
- No. of deliveries
Vehicle maintenance
cost
- Vehicle type
Volume of the
containers of the
vehicle
- -
How long the material
will remain in transit
- -
Real time vehicle
location
- -
Knowledge of past
incidents
- -
8 Overall analysis
This chapter will discuss the overall analysis of all four research questions
mentioned in this study.
The purpose of the study is to identify transport related performance measures
in order to evaluate construction logistic solution and then to see what data is
needed to calculate these identified transport performance measures and what
data is empirically available. It has been found that very few researchers have
discussed about transport flows within construction industry. This is due to the
fact that construction logistic solution is not a widely accepted area within
construction logistics.
With the help of theory, semi structured interviews and focus group transport
related performance measures have been identified under respective strategic
objectives. As Marr (2015) suggest that it is important to link performance
measures with the right strategic objectives. For this purpose, six strategic
objectives have been identified after theoretical and empirical research and
Table 35: Comparison between required and available data
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these are to plan transport effectively, to minimize transportation time, to
reduce transportation cost, to achieving environmental sustainability, to ensure
security and safety. Then performance measures have been identified under
these strategic objectives. According to business unit manager (14.1.2019)
there should be few but comprehensive performance measures thus making it
easy to track them and bring improvement. Considering this, the identified
performance measures were later consolidated and clubbed together in order
to find few but comprehensive performance measures which can be seen
below;
Identified performance measures
from RQ1
Identified performance measures
from RQ1
Objective: Effective transport
planning
-Total distance driven
-Total travel time
-No. of unplanned deliveries
-No. of shipments arrives at gate per
day
Objective: To achieve
environmental sustainability
-Road damage through vibrations
-CO2 emission
Objective: Minimizing
transportation time
-Truck turnaround time
Objective: To ensure security
-Packaging of hazardous material
-On road material theft
Objective: Cost minimization
-No. of in-transit damages
-Vehicle maintenance costs
-Vehicle utilization (time, capacity)
-No. of vehicle movements
Objective: To ensure safety
-No. of transport incidents
-Vehicle insurance
In addition to this, identified performance measures were also classified with
respect to terminal, checkpoint and their respective construction sites (Figure
17 and 18). After finding these performance measures the next objective was
to find ways of calculating them. In order to do that theory remained major
contributor as according to both case representatives, the case companies are
not measuring these performance measures and the data that they have is not
sufficient to calculate these performance measures. Despite this fact, an effort
has been made to develop some calculations and formulas to calculate
identified performance measures. After devising the formulas empirical data
was collected and analyzed. It has been found out that the empirical data from
both cases rely on duration of activities mainly and not providing any other
good amount of information with the help of which identified performance
measures can be calculated. The analysis such as efficient and in-efficient
117 | P a g e
orders, fast and slow companies, number of deliveries done by the Case 1-
Terminal per company, the delivery frequency for a company by terminal was
done. On the other hand, analysis on Case 2-Checkpoint empirical data has
been conducted by calculating mean and standard deviation. Extensive data
analysis for Case 2-Checkpoint could not be done due to limited number of
entries i.e. one week. After empirical data analysis, a comparison among
required data, available data at Case 1 and available data at Case 2 is made
in order to identify the gap. As a result, it has been found that empirical data
mainly consists of duration of different activities which does not fulfill the
requirement of data needed to calculate or verify identified transport
performance measures.
9 Conclusion
To conclude this paper, the research questions will be answered. As there are
four research questions in this study so the conclusion will give answers to all
research questions one by one. In the end, further research area will be
discussed.
The first question for this thesis is “what performance measures can be used
for the evaluation of construction logistic solutions and their respective
construction sites with respect to transport flows?” In order to answer this
research question, the use of literature review, semi structured interview and
focused group was made and as a result transport performance measures within
the connect of construction logistics has been identified. In addition to this,
these performance measures have also been classified according to
construction logistic solution (i.e. terminal and checkpoint) and their
respective construction site. This can be found in Figure 17. In order to suggest
few but comprehensive performance measures consolidation and grouping
have been done resulting in major performance measures aiming at achieving
the strategic objectives. This can be found in Figure 18.
The second research question which is “what kind of data is needed to
measure identified transport related performance measures in research
question 1?” For answering this research question, theory has remained
helpful as compared to empirical data because according to business unit
manager at Case 1 (14.1.2019) and logistic consultant at Case 2 (14.1.2019)
insufficient data has been recorded and they are making very less use of data.
With the help of theory and personal knowledge and experience the answer to
this research question has been provided in section 5.4.
118 | P a g e
The third research question of this study is “what transport data is empirically
available from construction logistic solution?” In order to answer this
question, empirical data was collected from Case 1 and Case 2. It has been
found that within both empirical datasets, mainly the duration of activities has
been provided which does not fulfill the purpose of calculating identified
performance measures in research question. The Case 1 data has been analyzed
by efficient and inefficient orders, by slow and fast companies, by number of
deliveries done by the Case 1 (per company), the delivery frequency for a
company by terminal mainly. Whereas the Case 2 data has been analyzed by
calculating average mean and standard deviation in order to see the variation
in entered data. The implication of this data analysis is that it can provide
enough insights to bring improvement in slow and underperforming processes.
The fourth question in this paper is “what is the gap between required and
available data to measure identified transport related performance measures
in research question 1? In order to answer this research question a comparison
has been made between the findings of RQ2 and RQ3. And a list of gaps has
been identified in the required and available data in Table 35.
9.1 Further study There is permanent need of further research in this area of studies because the
phenomena of construction logistics solution is not widely accepted. Besides
this, in order to verify identified transport performance measures there is need
to collect relevant data. So, a new study related to collection of relevant data
can be conducted further.
119 | P a g e
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11 Appendix
11.1 Semi structured interview guide for RQ1 The most important statements from semi structured interviews are mentioned
below;
General Information
- What is your position in the company?
- How long have you worked for the company?
- How long have you worked within the construction industry?
Questions related to the topic
- Do you think construction logistics will bring efficiency in the
construction industry?
- Do you agree that construction logistics solution is a new phenomenon
in the construction industry?
- Do you think it is effective to use construction logistics solutions?
- Do you think it is important to measure transport flows in construction
logistics?
- Are you using any transport related performance measures for your
company?
- Do you think transport performance measures should be align with
company objectives such as effective transport planning, transport time
reduction, transport cost minimization, to achieve environmental
sustainability, to ensure security and safety?
- What do you think should be the number of transport related
performance measures such as five, eight, twelve or more?
- Do you believe in the comprehensiveness and quality of performance
measures or it’s the quantity that matters the most?
- Do you consider early delivery as “on time delivery”?
- How do you ensure on time deliveries?
- How do you deal with late deliveries?
- Do you believe that you have enough no. of vehicles to meet daily
demand?
- What factor do you consider more in vehicle capacity i.e. weight a
truck carries or frequency of trips?
- What is the maximum vehicle utilization (in terms of kilometers) that
you have ensured so far?
- What is the maximum vehicle weight utilization that you have ensured
so far?
- Are shipping documents prepared on time?
- How often some error occurs in shipping documents?
- At what point/stage do you experience the most damages?
- How many vehicles do you have for running your operations?
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- Do you generally track your shipments/deliveries?
- What transport performance measures do you think are important to
measure?
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11.2 Protocol for focus group
The protocol for focus group discussion developed with the help of theory and
semi structured interviews
Objective:
Effective
transport
planning
Objective:
Reduce
transportation
time
Objective:
Transport
cost
minimization
Objective:
To achieve
environmental
sustainability
Objective:
To ensure
security
Objective: To
ensure safety
Reduced no. of
miles driven
outside of pre-
determined
routes
Ensure on time
delivery
Reduce no. of
trips per
vehicle
Reduction in
CO2 emission
Ensure good
quality
packaging
of
hazardous
material
Reduce no. of
transport
incidents
Reduce total
distance driven
Reduce truck
turnaround time
-Loading and
unloading time
- Easy access to
construction site
Ensure
vehicle fuel
efficiency
including idle
time
Minimize no.
of breaches in
noise limits
Reduce on
road
material
theft
Ensure vehicle
insurance
Ensure trucks
availability
(trucks
confirmed vs
requested)
Minimize
vehicle travel
time
Reduce no. of
empty miles
Road damage
due to vehicle
vibrations or
vehicle
overload
Reduced no. of
unplanned
deliveries
Reduce the time
taken in
preparing
shipping
documents
Minimizing
maintenance
cost
Optimal no. of
deliveries
arrives at gate
per day
Minimize the
time taken in
doing shipping
documents
corrections
Reduce no. of
damages
during
delivery
Maximize
vehicle travel
capacity
Reduction in
average waiting
time at gate
Optimize no.
of vehicles
movement
Maximize
vehicle weight
utilization
Vehicles
schedule
reliability
Reduce cost
of delivery
140 | P a g e
11.3 Interview guide for research question 2 The interview question for RQ2 were shared via email to the concerned contact
persons due to busy schedule of concerned contact persons and due to nature
of the questions;
- Do you record the data of vehicles regularly or not?
- Is the data available in excel sheets (ready to use format) or do we have
to organize the data to turn it into the meaningful format?
- What is average time that you take to deliver material at
site/terminal/checkpoint?
- How long the customer have to let the TPL know of the delivery i.e.
what time they have to book it?
- What is total distance between supplies and destination. Are supplies
far from the destination or are they closer? Is this convenient to supply
material from supplier to destination?
- What is the frequency of un-planned deliveries? How many times this
happen? How you deal with that and do you become successful in
making that delivery happen?
- How do you calculate no. of shipments per day?
- How do you make estimate that what amount of material will be used
in one day?
- How do you measure vehicle capacity?
- What is maximum vehicle capacity utilization that you have ensured
so far?
- How do you calculate vehicle weight utilization?
- How do you calculate vehicle turnaround time?
- At which point do you experience the most damages and how you
cover the cost for damages?
- How much cost you incur on maintaining vehicles?
- Is there any specific target that you have set for CO2 emission?
- How many vehicles do you have for running your operations?
- How do you ensure less safety and security incidents?
141 | P a g e
11.4 Excerpts of semi structure interview for RQ2
The excerpts of interview for research question 2 are shown here;
Q: What is average time that you take to deliver material at
site/terminal/checkpoint?
JH: We don´t have any measurement regarding lead times from incoming
materials (from the supplier) to delivered on site. Just that the material arrived
before 9 am should be delivered the same day to the project.
Q: What is the frequency of un-planned deliveries? How many times this
happen? How you deal with that and do you become successful in making that
delivery happen?
JH: We don´t have any data of unplanned deliveries, but it happens. Often
when it has happened it´s because something unforeseen has happened in the
project.
Q: How do you make estimate that what amount of material will be used in
one day?
JH: We don´t. See above answers. The materials delivered to the warehouse
before 9 am will be delivered to site the same date. Unfortunately, we don´t
get any data from the suppliers, making it hard to plan number amount of
incoming materials and also amount of outgoing trucks.
Q: What is maximum vehicle capacity utilization in terms of travel that you
have ensured so far?
JH: No exact measurement but about 95% (loading meters)
Q: What is maximum vehicle weight utilization that you have ensured so far?
JH: No exact measurement but close to 100%
142 | P a g e
11.5 Snapshot of original data by Case 1-Terminal The snapshot of data provided by Case 1 Terminal
Order nr
Företagskod
Artikelkod
Benämning (activities)
Datum Antal (amount)
Kundmärkning(customer marking/number)
Signatur
B1826
2822 T131 Materialförfrågning (material question)
11/1/2018
0.17 1881918302 MOKI
B1725
2450 T131 Inleverans 11/1/2018
0.5 25744 KAIV
B1725
2450 T132 Plock (gathering) 11/1/2018
0.08
MOKI
B2158
2009 T131 Uppmärkning Skanska (marking)
11/1/2018
0.25 114922 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 114875 7SÄT
B2158
2009 T131 Uppmärkning JRETAIL 11/1/2018
0.25 87520 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 975929-01 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 975932-01 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 975936-01 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 975931-01 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 975937-01 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 975935-01 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 975943-01 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 5000196005 7SÄT
B2158
2009 T131 Uppmärkning Skanska
11/1/2018
0.25 975991-01 7SÄT
B2158
2009 T131 Uppmärkning Assemblin
11/1/2018
0.25 MB001056 7SÄT
B2158
2009 T131 Uppmärkning Zengun 11/1/2018
0.25 49235-01 7SÄT
B2158
2009 T131 Uppmärkning JRETAIL 11/1/2018
0.25 5165343681 7SÄT
B2158
2009 T105 Transport inkl chaufför
11/1/2018
1.5
8BRU
143 | P a g e
B1870
2727 T131 Inleverans (delivery) 11/1/2018
0.17 25751 KAIV
B1870
2727 T132 Inleverans 11/1/2018
0.25 25751 KAIV
144 | P a g e
11.6 MATLAB code for the analysis of Case-1 Terminal Data
In this file, we try to formulate the performance measures based on the
data given.
Data Pre-Processing
ActivityDetails in row 9068 is changed from blank to 'undefined'
ActivityDetails in row 9217 is changed from blank to 'undefined2' Note that
only columns 1 through 6 have been considered here, as other columns were
suggested to be ignored. Moreover, time has been changed from hours to
minutes.
opts = detectImportOptions('Data/Case-1-terminal/case1Data.csv');
% Also specifying the number of columns that we need to be imported.
opts.SelectedVariableNames = [1 2 3 4 5 6];
% Reading the table according to the import options we created above
data = readtable('Data/Case-1-terminal/case1Data.csv',opts);
% Converting time into minutes
data.Duration = data.Duration * 60;
% Converting ActivityDetails to Lower Case
data.ActivityDetails = lower(data.ActivityDetails);
% Sorting the table based on order number
data = sortrows(data, 1);
Refining the Activities
As discussed via eMail, that the activity lossning av slingbil can be combined
together, we are going to consider all the activities that contain the term
"lossning av slingbil" as the same. This decreased unique activities from 2133
to 1036 Doing the same for the term "lastning div ställningar" (down to 1032)
for i = 1:size(data, 1)
if (contains(data.ActivityDetails(i), 'lossning av slingbil'))
data.ActivityDetails(i) = {'lossning av slingbil'};
elseif (contains(data.ActivityDetails(i), 'lastning div ställningar'))
data.ActivityDetails(i) = {'lastning div ställningar'};
end
end
Finding average time for one particular activity.
Here we find all the unique activities and the average time taken to complete
one activity.
145 | P a g e
activity = table(data.ActivityDetails,data.Duration);
uniqueActivities = sort(string(unique(data.ActivityDetails)));
for i = 1:size(uniqueActivities, 1)
temp = activity(activity.Var1 == uniqueActivities(i), 2);
uniqueActivities(i,2) = mean(temp.Var2);
end
Appending the average time in the main table.
Saving average time of activity in the table
data.ActivityDetails = categorical(data.ActivityDetails);
uniqueActivityNames = uniqueActivities(:,1);
uniqueActivityNames = categorical(uniqueActivityNames);
for i = 1:size(data.ActivityDetails, 1)
temp = uniqueActivities(uniqueActivityNames == data.ActivityDetails(i),
2);
data(i,7) = table(str2double(temp));
end
data.Properties.VariableNames(7) = {'AverageTime'};
data(:,8) = table(data.AverageTime - data.Duration);
data.Properties.VariableNames(8) = {'Efficiency'};
Splitting the main table
Splitting all the orders into separate tables and sorting according to the date
% Finding total unique order numbers
orderNumber = unique(data.OrderNumber);
orderNumber = string(orderNumber);
% Splitting
for i = 1:size(orderNumber, 1)
orderData{1,i} = data(data.OrderNumber==orderNumber(i),:);
orderData{1,i} = sortrows(orderData{1,i}, 5);
orderData{2,i} = orderNumber(i);
orderData{3,i} = mean(orderData{1,i}.Duration);
orderData{4,i} = std(orderData{1,i}.Duration);
orderData{5,i} = orderData{4,i}^2;
end
Now finding out which order number performed better than average
j = 1;
k = 1;
for i = 1:size(orderData,2)
orderData{2,i} = sum(orderData{1,i}.Efficiency);
if orderData{2,i} > 0
146 | P a g e
efficientActivities (j,:) = [orderData{1,i}.OrderNumber(1)
orderData{2,i}];
j = j+1;
else
inefficientActivities (k,:) = [orderData{1,i}.OrderNumber(1)
orderData{2,i}];
k = k+1;
end
end
Most Efficient and Ineffecient Activities (Overall)
efficientActivities = sortrows(efficientActivities, -2)
inefficientActivities = sortrows(inefficientActivities, 2)
efficientActivities = cell2table(efficientActivities);
efficientActivities.Properties.VariableNames = {'OrderNumber',
'EfficientByMinutes'};
inefficientActivities = cell2table(inefficientActivities);
inefficientActivities.Properties.VariableNames = {'OrderNumber',
'InefficientByMinutes'};
% writetable(efficientActivities,'Data/Case-1-
terminal/efficientActivities.xlsx');
% writetable(inefficientActivities,'Data/Case-1-
terminal/inefficientActivities');
Now splitting the main table based on the Company
Splitting all the orders into separate tables and sorting according to the date
% Finding total unique order numbers
companyCodes = unique(data.CompanyCode);
% Splitting
for i = 1:size(companyCodes, 1)
companyData{1,i} = data(data.CompanyCode==companyCodes(i),:);
companyData{1,i} = sortrows(companyData{1,i}, 5);
companyData{2,i} = companyCodes(i);
companyData{3,i} = mean(companyData{1,i}.Duration);
companyData{4,i} = std(companyData{1,i}.Duration);
companyData{5,i} = companyData{4,i}^2;
end
clear companyNames projects unloadingSites i;
Average activity times in different companies.
147 | P a g e
companyAnalysis = table(companyCodes);
M = containers.Map(uniqueActivities(:,1),1:size(uniqueActivities, 1));
uniqueActivityCodes = [(1:size(uniqueActivities,1))' uniqueActivities(:,1)];
companyAnalysis(:,2:size(uniqueActivities,1)+1) =
splitvars(table(zeros(size(companyAnalysis,1), size(uniqueActivities,1))));
for i = 1: size(companyData, 2)
companyAnalysis.companyCodes(i) = companyData{2,i};
activity = table(companyData{1,i}.ActivityDetails,
companyData{1,i}.Duration);
uniqueActivities =
sort(string(unique(companyData{1,i}.ActivityDetails)));
for j = 1:size(uniqueActivities, 1)
temp = activity(activity.Var1 == uniqueActivities(j), 2);
uniqueActivities(j,2) = mean(temp.Var2);
end
for k = 1:size(uniqueActivities, 1)
% companyAnalysis(i, k+1) = {uniqueActivities(k,2)};
companyAnalysis(i, M(uniqueActivities(k,1))+1) =
{uniqueActivities(k,2)};
end
clear activity uniqueActivities;
end
tempStr = string(uniqueActivityNames)';
for i = 1:size(tempStr,2)
tempStr(1,i) = strcat(toCamelCase(tempStr{1,i}), num2str(i));
end
companyAnalysis.Properties.VariableNames = ['companyCode', tempStr];
clear tempStr;
% Nowe we have the variable companyAnalysis with company codes per row and
% activities per column. One cell represents the average time taken by the
% company to complete that particular task. A value of 0 represents that
% that company doesn't perform that task.
% Now let's find out which companies perform a particular activity the
% fastest and slowest.
for i = 2:size(companyAnalysis,2)
tempT = table2array(companyAnalysis(table2array(companyAnalysis(:,1)) ~=
0, [1,i]));
Activity(i-1,1) = uniqueActivityCodes(i-1,2);
[~,FastComIdx] = min(tempT(:,2));
[~,SlowComIdx] = max(tempT(:,2));
FastCompany(i-1,1) = tempT(FastComIdx,1);
SlowCompany(i-1,1) = tempT(SlowComIdx,1);
end
SlowFastCompanies = table(Activity, SlowCompany, FastCompany);
SlowFastCompanies((table2array(SlowFastCompanies(:,2)) ==
table2array(SlowFastCompanies(:,3))), :) = [];
148 | P a g e
clear tempT Activity FastComIdx SlowComIdx FastCompany SlowCompany i j k;
% writetable(SlowFastCompanies,'Data/Case-1-
terminal/SlowFastCompanies.xlsx','Sheet',1,'Range','A1');
Saving other variables to table
byCompany = splitvars(table(companyData(2:end, :)'));
byCompany.Properties.VariableNames = {'CompanyName', 'Mean',
'StandardDeviation', 'Variance'};
% writetable(temp,'Data/Case-1-
terminal/byCompany.xlsx','Sheet',1,'Range','A1');
byOrder = splitvars(table(orderData(2:end, :)'));
byOrder.Properties.VariableNames = {'OrderName', 'Mean', 'StandardDeviation',
'Variance'};
% writetable(temp,'Data/Case-1-
terminal/byOrder.xlsx','Sheet',1,'Range','A1');
PIECHART
SC = SlowFastCompanies.SlowCompany;
FC = SlowFastCompanies.FastCompany;
[f1,f2] = hist(FC, unique(FC));
[s1,s2] = hist(SC, unique(SC));
f1c = categorical (FC);
temp = and(f1c ~= '2009', f1c ~= '2450');
fcOthers = FC;
fcOthers = string(fcOthers);
fcOthers(temp,:) = 'others';
fcOthers = categorical(fcOthers);
hFig1 = figure(1);
pie(fcOthers, [0 1 0]);
s1c = categorical(SC);
temp = and(and(and(s1c ~= '2009', s1c ~= '2450') , s1c ~= '2727'), s1c ~=
'2811');
scOthers = SC;
scOthers = string(scOthers);
scOthers(temp,:) = 'others';
scOthers = categorical(scOthers);
hFig2 = figure(2);
pie(scOthers, [1 0 0 0 0]);
Efficient/Inefficient per activity
149 | P a g e
%activities per order
ordNum = categorical(data.OrderNumber);
[apo1,apo2] = hist(ordNum, unique(ordNum));
apo1 = string(apo1)';
apo2 = string(apo2)';
apo1(:,2) = apo2;
efficientActivities = sortrows(efficientActivities, 1);
apo1 = sortrows(apo1,2);
for i = 1:size(efficientActivities,1)
idx = apo1(:,2) == efficientActivities.OrderNumber(i);
efficientActivities(i,3) =
{efficientActivities.EfficientByMinutes(i)/str2double(apo1(idx,1))};
end
efficientActivities = sortrows(efficientActivities,-3);
inefficientActivities = sortrows(inefficientActivities,1);
for i = 1:size(inefficientActivities,1)
idx = apo1(:,2) == inefficientActivities.OrderNumber(i);
inefficientActivities(i,3) =
{inefficientActivities.InefficientByMinutes(i)/str2double(apo1(idx,1))};
end
inefficientActivities = sortrows(inefficientActivities,3);
hfig3 = figure(3);
cat1 = categorical(efficientActivities.OrderNumber,
efficientActivities.OrderNumber);
bar(cat1, efficientActivities.Var3);
hfig4 = figure(4);
cat2 = categorical(inefficientActivities.OrderNumber,
inefficientActivities.OrderNumber);
bar(cat2, inefficientActivities.Var3 * -1);
Number of deliveries per company
for i = 1: size(companyData,2)
temp1 =
companyData{1,i}(contains(string(companyData{1,i}.ActivityDetails),
'inleverans'),:);
if (size(temp1,1) == 0 || size(temp1,1) == 1)
byCompany.DeliveryCount(i) = size(temp1,1);
byCompany.AverageTimeBetweenDeliveries(i) = -1;
continue;
end
temp1 = sortrows(temp1, 5);
temp1.Difference(1) = 0;
temp1.Difference(2:end) = datenum(string(table2cell(temp1(2:end,5)))) -
datenum(string(table2cell(temp1(1:end-1,5))));
150 | P a g e
byCompany.DeliveryCount(i) = size(temp1,1);
byCompany.AverageTimeBetweenDeliveries(i) =
sum(temp1.Difference)/(size(temp1,1)-1);
end
% To be exported to excel to make histogram
companyHistogramData = byCompany(byCompany.AverageTimeBetweenDeliveries >0 ,
[1 5 6]);
%Nr = normalize(A,'range')
companyHistogramData.NrDC =
normalize(companyHistogramData.DeliveryCount,'range');
companyHistogramData.NrAvg =
normalize(companyHistogramData.AverageTimeBetweenDeliveries,'range');
Other Functions
function returnString = toCamelCase (str)
str=lower(str);
idx=regexp([' ' str],'(?<=\s+)\S','start')-1;
str(idx)=upper(str(idx));
str(~ismember(str,['A':'Z' 'a':'z'])) = '';
returnString = str;
returnString = returnString(find(~isspace(returnString)));
end
11.7 MATLAB code for the analysis of Case-2 Terminal Data
KPI Formulation for Case-2-terminal data
In this file, we try to formulate the performance measures based on the data
given.
Data Pre-Processing
ActivityDetails in row 9068 is changed from blank to 'undefined'
ActivityDetails in row 9217 is changed from blank to 'undefined2' Note that
only columns 1 through 6 have been considered here, as other columns were
suggested to be ignored. Moreover, time has been changed from hours to
minutes.
clear;
clc;
opts = detectImportOptions('Data/Case-2-terminal/epData.csv');
% Also specifying the number of columns that we need to be imported.
151 | P a g e
opts.SelectedVariableNames = [1 3:13];
% Ignore those rows where item value is missing
opts.MissingRule = 'omitrow';
% Reading the table according to the import options we created above
data = readtable('Data/Case-2-terminal/epData.csv',opts);
Splitting the main table based on Unloading Site
Splitting all the orders into separate tables and sorting according to the date
% Finding total unique order numbers
unloadingSites = string(unique(data.UnloadingSite));
% Splitting
for i = 1:size(unloadingSites, 1)
unloadingSitesData{1,i} = data(data.UnloadingSite==unloadingSites(i),:);
unloadingSitesData{1,i} = sortrows(unloadingSitesData{1,i}, 3);
unloadingSitesData{2,i} = unloadingSites(i);
unloadingSitesData{3,i} = mean(unloadingSitesData{1,i}.Duration);
unloadingSitesData{4,i} = std(unloadingSitesData{1,i}.Duration);
unloadingSitesData{5,i} = unloadingSitesData{4,i}^2;
end
Splitting the main table based on Project
Splitting all the orders into separate tables and sorting according to the date
% Finding total unique order numbers
projects = string(unique(data.Project));
% Splitting
for i = 1:size(projects, 1)
projectsData{1,i} = data(data.Project==projects(i),:);
projectsData{1,i} = sortrows(projectsData{1,i}, 3);
projectsData{2,i} = projects(i);
projectsData{3,i} = mean(projectsData{1,i}.Duration);
projectsData{4,i} = std(projectsData{1,i}.Duration);
projectsData{5,i} = projectsData{4,i}^2;
end
Splitting the main table based on Company
Splitting all the orders into separate tables and sorting according to the date
% Finding total unique order numbers
companyNames = string(unique(data.CompanyName));
% Splitting
for i = 1:size(companyNames, 1)
companyData{1,i} = data(data.CompanyName==companyNames(i),:);
companyData{1,i} = sortrows(companyData{1,i}, 3);
152 | P a g e
companyData{2,i} = companyNames(i);
companyData{3,i} = mean(companyData{1,i}.Duration);
companyData{4,i} = std(companyData{1,i}.Duration);
companyData{5,i} = companyData{4,i}^2;
end
clear companyNames projects unloadingSites i;
Exporting the data
% Export by Company
byCompany = table(companyData(2:4, :)');
byCompany =
splitvars(byCompany,'Var1','NewVariableNames',{'CompanyName','MeanDuration','
StandardDeviation'});
% Export by Project
byProject = table(projectsData(2:4, :)');
byProject =
splitvars(byProject,'Var1','NewVariableNames',{'CompanyName','MeanDuration','
StandardDeviation'});
% Export by Unloading Site
byUnloadingSite = table(unloadingSitesData(2:4, :)');
byUnloadingSite =
splitvars(byUnloadingSite,'Var1','NewVariableNames',{'CompanyName','MeanDurat
ion','StandardDeviation'});
Saving to excel
writetable(byCompany,'Data/Case-2-
terminal/byCompany.xlsx','FileType','spreadsheet'); for i =
1:size(companyData,2) writetable(companyData{1,i},'Data/Case-2-
terminal/byCompany.xlsx','Sheet',1+i,'Range','A1'); end
writetable(byProject,'Data/Case-2-
terminal/byProject.xlsx','FileType','spreadsheet'); for i =
1:size(projectsData,2) writetable(projectsData{1,i},'Data/Case-2-
terminal/byProject.xlsx','Sheet',1+i,'Range','A1'); end
writetable(byUnloadingSite,'Data/Case-2-
terminal/byUnloadingSite.xlsx','FileType','spreadsheet'); for i =
1:size(unloadingSitesData,2)
writetable(unloadingSitesData{1,i},'Data/Case-2-
terminal/byUnloadingSite.xlsx','Sheet',1+i,'Range','A1'); end
clear byCompany byProject byUnloadingSite;