55,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
- Veröffentlichungsjahr: 2022
Designing Intelligent Construction Projects 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.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 348
Ähnliche
Table of Contents
Cover
Title Page
Copyright
Preface
Acknowledgements
About the Authors
1 Complexity, Cybernetics, and Dynamics
1.1 Complexity
1.2 Viable System Model
1.3 Modelling with the Viable System Model
1.4 System Dynamics
1.5 Findings, Criticism, and Reflective Questions
Notes
2 Lean Management and Lean Construction
2.1 Pioneers of Lean Management
2.2 Toyota Production System and Tools
2.3 Lean Management and Its Principles
2.4 Lean Construction and Tools
2.5 Tools, Tools, Tools
2.6 Practice Insights from Martin Jäntschke
2.7 Findings, Criticism, and Reflective Questions
Notes
3 Cybernetics and Lean
3.1 VSM and Lean (Construction) Thinking
3.2 Mapping the Viable System Model with Lean Construction Methods
3.3 Mapping the Viable System Model with Lean Management Methods
3.4 Performance Measurement
3.5 Case Studies and Practice Insights
3.6 Findings, Criticism, and Reflective Questions
Notes
4 Beyond Cybernetics and Lean
4.1 Control, Regulate, Steer
4.2 Self‐organisation
4.3 Viable, Lean, … and What About Agile?
4.4 Digital Transformation
4.5 Phases of Digital Change
4.6 Digitalisation in the Construction Industry
4.7 Changing the Game
4.8 Partnering
4.9 Success Patterns in Projects
4.10 Findings, Criticism, and Reflective Questions
Notes
5 Summary and Closing Remarks
5.1 Complexity, Cybernetics, and Dynamics
5.2 Lean Management and Lean Construction
5.3 Cybernetic and Lean
5.4 Beyond Cybernetic and Lean
References
Glossary
List of Figures
List of Tables
List of Equations
List of Abbreviations
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Cynefin framework (PDCA).
Table 1.2 Amplifier.
Table 1.3 Attenuator.
Table 1.4 Negotiation 1.
Table 1.5 Negotiation 2.
Chapter 2
Table 2.1 Example: flow efficiency.
Chapter 3
Table 3.1 Complexity criteria with assigned key figures and gradations.
List of Illustrations
Chapter 1
Figure 1.1 System states.
Figure 1.2 Cynefin framework.
Figure 1.3 The fractal structure.
Figure 1.4 The viable system model.
Figure 1.5 Varieties 1.
Figure 1.6 Varieties 2.
Figure 1.7 Horizontal and vertical variety according to Pfiffner (2020).
Figure 1.8 5 Variety balances.
Figure 1.9 System 1.
Figure 1.10 Environment and complexity driver.
Figure 1.11 Environment and complexity driver connected with retarding and r...
Figure 1.12 S2 and S3*.
Figure 1.13 S3, S4+ specific environment, S5.
Figure 1.14 VSM model construction site after discussion.
Figure 1.15 System archetype: balancing process with delay.
Figure 1.16 System archetype: escalation.
Figure 1.17 System archetype: success to the successful.
Figure 1.18 System archetype: limits to growth.
Figure 1.19 System archetype: shifting the burden.
Figure 1.20 System archetype: fixes that fail.
Figure 1.21 System archetype: eroding goals.
Figure 1.22 System archetype: accidental adversaries.
Figure 1.23 System archetype: tragedy of the commons.
Figure 1.24 System archetype: growth and underinvestment.
Chapter 2
Figure 2.1 Toyota Production System.
Figure 2.2 Supermarket pull system according to Rother and Shook (2011).
Figure 2.3 Difference of JIT and JIS.
Figure 2.4 Digital andon board of a production line.
Figure 2.5 Poka‐yoke system Matrix according to Bergbauer (2008).
Figure 2.6 Possible implementation of poka‐yoke.
Figure 2.7 Example of levelling and smoothing according to VDMA Forum Indust...
Figure 2.8 Relation between kaizen, PDCA, and standards.
Figure 2.9 Toyota leadership development model according to Liker and Convis...
Figure 2.10 Postponement of the time sequences of the trades.
Figure 2.11 The last planner system.
Figure 2.12 Takt planning.
Figure 2.13 Takt control board, graphic aligned on Demir/Theis, Drees & Somm...
Figure 2.14 Last planner system and takt planning and control.
Figure 2.15 Side view student housing.
Figure 2.16 Floor plan view.
Figure 2.17 Example determining work content of trade painting.
Figure 2.18 Determining trade sequence.
Figure 2.19 Mapping trade sequence with workload of trades.
Figure 2.20 Work distribution diagram including takt time.
Figure 2.21 Takt areas.
Figure 2.22 Takt strategy.
Figure 2.23 Takt plan.
Figure 2.24 Ishikawa diagram.
Figure 2.25 A3 report.
Figure 2.26 Central information board in production department.
Figure 2.27 5S method.
Figure 2.28 Project optimisation by measures and methodological approaches....
Chapter 3
Figure 3.1 Mapping VSM and LPS.
Figure 3.2 Mapping VSM and takt planning and control.
Figure 3.3 Mapping VSM and lean management tools.
Figure 3.4 Key figure system.
Figure 3.5 Beers' Triple: example buildings produced.
Figure 3.6 Case study: planning project.
Figure 3.7 Case study: organigram.
Figure 3.8 Case study: viable system model.
Figure 3.9 Case study: system dynamics model meetings.
Figure 3.10 Organisational chart.
Figure 3.11 The organisation as a whole.
Figure 3.12 Level −2, perspective parent company/group.
Figure 3.13 Level −1, perspective infrastructure division.
Figure 3.14 Level 0, perspective megaproject.
Figure 3.15 Level 1, perspective section.
Figure 3.16 Tension between internal and external demands and objectives in ...
Figure 3.17 Transformation of a hierarchical organisation structure into a v...
Figure 3.18 Newly created organisational structure with four system 1s based...
Figure 3.19 The ten criteria for defining complexity levels and their correl...
Figure 3.20 Derivation of the process map from the VSM organisational struct...
Figure 3.21 Creation of the new role profiles based on the assigned processe...
Figure 3.22 The organisational chart of the company as a simplified 3D model...
Figure 3.23 From 2D to 3D: the organiplastic in its full digital version.
Chapter 4
Figure 4.1 Controlling, regulating, and steering. Steering (left graphic): d...
Figure 4.2 Digitalisation in the construction industry: overview phases.
Figure 4.3 Digitalisation in the construction industry: phase 1.
Figure 4.4 Digitalisation in the construction industry: phase 2.
Figure 4.5 Digitalisation in the construction industry: phase 3.
Figure 4.6 Digitalisation in the construction industry: phase 4.
Figure 4.7 The MacLeamy curve.
Guide
Cover Page
Table of Contents
Title Page
Copyright
Preface
Acknowledgements
About the Authors
Begin Reading
References
Glossary
List of Figures
List of Tables
List of Equations
List of Abbreviations
Index
End User License Agreement
Pages
iv
xi
xii
xiii
xiv
xv
xvii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
221
223
224
225
227
228
229
230
231
232
233
234
Designing Intelligent Construction Projects
Michael Frahm
Aalen, Germany
Carola Roll
Passau, Germany
This edition first published 2022
© 2022 John Wiley and Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Michael Frahm and Carola Roll to be identified as the authors of this work has been asserted in accordance with law.
Registered Office
John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
Editorial Office
9600 Garsington Road, Oxford, OX4 2DQ, UK
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of Warranty
While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging‐in‐Publication Data Applied for:
Paperback ISBN 9781119690825
Cover Design: Wiley
Cover Image: © Rachael Arnott/Shutterstock
Preface
Intelligence is the ability to adapt to change.
Stephen Hawking
Why a book with a focus on management cybernetics, lean management, and digitalisation?
Because cybernetics, as a mixture of the natural sciences and the humanities, teaches a holistic and universal understanding of the control and regulation of machines, living organisms, and social systems. Exactly the right thing for people who dare to think outside the box. Because lean management – which emerged in postwar Japan, a country that had to relearn industrial production in the face of huge unemployment, a lack of space, and being restricted by being an archipelago of many islands – brought a common‐sense approach to industry, moving companies away from sluggish or unhealthy practices and conflict behaviour towards an attitude of trust and cooperation that focused on the product and the customer once more.
Because digitalisation is a great opportunity and the founders of cybernetics foresaw the technical possibilities of control and regulation regarding feedback and automation.
In addition to these changes, the combination of these topics offers incredible potential in terms of networking, enhancement, and feedback. The authors would like to mention here that the main purpose of this book is to show the interconnection between these disciplines. Methods and concepts are described concisely.
But what is an intelligent construction project organisation for us? In our understanding, construction project organisations are not only construction sites and construction companies but also engineering offices and client organisations, as well as other participants in the production of planning and construction processes that have an influence and must be considered. In our opinion, it is important to see the entire production system and its alignment and coupling internally and externally for a successful implementation.
For us, ‘intelligent’ stands for adaptability and robustness in order to produce products to the best possible extent, created according to logical and sensible processes and meeting the exact customer requirement. ‘Intelligent’ also stands for low‐waste processes that promote the application of new technologies to reduce waste and put the cooperative collaboration in the foreground. Everyone knows (even if not everyone practises this) that the best way to work is cooperatively, that is with a bit of give and take. ‘Intelligent’ for us also means that we create production systems in which it is fun to work, in which there is a working culture of motivation. We are convinced that this is more intelligent than the 70‐hour week.
In addition, ‘intelligent’ for us means the responsible and sustainable use of resources, as well as the use of technical and digital systems to relieve the human workforce of tasks that can be done better and more economically by nonhuman solutions.
This book does not claim to be exhaustive. Rather, the authors present topics that they consider relevant. This book is also not a research paper, but written for the interested user who wants to get to know and try out approaches in themselves and in their combination. Accordingly, we have intentionally avoided scientific terms where possible. Our aim is to make the text flow, to make the book an entertaining read, and so we have chosen its language, structure, and length with this very much in mind. Nevertheless, the book contains some theory that has to be relearned and internalised before it can be applied. And with complex questions there are usually no, or few, simple solutions. Therefore, the book avoids patent remedies. Practical examples, anecdotes, and questions for thought and reflection are offered instead. And we do not pretend to offer every answer, as we have also aimed to be succinct rather than exhaustive.
Both authors are German and so much of their environment and experience is German. However, whilst the book contains many examples from Germany there are also examples from around the world.
We have deliberately refrained from an epic approach to system theory and also the scientific discourse about definitions, terms, and which approach is ultimately the better one but have of course written about topics, areas, and approaches that interest us and with which we ourselves have gained experience. When this book talks about systems, it generally refers to organisations in an entrepreneurial context.
Donella Meadows, acclaimed author of The Limits to Growth (Meadows et al. 1972), and one of the most important ‘systems thinkers’ of her and our time (Meadows 2017), describes a system from elements, their relationships, and behaviour, and from its purpose or function. She explains in a generally understandable way that a system is, for example, a soccer team with the elements: players, coaches, the field, and the ball, whose relationships or connections are the rules of the game, strategies, communication of the players, and the laws of physics, whose purpose is to win games, play sports, or make money.
This understanding of systems also applies to other systems, such as a company, a city, an economy, an animal, a tree, a forest, which includes the subsystems trees and animals, the earth, the solar system, as well as the galaxy. Conglomerates without certain connections or functions are not systems.
Today, no one can avoid the design, analysis, and adaptation of systems, organisations, and processes. Understanding this is the key to dealing intelligently with increasing complexity.
Acknowledgements
We would like to thank Hamid Rahebi for making available his preliminary work, which he prepared as co‐author for the German‐language basis of this book and also for his support in the present expanded English version of this book. We thank DeepL, and especially Marc Beament who helped us with the translation into English.
Any remaining ‘German English’ we happily take the blame for!
We thank Wiley for their patience and support in the preparation of this book, Tim Bettsworth for copyediting it, and Paul Sayer, Amy Odum, and Mike New. We also thank Martin Jäntschke for his practical insights on implementing lean management in large organisational units working on large projects.
We thank all those from whom we were able to learn and continue to learn. Learning never stops.
About the Authors
Michael Frahm, born 1979. Educated in Stuttgart, Kaiserlautern, Saarbrücken in engineering and business law. Management courses at the HEC Paris, HHL Leipzig, and Northwestern University. Fifteen years of professional experience in mega construction project management. He is director of the nonprofit association for System and Complexity in Organisation (SCIO) for Germany and a Certified Advanced System Practitioner for this organisation.
Carola Roll, born in 1978. Educated in Straubing and Krems (Austria), in technical business administration, lean operations management, and integrated management systems. Over 20 years of professional experience in interface positions between business administration and technology in various medium‐sized companies. She is director of the nonprofit association for System and Complexity in Organisation (SCIO) for Germany and head of Bavaria's related practice group.
1Complexity, 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).
Table 1.1 Cynefin framework (PDCA).
PDCA circle
Plan
Do
Check
Act
Simple systems
Sense
Categorise
—
Respond
Complicated systems
Sense
Analyse
—
Respond
Complex systems
—
Probe
Sense
Respond
Chaotic systems
—
Act
Sense
Respond
Disordered/Confused system:
The system cannot be classified by the actor or observer in confusion. In this case, the task can be broken down into smaller tasks, for example.
In the case of disorientation, the system cannot be classified by the assessor. In such situations, people often withdraw to their comfort zone without assessment and make decisions based solely on their own experience. This is not necessarily wrong. Referring to ‘intuition’ and the researcher Gerd Gigerenzer's (2008) research results, we see that intuition can also often lead to good and quick decisions in complex and chaotic situations.
In general, the Cynefin framework supports system understanding and classification and provides a recommendation for acting.
Mapping of the Cynefin framework with the Deming circle2 (Plan, Do, Check, and Act) is shown in Table 1.1.
1.1.4 Complexity in Construction Management
Patzak (2009) and IPMA (2016) provide a complementary approach for the classification and partly also for measuring complexity in project management. The procedure proposed by Patzak is cumulative and uses a scoring table and includes the following areas to be scored:
project goal
project subject
project task
project executors
project environment.
The complexity of the building industry is determined by its institutions and actors. Many participants at the administrative and operational level make for a great deal of complexity. This is intensified by a high cost and time pressure. Dirnberger (2008) notes that complexity arises at the interfaces since even small construction projects today involve more than 100 participants.
Schwerdtner (2007) distinguishes between organisational and technical complexity and takes into account the unique nature of construction projects. ‘Technical complexity’ refers to the building structures, and ‘organisational complexity’ relates to the systems they create and operate.
Hoffmann 2017 distinguishes between:
the complexity of the building (object)
the complexity of the processes (project)
the complexity of the relationship structure (behaviour).
In the case of the long term (10 years or more), changes in standards and the legal situation are also relevant. Also, the growing critical interest for a project increases its complexity. Just‐in‐time planning, lifecycle orientation, the decreasing level of trained site personnel, conflicting implementation cultures, competitive constraints, and the fact that construction is often started on a greenfield site are all factors that increase a project's complexity.
For the purposes of classification, specifically for construction project management, complementary approaches were developed by Lechner (2015) and especially by Hoffmann (2017).
1.1.5 How to Cope with Complexity
In the context of complexity, understanding what it is and what approaches can be implemented is essential. Specific approaches are suitable as already described, e.g. with the Cynefin model.
According to Dietrich Dörner (2012), the following are common mistakes in dealing with complexity.
First error: Wrong target description. Individual objectives are worked on and the overall system and its effects are disregarded.
Second error: No networked analysis. No order principles are created to evaluate data or large amounts of data, e.g. using feedback loops.
Third error: Wrong emphasis. One concentrates, e.g. only on one pathological focus; other problems are disregarded.
Fourth error: Side effects are ignored. One works single‐mindedly on a problem without caring about the side effects.
Fifth error: Tendency to override. If small interventions show no effect in cases of maladministration, a system is heavily intervened in. If there is a time delay in accumulating unexpected results, the override is completely stopped.
Sixth error: The tendency to authoritarian behaviour. Whoever has the power in the system thinks they have seen through it. This can be fatal if you think you are in control. It is more important to activate self‐regulation in the system.
Jurgen Appelo (2010), developer of the Management 3.0 approach, recommends the following:
Address complexity with complexity (see also Ashby's Variety in
Section 1.2.2
).
Use a diversity of models.
Assume dependence on context.
Assume subjectivity and coevolution.
Anticipate, adapt, and explore.
Develop models in collaboration.
Copy and change.
The following heuristics are also the first guidelines in dealing with complexity:
Take the best: Using exclusion strategy – exclude irrelevant information.
Tit for tat/tit for two tat: Using cooperation strategy – reduction of complexity (disturbances) through conflicts (see also
Chapter 4
,
Section 4.7.2
).
Simple frame: Through a framework strategy (e.g. 10 commandments), means face/select/ignore complexity through the definition of a simple framework.
Pareto strategy: Using the Pareto principle (80/20) to counter complexity through effort and benefit.
For construction management, the kpbm® heuristic of Frahm (2015) can be taken into account. It concentrates on the following system characteristics to build a robust system behaviour for construction endeavours.
Viability:
Viability is to be understood in the entrepreneurial sense. This means:
○ Adaptability to change.
○ Ability to influence and shape the environment.
○ Ability to open up new environments.
○ Ability to make a positive contribution to big picture delivery.
Viability expresses that construction projects should be designed so that there is sufficient capacity to function effectively as an organisation internally and with its environment externally.
Attenuation and amplifying:
Interrelationships come to the fore by attenuation and amplifying. See, for example,
Section 1.2.2
.
Bottleneck concentration and flexibility:
Key elements in the planning and construction process are resources. Bottlenecks in this respect must be identified and overcome. Furthermore, appropriate buffers have to be considered.
Cooperation:
Cooperation is the superior strategy for achieving something together and achieving the best for everyone involved.
After all these explanations, it is clear that dealing with complexity is not an easy task. Umberto Eco (1988) writes in his novel Foucault's Pendulum something of a metaphor: ‘For every complex problem, there is a simple solution, and it is wrong.’
Often there are no simple rules to cope with complexity. As a rule of thumb, one can say, the more complex the situation is, the greater the mix of people, disciplines, and methods is needed.
1.1.6 Interaction and Autopoiesis
Interactions are essential elements in systems. The Chilean biologists and neuroscientists Humberto Maturana and Francisco Varela (1992) present the concept of structural coupling to a broad public in their book Tree of Knowledge. Structural coupling occurs in interactions, which means the structure changes the environment evolutionarily, and vice versa.
There are many possibilities for design, and the more accurate a project becomes, the more supporting and hindering forces are released. Within the framework of this design process, a structural coupling must take place. The management of a project requires a stable project environment. A vital foundation stone is laid in the design process.
The management of construction projects happens mainly in the beginning and with large‐scale projects is often more a social process than the implementation of a technical project. Those responsible and owners quickly realise that technological and economic professionalism alone is not enough. The formation of coalitions, political connections, and public perception are essential areas in the process.
The task of forming projects before they can be planned, built, and operated on can be an extraordinarily disorganised and complex process. This is expressed by the process of coordination and design that takes many years or decades with the slow and challenging consensus building. Through many small steps, the integration of the system into the environment takes place. Many independent questions cannot be solved simultaneously. Issues that have already been solved and defined are often restarted through long design processes, for example when marginal conditions and political power relations change, even if these have already been formally decided.
This evolutionary mechanism reflects the Darwinian principle from 1859 of the interaction between variation (specialisation) and selection (adequacy of the most suitable), which is still valid today (Darwin 1859). The relationship is coupled when recursive interactions have reached stability, and further changes continue for the long run co‐evolutionarily and in the same direction.
With the look at structural coupling, you can answer operational and strategic questions for your organisation and your project alike, what a specific relation does, where it takes you, what identity can be created out of this, and how can you build stability?
Maturana and Varela also established the concept of autopoiesis (ancient Greek for ‘self‐creation’). Autopoiesis describes systems that refer to themselves, and create and maintain themselves out of themselves. The basis of their self‐organisation is always directed towards a state of equilibrium and thus towards self‐preservation. When a form of stability is reached, the system is structurally coupled. This can be repeated many times.
An example of an autopoietic system approach is the viable system model (VSM).
1.2 Viable System Model
The Conant–Ashby theorem (Conant and Ashby 1970; Conway 2021), also known as the Good Regulator, means:
Every good regulator of a system must be a model of that system.
This means that the management of an organisation can only be as good as the model on which it is based. Conway's law gives the Conant‐Ashby theorem practical meaning. Melvin Conway, an American computer scientist, was made public in 1986 and became more widely known in digital transformation and the implementation of software projects. It reads:
Organizations which design systems … are constrained to produce designs which are copies of the communication structures of these organizations.
This means that, in the case of insufficient communication between departments, defects in the product manifest themselves exactly where the interfaces do not work. Products are, therefore, results of the communication structures of their organisations and thus the results of the underlying models. A Harvard study confirmed Conway's law (MacCormack et al. 2008). To map the behaviour of systems in an organisational context, a suitable model is recommended.
As described already, construction projects are usually complex projects. To counter ‘complexity’ and ‘chaos’ in the entrepreneurial context and create stability, management cybernetics was developed by Stafford Beer (1979, 1995), a professor of business administration and operations research at the Business School of Manchester. He transferred cybernetics approaches to companies and the business world generally.
Beer's most well‐known application is the VSM. As an alternative to a hierarchical organisation structure, Beer oriented the development of the model on the successful model of evolution in terms of viability: ‘the central nervous system of mammals’. The VSM attempts to balance and order the system and provides a fractal (see Figure 1.3) or self‐similar structure. This means that, on the one hand, a balance between control and autonomy in an organisation is striven for. On the other hand, a fractal structure at all organisational levels exists.
The same or similar generic organisational code can deal with complexity and chaos in a relatively simple way and help create order. The VSM serves as a basic structure or as a map with which one can orientate oneself.
The rule is:
The purpose of the system is what it does – keyword POSIWID
This means that production is the reason the organisation exists. The organisation must follow the production process. As the organisation can exist out of more than one viable system, there can be more than one purpose.
1.2.1 The Static Perspective on the VSM
The static perspective of the VSM is about analysing existing systems with the help of it.
Thus, in addition to the viability of a system, any existing problems in the corporate structure and the structural and procedural organisation can be examined.
The goal of this is to identify cybernetic approaches and missing elements about cybernetics (as with a checklist), if present, and use them as a basis for the conceptual design of an optimised system.
This has the advantage that there is no need to worry about forgetting anything.
The idea of making the VSM equally useful as a diagnostic tool goes back to its creator, Stafford Beer, who in his 1985 book Diagnosing the System for Organizations provides the reader not only with a description but also with a workbook in the best sense with concrete working instructions. At the same time, Beer is aware that this approach and its presentation are entirely new (Beer 1985). In his chapter ‘A Cybernetic Method to Study Organizations’, Raul Espejo explicitly emphasises that the VSM, although perceived mainly as a diagnostic tool, also opens up other possibilities (Espejo 1989).
Figure 1.3 The fractal structure.
The VSM consists of six horizontal system levels (see Figure 1.4):
System 1 Operation
System 2 Coordination
System 3 Control and Cohesion/Operational Management (inside and now)
System 3* Audit and Monitoring
System 4 Intelligence/Strategic Management (outside and then)
System 5 Policy/Normative Management
and six vertical information channels:
Channel 1 Intervention and Regulate
Channel 2 Allocation of Resources
Channel 3 Operational Interrelationships
Channel 4 Interrelationships of the Environment
Channel 5 Coordination (Sympathicus) = System 2
Channel 6 Monitoring (Parasympathicus) = System 3*
Figure 1.4 The viable system model.
Furthermore, the VSM includes an algedonic channel and transducers. Algedonic signals are alarm signals that transmit either positive or negative messages directly into system 5. Transducers are converters that form the interface between the subsystems. They ensure the maintenance of information authenticity.
1.2.1.1 System 1: Operation
System 1 (S1) consists of three elements:
environment
operation
management.
These three units build the:
‘System in focus’, in other words the reason the organisation exists.
In this system, all the main activities of an organisation are collected. All three elements interact with each other. The entire company must always pay great attention to this system level.
Examples of an S1 are producing units such as construction teams, construction companies, or designers.
It must be noted here that organisations may consist of several S1s. These usually represent strategic business units, product lines, or similar. When modelling a VSM, urgent attention must be paid to the viability of the S1s.
1.2.1.2 System 2: Coordination
System 2 (S2) has two tasks. First of all, it represents a communication medium between S1 and S3 through a standardised processes, a common language, and it coordinates all S1 systems with each other. Secondly, it is the institutional place where self‐organisation takes place. It has an activating, or sympathetic, effect.
Examples are daily short informal meetings, operation and production plans of any kind, scrum/Kanban boards, project control, production planning and control, six sigma, the last planner system, or takt planning and control.
1.2.1.3 System 3: Operational Management
System 3 (S3) deals with the present business of systems 1–3 and must make all operational activities as efficient and effective as possible. It allocates resources and demands results. S3 competes with S4 for resources and receives normative specifications from S5.
Examples for an S3 instance are project, team, or department leader, or a managing director.
1.2.1.4 System 3*: Monitoring/Audit
System 3* (S3*) is a review channel with an institutional absorbing, or parasympathetic, effect.
An example of this subsystem is a regular walk by management around the construction site, having conversations with the foreman and workers to get an additional picture of the production process or a project audit with an objective view from the outside into the project process. Lean tools such as 5S audits or generalist layered process audits can also be located here.
1.2.1.5 System 4: Strategic Management
System 4 (S4) focuses on the strategic issues of the overall organisation and deals with future problems from the environment. It competes with S3 for resources.
Examples of S4 are strategic purchasing or developing new business areas such as a building information modelling (BIM) strategy for the entire company.
1.2.1.6 System 5: Policy
System 5 (S5) represents the identity of the organisation. Topics such as values, norms, ethics, and culture are addressed here and transferred into the organisation. It is the highest decision‐making unit, makes fundamental choices, and, if necessary, regulates between S3 and S4.
