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A structural measurement system for engineering design processes [Elektronische Ressource] / Matthias F. Kreimeyer


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420 Pages



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Published 01 January 2009
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Language English
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Lehrstuhl für Produktentwicklung

A Structural Measurement System
for Engineering Design Processes

Matthias F. Kreimeyer
Vollständiger Abdruck der von der Fakultät für Maschinenwesen der Technischen Universität
München zur Erlangung des akademischen Grades eines
genehmigten Dissertation.
Vorsitzender: Univ.-Prof. Dr.-Ing. habil. Boris Lohmann
Prüfer der Dissertation: 1. Univ.-Prof. Dr.-Ing. Udo Lindemann
2. Prof. Peter John Clarkson, Ph. D., University of
Cambridge / UK

Die Dissertation wurde am 21.09.2009 bei der Technischen Universität München
eingereicht und durch die Fakultät für Maschinenwesen
am 25.11.2009 angenommen.
The thesis at hand addresses a significant issue in the field of development processes. The
development of complex technical systems brings about highly complex processes; to
improve these processes and manage them, an important issue that needs tackling is the
analysis, interpretation and goal-oriented improvement of such processes. Although
approaches for managing complex processes exist, a systematical, method-based analysis is
highly difficult. Thus, this thesis proposes a method for analyzing such processes in order to
identify typical structural constellations among the process’ entities and interpret them by
drawing inferences about their behavior. Thereby, knowledge about existing process models
can be extracted and applied to reduce risks in process planning through better understanding
how the structure of a process impacts its behavior. Generating such a means of process
analysis and management provides a major contribution both for academia and industry,
especially for the improvement of large and complex development processes.
Approaching this overall aim, the thesis considers a wide state of the art that is related to the
analysis and improvement of processes. Major conclusions are drawn from the fields of
system theory, graph-theory, matrix-based methods for structural complexity management,
network theory, process management as well as software engineering. Subsequently, this
knowledge is combined, laying the ground for elaborating the overall solution approach.
Moreover, major deficits in these fields are identified. The fundamental conclusion from this
review states that a framework for a goal-oriented analysis and improvement of systems is
still inexistent. Moreover, a systematic collection of methods of analysis for process structures
is missing. Existing methods for analyzing process structures remain too abstract and only
marginally allow drawing conclusions about the behavior of a system. Lastly, clear deficits in
modeling the structure of process exist – namely the description of logical operators and the
appointment of attributes remain insufficient.
Based on these findings, the overall framework of the suggested approach is outlined as well
as its design process. The overarching method of analysis is oriented at the established
approach of system analysis, enhanced with a clear goal-orientation. This leads to three main
parts of the solution: First, an approach for enhanced process modeling is described, laying
the ground for the development of the analysis approach. Second, the analysis of process
models through complexity metrics is addressed. Third, the results of analysis are classified
by the possible aims of process analysis, thus offering a goal-oriented conduction of process
The modeling basis uses multiple-domain matrices, combining existing process models via a
new meta-model, enhanced with additional, newly developed constructs of modeling bridging
existing dependency models and established process models. It therefore extends the
modeling capabilities available in process management, closing the gap to structural
complexity management without introducing yet another modeling scheme.
The broad set of complexity metrics is based on the current state-of-the-art in different
disciplines that have, to this extent, not been reviewed in depth. All metrics are systemized by
requirements initiating from process management as well as by potential objects of
conclusion. To ensure their validity, these metrics are verified theoretically based on
measurement theory and practically using several examples from industrial process
management. To support the application of the metrics, comprehensive guidelines for
interpretation are set up with the meta-model as a semantic basis. As such, especially the use
of interpretation guidelines renders the approach more complete and applicable and extends
the body of knowledge substantially.
Ultimately, the author introduces the new aspect of a goal oriented analysis in a concrete
manner, guided by the most common goals of process analysis. To enable a flexible
application, a modular set-up consisting of three steps is chosen: As a starting point, the
strategic level is addressed using common goals of process analysis. Then, these goals are
concretized by typical questions that can be posed in their context. Finally, these questions are
answered using the metrics and parts of the meta-model. This way, the method generated is
both straightforward and extensible to suit differing future needs.
The results are validated with two major case studies from automotive development. To
ensure the validity of the findings of these studies, they are cross-checked with engineers and
managers involved in the industrial processes. Both case studies confirm the initial hypothesis
and the validity of the chosen solution approach.
Subsuming, the thesis by Matthias Kreimeyer offers a comprehensive, broad, and complete
solution. By elaborating a modular approach, a consistent, methodical analysis of process
structures is possible. While the suggested approach remains highly abstract, it offers a
precise scientific contribution, filling the academic void addressed, and it provides, at the
same time, substantial significance for process improvement in any industrial context.

Garching, February 2010 Prof. Dr.-Ing. Udo Lindemann
Institute of Product Development Technische Universität München

Industry and scientific research both require methods to support the management of complex
engineering development processes in a way that recognises and exploits the characteristics of
their structural complexity. This thesis addresses this need through the development of a
systematic and scientifically rigorous approach to modelling and analysing processes,
demonstrated by its application to two case studies of automotive design. These studies
highlight the complexity of such processes and outline the problem that, even when models of
the activities, information flows, resources etc. are available, such models are sufficiently
complex that potential ‘problem areas’ cannot be identified solely by inspection. This leads to
the introduction of structural analysis as a possible means to identifying such challenges.
An extensive literature shows how a model of process structure can be used to analyse a
process and thereby to identify possible improvements against goals such as quality and
transparency. This is a major contribution to the engineering design literature, summarising
the many different approaches found in this area. A conceptual framework for structural
analysis, comprising three main aspects is also proposed: to model system structure; to apply
metrics to summarise system structure and identify potential areas for improvement; and to
apply the approach in a manner directed towards improvement goals.
A new meta-model is developed which synthesises existing notations and approaches to
address several important limitations of such matrix-based modelling approaches. This model,
along with a substantial collection of graph-theoretic metrics, enables structural analysis to be
performed across different domains and different perspectives of a process. The metrics are
placed into context by showing how they can be selected and used to address particular
process improvement goals. A framework is also presented to decompose such high-level
goals into specific questions regarding how to improve a process, and metric is related to one
or more of these questions.
Further case studies illustrate how the conceptual framework can be applied in the automotive
industry. In the first study, a model is synthesised from almost 200 items of company
documentation and it is shown how structural analysis can be used to explore the process and
look for potential problem areas. In a second, data is gathered from almost 70 interviews with
company personnel and metrics selected and applied to meet specific improvement objectives.
Taken together, these studies illustrate how the practical application of structural analysis can
be used to understand and improve complex processes.
In summary, this is an excellent piece of research embodied in an equally excellent thesis.
There is much in this work that is original and that will be of great value to other researchers
and practitioners.

Garching, February 2010 Prof. P. John Clarkson
Engineering Design Centre University of Cambridge
The optimization of processes is critical for success in managing companies in a value-driven
manner. Today, it is considered common sense that a sustainable value increase means
investing in the improvement of processes. The most important leverage to increase process
performance is to improve processes at a very early stage of the product life cycle; to this end,
the systematic improvement of engineering design processes in particular merits particular
One of the most successful management tools in practice over the last two decades has been –
and continues to be – the Balanced Scorecard System, consisting of a balanced set of key
performance indicators. To build up powerful Balanced Scorecards in practice, substantial
research needs to be done in advance to develop proposals for consistent sets of
This research proposes a measurement system that makes use of complexity metrics to
embody various patterns of the interplay of process entities in the spirit of a Balanced
Scorecard, all the while adapted to the needs of process improvement. The metrics are used to
draw inferences about the process’s behavior. This way, knowledge about a process can be
extracted from existing process models, or new process models can be structured
systematically by addressing desirable patterns.
Generating such a means of process analysis and management provides a major contribution
both for academia and industry, especially for the improvement of large and complex
engineering design processes in a balanced and comprehensive manner and at a high level of
The leading industry in developing and improving process management systems has been –
over the last years - the automotive industry. Hence, the case studies from automotive design
as used in this research show what is currently possible in terms of a balanced and systematic
process analysis. With this industry facing fundamental and structural changes, each
methodological contribution allowing for professionalizing and accelerating design processes
can strengthen the competitiveness of companies using such approaches.

Garching, February 2010 PD Dr. rer.-pol.Werner Seidenschwarz
Seidenschwarz & Comp.
This work results from my occupation as a researcher at the Institute of Product Development
at the Technische Universität München from September 2004 to August 2009. First and
foremost, I would like to thank my doctoral advisor Udo Lindemann for his continuous
support and encouragement and providing me the liberty to try out new ideas, develop new
concepts, and have access to different industries to advance them. In granting me scientific
freedom and providing me with valuable reviews, Professor Lindemann helped me develop
this thesis to its current state. I am also grateful to my second advisor John Clarkson of the
Engineering Design Center at the University of Cambridge. His constant belief in my work
helped me develop the idea of using complexity metrics as a sound basis for process analysis.
Also, I am very thankful to Boris Lohmann, who agreed to act as chairperson of the
examination board and manage the dissertation process.
An important tribute goes to my colleagues at the institute, many of whom have become close
friends through our close collaboration. In particular the support of my friends Frank Deubzer
and Ulrich Herfeld need to be mentioned. I have gained many valuable ideas and much
energy during our ongoing discussions and our fruitful collaboration in different contexts.
Maik Maurer, Thomas Braun, and Wieland Biedermann have helped me develop the ideas of
complexity management much further, both at the institute and at Teseon GmbH. Finally, I
want to thank those colleagues who have helped me proofread this work and finalize all the
little details, especially Stefan Langer, Christoph Baumberger, Ralf Stetter, Udo Pulm, and
Willem Keijzer. All of those colleagues who covered for me during the time I wrote lightened
my load, too.
In addition, special thanks goes to the international research community, especially Tyson
Browning, Mike Danilovic, Steven Eppinger, Ed Crawley, Andrew Kusiak, Anja Maier, and
David Wynn. Their input and ideas and their continuous provision of new references helped
me to complete many of my concepts, whose full development would otherwise not have
been possible.
I would also like to thank the students, whose work has contributed largely to the maturation
of this thesis. I supervised many of their works during my time at the institute, especially
those of Thomas Lorenzer, Susanne Hogger, Markus Eichinger, Vadim Scheinin, Matthias
Gürtler, Caspar Sunder-Plassmann, Nikolas Bradford, and Christian Schmied, and this helped
me try out new concepts and gain new insights.
Lastly and mostly, I am deeply grateful to my parents and my friends for supporting me and
believing in me. Their patience and comprehension even during stressful times and long
nights at the office gave me the strength to give this thesis the finishing touch.

Garching, March 2010 Matthias Kreimeyer