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CRANFIELD UNIVERSITY

Marco Pugi

Development of a model for the integration of the factor “human” in factory planning

Cranfield University RWTH Aachen University

Università di Pisa Engineering and Management of Manufacturing Systems

MSc Academic Year: 2015 - 2016

Supervisors: Dr Konstantinos Salonitis Mr Matthias Dannapfel

Dr Gino Dini

CRANFIELD UNIVERSITY

Cranfield University Università di Pisa

MSc Engineering and Management of Manufacturing Systems Engineering Management

MSc Double Degree Programme

Academic Year 2015 - 2016

Marco Pugi

Development of a model for the integration of the factor “human” in factory planning

Supervisor: Dr Konstantinos Salonitis Mr Matthias Dannapfel

Dr Gino Dini

This thesis is submitted in partial fulfilment of the requirements for the degree of MSc Engineering and Management of Manufacturing Systems © Cranfield University 2015/2016. All rights reserved. No part of this publication may be reproduced without the written permission of the

copyright owner.

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To my mom and dad, landmark of my life.

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ABSTRACT

Nowadays factory planning has to face a turbulent environment due to changes in

customer demand and rules of competition. Thus, there is a growing need for

responsiveness, rapidity and predictability of the outcome in the planning stage. This

can be obtained only if the planner is fully integrated with the system and is able to

“turn the route “ promptly.

The aim of this work is supporting manufacturing industry to facilitate the full

integration of the planning team with factory planning processes in order to minimise

the variability of the expected output of the project, which is introduced by the

involvement of the human factor. This variability can affect the desired outcome

because it induces uncertainty.

“Factory planning project is not a black box as well as human factor is not a

machine”.

The trigger of the study is an evident lack of a holistic approach in the planning stage

due to a widespread “watertight compartments view” which avoids the integration of

each factory planning area with the system and mainly with the brain, the planner.

Accordingly, The innovation of this study is contained in the ability of providing a

comprehensive approach to avoid local optimisations and detachment of planning

decision points within compartments. Another key aspect is the analysis that derives

from the awareness that it’s impossible to achieve synergy between planner and

system just considering technical aspects and neglecting the human and

psychological ones.

The outcome of the research is a set of structured methodologies and a qualitative

model with the aim of avoiding local optimisations and supporting the planner in the

decision making process. Human issue cannot be ignored without huge benefits

reduction in the production system.

Keywords: Team Selection, Conflict Management, Virtual Production Planning,

System Integration, Collaborative Based Factory Planning, Motivation strategy,

Competency development programme, Project Management.

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ACKNOWLEDGEMENTS

The SATM Department of Cranfield University, the factory planning department

of WZL RWTH Aachen University and the DICI department of Università di

Pisa, support this work.

I would like to reach with this section all the people around me that have made

this achievement possible, unfortunately I cannot list everybody here.

I want to express my deepest thankfulness to Borgo de’ Medici srl, Borgo de’ Medici USA Srl, Gucci spa and Amazon Italia for their valuable help and

information. The availability and cordiality of their teams have brought great

value to the research during the model development and the validation stages.

My special gratitude is for my supervisor Dr Konstantinos Salonitis for the

valuable and constant direction that have driven successfully this work, Mr Matthias Dannapfel for the introduction in this field of study and for the

continuous feedback, Dr Gino Dini for the support and availability.

Finally, I would like to thank Matteo Pugi, Borgo de’ Medici’s finance and

planning manager for the help in the validation phase of the model.

Apart from all the people already listed, I want to acknowledge my colleague

and friend Gael Masullo for his invaluable collaboration and help in the

development of the literature review database.

Last but not least, I would like to express my gratitude to my father Mauro for

his invisible support during my projects and challenges.

Cranfield, September 2016

Marco Pugi

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TABLE OF CONTENTS

ABSTRACT ................................................................................................................................ III

ACKNOWLEDGEMENTS ............................................................................................................ IV

LIST OF ABBREVIATIONS .......................................................................................................... VII

1 INTRODUCTION .................................................................................................................... 10

1.1 RESEARCH BACKGROUND ................................................................................................................ 10

1.2 VISION, RESEARCH OBJECTIVES AND QUESTIONS .................................................................................. 12

1.3 GAP ANALYSIS ............................................................................................................................... 15

2 RESEARCH METHODOLOGY .................................................................................................. 16

2.1 SCIENTIFIC APPROACH .................................................................................................................... 16

2.2 RESEARCH METHOD ....................................................................................................................... 16

3 FACTORY PLANNING AND HHFI MODEL ................................................................................ 19

3.1 MODEL OVERVIEW ......................................................................................................................... 19

3.2 ADAPTABILITY ............................................................................................................................... 22

3.2.1 Team Selection And Management ......................................................................................... 23

3.2.2 Factory Planning Methodology Selection .............................................................................. 36

3.2.3 Technology Support ............................................................................................................... 38

3.3 REPEATABILITY .............................................................................................................................. 43

3.3.1 Motivation Strategy ............................................................................................................... 44

3.4 RESPONSIVENESS ........................................................................................................................... 53

3.4.1 System Integration ................................................................................................................. 54

3.5 PREDICTABILITY & CONTROLLABILITY ................................................................................................. 56

4 VALIDATION & FUTURE IMPROVEMENTS ............................................................................. 57

4.1 VALIDATION .................................................................................................................................. 57

4.2 FUTURE IMPROVEMENTS ................................................................................................................. 60

5 CONCLUSION ........................................................................................................................ 62

6 APPENDIX ............................................................................................................................ 64

6.1 A1-‐BELBIN’S REPORT ..................................................................................................................... 64

6.2 A2 – VOICEPRINT REPORT ............................................................................................................... 70

6.3 A3 – PROJECT MANAGEMENT: PMP AND PRINCE2 ............................................................................. 79

7 BIBLIOGRAPHY ..................................................................................................................... 82

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LIST OF FIGURES

FIGURE 1 INTERNAL AND EXTERNAL CHANGE DRIVERS ................................................... 11

FIGURE 2 RESEARCH FRAMEWORKS ................................................................................. 17

FIGURE 3 RESEARCH METHOD’S SUB-‐TASKS .................................................................... 18

FIGURE 4 PLANNING PROJECT .......................................................................................... 19

FIGURE 5 HHFI MODEL ..................................................................................................... 21

FIGURE 6 TEAM SELECTION AND MANAGEMENT PROCESS ............................................. 23

FIGURE 7 FACTORY PLANNING KEY ROLES INVOLVED ...................................................... 24

FIGURE 8 BELBIN'S TEAM ROLES ...................................................................................... 26

FIGURE 9 BELBIN'S MODEL CATEGORIES .......................................................................... 27

FIGURE 10 BELBIN'S REPORT EXTRACT ............................................................................. 28

FIGURE 11 THE 9 VOICEPRINT'S VOICES ........................................................................... 29

FIGURE 12 VOICEPRINT OVERALL SHAPE .......................................................................... 30

FIGURE 13 VOICEPRINT PREFERENCES ............................................................................. 31

FIGURE 14 INTERNATIONAL MANAGEMENT’S TRAPS ...................................................... 32

FIGURE 15 FOSTERING THE ABILITY OF WORKING TOGETHER ......................................... 34

FIGURE 16 SCENARIO TECHNIQUE .................................................................................... 35

FIGURE 17 PLANNING MODULES ...................................................................................... 36

FIGURE 18 PLANNING TOOLS IN DIGITAL FACTORY ......................................................... 38

FIGURE 19 FP TECHNOLOGIES .......................................................................................... 42

FIGURE 20 PORTER AND LAWLER'S MODEL ..................................................................... 45

FIGURE 21 COMPETENCE FRAMEWORK ........................................................................... 47

FIGURE 22 INCENTIVES OPTIONS ...................................................................................... 51

FIGURE 23 VPI LOGIC ........................................................................................................ 55

FIGURE 24 VALIDATION PROCESS ..................................................................................... 59

FIGURE 25 FUTURE IMPROVEMENTS ............................................................................... 60

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LIST OF ABBREVIATIONS

BdM CAD CBFP DF DICI FP HHFIM IEA IT OLAP PM PMI PMP PRINCE2 PTC RWTH Srl SW UK US USA VDI VPI VR WZL 2D 3D

Borgo de’ Medici Computer-Aided Drafting Collaborative Based Factory Planning Digital Factory Dipartimento di ingegneria civile ed industriale Factory Planning Holistic Human Factor Integration Model International Ergonomics Association Information Technology On-line Analytical Processing Project Management Project Management Institute Project Management Professional Projects IN Controlled Environments 2 Parametric Technology Corporation Rheinisch-Westfälische Technische Hochschule Società a Responsabilità Limitata Software United Kingdom United States United States of America Verein Deutscher Ingenieure Virtual Production Intelligence Virtual Reality Werkzeugmaschinenlabor 2 Dimensions 3 Dimensions

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1 INTRODUCTION

1.1 Research Background

Actually companies are facing various challenges due to a lack of a

systematic and structured way to run factory-planning projects. Moreover,

there is an old-fashion vision of factory planning, which too often considers

it like a black box. This kind of perspective implies to take into account

only required input and desired output and to ignore other types of

relationships and complexities. While the organisation is influenced by

external change factors that raise new challenging requirements, the

planner gropes in the dark because of the lack of a defined set of

procedures, methodologies and tools for facing the change. Furthermore,

most of the areas of interest of Factory Planning research are analysed

individually without identifying links and relations among them. This

obsolete approach is called “watertight compartments view”.

Factory planning is a relatively modern field of study and the effort in the

research has strongly increased over the time in the last two decades but

a holistic point of view is still missing. As shown in Figure 1, Companies

are struggling to satisfy the customer demand because of several change

drivers that have increased the turbulence in the market. This turbulence

asks for the development of factory planning projects with high level of

flexibility, responsiveness, re-configurability and predictability of the

output. It can be achieved only if there is a full integration and compatibility

within the human factor, the systems in place and the planning process.

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Figure 1 Internal and External Change Drivers

The most critical resource in FP is the brain, the planner. Very often the

factor “human” is considered as such a core element. If human aspects

are ignored, the system takes the risk of not achieving the optimal

performance.

The International Ergonomics Association (IEA) has defined human factor research as “understanding of interactions among humans and other

elements of a system in order to optimize human well being and overall

system performance” (IEA Council, 2000). In the recent past, the planner

is ignored or taken into account too late in the FP process, making the

great majority of planning choices difficult to modify. There are critical

challenges in the integration of the human factor in FP because of the

incredible variability in individual competence and skills and the way of

behave in team contexts.

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1.2 Vision, Research Objectives and Questions

This study sets the aim of achieving a structured approach to integrate the

key resource of the plant, the planner, in the factory planning process. The

developed model explores in a systemic way all the core areas for the

planning process in order to minimize the output variability due to

incompatibility between human factor and system. This goal can be

achieved analysing the problem mainly from two different perspectives:

• Methodological point of view: taking into account which tools are

needed, which factory planning methodologies deserve to be chosen on

the basis of the advantages they bring and which support programmes are

needed to keep high standards of human resources’ performances.

• Psychological point of view: considering the variability introduced

inside the system by the planner in terms of individual attitude and skills,

competences and experience and how to avoid conflicts and find

synergies.

The research strives for answering unexplored dilemmas in a structured

way with the ambitious goal of replacing every single and not

comprehensive company approach. The boundary of the research can be

highlighted from the following questions:

1. Can human factor introduce a degree of variability in the factory planning process if not supported with a systemic and comprehensive approach (tools, methodologies…)?

The current perception, which is widespread in industry, is to consider

factory-planning projects like black boxes, so the prime focus deserves to

be on required inputs (as software, labour hours, human resources…) and

the desired outputs (deliverables). Ignoring the level of complexity of

human relationships, conflicts management and individual attitudes,

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organisations take the risks of project delays, budget overruns and

unsatisfying results.

2. How can I ensure the effectiveness of factory-planning projects’ results and increase the predictability of the output?

The variability needs to be managed and mitigated. This study aims to

provide a model that supports the human factor integration in the system

by managing planning critical areas. From this research perspective “Full

integration with the system” means: “complete availability of tools,

methodologies, resources and programmes in a holistic approach which

takes into account requirements and constraints of each area and avoids

local optimisations”. For example, nowadays the planner, because of a

lack of a big picture, makes decisions about planning methodologies

without considering the integrative information model managing all data

which are contained in the project. Furthermore, planning team is built

regardless the resources composition and without a clear programme to

sustain its performance is very common in industry. This kind of “watertight

compartments view” which induces to make decision considering all the

planning areas as isolated and ignoring the interfaces among them

introduces variability.

This research wants to emulate what was made for the Toyota’s House of

Quality in manufacturing industry:

• Identifying the most critical planning areas

• Presenting and discussing the most relevant decision points

• Providing a model and a set of tools/methodologies to achieve satisfying

results in each area.

3. What is the focus of the research in a vast and not so much explored field like factory planning?

The focus of this research is on the psychological and human aspects

rather than technical and “hard” ones. This work aims to produce an

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integrated model to achieve the full involvement of the planner in project

by listing and describing the most important technical areas (like

technology support, planning methodology) but going through the ones

based on a “soft” and “human-based” analysis (like team management,

conflict management, motivation, career expectations) that are neglected

by the current literature.

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1.3 Gap Analysis

The triggers of this research are some gaps easily identifiable in factory

planning literature that we have decided to face in a holistic way.

1. Factory planning main areas are analysed and debated separately like watertight compartments. The lack of a holistic approach may sacrifice global effectiveness for local optimisation.

Literature about Factory Planning spreads across several different areas

of interest like information management, planning methodologies,

technology and planning tools. However, actually no academic study has

elaborated a holistic and interconnected work, which establishes the

relationships and the criticalities derived from those areas. How an event

that affects one area may affect the others. Current literature doesn’t offer

guidelines for finding synergies, for example the planning methodology

(0+5+X, CBFP) is selected regardless the team members, the SW tools

available, the size or the structure rigidity of the organisation. One decision

in a specific area could affect negatively the global efficiency of the system

and generate sub-optimisations. This research wants to add a systemic

perspective of factory planning besides the stand-alone one.

2. The importance of human factor integration and anthropological aspects like conflict management and collaborative interactions are almost completely ignored.

Factory planning literature involves mainly technical aspects regardless

the importance of the variable “Human” in terms of individual attitude,

skills, competence, experience, work approach and what is needed for a

full and natural integration with the system. The innovative aspect of this

research is to consider human resources (the planning team) “pivot” of the

whole factory-planning project.

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2 Research Methodology

2.1 Scientific Approach

Very often in manufacturing engineering it is hard to identify and to adopt

effectively one scientific methodology and rigorously respect it. The main

reason why the partnership between academia and industry is solid:

research has to be profitable and has to develop concrete aspects.

In addition, a second layer of complexity is introduced by the psychological

and anthropological research approach. Factory planning is a process

within a socio-technical system composed by infrastructure, technology,

procedure but also people, expectations, skills and hierarchy. In the end it

is almost impossible to apply a methodology and faithfully follow it.

However, G. Sohlenius has developed the paradigm “Science of

engineering” which takes inspiration from Theodore von Karma and his

statement:

“The scientist explores what is, the engineer creates what has never been”

This paradigm allows researchers to adopt a flexible methodology to

proceed in the study. In the next paragraph there is a brief explanation of

the methodology and how it has been adapted for this research.

2.2 Research Method

The framework for the development of the research is shown in figure 2

and broken down into specific sub-tasks in figure 3. The science of engineering method is the criteria at the base of this research study. A

brief explanation of each step is highlighted below:

• Analyse what is: collecting information, developing a big picture of the

problem, identifying criticalities and areas of improvement, evaluating the

applicability of state-of-art methodologies and industrial best practices,

trying to identify the TO-BE scenario and the vital requirements to arrive

there.

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• Imagine what should be: based on the development of a “vision for a

better future, clear identification and structure of requirements, exploration

of applicability of existing standards, suggestions about areas integration.

• Create what has never been: The development of the model for the

integration of human factor and support methodologies for each specific

area of interest.

• Analyse the result of the creation: Verifying results, validating the

model from industrial experts and identifying gaps and needs for future

improvements.

Figure 2 Research Frameworks

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Figure 3 Research Method’s sub-tasks

During the phase of Information collection and Model validation three

multinational companies of different size have been interviewed and

visited in order to bring value and dependability to the research. The

basement of this research is the joint support of academia with the

opinions of the industrial experts.

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3 Factory Planning and HHFI model

3.1 Model Overview

According to factory planning manual of Michael Schenk, Planning and

innovation of manufacturing plants can be translated in “envisioning”

production process in advance. This perspective of factory planning

involves tools that effectively design the planning process. A systemic,

holistic and structured approach is influenced by situation-driven decisions

and it should support the development of a planning project through the

management of direct and indirect planning activities.

Project Design reflects a creative design activity that uses prepared

technical building blocks/modules (components, assemblies, individual

systems, etc.) and organisational solutions to design, dimension, structure

and configure a user-friendly technical unit (device, machine, plant,

building, manufacturing plant, etc.). The output is a planning project that

involves the development of the building of manufacturing plants and it is

an input for it in the first phases of factory planning. Figure 4 shows

planning project’s features in detail.

Figure 4 Planning project

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Due to the turbulence of the market and the speed at which factors

influence factory change, a plant or even a network can lose

competitiveness and become a stagnant factory. This is the main risk for a

manufacturing organisation and it happens when a complex organisational

body has been structured over the time and relies on tradition and

complacency. Hierarchical levels are numerous and strictly separated with

a precise definition of tasks and roles. Employee contribution in terms of

creativity and participation is not considered as essential and the salary

system relies only on output rather than results. The attention is on the

functional optimization of commercial, design and manufacturing areas. As

a result decision-making process is not responsive and fast. This situation

may highlight a lack of a propensity for long-term goals, for innovation,

systems harmonisation, complacency and mainly for human factor

integration.

The model developed is called Holistic Human Factor Integration Model (HHFI) and it is the first effort in the whole factory planning literature in:

• Identifying and discussing critical areas of interest for the full integration

of the human factor with factory planning processes, procedures, tools in

other words with the system.

• Providing a comprehensive set of methodologies for the decision making

process in “soft” areas like conflict management and team selection.

• Highlighting the importance of fitting the project out with strategic

attributes and showing how to pursue them. The model is based on the

idea that the planner can be integrated in the project only if the project

itself stands on solid pillars:

1. Adaptability & Flexibility 2. Responsiveness 3. Predictability and controllability 4. Repeatability

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Each pillar is broken down into some critical decision areas and the

planner needs a set of structured methodologies to support the decision

making process in each of these areas. Figure 3 shows the HHFIM model.

Figure 5 HHFI Model

The following sections will explain the importance of each attribute (pillar)

and the most critical decision points to take into account. Some areas are

common for every type of project; on the contrary other areas are specific

for factory planning projects. Even if the focus of the planner has to be

constant on all of these areas, this research doesn’t aim to replace

manuals of factory planning, so each area will concern only particular key

aspects not already discussed in literature rather than a generic

explanation.

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3.2 Adaptability

The first area of interest for the full integration of the human factor

concerns the ability of the project itself to change its features and structure

on the basis of the manifestation of external events. Adaptability is defined

as “the ability of exchange, remove, adjust or add parts of the

manufacturing resources after the manifestation of an external event or a

change driver. In other words, adaptability shows the potential of putting in

place reactive or proactive solutions with little expanses at several

hierarchical levels.” The pillars of adaptability are modularity, scalability,

compatibility and integration.

This research validates the vision that a project can achieve adaptability

only if well-manages and takes effective decisions in 3 key areas of

interest:

1. Team Selection and Management: This area relies on the idea

of compatibility. An adaptable project needs the right human resources

with the right skills and attitude and a structured methodology to make the

team integrated with the project.

2. Factory Planning Methodology selection: this area is based on

the ideas of modularity and scalability. The newest factory planning

methodology called Collaborative Based Factory Planning (CBFP) is a

modular framework able to allocate every piece of project to the most

suitable team or person. “The right task to the most suitable partner”.

3. Technology support: in the era of industry 4.0, simulation, virtual

and augmented realities have gained core relevance for factory planning.

This paragraph establishes a link between expectations and output,

planner and tools, human and factory planning. The planner cannot

envision in advance future development and anticipate obstacles without

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the necessary equipment. The ability of turning the route promptly needs

more and more cutting-edge technologies.

3.2.1 Team Selection And Management

The effective management of the planning team involves aspects like

team selection and conflict management. This research wants to proof

that the full human factor integration in the factory planning process is

possible only if the right person operates in the right team and receives the

right and adequate support from the management and the organisation

itself. A theoretical framework has been developed in order to guide the

management in the right direction. As shown in figure 6, each step aims to

recommend also some specific tools in order to make the process the

most effective possible.

Figure 6 Team selection and Management process

Team selection methodology concerns 3 key steps:

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1. (Requirements Definition) Professional profiles & qualifying skills: the first step in the team selection phase is the requirements

definition. The management needs to identify the most desirable

professional profiles for the accomplishment of the project. Figure 7 shows

the most common characters involved in factory planning projects.

Figure 7 Factory Planning key roles involved

On the basis of the project features the management has to select

• Number of teams

• Size of the team

• Hierarchical relationships and organisation of the team

• External support (consultants)

• Points of contact with the rest of the organisation (interfaces)

• Ownership and responsibilities (approvals)

• The key roles involved

• The skills required for the full achievement of the tasks.

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As well as the first 7 choices rely mostly on management’s experience; the

last decision point “The skills required for the full achievement of the tasks”

needs a deeper way of exploration. The first step is the identification of the

skills classis.

• Worker skills include communication skills, leadership skills, listening

skills, management skills, planning skills, problem-solving skills, teamwork

skills, and technical work skills.

• Personal attributes include worker calm, clear thoughts, constructive,

creative, dynamic, educated, efficient, energetic, focused, healthy,

intelligent, integrity, knowledgeable, organized, previous success in work,

relationship with others, responsible, seek improvement, and strong.

In order to assess the level of the skills, a special self-questionnaire needs

to be structured and validated. Each questionnaire will be matched with a

checklist developed before the selection phase.

2. (Assessment) Individual attitude: The second step starts from

the idea behind the Belbin’s model that a competitive team needs to be

well balanced differentiating the roles within the members. The individual

role is defined on the basis of the natural and instinctive way of behaving

of a person, in other words a team role is defined as “a tendency to

behave, contribute and interrelate with others in a particular way”. Every

team needs to be formed by the highest number of different roles. The

differentiation of the roles mitigates the outcome variation due to the

introduction of the human factor in the planning process. Every extremist

or one-direction behaviours are limited by the different points of view of the

other team partners. According to the Belbin’s model each human being is

characterized by a different way of behave in team working. As shown in

figure 8, Belbin has identified and described 9 characters that comprehend

the great majority of people behaviours during teamwork.

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Figure 8 Belbin's Team Roles

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As shown in figure 8 and 9 each team roles belongs to a specific category

of behaviour:

Task oriented roles: they are prone to accomplish the task the better and

the faster regardless to people feelings and creativity

People oriented roles: the focus is mainly on coordination and support of

the team members. The achievement of the task needs to take care of

people’s feelings. Ideas Oriented Roles: Creativity is the key concept. Who belongs to this

category shows to be innovation oriented and idea-generators. Sometimes

they lack of judgement about practicality of their ideas.

Figure 9 Belbin's Model Categories

The issue that needs to be addressed is how to link every potential team

member with his related Belbin’s character. Belbin website provides an

online questionnaire that needs to be filled by each potential team member

and in the end generates a report with an extensive description of your

individual attitude and tendencies. This material can bring huge benefits

during the team structure also called “Phase 0”.

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Figure 10 shows an extract of the final output contained in the report with

a bar diagram that highlights the roles in order of preference for the user.

This analysis is based on self-perception only. In appendix A-1 is shown

an example of the report.

Figure 10 Belbin's report extract

This type of analysis is suitable for every type of organisation because of

its high level of scalability. It provides an effective approach to set well

balanced teams and it can be considered the first proactive intervention to

avoid conflicts and delays.

3. (Configuration) Communication and integration

The last step in the team selection involves the potentiality of the

integration within the team. This section concerns mainly 2 areas:

v Completeness of communication repertoire

According to Voiceprint’s point of view, Talk is action. How we

communicate to partners – the content and the manner – implies instant

and long-lasting consequences. It may strongly affect people’s thought,

feelings and behaviour. The focus on this aspect becomes vital in team

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management roles or in team relationships. The core criticality is the lack

of attention in the communication. Speech can often be unintended,

undesirable and unproductive. Voiceprint investigates the way people

adopt 9 distinctive modes of expression or “voices” (shown in figure 11).

Sooner or later all of these voices need to be adopted for an effective

development of the job, primarily in team contexts.

Again the team needs to be well balanced in terms of proficiency of the 9

voices. High level of expertise in the use of the whole repertoire is an

additional effort in avoiding conflicts within the members.

Figure 11 The 9 Voiceprint's voices

The VoicePrint questionnaire defines the self-perceptive portrait of how

individuals use the 9 voices, in other words the way people see

themselves adopting the 9 voices. This self-perception report is an

indication of the shape and completeness of the repertoire, whether

people rely on some voices more than others and whether the approach

changes under pressure. The report is designed to give a structure for

some initial self-reflection, but is certainly not intended to be a substitute

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for an interpretive conversation with a trained and accredited Voiceprint

consultant. The results and the trends are just a starting point for an

introspective analysis and for the understanding the importance in practice

of the repertoire completeness in teamwork. Facilitated exploration is

therefore the best way to explore and contextualise the patterns in

individual distinctive VoicePrint, achieving the awareness of the effects

and consequences of the way you adopt the voices, and concentrating the

efforts in the improvement of the proficiency in the management of this

important collaborative competence. Appendix A-2 provides an example

of the Voiceprint questionnaire report.

The output of the questionnaire is composed by 2 analyses:

This first diagram shown in figure 12 highlights the overall shape of your

VoicePrint. It provides a qualitative representation of the level of the voices

to which the person relies on or use to adopt more frequently.

Figure 12 Voiceprint overall shape

This second diagram, shown in figure 13, ranks your (self-reported) use of

the modes from most to least. The team members can gain benefits

thinking about and reflecting on the patterns. The figure can even give a

brief overview of the individual way of approaching conversations, since

most of the times people start with the most proficient voice and then shift

to other voices of the repertoire. This way of interpreting the individual

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portrait could even provide to the team member insights into his internal

dialogue and the thinking process features.

Figure 13 Voiceprint preferences

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v Cultural factors & Conflict management

Since very often partners of the same team are not trained to perform

outside their cultural boundaries, they need to concentrate their attention

on cultural factors (values, behaviours and beliefs). Neglecting these

aspects may affect integration of the planning team and the achievement

of the optimal result. Furthermore, this challenge can be converted into

benefit taking advantage from diversity intelligently. Figure 14 highlights

the process that can bring the team partner to fall into the similarity or

suspicion traps.

Figure 14 International Management’s Traps

Cultural impact can be broken down mainly into 3 key aspects:

• Political: involves the influence of the educational and legal systems on

culture.

• Sociological: includes religion and people’s belief

• Psychological: highlights the influencing process of our minds when

operating in the political and sociological surroundings.

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Logically, one culture can be defined as the collective programming of the

mind. According to current researches, we can identify seven dichotomies,

which are like decision points. The way of finding a solution for these

dichotomies differentiates the cultures:

• The universal truth versus the particular instance.

• Individualism versus collectivism.

• Affective versus neutral relationships.

• Specific versus diffuse cultures.

• Achievement versus ascription.

• Past, present or future orientation.

• External versus internal control (nature orientation).

Collaboration within international teams is likely to be influences by the

way of facing these dichotomies.

The greatest majority of differences in FP project rise mainly at 2 levels:

• The method of work: The work approach gap can be identified in the

planning and coordination stages. The cultural impact is caused by the

different solutions of the dichotomies of individualism versus collectivism

and neutral versus affective relationships.

• The Preferences in design: Very often, can be identified a limited

consensus within team partners of different cultures

The failure in fostering a collaboration spirit can be partially mitigated

adopting a very common strategy: Dividing the task in multiple sections

and split each of them to every single team member. This makes every

member responsible for his part and avoids conflicts. Clearly, in this case

the quality of the output it is likely to be lower because the result achieved

taking advantage of the diversity is greater that the sum of the single parts.

Another option is to try to create a joint vision by investing in sustainable reconciliation. In order to reach integration and consensus, a method of

fostering the ability to work together have been developed by creating a

joint vision of the system under design. The collaborative vision supports

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the process of selecting the most suitable methodology, i.e. methods of

work and preferences in design. The logical consequence is that the

factory planning team can take benefit of diversity. Figure 15 shows a

model for developing the competence of working together.

Figure 15 Fostering the ability of working together

Another valuable tools is the Pest analysis. It is commonly used for

investments abroad in order to be able to take into consideration all

external drivers like tributary system, legislation, religion, exchange rates,

government stability and so on that can affect the decision. This tool has

been converted currently to anticipate conflicts in teams. It analyses in

advance all the critical aspects related to a joint collaboration of team

partners with different background and cultures. The outcome of this tool is

a detailed report on the most critical Political, Economic, Social and

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Technological factors that deserve to be managed in order to avoid future

conflicts.

This research recommends also the adoption of a very common tool called

Scenario Technique in order to foster the required joint vision. The

technique is an established tool with clear directives for practice and has

high acceptance in industry. Providing beside quantitative analysis also

assistance in the development of the prediction of every team member of

future events. The final goal is to convert individual knowledge and

perception in a shared strategy. Figure 16 shows the scenario technique’s

phases.

The reconciliation scenario technique gathers the preferences,

expectations and policies of different partners in order to provide a joint

vision of the project. In international factory planning the scenario

technique is not only a tool for systematically developing complex

expectations of the future but is also a method for dealing with group

dynamics in teams.

Figure 16 Scenario Technique

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3.2.2 Factory Planning Methodology Selection

Condition Based Factory Planning (CBFP) is a modular, parallel planning

methodology, which can be reconfigured in response to specific conditions

both of the factory-planning project and of the organisation. This is

considered the most effective methodology for the planning phase of the

project and needs to be adopted from the company, understood from the

team and continuously improved project by project.

A model consisting of 28 basic modules (example shown in Figure 17)

composes the CBFP and eight planning domains, which are defined

based on project experience of the Laboratory for Machine Tools and

Production Engineering (WZL). The modules involve several different

planning tasks like: capacity planning or segmentation.

Figure 17 Planning Modules

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The main concept behind CBFP is to develop the planning duties without

taking into account the temporal sequence but just the contents. In CBFP,

the single planning tasks are encapsulated in different planning modules

with defined input and output information. Due to the resulting

dependencies between the modules, the planning process is determined.

The planning process within the single modules is standardized, which

leads to significant timesaving in factory planning projects. Each module

needs specific information, which are linked to specific dependencies. The

transparency of the project is guaranteed by the identification and study of

those dependencies. The main advantage of this approach is that if

changes occur, the planning teams are able to trace the modifications and

effects derived from the new scenario identifying the dependencies

between input and output in terms of information of the modules. Within

the single modules, the transformation of input into output information

takes place. For single modules, it has also been investigated whether

specific pieces of output information can be generated automatically

without being in need of a human planner using his experience and

intuition to derive and evaluate his planning results. For each module,

software tools have been developed over the time, (as shown in the next

chapter) which are vital and specific for each planning duty. The

criticalities of this approach are mainly 2:

• Data management becomes very complex due to the amount of

dependencies between modules and information.

• CBFP structures a theoretical model to support the information flow but it

doesn’t include an integrative information model managing all data

contained in the project. This can be fixed merging CBFP with Virtual

Intelligence Production (VPI).

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3.2.3 Technology Support “Digital Factory” (DF) consists of the set of tools for the support of the

planner in factory planning and it is considered the planning basement of

the future. The VDI (Verein Deutscher Ingenieure) defines DF as “a

comprehensive network of digital models, methods and tools, including

simulation and 3D/VR visualisation, which are integrated through

continuous data management”. The main goal of DF is a comprehensive

and holistic planning, implementation, control and continuous

improvement of all key processes and resources linked with the finished

product. Digital Factory can be considered formed by 3 elements:

• Modelling and visualisation,

• Simulation and evaluation

• Data management and communication

Figure 18 shows the most common categories of planning tools in the

digital factory. The challenge for the full integration of the planner in the

factory planning process is the selection of the right dimension of

technological planning tools in order to have an effective and efficient

support for the phases of design and simulation. The features of the

company (size, structure rigidity, organisational shape, etc.) influence the

suitability of the tools, for instance a small enterprise with high rigidity in

the structure, which sells commodity products, won’t achieve any type of

benefit from the utilisation of augmented reality tools for product design.

Figure 18 Planning Tools In Digital Factory

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Technological solutions on the market Regarding this huge number of tasks, a vast number of commercial tools

and solutions are available on the market as shown in figure 19

Figure 19 FP technologies

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Challenges for the Planning team in DF

DF is considered the vanguard of factory planning, but "from great power

comes great responsibilities”, so the planner needs to put efforts in the

management of 3 key critical aspects in order to avoid disruption and

inefficiencies:

1. Limited time for the planning process and the complexity of the

organisation’s processes and relations need continuous support from all

areas to ensure up-to-date planning data and avoid silos effect. The whole

planning process in the virtual world influences the concrete production

factory, the logical consequence is that each modifications in the real world have to be converted in the virtual planning data.

2. The tools of the Digital Factory fully rely on the digital database. Errors or defects in digital data may affect the planning process

output and delays in implementation. Re-planning phase could be very

expensive, provoke delays in the launch of production and impede the

achievement of an optimum result.

Theoretically, the DF and the concrete world are identical but,

unfortunately, they differ because there is no a physical link between the

two worlds and In DF the manufacturing plant is virtually built. The hardest challenge is to overlap these two worlds, minimise the divergence and keep DF up-to-date with the real world. Several

problems can be identified during the process of achieving this level of

consistency:

• 3D data is usually not available for all existing plants (Only 2D layout

or production designs).

• Very often there are still planning processes that do not yet use these tools.

• Furthermore, the factory is a dynamic environment. In most cases, changes are not documented and can thus not be transferred back to the virtual planning data of the Digital Factory.

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Focus on Augmented Reality

To find a definition of Augmented Reality (AR), different approaches are

possible. A very general classification is given by Milgram, who used the close

relation of Augmented Reality with Virtual Reality to create a so called Reality-

Virtuality (RV) Continuum. In this continuum, AR finds its place in-between the

Real Environment and the Virtual Environment (see figure 20), in the Mixed

Reality space. In contrast to Augmented Virtuality (AV), Augmented Reality is

situated closer to the real world than to the virtual world. Thus, Augmented

Reality denotes an enhancement of the user’s perception of the real

environment. Another more technical but still technology-independent definition

can be found in the AR survey of Azuma, where the following characteristics for

AR systems are stated:

• It combines real and virtual.

• It is interactive in real-time.

• Real and virtual objects are registered in 3D.

Figura 20 Reality vs Virtuality Continuum

Augmented Reality as a technology requires several system components, which

need to take care of the different functional aspects related to an AR system,

such as tracking the interesting objects in the real world, presenting the

augmented world to the user or controlling interactions between the user and

the system. Reicher conducted a study on software architectures for

Augmented Reality systems to identify a reference architecture built from

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components common to most AR systems. The architecture consists of the

following components:

• Application: containing application specific logic and content,

• Tracking: responsible for determining the users’ and other objects’

poses,

• Control: gathers and processes user input,

• Presentation: uses 3D and other output modalities

• Context: collects different type of context data and makes it available to

other subsystems and

• World Model: stores and provides information about real and virtual

objects around the user.

AR & VR are able to support the full integration of the human factor in factory

planning giving the power of envisioning in advance what some potential

scenarios to the planner. This technology provides to the project the adaptability

and the robustness needed to face the external change drivers and the internal

unexpected requirements simulating in advance what will be.

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3.3 Repeatability

This attribute plays a key role in the integration of the human factor in

factory planning process. It refers to the ability of the organisation to create

a solid basement to make the project sustainable and the ability to

enhance the effectiveness and efficiency of the outcome project by

project. The organisation has to develop solutions for keeping inside:

• Experience and competency developed: the organisation has to

structure a programme in order to support the development of the human

resources’ competences and to preserve them to get out the company.

The human factor cannot be seen as driver of the system if he is not

aware of the competencies he is going to develop. Effective Factory

planning anticipates the future.

• Data and information collected: the information flow needs to be well

structured, precise and accurate (the right information to the right person

at the right level of detail), well timed and efficient. The planner is able to

manage the project effectively only if can promptly collect and find the right

data. The organisation has to defeat the “Silos effect” which is a watertight

compartment view for information. This set of data, information and lesson

learned will constitute the base for the future projects.

A key decision point to make the project sustainable in the long term is

motivation: It’s impossible to preserve experience, competencies and

skilled people inside the company without a clear motivation strategy (in

terms of salary system and career opportunity). The effective approach

aims to push performance to the limit and to preserve talented people from

the migration. The planner and the team cannot be fully integrated in the

project if they don’t see a compensation for their work or the opportunity

for achieving the individual goals beside the company’s ones. Great

expectations demand great opportunities.

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3.3.1 Motivation Strategy

The optimum outcome of the project can be achieved only if there is in

place the right strategy for sustaining and improving employee’s

motivation. The first challenge to be faced is that, of course, motivation

cannot be measured but just indirectly observed as positive delta

performance. The trigger of lack in/good performance can be led back to

two key concepts:

• Intrinsic motivation (i.e. when a job is considered independent,

professionally challenging and as a basis for personal growth)

• Extrinsic motivation (i.e., by payment and threatened punishment).

Literature of motivation stresses mainly two blocks of theories but very

often the structure of the motivation strategy can be derived from both of

them:

• Content theories the concept at the basis of these theories is that the

interpretation of people behaviour is linked to the need of filling some

specific deficiencies. In literature about motivation the main model is

considered the one developed by Abraham Maslow as shown in figure 21.

Figure 21 Maslow's Pyramid

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• Process theories explain actions against the background of complex,

multi-staged decision processes. According to these approaches,

motivation originates in the work process and the mental anticipation of it.

The majority of process theories work with the value which specific

processes and their results have for an employee.

Figure 22 Porter and Lawler's Model

From this last group of theories we have identified the reason why

motivation is related to an important aspect of human motivation deeply

described in literature: there is a lack of motivation where there is no

evidence of future individual improvement. The term individual

improvement can be roughly broken down into 2 main areas:

• Competency development

• Career related aspects (salary system, hierarchical position)

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3.3.1.1 Competence Development

The term “competence” is vaster than qualification; it defines the boundary

of the expertise as a mix of theoretical knowledge and capabilities that

people has command over in determined scenarios.

“Competency refers to the motivation and aptitude of a person to

independently further develop knowledge and ability in an area to a level

that can be characterized as expertise”

From this derives the idea that the learning process should not involve just

instilled and accumulated pre-structured notions because people learn

linking new notions with old knowledge, theoretical contents with practical

experience, suggested solutions with concrete problems. The main

consequence of this concept is that the learning programme has to be

structured to show the real world sense and context of the course content.

Learning programmes, in order to develop competencies, are meaningless

if they are not preceded by a gap determination phase. It’s vital to identify

and distinguish the individual elements of competence. A common model

has defined 4 areas of competency:

• Technical

• Methodological

• Individual

• Social

Figure 23 shows and describes these terms and elucidates the relations

inside the framework.

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Figure 23 Competence Framework

Literature about knowledge distinguishes explicit and implicit knowledge

and agrees that both compose professional competence. The term

explicit knowledge refers to conscious, logically organized knowledge

that can be communicated. Implicit knowledge stems from experience; it

allows tasks to be executed with certainty, however it is not consciously

present and cannot be verbally communicated.

After the identification of the desired competences and the current gap,

the management should design before the project launch and during the

entire course of the project the features of the competence development

programme facing two main aspects:

1. Areas of human resources development: The specific area of

competence that the management wants to improve in the team

(Knowledge & skills oriented, Behaviour oriented)

2. Type of learning: The most suitable way to develop competences

in terms of knowledge and skills. (Formalized learning, Partly formalized

learning, Informal learning)

These two areas are strictly interrelated. After the decision of what

competence area the management wants to improve, it has to decide what

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kind of actions to adopt. The main goal is to align organisation and project

requirements and team competence in the short and long term both. The

initiatives can be divided following mainly 2 main criteria: Level of formalisation and orientation.

According to the level of formalisation we can distinguish:

• Formalized learning is driven by an expert through classes, training and

explanations and is adopted mainly for technical and methodological

competences (more effective for transmitting explicit knowledge)

• Partly formalized learning is most of the times conducted in learning

environments. The experts follow a conscious path but do not pre-

structure the lesson. (effective for implicit and explicit knowledge and for

the transmission of all 4 dimensions of competence). Advantage: very

close to experience and very effective for the development of social and

individual competence. Disadvantage: Lack of exploration of theoretical

elements.

• Informal learning is unstructured and experience oriented. It can be

performed during the project tasks, transmitting informal knowledge to the

partners

In addition on the basis of the orientation we have:

• Knowledge oriented approaches’ goal is the effective alignment of

individual competences with the project’s work task prerequisites. Main

tools:

1. Requirement analysis

2. Scenario building

3. Staff appraisals (provide information about the current capabilities

and employee’s potential. It involves an analysis stage before the structure

of development plans that contain metrics, timeline and the final

evaluation).

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The most up-to-date knowledge oriented approaches take into account

individual learning styles and contain challenging learning purposes based

on work related scenarios.

• Behaviour-oriented measures’ goal is facilitating behaviour

modification in order to align people’s way of acting with the organisation

vision. Main tools:

1. Training measures (group training or outdoor training)

2. Coaching

3. Mentoring

4. Consulting

Change management gains a vital importance to support the change and

avoid delays or failure. The most effective approaches couple training

measures with measures related to design of workplace.

3.3.1.2 Career related aspects

The purpose of career-related area is to structure career development

programme (including the salary system) consistent with the organisation

vision that overlaps the prerequisites of the company with the expectations

of the individuals. Who takes part of a challenging factory-planning project

needs to be aware of the reward for his work. After the conclusion of the

project, on the basis of the employees’ expectations, performance and

willingness, career should move in at least one direction:

• Vertical careers: involve the climb or descent of the hierarchical scale.

For example after the successful completion of the FP project a team

member that has shown leadership and managerial skills should be

selected as team leader for the next project and obtain more commitment.

• Horizontal careers: do not involve hierarchical movements but changes

in department, project or role (if on the same hierarchical level). For

example, at the end of the project the financial responsible of the planning

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team can be moved from the FP project to an ERP introduction one in

order to extend his competences.

Evidently, in the development of the career-system, the management

needs to identify, clarify and include the expectations of the employees.

Furthermore, it is important to consider some vital aspects like for example

the readiness of people to move vertically that is commonly stronger than

to move horizontally. This desire of climbing vertically is relies on the wish

for autonomy, power, self-development and higher salary. On the other

hand, the desire of changing horizontally can be justified by more involving

and fascinating tasks or the ambition of enlarging the boundaries of

competence in different fields. Finally, horizontal changes can facilitate

future vertical climbs but sometimes are stopped by the desire of a stable

career development.

Accordingly, the salary system should pursue two main goals

• Developing a payment which is perceived as appropriate

• Controlling employee performance

The challenge for the management is to identify the efficient and effective

combination of incentives between monetary and non-monetary ones in

order to raise the team members’ motivation at the lower cost. Figure 24

shows the most common type of monetary and non-monetary rewards.

The salary systems development process should pursue the fairness

criteria, which is dependent on normative decisions and cultural backgrounds.

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Figure 24 monetary/non monetary incentives

In order to ensure that the salary system developed is expression of the

fairness criteria, the planner should consider several different aspects:

• The relationship between the offer and demand on the job market,

• Acquired qualifications and professional certifications,

• Acquired social privileges, e.g., the length of time someone has belonged

to an organization or their length of service,

• Social needs such as the responsibility for spouses and children,

• The general difficulty of the work task,

• The specific performance of the employee.

Finally, the planner should always follow 3 main guidelines before the

project launch and during the project development:

• The management has to build over the time a goal orientation and clarify

individual targets, which have been agreed upon together and written “on

paper”.

• Management should identify and discuss the conditions under which the

project team can accomplish the task and achieve the required

performance. The new challenge for management is the elimination of

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technological and organisational obstacles beside the design of a

programme for the development of competences.

• The management should be able to vary the management style (from

participative to a performance oriented and vice versa) on the basis of the

features of the planning team (experience, hierarchical rigidity, work

approach, etc.)

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3.4 Responsiveness

The third section describes the key aspects of responsiveness. We can

define it as “the ability of communicating and problem solving in a timely

and efficient manner after the effective analysis of the contextual

conditions”. The potential responsiveness of the project is a key indicator

of the full integration of the human factor because no project is completely

manageable if the planner doesn’t receive promptly the information and

the tools to change the course and bypass in advance obstacles.

Responsiveness of the project can be achieved making effective and

efficient three key elements:

• Communication: A project to be responsive requires a validated and

reliable communication system, cutting-edge technology and a low degree

of hierarchy in the organisational structure. Information flow has to be

precise, timely and structured.

• Problem solving: The organisation needs to stand on solid and

experienced procedures to face challenges. Furthermore, the

organisational structure of the team has to include a body for problem

solving with the responsibilities of crisis management, risk management

and problem solving.

• Compatibility & integration: The organisation has to pursue the full

integration of the systems under the organisation’s umbrella.

Incompatibility among systems or technologies and between planner and

production system can affect the responsiveness of the project to change

route promptly.

The following section will briefly explain the virtual production intelligence,

a vital concept of factory planning, with the aim of increasing the speed

and the quality of the decisions.

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3.4.1 System Integration

Virtual Production Intelligence (VPI) can be defined as an umbrella for

different applications of every description, which supports and makes

easier the access to and the manipulation of information with the final goal

of enhancing the quality of the decisions, overall efficiency and the level of

performance. The key focus of VPI is on the management and analysis of

information produced in digital manufacturing. It is tightly linked to the

concept of “business intelligence system” of Luhn. The VPI strives for

achieving 3 core objectives:

• Holistic: Addressing all sub processes of product development, factory

and production plan etc.

• Integrated: Supporting the adoption and the combination of existent

approaches instead of producing new standards.

• Collaborative: Considering roles, which are part of the planning process,

as well as their communication and delivery processes.

The importance of this section can be identified in the planner’s need of

avoiding the silos effect and the applications incompatibility within the

organisation. The lack of system integration can be disruptive and

obstacle the ability of the planner of managing the project. Effective

decision-making process requires a well-timed, precise and efficient

information flow and this can be achieved only with a full integration of the

system in terms of applications. VPI is the answer to this challenge.

Furthermore, VPI addresses the most critical challenges for the

implementation of Digital Factory like: interoperability, user interaction,

visual analysis and simulation. The IT system that makes the

organisation able to manage all the functionalities and visualisation

capabilities to offer the power to the users to face these critical aspects is

called VPI platform. The core objectives of VPI are the minimisation of planning efforts and the enhancement of planning efficiency giving a

comprehensive analysis and the visualisation in terms of control panel.

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This needs to structure the integrated information model. Using the

adaptive information integration, data can automatically be transferred

between the sources and the data sinks. Furthermore, data can be

consolidated for analysis. Such an information centred approach facilitates

a structured information management that makes an integrated and

efficient data access possible, e.g. by supporting structured query

languages. Methods and tools for data analyses and evaluation, such as

Data Warehousing and OLAP (On-Line Analytical Processing), facilitate

various possibilities to manipulate and to maintain existing data. The

platform serves for planning and support concerns by providing an

integrated and explorative analysis in various fields of application. Figure

25 shows how the platform issued by various user groups in these fields of

application.

Figure 25 VPI logic

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3.5 Predictability & Controllability

The planner and the project team can be seen as foreign bodies of the

system if they have not the power of controlling the process advancement

and the tools to standardise and make predictable the desired outcome.

The clarification of the objectives and the deployment of a shared and

common strategy as well as the ability of project management are vital for

the full control of the project and the avoidance of accidents and delays.

• Strategy and vision deployment guarantees the alignment between

management and planning team, vision and related strategy, stakeholders

and project. This area anticipates every activity of project management like

the project charter or the PM plan development. This area concerns the

ability of the organisation of spreading to several hierarchical levels the

guidelines of his mission. The full integration of the planner can be

achieved only if he is aware and shares the objectives of the company.

The right route can be chosen only if the planner is aware of where the

organisation aims to arrive.

• Project management gives to the planner the ability to control the

project and to avoid budget overruns, delays and stakeholders

dissatisfaction. This area is common to several types of project so it is not

explained in detail here. Appendix A-3 shows a brief discussion about the

differences between two standards: PMP and Prince2. This section is

inserted in order to go beyond the old fashion view of a mutual exclusive

choice between these two standards and to help the organisation to

achieve additional benefits related to PM.

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4 Validation & future improvements

4.1 Validation

Theoretically, there are 3 ways for the model validation and any of them

can be utilised to different sections of the model:

• Judgement of experts (from academia and/or industry)

• Measurements during the on field implementation

• Theoretical results/analysis

The first one has been chosen mainly because of the nature of the model

and of the topic. HFIM is a qualitative and not quantitative model with the

aim of creating an awareness of the decision points that need to be taken

into account in factory planning projects and supporting the decision

making process itself. Furthermore, this model includes some

psychological and “soft” factors that are not easily measurable.

The critique of he model has to be conducted by experts of the system

(and not of the model) and not by the modeller itself. For this research

three companies of different size and sector have been involved. After the

phase of iteration the model has been completed and refined.

The configuration and refinement of the model were conducted basically

with questionnaires (sent by mail) and interviews (by mail, calls and on-

site visits) to:

• General Manager, finance and planning manager, project manager of

BdM (small size company in the food sector)

• Controller of Gucci (medium size company in the fashion sector)

• Operations Manager, project manager of Amazon Italia (large size

company in the e-commerce sector).

The experts’ selection has been developed on the basis of the current

network of the researcher. The advantage of this choice consists in having

a closer relationship with the interviewees and greater availability in terms

58

of time and resources. The trigger of this choice is the unsuitableness of

the questionnaires approach for this psychological/soft topic of human

factor integration. The research gains value only if we are able to include

during the refinement iterations and during the development of the model

also the experience and intuition of the experts. The entire panel has

experience in factory planning projects and has provided valuable

modifications to the model’s structure and contents by the fulfilment of the

questionnaire but mainly with the on-site visits and the interviews. The set

of methodologies containing “team selection”, “conflict management” and

“motivation strategy” has been reviewed, analysed and configured

according to the experts’ perspective. The figure 26 below shows at a high

level the validation process.

In the end, after several iterations the entire panel of experts has agreed

on the value of the model and its innovative ability to fully support the

decision making process in a “fuzzy” and turbulent context as factory

planning. The model has respected the expectations of the user (the

planner) in an innovative way and created a first step for the future

development.

The validation process outcome consists of:

• Experts' approval of the model

• Gap in the research and future improvements

The model has not been tested on field because of the limited timeframe

and the “soft” nature of the topic, which requires incredible efforts in terms

of time for the validation. Accordingly, the next step of the research may

consist of a business case and the physical implementation of the model

inside a real production system and a real factory-planning project in order

to identify more strengths and weaknesses.

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Figure 26 Validation process

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4.2 Future Improvements

After the validation process are emerged some points for future

improvements:

Figure 25 Future Improvements

• Qualitative not quantitative model: because of the nature of the topic,

(which includes human factor) and the nature of the model it is hard to

build a quantitative model. Literature has some techniques to develop

quantitative analysis for the different areas of interest. A methodology to

integrate all of these technics is still missing.

• Specific for Factory Planning: HFIM is specific for factory planning

project and loses relevance when we decide to change the field of

application. Some areas of interest of the model are “commodity”, so they

can be applied to every project (like project management area) but there

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are still some areas specific for factory planning (like methodology

selection).

• Requires a lot of efforts in the planning phase: this model provides a

framework to minimise the output variation due to the introduction of the

human factor but taking into account each single aspect and following the

methodologies in order to make an effective decision is costly in terms of

time. “The optimal decision is a luxury”.

• Other important areas of interest need to be added and discussed: The selection of the areas has been conducted according to the literature

review and the interview with the experts. It is likely that some relevant

areas have been neglected. The future step is to develop a second

iteration of interviews with industry and integrate the residual areas.

• Validation on field: The model needs in the future to be tested on site in

a real project of factory planning and refined accordingly with the result of

the project.

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5 Conclusion

Factory planning is a relatively new field of study, which deserves an

increasing joint research effort from academia and industry. Too often

psychological and human aspects are neglected in literature of FP and too

often the technical areas of study about FP (like simulation technology,

virtual reality, planning methodologies…) are discussed separately. This

research aims to going beyond the old fashion views of “FP as a black

box” and “watertight compartments”. Accordingly, HFIM model is the first

step in the direction of defining the FP boundary and introducing shared

methodologies and practices. The model aims to emulate what the ------‘s

“House of Quality” represents for manufacturing and pursue the objectives

of:

• Identification of the most critical areas

• Development of practices and methodologies to face challenges in each

area.

The research has been conducted developing a “big picture” of the topic,

then defining a generic background of each area but deepening only the

“soft” and “human-based” aspects like Project Management, conflict

management, team selection and management, Motivation and

Competence development.

The judgement and the support of FP experts have brought incredible

value to the model and to the research as a whole. The future steps

consist of the test on field of the model in order to investigate the concrete

applicability and the development of a quantitative model.

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6 Appendix

6.1 A1-Belbin’s Report

Belbin Team RoleReport for

Jo Pink

Colourful Company PLCRainbow HR

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6.2 A2 – Voiceprint Report

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6.3 A3 – Project Management: PMP and Prince2 PM is a vital area of interest not specific for factory planning projects, so

we refer to generic manuals for the basics.

The PRINCE2 and PMP certifications involve two different project

management frameworks that belongs to two different institutions: PMI

and Axelos. Both offer a body of knowledge and a proven approach to

managing projects effectively.

A critical decision point in the planning phase is the selection of the Project

Management Standards. The two main methodologies are Prince2 and

Project Management Professionals. In this section it is interesting to

analyse how the industrial perspectives sometimes relies on erroneous

assumptions like considering these two standards like a mutual exclusive

choice.

The literature of project management and the institutions themselves

recommend a methodology rather than another in function of various

factors, for example, the great majority of literature argues that the optimal

choice should be based on the industry or company you are aiming to

build a career in, the type of project one is leading or directing or the

country the organisation belongs to. Figure 24 shows the current

certification adoption region by region

The nature of these two frameworks is deeply different mainly for three

key aspects:

PMP Prince2 Knowledge book (set of

standards)

Methodology

Descriptive (explains best

practices)

Prescriptive (explains correct

approach)

Answer to “HOW?” Answer to “WHAT, WHEN, WHO?”

80

The first core difference is that PMP is a collection of best practices that

constitute a standard but Prince2 is a “process-based project management

method” that offers a systematic method for delivering a successful project

with clear templates, processes, and steps. The certification is both,

process and project focused. It is a broad, high-level, general framework

of project management principles, which means it is recommended for and

implemented on just about any kind of project. Accordingly PMP is a

descriptive framework, which shows and discusses these best practices

and leaves a certain degree of freedom in the implementation. Prince2 is a

prescriptive model, a set of guidelines that explains in every situation what

to do, when and who.

The result of the interviews to Amazon and Bdm’s project managers

highlights a new perspective of these two frameworks. Because of their

81

nature and application this is no more a mutual exclusive choice, on the

contrary, the joint adoption may bring important benefits to the project.

According to a survey conducted by PMI-NIC (Project Management

Institute – Northern Italy Chapter), the PMI’s certification is achieving more

and more recognition but the integration with Prince2 may bring relevant

benefits:

• Exhaustive Body of Knowledge: provides to the project manager a set

of tools to analyse the project from all angles, ensuring the feasibility

before starting. The probability of failure is minimised clarifying in the

planning stages user requirements and potential risks.

• Well Laid-out Methodology: it avoids waste of time and resources

• Standardization: Confusion in project execution is eliminated since the

same, standard approach is used throughout, with common filing systems,

procedures, and documents.

• Driven by Business Case: PRINCE2 ask for updating the business

case simultaneously to the progress of the project. This can avoid that the

project will not create value for the company and for the customer.

Failure to do so will eliminate the justification for the continuity of the

project.

• Eliminates ambiguity: It divides the master project plan into Project

Plans, Stage Plans, and Team Plans, which simplify the execution of the

project.

82

7 Bibliography

[1] Seliger, G. 1997. Cultural Factors in Factory Planning, Int J Adv Manuf

Technol, 113 pp. 478-487

[2] Jumyung Um, 2014. Factory planning system considering energy-

efficient process under cloud manufacturing, 47th CIRP Conference on

Manufacturing Systems CIRP 17 pp. 553 – 558

[3] Pedrazzoli P.et al., (2007), “Virtual factory framework: Key enabler for

future manufacturing”, Springer.

[4] Redaelli, C., Lawson, G., Sacco, M., D’Cruz, M., (2009), “DiFac: Digital

Factory for Human Oriented Production System, Human” In:Computer

Interaction, InTech, 339-354.

[5] Heragu S., (2006),“Facilities design”, Iuniverse.

[6] Schenk M., Wirth S., Müller E.,(2010), “Factory planning manual”,

Springer.

[7] Tompkins J.A., White J.A., Bozer Y.A., Tanchoco J.M.A., (2010),

“Facilities planning”, 4th edition, John Wiley & Sons.

83

[8] Constantinescu C., Riexinger G., (2011),“Implementation and

enhancement of a graphical modelling language for factory engineering

and design”, In:7th International Conference on Digital Enterprise

Technology.

[9] Viganò G.P. et al., (2011), “GIOVE Virtual Factory: A New Viewer for a

More Immersive Role of the User During Factory Design”, In:Digital

Factory for Human‐oriented Production System,Springer.

[10] Iqbal M., Hashmi M.S.J., (2001), “Design and analysis of a Virtual

factory layout”, In:Journal of Materials Processing Technology,‐ Vol. 118.

[11] Nyhuis P, Wiendahl H.P.,(2009), “Fundamentals of Production

Logistics: Theory, Tools and Applications”, Springer.

[12] Arnott D, Pervan G. A Critical Analysis of Decision Support Systems

Research. In: Journal of Information Technology. 20 (2); 2005. p. 67-87.

[13] Luhn H P. A Business Intelligence System. In: IBM Journal of

Research and Development. 2 (4); 1958. p. 314-319.

[14] Baars H, Lasi H, Kemper H-G, Koch M. Manufacturing Execution

Systems and Business Intelligence for Production Environments. In:

Proceedings of the 16th Americas Conference on Information Systems

(AMCIS), Lima, Peru; Paper 436; 2010.

84

[15] VDI guideline 4499 part 1. Digital Factory – Fundamentals. Berlin:

Beuth; 2008.

[16] Schuh G, Kampker A, Wesch-Potente C. Condition based factory

planning. In: Prod. Eng. Res. Devel. 5 (1); 2011. p. 89-94.

[17] Büscher, Christian Voet, Hanno Meisen, Tobias Krunke, Moritz

Kreisk??ther, Kai Kampker, Achim Schilberg, Daniel Jeschke, Sabina.,

2014, Improving factory planning by analyzing process dependencies

[18] Reinhard R, Büscher C, Meisen T, Schilberg D, Jeschke S. Virtual

Production Intelligence. A contribution to the Digital Factory. In: Proc. of

the 5th International Conference on Intelligent Robotics and Applications

(ICIRA 2012). (1); 2012. p. 706-715.

[19] Büscher C, Hoffmann M, Reinhard R, Schilberg D, Jeschke S.

Performance Indicators for Factory Planning on the Basis of the Virtual

Production Intelligence Approach. In: Proc. of the 22nd International

Conference on Production Research (ICPR 22), July 28 – August 1.

Iguazu Falls, Brazil: ICPR; 2013.

[20] (VDI – Association of German Engineers) Verein Deutscher

Ingenieure. 2009. Digital Factory – Fundamentals. VDI 4499-1:2008.

[21] Bracht, U., and T. Masurat. 2005. “The Digital Factory between Vision

and Reality.” Computers in Industry 56 (4): 325–333. Dassault Systems.

2011. DELMIA Digital Manufacturing & Production – DELMIA – Dassault

85

Systèmes. Dassault Systems. Accessed October 4, 2011.

http://www.3ds.com/products/delmia

[22] Kühn, W. 2006. “Digital Factory – Integration of Simulation Enhancing

the Product and Production Process towards Operative Con- trol and

Optimisation.” International Journal of Simulation 7 (7): 27–39.

[23] Westkämper, E. 2009. “Digital Production.” In Technology Guide:

Principles, Applications, Trends, ed. H.-J. Bullinger, 482–485. Ber- lin:

Springer.

[24] Gregor, M., and S. Medvecky. 2010. “Digital Factory – Theory and

Practice.” In Engineering the Future, ed. L. Dudas, 355–376. In Tech.

Accessed October 4, 2011.

http://www.intechopen.com/articles/show/title/digital-factory-theory-and-

practice

[25] Wagner, U., H. A. ElMaraghy, T. AlGeddawy, E. Müller. 2012. “The

State-of-the-Art and Prospects of Learning Factories.” In Pro- ceedings

45th CIRP Conference on Manufacturing Systems, ed. D. Mourtzis and G.

Chryssolouris, May 16–18, 2012, 121– 128. Athens: University of Patras.

[26] Collaborative Factory Planning in Virtual Reality N. Menck,*, X. Yang,

C. Weidig, P. Winkes, C. Lauer, H. Hagen, B. Hamann, J.C. Aurich, ,

In:Procedia CIRP 3 ( 2012 ) 317 – 322

86

[27] Smparounis, K., Alexopoulos, K., Xanthakis, V., Pappas, M.,

Mavrikios, D., Chryssolouris, G., 2009, A Web-based platform

forCollaborative Product Design and Evaluation, 15th International

Conference on Concurrent Enterprising (ICE).

[28] Grundig, C.-G., 2006, Fabrikplanung, Planungssystematik –

Methoden – Anwendungen, Hanser, München.

[29] Baecker, R. M., 1993, Readings in Groupware and Computer-

Supported Cooperative Work, San Francisco, Morgan Kaufmann

Publisher, San Francisco.

[30] VDI guideline 5200 part 1. Factory planning – Planning procedures.

Berlin: Beuth; 2011

[31] Kampker A, Meckelnborg A, Burggräf P, Netz T. Factory Planning

Scrum: Integrative Factory Planning with Agile Project Management. In:

Proc. of the

International Conference on Competitive Manufacturing 2013,

Stellenbosch, South Africa, January 30 – February 1; 2013.

[32] Plenert G, Installing Successful Factories in Developing Countries. In:

International Executive Journal, 1990, Vol.32(2) pg.29-32

87

[33] Chen D, Information Management for Factory Planning and Design.

In: Production Engineering, 2012, pg 68

[34] Sohlenius Gunnar The manufacturing system ‐ Our motor of welfare

[Book]. , 2000, Stockholm : Royal Institute of Technology, 2000. ‐ ISSN

1650‐1888.

[35] Diemer H, Factory Organization and Administration, 1921, McGraw

Hill

[36] Strategic Planning for Factory Modernization: A Case Study, Mize,

Joe H;Seifert, Deborah J;Berry, Gayle, In: National Productivity Review

(pre-1986); Dec 1, 1984; 4, 1; ProQuest pg. 33

[37] Mize, J. H. 1986. Strategic Planning for Factory Modernization: A

Case Study. National Productivity Review 4, 1; ProQuest pp. 33.

[38] Politze, D. P., Jufer, N., Bathelt, J., Kunz, A. and Wegener, K. AMOR

– an agent for assisting monitoring, optimization and (Re-)Design in

Factory Design. In Proc. 43st CIRP Conference on Manufacturing

Systems. Vienna, Austria, 2010.

[39] Wilfried Sihn ; Juergen Bischoff ; Ralf von Briel and Marcus Josten

"Team table: a framework and tool for continuous factory planning", Proc.

SPIE4192, Intelligent Systems in Design and Manufacturing III, 206

(October 13, 2000); doi:10.1117/12.403652

88

[40] Aggteleky, B., 1987-1990, Fabrikplanung, Band 1-3 München, Wien:

Hanser.

[41] Schmigalla, H., 1995, Fabrikplanung. München, Hanser.[42] Ueda, K.,

1997, Modelling of Biological Manufacturing Systems for Dynamic

Reconfiguration, Annals of CIRP Bionic Manufacturing, Annals of CIRP

46/1 , p. 343-346.

[43] Wirth, S., 1996, Methods and Tools for Permanent Factory

Replanning, Proceedings of Israel- Germany Bi-national Conference on

Computer Integrated Extended Manufacturing Enterprises, 28-29.02.96,

Herziliah, Israel.

[44] Procedure of modular Green Factory Planning to enhance

collaboration and decision making, Florian Muellera, *, Alessandro

Cannataa , Christoph Herrmannb, In: 21st CIRP Conference on Life Cycle

Engineering

[45] Keddis, N., 2015. Adaptable Capability-Based Planning. IFAC-

PapersOnLine 48-3 pp. 1204–1209

[46] Menck, N. 2012. Collaborative Factory Planning in Virtual Reality.

45th CIRP Conference on Manufacturing Systems 2012, Procedia CIRP 3

pp. 317 – 322

[47] Horbach, S. 2011. Building blocks for adaptable factory systems.

Robotics andComputer-Integrated Manufacturing 27 pp. 735–740

89

[48] Chen, D. 2009. Information Management for the Factory Planning

Process. Licentiate Thesis in Production Engineering, KTH Royal Institute

of Technology.

[49] Othman, M. 2012. Integrating workers’ differences into workforce

planning. Computers & Industrial Engineering 63 pp. 1096–1106

[50] Johnson, J.P., Lenartowicz, T., and Apud, S., 2006. Cross-cultural

competence in international business: toward a definition and a model.

Journal of International Business Studies, 37, 525–543

[51] Innovative Approaches for Global Production NetworksGlobal

Footprint Design – an evolutionary approachSchuh1, G.; Potente1, T.;

Kupke1, D.; Varandani1*, R.

[52] Iyengar, S., 2012. How to make choosing

easier,http://www.ted.com/talks/sheena_iyengar_choosing_what_to_choo

se.html, Download:13.03.2012

[53] Karl, F. 2015. Reconfigurations on manufacturing resources:

identification of needs and planning. Prod. Eng. Res. Devel. 9 pp. 393–

404

[54] Büschera, C. 2016. Semantic information modelling for factory

planning projects. 48th CIRP Conference on MANUFACTURING

SYSTEMS - CIRP CMS 2015, Procedia CIRP 41 pp. 478 – 483

90

[55] Shariatzadeh, N. 2012. Software Evaluation Criteria for Rapid Factory

Layout Planning, Design and Simulation. 45th CIRP Conference on

Manufacturing Systems 2012, Procedia CIRP 3 pp. 299 – 304

[56] Cohen, M.A., Lee, H.L., 1985. Manufacturing Strategy Concepts and

Methods, in Kleindorfer, P. R. Ed., The Management of Productivity and

Technology in Manufacturing, 153- 188.

[57] Nyhuis, P. 2010. Integrative factory, technology, and product

planning-systemizing the information transfer on the operational level.

Prod. Eng. Res. Devel. 4 pp. 231–237

[58] ElMaraghy, H.A., Kuzgunkaya, O., Urbanic, R.J., 2005. Manufacturing

Systems Configuration Complexity, Intelligent Manufacturing Systems

(IMS) Centre, CIRP Annals - Manufacturing Technology, Volume 54, Issue

1, Pages 445–450

[59] Papakostas, N., Efthymiou, K., Mourtzis, D., Chryssolouris, G., 2009.

Modelling the complexity of manufacturing systems using nonlinear

dynamics approaches, CIRP Annals – Manufacturing Technology, Volume

58, Pages 437-440.

[60] Menck, N. 2013. Virtual Reality as a Collaboration Tool for Factory

Planning based on Scenario Technique. Forty Sixth CIRP Conference on

Manufacturing Systems 2013, Procedia CIRP 7 pp. 133 – 138

[62] Unzeitig, W. 2014. An approach for considering uncertainties during

the early phase of factory planning for an automotive contract

manufacturer. Elektrotechnik & Informationstechnik 131/7 pp. 212–218.

91

[63] Strumar, A. J. 1984. Management’s Role in Planning the Automated

Factory. IEEE TRANSACTIONS ON COMPONENTS, HYBRIDS, AND

MANUFACTURING TECHNOLOGY, VOL. CHMT-7, NO. 3

[64] Ling-Chieh Kung, 2009. Heuristic factory planning algorithm for

advanced planning and scheduling. Computers &Operations Research 36

pp. 2513--2530

[65] Nyhuis, P. 2007. Adequate and economic factory transformability and

the effects on logistical performance. Int J Flex Manuf Syst 19 pp. 286–

307

[66] Kobrin, S.J. 1991. An Empirical Analysis of the Determinants of

Global Integration. Strategic Management Journal, 12: 17-31.

[67] Hoffmann, M. 2014. A Modular Factory Planning Approach Based on

Vertical Integration of the Information Flow. Applied Mechanics and

Materials. Frontiers of Mechanical Engineering and Materials Engineering

II.

[69] Kung-Jeng Wang, 2012. A Negotiation-Based Capacity-Planning

Model. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND

CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 42,

NO. 6.

92

[70] Garwin, D.A. 1993. Building a Learning Organization. Harvard

Business Review, 71(4): 78-91.

[71] Duncan, R.B. 1972. Characteristics of Organizational Environments

and Perceived Environmental Uncertainly. Administrative Science

Quarterly, 17:313–327.

[72] Katja WindtRobust Manufacturing Control: Proceedings of the CIRP

Sponsored Conference RoMaC, 2012, Springer

[73] Bornschlegla, M. 2015. A Method for Forecasting the Running Costs

of ManufacturingTechnologies in Automotive Production during the Early

Planning Phase, 12th Global Conference on Sustainable Manufacturing,

CIRP pp. 412–417

[74] Kopf, R. 2016. Adjusting the factory planning process when using

immature technologies. Conference on MANUFACTURING SYSTEMS -

CIRP CMS 2015, Procedia CIRP 41 pp. 1011 – 1016

[75] Ackermann, J. 2013. Approaches for planning and operation of

adaptable factories. International Journal of Production Research, Vol. 51,

No. 15, pp. 4618–4629

[76] Pentenrieder, K. 2009. Augmented Reality based Factory Planning.

Master thesis.

[77] Burggraef 2011 - Wertorientierte Fabrikplanung

93

[78] Tichy, N.M., Tushman, M.L., & Fombrun, C.J. 1979. Social Network

Analysis for Organizations. Academy of Management Review, 4: 507–519.

[79] zpeynirci, S. 2009 . Bounding approaches for operation assignment

and capacity allocation problem in flexible manufacturing systems.

Computers &Operations Research 36 pp. 2531--2540

[80] Lee, J. 2011. Construction of a computer-simulated mixed reality

environment for virtual factory layout planning. Computers in Industry 62,

pp. 86–98

[81] Westkamper, E. 2012 E. Continuous Improvement and Participative

Factory Planning by Computer. Master thesis.

[82] Mintzberg, H., & Waters, J.A. 1985. Of Strategies, Deliberate and

Emergent. Strategic Management Journal, 6: 257-272.

[83] Loos, M. N. 2012. Factory planning in focus – Between machines and

structural works. Ernst & Sohn Verlag für Architektur und technische

Wissenschaften GmbH & Co. KG, Berlin. Bautechnik 89, Heft 4

[84] Wiendahl, H. P. 2015. Handbook Factory Planning and Design.

Springer.

94

[85] Cheng Leong Ang, 1987. Technical Planning of Factory Data

Communications Systems. North-Holland Computers in Industry 9 pp. 93-

105

[86] Amason, A. C. (1996). Distinguishing the effects of functional and

dysfunctional conflict on strategic decision making: Resolving a paradox

for top management teams. Academy of Management Journal, 39(1),

123–148. doi:10.2307/256633.

[87] Entrialgo, M., Fernandez, E., & Vazquez, C. J. (2000). Characteristics

of managers as determinants of entrepreneurial orientation: Some

Spanish evidence. Enterprise & Innovation Management Studies, 1(2),

187–205. doi:10.1080/14632440050119596.

[88] Elbanna, S., & Child, J. (2007). the influence of decision,

environmental and firm characteristics on the rationality of strategic

decision-making. Journal of Management Studies, 44(4), 561–591.

doi:10.1111/j.1467-6486.2006.00670.x.

[89] Lindsay, W. M., & Rue, L. W. (1980). Impact of the organization

environment on the long-range planning process. Academy of

Management Journal, 23(3), 385–404. doi:10.2307/255507.

[90] Miller, D., & Friesen, P. H. (1983). Strategy-making and environment:

The third link. Strategic Management Journal, 4(3), 221–235. doi:10.1002/

smj.4250040304.

95

[91] Steers, R. M., & Braunstein, D. N. (1976). A behaviorally-based

measure of manifest needs in work setting. Journal of Vocational

Behavior, 9, 251–266. doi:10.1016/0001-8791(76)90083-X.

[92] Goll, I., & Rasheed, A. M. A. (1997). Rational decision-making and

firm performance: the moderating role of environment. Strategic

Management Journal, 18(7), 583–591.

doi:10.1002/(SICI)10970266(199708)18:7<583::AID-SMJ907>3.0.CO;2-Z.

[93] Miller, S., Wilson, D., & Hickson, D. (2004). Beyond planning

strategies for successfully implementing strategic decisions. Long Range

Planning, 37(3), 201–218. doi:10.1016/j.lrp.2004.03.003.

[94] Fredrickson, J. W., & Iaquinto, A. L. (1989). Inertia and creeping

rationality in strategic decision processes. Academy of Management

Journal, 32(4), 516–542. doi:10.2307/256433.

[96] Liberman-Yaconi, L., Hooper, T., & Hutchings, K. (2010). Toward a

model of understanding strategic decision-making in micro-firms: Exploring

the Australian information technology sector. Journal of Small Business

Management, 48(1), 70–95. doi:10.1111/j.1540-627X.2009.00287.x

[97] Srivastava, S. (2009). Locus of control as a moderator for relationship

between organisational role stress and managerial effectiveness. The

Journal of Business Perspective, 13(4), pp49–61.

doi:10.1177/097226290901300405.

96

[98] Di Zhang, D., & Bruning, E. (2011). Personal characteristics and

strategic orientation: entrepreneurs in Canadian manufacturing

companies. International Journal of Entrepreneurial Behaviour &

Research, 17(1), 82–103. doi:10.1108/13552551111107525

[99] Papadakis, V. M., Lioukas, S., & Chambers, D. (1998). Strategic

decision-making processes: The role of management and context.

Strategic Management Journal, 19, pp115–147.

doi:10.1002/(SICI)10970266(199802)19:2<115::AID-SMJ941>3.0.CO;2-5.

[100] Mishra, S., & Lalumière, M. L. (2010). You can’t always get what you

want: The motivational effect of need on risk-sensitive decision-making.

Journal of Experimental Social Psychology, 46(4), 605–611.

doi:10.1016/j.jesp.2009.12.009.

[101] Forlani, D., Mullins, J. W., & Walker, O. C. (2002). New product

decision making: How chance and size of loss influence what marketing

managers see and do. Psychology and Marketing, 19(11), 957–981.

doi:10.1002/mar.10046.

[102] Taylor, R. N., & Dunnette, M. D. (1974). Relative contribution of

decision-maker attributes to decision processes. Organizational Behavior

and Human Performance, 12, 286–298. doi:10.1016/00305073(74)90052-

X.

[103] McClelland, D. C. (1961). The achieving society. Princeton, NJ: Van

Nostrand.

97

[106] PMI, Project Management Body Of Knowledge (PMBOK Guide), 5th

edition, ANSI.

[107] Hinde, D. PRINCE2 study guide. SYBEX

98

8 Sitography

[1] http://www.belbin.com/about/belbin-team-roles/

[2] https://www.pmi.org/certifications/types/project-management-pmp

[3] https://www.prince2.com/eur

[4] https://letstalk.voiceprint.global

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