Designing Intelligent Construction Projects
Description
Explore the potential impact of management cybernetics, lean methodologies, and digitalization on the construction sector
As a heavily asset-driven industry, construction is at the crossroads of a transformation. Digitalization has already begun and is acting as a beacon. Intelligently designed project organizations and systems must follow to make construction projects fit for the future.
In Designing Intelligent Construction Projects, a distinguished project manager and engineer and a lean and integrated management system manager deliver a comprehensive exploration of the fundamentals of management cybernetics, lean management in general and lean construction in particular, and construction-oriented digital tools. In the book, the authors describe how these disciplines can be combined to successfully transform construction projects.
Preliminary discussions of management cybernetics and lean management are followed by specific discussions of how these topics can be adapted to the construction industry. The book connects the principles of management cybernetics and digitalization, accessibly describing the potential impact of digitalization on construction projects.
Readers will also find:
* Illuminating case study material that highlights how change management methodologies, game theory, and collaborative contractual design can deliver results
* Strategies for achieving lean, viable, and digitally oriented construction leadership fit for the modern market
* Rigorous discussions of the current and potential future impact of digitization on construction firms
Perfect for built environment professionals and practitioners, Designing Intelligent Construction Projects will also earn a place in the libraries of postgraduate and advanced undergraduate students of civil engineering, architecture, and project management with an interest in construction management.
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Persons
Michael Frahm was educated in Stuttgart, Kaiserlautern, and Saarbrücken in engineering and business law, and has fifteen years of professional experience in mega construction project management. He is director of the non-profit association for Systems and Complexity in Organisation (SCiO) for Germany and is a Certified Advanced System Practitioner of this organization.
Carola Roll was educated in Straubing and Krems in technical business administration, lean operations management, and integrated management systems and has over 20 years of professional experience in interface positions between business administration and technology in various medium-sized companies. She is director of the non-profit association for System and Complexity in Organisation (SCIO) for Germany and head of Bavaria's related practice group.
Content
Preface xi
Acknowledgements xv
About the Authors xvii
1 Complexity, Cybernetics, and Dynamics 1
1.1 Complexity 2
1.1.1 Complexity in the Mathematical Sciences 2
1.1.2 Complexity in Sociology 3
1.1.3 Complexity in Management 3
1.1.4 Complexity in Construction Management 6
1.1.5 How to Cope with Complexity 7
1.1.6 Interaction and Autopoiesis 9
1.2 Viable System Model 10
1.2.1 The Static Perspective on the VSM 11
1.2.1.1 System 1: Operation 14
1.2.1.2 System 2: Coordination 14
1.2.1.3 System 3: Operational Management 14
1.2.1.4 System 3*: Monitoring/Audit 15
1.2.1.5 System 4: Strategic Management 15
1.2.1.6 System 5: Policy 15
1.2.2 Ashby's Variety 16
1.2.2.1 The Variety Number 18
1.2.2.2 The Degree of Variety 18
1.2.3 The Dynamic Perspective on the VSM 23
1.2.3.1 Variety Balance 1: Workload 26
1.2.3.2 Variety Balance 2: Line Balancing 26
1.2.3.3 Variety Balance 3: Autonomy vs. Cohesion 26
1.2.3.4 Variety Balance 4: Change Rate 27
1.2.3.5 Variety Balance 5: Change vs. Status Quo 27
1.3 Modelling with the Viable System Model 28
1.3.1 Modelling Steps 28
1.3.2 Create a VSM Model Using an Example 29
1.4 System Dynamics 34
1.4.1 Systemic Archetypes 34
1.4.2 Modelling with System Dynamics 43
1.4.3 Example: Managing Risks with System Dynamics 43
1.5 Findings, Criticism, and Reflective Questions 44
1.5.1 Findings 44
1.5.2 Criticism 45
1.5.3 Reflective Questions 46
2 Lean Management and Lean Construction 47
2.1 Pioneers of Lean Management 48
2.2 Toyota Production System and Tools 49
2.2.1 Waste, Kanban, and Just-in-time Principle 51
2.2.2 Jidoka and Related Elements 54
2.2.3 Heijunka 58
2.2.4 Single Minute Exchange of Die (SMED) 60
2.2.5 Kaizen and Standards 60
2.3 Lean Management and Its Principles 61
2.3.1 Resource and Flow Efficiency 63
2.3.2 Examples for Resource and Flow Efficiency 64
2.3.2.1 The Machine and Plant Manufacturer 64
2.3.2.2 The Vacation Flight 65
2.3.2.3 The Healthcare System 65
2.3.2.4 The Automotive Industry 65
2.3.3 Four Important Principles 65
2.3.3.1 Flow Principle 66
2.3.3.2 Takt Principle 66
2.3.3.3 Pull Principle 66
2.3.3.4 Zero-defect Principle 66
2.3.4 Lean Leadership 66
2.3.4.1 Excursion: Kata 67
2.4 Lean Construction and Tools 70
2.4.1 Last Planner System 72
2.4.1.1 Milestone Planning 74
2.4.1.2 Collaborative Programming 74
2.4.1.3 Making Ready 74
2.4.1.4 Production Planning 74
2.4.1.5 Production Management and Learning 74
2.4.2 Takt Planning and Control 76
2.4.2.1 Takt Planning 77
2.4.2.2 Takt Control 78
2.4.3 Last Planner System and Takt Planning and Control 78
2.4.4 Lean Construction Case Study 80
2.4.4.1 Takt Planning 81
2.4.4.2 Takt Control 85
2.5 Tools, Tools, Tools 87
2.5.1 First-run Study 88
2.5.1.1 Phase Plan 88
2.5.1.2 Phase Do 88
2.5.1.3 Phase Study 89
2.5.1.4 Phase Adjust 89
2.5.2 Waste Walks 89
2.5.2.1 5 Why and 6W Questioning Technique 90
2.5.3 Ishikawa Diagram 91
2.5.4 A3 Method and Report 92
2.5.5 Visual Management 94
2.5.6 5s/5a 96
2.5.6.1 Seiri - Sort 96
2.5.6.2 Seiton - Set in Order 96
2.5.6.3 Seiso - Shine 97
2.5.6.4 Seiketsu - Standardise 97
2.5.6.5 Shitsuke - Sustain 97
2.5.7 Plus/Delta Review 97
2.5.8 Big Room 99
2.6 Practice Insights from Martin Jäntschke 101
2.6.1 Infrastructure Railway - Introduction of Lean Construction in Large Projects 101
2.6.2 Implementing Change in an Infrastructure Organisation 104
2.6.3 Conclusion 108
2.6.3.1 To Section 2.6.1 108
2.6.3.2 To Section 2.6.2 109
2.7 Findings, Criticism, and Reflective Questions 109
2.7.1 Findings 109
2.7.2 Criticism 110
2.7.3 Reflective Questions 112
3 Cybernetics and Lean 113
3.1 VSM and Lean (Construction) Thinking 115
3.2 Mapping the Viable System Model with Lean Construction Methods 116
3.2.1 Mapping VSM and the Last Planner System 117
3.2.2 Mapping VSM and Takt Planning and Control 118
3.2.3 Mapping Information Channels and Lean Construction 118
3.3 Mapping the Viable System Model with Lean Management Methods 119
3.4 Performance Measurement 124
3.4.1 General Measurement 124
3.4.2 Lean Measurement Construction 126
3.4.3 Beers' Triple 126
3.5 Case Studies and Practice Insights 128
3.5.1 Case Study: Planning Project 128
3.5.2 Case Study: Major Project (Planning and Execution) 130
3.5.2.1 Design Phase and Approval Phase 131
3.5.2.2 Tendering and Awarding Phase 132
3.5.2.3 Construction Phase 133
3.5.3 Case Study: Megaproject (Execution) 134
3.5.3.1 Boundary Conditions 134
3.5.3.2 Analysis of the Megaproject 140
3.5.3.3 Section Analysis 140
3.5.4 Practice Insights from a Medium-sized Mechanical Engineering Company 143
3.5.4.1 Challenges for the Industry 143
3.5.4.2 The Solution: The Creation of a Hybrid Corporate Form Based on the Vsm 144
3.5.4.3 From Theory to Practice: The Organisational Structure 146
3.5.4.4 Levels of Complexity 147
3.5.4.5 Process Organisation 149
3.5.4.6 Role Profiles 154
3.5.4.7 Organiplastic as a Base for the Management Cockpit 154
3.5.4.8 Conclusion 157
3.5.4.9 Adaptability 159
3.6 Findings, Criticism, and Reflective Questions 159
3.6.1 Findings 159
3.6.2 Criticism 161
3.6.3 Critical Reflection to Practice Insights from a Medium-sized Mechanical Engineering Company 161
3.6.4 Reflective Questions 162
4 Beyond Cybernetics and Lean 163
4.1 Control, Regulate, Steer 164
4.2 Self-organisation 165
4.3 Viable, Lean, ... and What About Agile? 166
4.4 Digital Transformation 167
4.5 Phases of Digital Change 169
4.6 Digitalisation in the Construction Industry 170
4.6.1 Status Quo 171
4.6.2 Phase 1: BIM, VR, AR, MR 172
4.6.3 Phase 2: Intelligent Project Management 174
4.6.4 Phase 3: Artificial Intelligence in Construction 176
4.6.5 Phase 4: Autonomous Project Management 179
4.7 Changing the Game 180
4.7.1 Nudge Management 180
4.7.2 Tit for Tat 181
4.8 Partnering 183
4.9 Success Patterns in Projects 186
4.10 Findings, Criticism, and Reflective Questions 190
4.10.1 Findings 190
4.10.2 Criticism 191
4.10.3 Reflective Questions 193
5 Summary and Closing Remarks 195
5.1 Complexity, Cybernetics, and Dynamics 196
5.2 Lean Management and Lean Construction 196
5.3 Cybernetic and Lean 197
5.4 Beyond Cybernetic and Lean 197
References 199
Glossary 209
List of Figures 215
List of Tables 219
List of Equations 221
List of Abbreviations 223
Index 227
1
Complexity, Cybernetics, and Dynamics
The Toyota style is not to create results by working hard. It is a system that says there is no limit to people's creativity. People don't go to Toyota to 'work' they go there to 'think'.
Taiichi Ohno
In contrast to lean construction, cybernetics or a system-oriented approach is relatively unknown or unused in construction. Norbert Wiener, a mathematics professor at the Massachusetts Institute of Technology (MIT), coined the term 'cybernetics' in 1943. At the time, he was leading an interdisciplinary research project and was confronted with the problem of coordination and communication between different experts and disciplines. This was the official birth of a new science of communication and regulation. Hermann Schmidt, professor of control engineering in Berlin, is regarded as the founder of cybernetics in Germany. In addition to Wiener and Schmidt, other historical protagonists such as Heinz von Foerster, W. Ross Ashby, Humberto Maturana, Stafford Beer, Frederic Vester, and many others have lent significant meaning to the term 'cybernetics'. Meanwhile, cybernetics is used in different disciplines.
As described by Christoph Keese in 2016 in his bestseller The Silicon Valley Challenge, the understanding of cybernetics and system science is more relevant than ever. It serves as an essential model for digital transformation. Cybernetics is based on the idea that everything is connected to everything. It, therefore, encourages people to think out of the box - an essential characteristic in a networked world in which great importance is accorded to the effective confrontation of complexity and chaos.
Wiener, his colleagues, and successors would be delighted by the possibilities of today: digitalisation, the Internet of Things, and the chance to model and simulate systems with enormous computing power to make more and sounder predictions. Here is an introduction to cybernetics and systems science, which we think is essential to gain an understanding of the past, the present, and the future of (construction) organisations.
1.1 Complexity
The term 'complexity'1 found its way into the language in the 1970s and has been defined in a number of ways since then. There are different approaches to and views on complexity. This reflects the subjective nature of complexity and that it depends on the context, actors, and observers. This section contains examples of 'complexity' from various fields.
1.1.1 Complexity in the Mathematical Sciences
In mathematics, 'complexity' is defined by the number of elements in the system and the variability of the feedback. The term 'complexity' is also associated with nonlinear system behaviour. Nonlinearity describes a system's sensitivity to even the slightest changes in the initial conditions. The so-called butterfly effect gives colloquial meaning to this behavioural phenomenon and explains that, theoretically, even the most minor changes in initial conditions (e.g. the wingbeat of a butterfly) can have a significant impact (hurricane) on the results. In geotechnics, construction mechanics, and structural analysis, the consideration of the nonlinearity of system behaviour is of great importance. In computer science, 'complexity' stands both for the computational effort required to solve a problem and for the information content of data.
Therefore, 'complexity' in the broader sense can be equated with calculability and system sensitivity.
1.1.2 Complexity in Sociology
In sociology, a distinction is made between factual, social, temporal, operative, and cognitive complexity. 'Objective complexity' describes the variety of different types of elements that can interact with each other. 'Social complexity' describes the interactions and feedbacks within the system. Extended by a temporal component, one speaks of 'temporal complexity'. 'Operational complexity' describes the fact that the system sets goals and that the system itself can bring about changes in the state. If there is a pronounced degree of controllability, we speak of 'operational complexity'. For Niklas Luhmann, the authoritative representative of sociological systems theory, complexity is an observer/observation-dependent fact and leads to a compulsion to select in systems (Luhmann 1994).
1.1.3 Complexity in Management
Hans Ulrich (Ulrich and Probst 1988; Ulrich 2001), a former professor of business administration at the University of St. Gallen, distinguishes between 'complicated' and 'complexity' as follows. He associates complicated more with the composition of a system, whereas complexity describes temporal variability more.
He expresses this as: 'Complexity is the ability of a system to assume a large number of different states in short periods of time. Machines are non-trivial systems whose behaviour is predetermined and predictable. Ecological and social systems are complex, "non-trivial" systems whose behaviour at certain points in time cannot be predicted' (Ulrich and Probst 1988).
As a rule of thumb, 'complexity' means that a system has many elements (E), relationships (R), and states (S) that change over time (t), as shown in Figure 1.1. An extension to this is 'chaos', which describes a state of complete disorder and independent causality. Much modern management literature has been based on Ulrich's understanding.
Following the acronym VUCA and Cynefin Framework, which has a solid link to complexity in management matters, were presented below for a better understanding. VUCA (Mack et al. 2016) or the VUCA world stands for:
Figure 1.1 System states.
- Volatility: e.g. frequent and rapid changes in the environment
- Uncertainty: not predictability of the future
- Complexity: many unknown elements exist internally and externally
- Ambiguity: information can be interpreted in different ways
It can be deduced from this that managers' previously tried and tested skills and abilities no longer endure in this new world and must be replaced by adapted leadership skills that are more strategically oriented and better suited to handling complexity (Lawrence 2013).
In response to the VUCA world, a VUCA acronym is again used. This is: vision, understanding, clarity, and agility.
The Cynefin framework (see Figure 1.2) provides an admired approach to reflecting complexity in a system context by Dave Snowden (Snowden and Boone 2007), a management consultant and researcher from Wales. Cynefin is the Welsh word for 'habitat' and is intended to reflect the point of view of the actor or observer on the context.
According to Snowden's Cynefin framework, a system will be classified between (Snowden and Boone 2007):
- simple
- complicated
- complex
- chaotic
- and disordered/confuse.
Figure 1.2 Cynefin framework.
For each of these categories, a pattern of action is proposed. These are:
- Simple system: A simple system can be understood without further analysis and at the first attempt. Cause and effect are clear to all participants.
The pattern of action: perceiving, categorising, and reacting is recommended. The existing facts are to be analysed, categorised further, and then implemented accordingly with a suitable procedure.
Typical for this are tasks that can be implemented using predefined processes. This procedure is called 'best practice'.
- Complicated system: A complicated system is characterised by many cause-and-effect relationships. Cause and effect are no longer immediately comprehensible. A complicated system requires specific expertise and time to understand the elements in the system.
The pattern of action: perceive, analyse, and react is recommended. This means that, analogous to the simple system, facts are to be explored, information is to be obtained, and expert knowledge is to be used on this basis.
'Good practice' is recommended as the correct procedure. This means that there are various accurate solutions.
- Complex system: In a complex system, the cause-and-effect relationship can only be understood after detailed analysis and retrospectively.
The pattern of action is: try, perceive, and react.
'Emergent practice' is recommended. This means that a diverse approach is recommended, which considers a mixture of methods, working with cross-functional teams, and experimentation.
- Chaotic system: In a chaotic system, it is not predictable how small changes in the initial conditions will affect the system's behaviour in the long run.
The pattern of action act, perceive, and react is recommended.
For the chaotic system, Snowden advises the use of just a handful of authorised people to act to achieve an immediate effect and stabilise the system and manoeuvre it into another system state. He calls this 'novel practice' (Snowden and Boone 2007).
