analysis of factors affecting on labour productivity … · construction labour productivity has...

1

ANALYSIS OF FACTORS AFFECTING ON LABOUR PRODUCTIVITY IN

CONSTRUCTION INDUSTRY BY USING RII METHOD

R.chitra1, Ruchi Kumari

2

Assistant Professor1,Student

2 ,Department of Civil Engineering

1,2

BIST, BIHER, Bharath University

[email protected]

Abstract— Productivity is dominantly aspect in the

construction industry. It is important both in

developed and under developing countries. Objective

of this is to identify and rank. Questionnaire survey

was conducted to collect the data. Productivity is the

most dominating factor in the construction industry.

Productivity is important in both developed and

developing countries. The objective of this study is to

identify the main factors affecting labour productivity

in various construction industries and assessing the

impact of the most influenced factors using RII

method and lastly, some recommendations are made to

minimize the factors affecting labour productivity.

The above objectives have been achieve through the

analysis of 25 questionnaires and the result of the

analysis shows that there are eight groups which have

significant impact on the labour productivity they

Manpower, Managerial, Motivation, Environmental,

Schedule, Safety, Equipment, Quality group.

Keywords— Labour productivity, RII, Influence

factor, construction.

I.INTRODUCTION

GENERAL

Construction projects suffer from various

problems and complex factors which affect each

phase of the project life cycle. Construction labour

productivity has become a big problem in

construction industry in most countries, hence it is

necessary to see how the human factors will affect

the labour productivity in construction projects.

Labour productivity is one of the least studied areas

within the construction industry. Productivity improvements achieve higher cost savings with

minimal investment[1-7]. Due to the fact that profit

margins are small on construction projects, cost

savings associated with productivity are crucial to

becoming a successful contractor. In construction, the

output is usually expressed in weight, length, or

volume, and the input resource is usually in cost of

labour or man-hours.

There are many standards available in the

construction industry for contractors as reference

values for purposes of construction cost estimation.

These standards may vary in values but most are

similar in principle. The paper attempts to highlight

some of the methods to study Labour productivity, its

importance and most factors which affect labour

productivity on construction project[13-17].

Productivity has been generally defined as the ratio

of outputs to inputs. Construction projects are mostly

labour based with basic hand tools and equipment, as

labour costs comprise 30% to 50% of overall project

cost. Productivity in economics refers to measure of output from production processes, per unit of input.

Productivity may be conceived of as a measure of the

technical or engineering efficiency of production.

Construction labour productivity is influenced by

various factors whose impact can be quantified in

productivity models play an important role in

estimating cost, in scheduling, and in planning.

A number of models have been developed using

regression analysis to provide a qualitative evaluation

of the impact of different factors on construction

labour productivity. The present study intends to

quantify these factors and to provide a model for

predicting labour productivity. Modernization and

industrialization has helped the construction industry

grow in leaps and bounds, small towns and cities

have become more urbanized and, the construction

sector[8-12].

LABOUR PRODUCTIVITY:

Productivity can be defined in many ways. In

construction, productivity is usually taken to mean

labour productivity, that is, units of works placed or

produced per man- hour.

Definition

Productivity is the ratio of output to all or some of the

resources used to produce that output. Output can be homogenous or heterogeneous, resource comprise,

labour, capital, energy, raw material, etc.

International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 9399-9411ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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TYPES

(a) Partial Productivity:

It is the ratio of output to one class of

input. For example output per man – hour is partial

productivity concept. So output per ton of material

and interest revenge generated per dollar of capital

and so on[18-23].

(b)Total factor productivity:

It is the ratio of net output to the sum of

associated labour and capital input. The net output

here is sometimes called value – added output. In

this, ratio, we explicitly consider only the labour and

capital input factors in the denominator.

(c) Total Productivity:

It is the ratio of total outputs to the sum of

all input factors. This is a holistic measure that takes

into consideration the joint and simultaneous impact

of all the input such as labours, materials, machine,

capital, energy, etc. This measure has received much

attention over the past ten years, as evidence by many

paper and case studies.

NEED FOR STUDY

To analyse the economical and statistical

analysis of a country or a particular

construction firms in a country.

To improve occupational educations,

training and living standards of

constructions labours.

To ensure safety and healthy environment

for construction labours.

To attain work satisfaction.

To reach better economical and social development.

To offer a dynamic measure of economic

growth.

IMPORTANCE OF THE STUDY

Productivity has a great importance in

construction. Labour productivity constitutes a

significant part of production input for construction

projects. In the construction industry many external

and internal factors are never constant and are

difficult to anticipate. This factor leads to a

continuous variations in labour productivity. It is

necessary to make sure that a reduction in

productivity does not affect the plan and schedule of

the work and does not cause delays[24-30].

SCOPE OF THE STUDY

The scope of present work is to identify

the factors affecting labour productivity in

construction. This study will help to identify future

work to be carried out to improve productivity in

construction. The main scope of study are-

The identification of factors that affecting the labour

productivity is confined to construction projects undertaken based construction companies.

The data will be collected from residential and

commercial construction projects.

OBJECTIVE OF THE STUDY

This study is conducted to achieve the following

objectives:

To identify the factors affecting the variation of

labour productivity in the construction projects.

To assess the impact of influenced factors on the

variation of labour productivity.

To suggest recommendations in order to reduce variation of labour productivity in the construction

projects.

To identify the current scenario followed in human

resources management in civil engineering field.

III. METHODOLOGY

Productivity = output / labour cost

QUESTIONNARIE SURVEY

IDENTIFICATION OF FACTORS AFFECTING

LABOUR PRODUCTIVITY

DATA COLLECTION

LITRETURE REVIEW

ANALYSIS OF DATA USING RII METHOD

International Journal of Pure and Applied Mathematics Special Issue

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Flow Chart on Methodology

IV. QUESTIONNAIRE METHODOLOGY

METHOD OF SURVEYING

The basic procedure of this study relies

on the survey questionnaire which will be gathered

from the construction sectors employees of various

sizes via mail or by faculty meeting. A thorough

literature review was first directed to recognize the

labour productivity that influence the performance of

construction sector in general[37-41].

This review has embraced the more broad and expansive meaning of labour productivity

in construction sectors and more labour productivity

components from other literature.

QUESTIONNAIRE STRUCTURE

The initial segment comprises of general

data like kind of organization, experience; cost of

their project and so forth and the second part

comprises of the project life cycle stage wise

productivity elements for assessment. The

questionnaire is made outlined upon the accompanying sorts of productivity in construction

sectors

FACTORS AFFECTING ON LABOUR

PRODUCTIVITY

Manpower

Managerial

Motivation

Environmental

Schedule

Safety

Equipment

Quality

QUESTIONNAIRE DESIGN

The review questionnaire is intended to test

the cross-sectional behavioural pattern of labour

productivity involved in construction project life

cycle. The questionnaire was set up for analyzing information for enhancement of productivity through

mathematical approach; review was planned by

observing the pertinent literary works in the range of

construction sector risk. The primary reason for the

study is not to build up another list of productivity

but rather to break down the relative criticalness

among the productivity distinguished and to

highlight the major productivity[31-36].

PRODUCTIVITY RATING

A Likert scale of 1-5 was used in the

questionnaire. A Likert scale is a type of psychometric response scale often used in

questionnaires, and is the most widely used scale in

survey research. When responding to a Likert

questionnaire item, respondents specify their level of

agreement to a statement. The scale is named after

Rensis Likert, who published a report describing its

use ( Likert 1932).The respondents were required to

indicate the relative criticality/ effectiveness of each

of the probability of labour productivity factors and

their impact to the management.

Table 2 Likert scale index.

Likert scale

index

Level of Productivity Rating

One Very low level

Two Low level

Three Medium level

Four High level

Five Very high level

CONCLUSION

RECOMMENDATIONS AND SUGGESTIONS


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ANALYSIS METHOD USED

RELATIVE IMPORTANCE INDEX:

The questionnaire are collected and analysed using

RII Method. Ranking of factors was calculated based on Relative Importance Index.

RII (%) = ∑ W / A * N

Where,

RII = Relative Important Index

W= weighting given to each statement by the

respondent and ranges from 1 to 5

A = Higher response integer (5)

N = total no. of respondents.

Table 4 Statistical Data of Questionnaire Sent and

Received

Number of respondents

Total Questionnaire sent 30

Total Questionnaire

received

10

Total Questionnaire

pending

20

V. RESULT & DISCUSSION

RANKING OF FACTORS AFFECTING ON

LABOUR PRODUCTIVITY

The overall factors are calculated by using

RII method. This is one of the management of tools

help to analyse by 5-scale likert factors analysis. The

mean value is found out for the various factors

affecting on labour productivity and determined

ranking of the factors affecting on labour

productivity[42-45]. Table shows

Table 3 Manpower

Manpower

Factors N Mini

mum

Maxi

mum

RII

Mean

Rank

Labour

absentation 25 5 3 0.536 4

Lack of

experience work 25 5 2 0.536 4

Labour

disruption 25 5 2 0.56 3

Labour personal

problems 25 3 2 0.584 2

Difficulty in

recruitment of

construction

works

25 4 3 0.6 1

Figure 1 Manpower

The above figure shows the ranking of Manpower. It

shown that difficulty in recruitment of construction works ranks first and labour personal problems ranks

second.



The overall factors affecting on labour

productivity are calculated by using RII method.

This is one of the management of tools help to analyse by 5-scale likert factors analysis. The mean

value is found out for the various factors affecting


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on labour productivity and determined ranking of

the labour productivity[46-50]. Table 4 shows the

Table 4 Managerial

Figure 2 Managerial

The above figure 5.2 shows the ranking of

Managerial. It shown that the financial difficulties of

the owner ranks first and poor communication ranks

second.

5.3. RANKING OF FACTORS AFFECTING ON


The overall risk factors are calculated by using RII

method. This is one of the management of tools

help to analyse by 5-scale likert factors analysis.

The mean value is found out for the various factors

affecting on labour productivity and determined

ranking of the labour productivity.

Table 5 Motivation

Motivation

Factors N Mini

mum

Maxi

mum

Mean Rank

lack of financial

motivation

systems

25 1 3 0.408 4

unclear

instruction to

labour

25 5 2 0.496 3

delay of salaries 25 4 3 0.536 2

lack of

transport

facilities 25 5 1 0.598 1

Figure 3 Motivation

Managerial

Factors N Mini

mum

Maxi

mum

RII

Mean

Rank

Poor site

management 25 5 1

0.45

6 4

Lack of

periodic with

labours

25 5 1 0.48

8 3

Poor

communication 35 5 3

0.57

6 2

Financial

difficulties of

the owner

25 5 3 0.60

8 1


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The above figure 5.3 shows the risk ranking of the

Motivation. It has shown the lack of transport

facilities ranks first and delay of salaries ranks

second.

5.4 RANKING OF FACTORS AFFECTING ON


The overall risk factors affecting on labour

productivity are calculated by using RII method tools.

This is one of the management of tools help to analyse by 5-scale likert factors analysis. The mean

value is found out for the various factors affecting on

labour productivity and determined ranking of the

factors affecting on labour productivity. Table 5.4

shows the

Table 6 Environmental

Environmental

Factors N Mini

mum

Maxi

mum

RII

Mean

Rank

working and

confined place 25 4 1

0.45

6 4

Location 25 5 1 0.49

6 3

weather

changes (heavy

rainfall, flood,

etc.)

25 5 8 0.50

4 2

project size 25 1 2 0.64

8 1

Figure 4 Environmental

The above 5.4 figure shows the ranking of

Environmental. It has shown the project size ranks

first and Weather changes ( heavy rainfall, flood, etc,)

ranks second.



The overall factors affecting on labour productivity are calculated by using RII method.

This is one of the management of tools help to

analyse by 5-scale likert factors analysis. The mean


on labour

productivity and determined ranking of the factors

affecting on labour productivity. Table 7

showsSchedule

Factors N Mini

mum

Maxi

mum

Mea

n

Rank

Overcrowding 25 5 1 0.464 4

Delay in

project 25 5 1 0.488 3

Misuse of time

schedule 25 5 2 0.520 2

Improper work

planning

25 4 3 0.616 1


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Figure 5 Schedule

The above figure shows the risk ranking of Schedule.

This shows the Improper workplanning ranks first

and Misuse of time table ranks second.

5.6 RANKING OF FACTORS AFFECTING ON








the factors affecting on labour productivity. Table 8

shows.

Table 8 Safety

Safety

Factors N Mini

mum

Maxi

mum

Mean Rank

Fire explosion 25 5 1 0.432 4

improper

instruction of

safety

25 5 1 0.544 3

Accidents 25 3 1 0.568 2

Insufficient

lighting 25 2 3 0.696 1

Figure 6 Safety

The above figure shows the ranking of safety. This

shows the Insufficient lighting ranks first and

Accidents ranks second.








on labour productivity and determined ranking of the factors affecting on labour productivity. Table 9

shows

Table 9 Equipment

Equipment

Factors N Mini

mum

Maxi

mum

RII

Mea

n

Rank


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Delay of

arrival of

equipment/mat

erials

25 5 1 0.47

2 3

Machineries

and equipment

failure

25 5 1 0.48

8 2

Old and

insufficient

equipment/

materials

25 5 1 0.55

2 1

Figure 7 Equipment

The above figure shows the ranking of Equipment.

This shows the Old and in sufficient equipment

ranks first and Machineries and equipment ranks

second.



The overall factors are calculated by using

RII method. This is one of the management of

tools help to analyse by 5-scale likert factors

analysis. The mean value is found out for the

various risk factors and determined ranking of

the risk factors. Table 10 shows the Dominant

risk factors mean & ranked accordingly.

Table 10 Quality

Quality

Factors N Mini

mum

Maxi

mum

RII

Mean

Rank

No proper

checking & test

of construction

materials

25 5 1 0.424 3

low quality of

materials 25 5 1 0.48 2

quality

inspection delay 25 5 4 0.56 1

Table 8 Quality

The above figure shows the ranking of Quality. This shows the Quality inspection ranks first and Low

quality ranks second.









the factors affecting on labour productivity. Table 11 shows

Table 11 Dominant factors on labour

productivity

Sl.

NoFactors Mea Ran


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. n k

1 Accidents 0.696 1

2 Project size 0.648 2

3 Improper work planning 0.616 3

4 Financial difficulties of the

owner 0.608 4

5 Difficulty in recruitment of

construction works 0.6 5

6 Lack of financial motivation

systems 0.592 6

7 Labour personal problems 0.584 7

8 Poor communication 0.576 8

9 Quality inspection delay 0.568 9

10 Old and insufficient

equipment/ materials 0.552 10

11 Delay of salaries 0.536 11

12 Misuse of time schedule 0.52 12

13 Weather changes (heavy

rainfall, flood, etc.) 0.504 13

14 Machineries and equipment

failure 0.496 14

15 Low quality of materials 0.48 15

5.10CONCLUSION: The theoretical model of this study proposed

eight independent groups affecting the variation of

Labour Productivity in the construction projects

namely Manpower group, Managerial group,

Environmental group, Motivation group, Material/Equipment, Schedule group, Safety group

and quality group.In construction projects, the

contractors used to think that labour productivity is

the maximum to complete project in short time. But

at the same time due to speed execution of work,

occurrence of error is high and if that happens

considerable amount of money and time will be

wasted to set right error. Decision making such as

factors affecting on labour productivity in

construction projects is very important in the construction management. The identification and

assessment of project labour productivity are the

critical procedures for projecting success. This study

determines the dominant labour productivity in

construction sectors. The results of the questionnaire

survey shows that accidents, project size and

improper work planning among site management

people is the prominent reason for the reduction in

productivity. Hence concluded that these factors have

to be kept in mind while executing a construction

project to achieve the optimal. This approach

provides a more effective, accurate and organized

decision support tool.

REFERENCE

1. Dharani K, ―Study on labour

productivity management in construction

industry‖, Chennai, India, Vol. 6, 2015.

2. Shashank K, Dr. Sutapa Hazra,

Kabirdra Nath Pal, ―Analysis of key factors

affecting the variation of labour productivity

in construction projects‖, Associate

Professor, Department of Civil Engineering,

India, Volume 4, May 2014.

3. Mahesh Gundecha, ―Study of factor

affecting labour productivity at a building

construction project in the USA‖, 2013.

4. M Dheenna Dhayalon, S. Alean, ―To

study the key factor that affect the labour

productivity‖, Coimbatore, 2015.

5. Vijay Antony Raj, Mrs.P.S.Kothai,

―To study on the factors affecting the

performance labour in India construction

industry‖, Vol. 3,2014.

6. Ramamoorthy, R., Kanagasabai, V.,

Kausalya, R., Impact of celebrities' image on

brand, International Journal of Pure and

Applied Mathematics, V-116, I-18 Special

Issue, PP-251-253, 2017


9407

17

7. Ramamoorthy, R., Kanagasabai, V.,

Vignesh, M., Quality assurance in operation

theatre withreference to fortis malar

hospital, International Journal of Pure and


Issue, PP-87-93, 2017

8. Ramya, N., Arthy, J., Honey comb

graphs and its energy, International Journal

of Pure and Applied Mathematics, V-116, I-

18 Special Issue, PP-83-86, 2017

9. Ramya, N., Jagadeeswari, P., Proper

coloring of regular graphs, International

Journal of Pure and Applied Mathematics,

V-116, I-16 Special Issue, PP-531-533, 2017

10. Ramya, N., Karunagaran, K., Proper,

star and acyclic coloring of some graphs,

International Journal of Pure and Applied

Mathematics, V-116, I-16 Special Issue, PP-

43-44, 2017

11. Ramya, N., Muthukumar, M., On

coloring of 4-regular graphs, International



12. Ramya, N., Muthukumar, M., On

star and acyclic coloring of graphs,



467-469, 2017

13. Ramya, N., Pavi, J., Coloring of

book and gear graphs, International Journal

of Pure and Applied Mathematics, V-116, I-

17 Special Issue, PP-401-402, 2017

14. Ramya, P., Hameed Hussain, J.,

Alteration framework for integrating quality

of service in internet real-time network,



57-61, 2017

15. Ramya, P., Sriram, M., Tweet

sarcasm: Peep, International Journal of Pure

and Applied Mathematics, V-116, I-10

Special Issue, PP-231-235, 2017

16. Sabarish, R., Meenakshi, C.M.,

Comparision of beryllium and CI connecting

rod using ansys, International Journal of

Pure and Applied Mathematics, V-116, I-17


17. Sabarish, R., Rakesh, N.L., Outcome

of inserts for enhancing the heat exchangers,



419-422, 2017

18. Sangeetha, M., Gokul, N., Aruls, S.,

Estimator for control logic in high level

synthesis, International Journal of Pure and


Issue, PP-425-428, 2017

19. Sangeetha, M., Gokul, N., Aruls, S.,

Image steganography using a curvelet

transformation, International Journal of Pure

and Applied Mathematics, V-116, I-20


20. Saraswathi, P., Srinivasan, V., Peter,

M., Research on financial supply chain from

view of stability, International Journal of

Pure and Applied Mathematics, V-116, I-17


21. Saravana Kumar, A., Hameed

Hussain, J., Expanding the pass percentage

in semester examination, International



22. Saravana, S., Arulselvi, S., AdaBoost

SVM based brain tumour image

segmentation and classification,



399-403, 2017

23. Saravana, S., Arulselvi, S., Dynamic

power management monitoring and

controlling system using wireless sensor

network, International Journal of Pure and


Issue, PP-405-408, 2017


9408

17

24. Saravana, S., Arulselvi, S., Clustered

morphic algorithm based medical image

analysis, International Journal of Pure and


Issue, PP-411-415, 2017

25. Saravana, S., Arulselvi, S.,

Networks, International Journal of Pure and


Issue, PP-393-396, 2017

26. Saritha, B., Chockalingam, M.P.,

Adsorptive removal of heavy metal

chromium from aqueous medium using

modified natural adsorbent, International

Journal of Civil Engineering and

Technology, V-8, I-8, PP-1382-1387, 2017


Adsorptive removal of brilliant green dye by

modified coconut shell adsorbent,



211-215, 2017


Photodegradation of eriochrome black-T dye

from aqueous medium by photocatalysis,



183-187, 2017


Photodradation of malachite green DYE

using TIO<inf>2</inf>/activated carbon

composite, International Journal of Civil

Engineering and Technology, V-8, I-8, PP-

156-163, 2017


Synthesis of photocatalytic composite Fe-

C/TiO2 for degradation of malachite green

dye from aqueous medium, International




Removal of heavy X`X`l from aqueous

medium using modified natural adsorbent,



205-210, 2017


Degradation of malachite green dye using a

semiconductor composite, International



33. Sartiha, B., Chockalingam, M.P.,

Photocatalytic

decolourisationoftextileindustrywastewaterb

y TiO2, International Journal of Pure and


Issue, PP-221-224, 2017

34. Sartiha, B., Chockalingam, M.P.,

Study on photocatalytic degradation of

Crystal Violet dye using a semiconductor,



209-212, 2017

35. Shanthi, E., Nalini, C., Rama, A.,

The effect of highly-available

epistemologies on hardware and

architecture, International Journal of

Pharmacy and Technology, V-8, I-3, PP-

17082-17086, 2016


Drith: Autonomous,random communication,

International Journal of Pharmacy and

Technology, V-8, I-3, PP-17002-17006,

2016

37. Shanthi, E., Nalini, C., Rama, A., A

case for replication, International Journal of


17234-17238, 2016


Elve: A methodology for the emulation of

robots, International Journal of Pharmacy

and Technology, V-8, I-3, PP-17182-17187,

2016


Autonomous epistemologies for 802.11

mesh networks, International Journal of


9409

17


17087-17093, 2016

40. Sharavanan, R., Golden Renjith,

R.J., Design and analysis of fuel flow in

bend pipes, International Journal of Pure and


Issue, PP-59-64, 2017

41. Sharavanan, R., Jose Ananth Vino,

V., Emission analysis of C.I engine run by

diesel,sunflower oil,2 ethyl hexyl nitrate

blends, International Journal of Pure and


Issue, PP-403-408, 2017

42. Sharavanan, R., Sabarish, R., Design

of built-in hydraulic jack for light motor

vehicles, International Journal of Pure and


Issue, PP-457-460, 2017

43. Sharavanan, R., Sabarish, R., Design

and fabrication of aqua silencer using

charcoal and lime stone, International



44. Sharmila, G., Thooyamani, K.P.,

Kausalya, R., A schoolwork on customer

relationship management with special

reference to domain 2 host, International



45. Sharmila, S., Jeyanthi Rebecca, L.,

Anbuselvi, S., Kowsalya, E., Kripanand,

N.R., Tanty, D.S., Choudhary, P.,

SwathyPriya, L., GC-MS analysis of biofuel

extracted from marine algae, Der Pharmacia

Lettre, V-8, I-3, PP-204-214, 2016

46. Sidharth Raj, R.S., Sangeetha, M.,

Data embedding method using adaptive

pixel pair matching method, International




Android based industrial fault monitoring,



423-427, 2017


Mobile robot system control through an

brain computer interface, International



49. Sivaraman, K., Sundarraj, B.,

Decisive lesion detection in digital fundus

image, International Journal of Pure and


Issue, PP-161-164, 2017

50. Sridhar, J., Sriram, M., Cloud

privacy preserving for dynamic groups,



117-120, 2017


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analysis of factors affecting on labour productivity … · construction labour productivity has...

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