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Engineering organizations developing large complex systems are usually not capable of determining an "overall optimal" system design. Rather, the system is divided in "com ponents" or subsystems (such as an axle in a car or a module in a software product), for each of which a performance can be measured, an optimal design can be found or at least approximated, and for which a designer (or engineer or team of engineers) is responsible. Each engineer then makes, at first, decisions to optimize "his" component. In real orga nizations, designers often develop considerable pride in the solutions they have found for their components. However, it is the very nature of complex systems that the components cannot be optimized in isolation, but that they interact in determining the quality of the overall system (via space constraints, or via the exchange of fluids, air, force, electricity, or information). To some degree, these interactions are known from experience and can be anticipated, or are embedded in accepted design principles. However, in any complex design project that is not entirely routine and marginal, many such interactions are not known at the outset.
List of contents
1 Introduction.- 2 Literature Review.- 2.1 Analytic models of design iteration.- 2.2 Models based on complexity theory.- 2.3 Models from the empirical or descriptive literature.- 2.4 Models based on the simulation of agent populations.- 2.5 Summary.- 3 Model Description.- 3.1 Structure of the NPD process.- 3.2 Component performance and interdependence.- 3.3 Role of time.- 3.4 Decision making and coordination.- 3.5 Model discussion.- 4 Analytic Results.- 4.1 Closed form analysis for the base case.- 4.2 Numerical example.- 4.3 Implications for the base case.- 5 Simulation Results.- 5.1 Definition of simulation technicalities.- 5.2 Simulation results.- 6 Discussion and Conclusion.- A Properties of the Error Function.- B Simulation Data.- B.1 Data for the base series of simulations (25,000 time units).- B.2 Data for the 10,000 time units verification run.- B.3 Data for the 40,000 time units verification run.- C Program Listing.- C.1 Base case.- C.2 Adaptations for instantaneous broadcast.- C.3 Adaptations for the simulation of cooperation.- C.4 Adaptations for the error function case.- C.5 Adaptations for the depleted case.
About the author
Dr. Jürgen Mihm promovierte bei Prof. Dr. Arnd Huchzermeier am Lehrstuhl für Produktionsmanagement der Wissenschaftlichen Hochschule für Unternehmensführung (WHU) in Vallendar. Er ist als Unternehmensberater bei McKinsey & Co., Inc. tätig.
Summary
In organizational theory the coordination of many interdependent actors in complex product development projects is recognized as a key activity. With increasing project compexity this coordination becomes more and more difficult, and it is not yet known whether this effect can be controlled by frequent and intense communication among project members.
Jürgen Mihm analyzes which factors create complexity in engineering projects and how the negative effects of complexity can be mitigated. He builds a mathematical model of a complex distributed design project demonstrating how complexity inevitably arises from the interaction of simple components. He characterizes the dynamic behavior of the system analytically and with the aid of simulations, and he derives classes of managerial actions to improve performance dynamics.
