Complexity in New Product Development: Mastering the Dynamics of Engineering Projects by Jürgen MihmComplexity in New Product Development: Mastering the Dynamics of Engineering Projects by Jürgen Mihm

Complexity in New Product Development: Mastering the Dynamics of Engineering Projects

byJürgen Mihm

Paperback | April 29, 2003

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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.
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.
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Title:Complexity in New Product Development: Mastering the Dynamics of Engineering ProjectsFormat:PaperbackDimensions:273 pagesPublished:April 29, 2003Publisher:Deutscher UniversitätsverlagLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3824477017

ISBN - 13:9783824477012

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Table of Contents

1 Introduction.- 2 Literature Review.- 2.1 Analytic models of design iteration.- 2.1.1 Models based on the concurrent engineering paradigm.- 2.1.2 Models based on queuing theory.- 2.1.3 Models based on the design structure matrix.- 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.- 3.2.1 Influence of the individual decision maker on the component.- 3.2.2 Influence of other decision makers on the component.- 3.2.2.1 Influence on the optimal component decision.- 3.2.2.2 Influence on the component performance.- 3.2.2.2.1 Piecewise linear formulation of bounds.- 3.2.2.2.2 Boundary conditions as error function.- 3.2.2.3 Interaction of influences.- 3.2.3 Performance of the individual decision maker.- 3.2.4 Total performance of the NPD network.- 3.3 Role of time.- 3.3.1 Decision making and time.- 3.3.2 Communication and time.- 3.4 Decision making and coordination.- 3.4.1 Decisions of the uncooperative decision maker.- 3.4.2 Decisions of the cooperative decision maker.- 3.4.2.1 Optimization in the piecewise linear case.- 3.4.2.2 Optimization in the error function case.- 3.5 Model discussion.- 3.5.1 Model limitations.- 3.5.2 Model characteristics in view of the NK model.- 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.- 5.2.1 Base case.- 5.2.2 Cooperation among agents.- 5.2.2.1 Cooperation among agents assuming piecewise linear boundary conditions.- 5.2.2.2 Cooperation among agents assuming erf-boundary conditions.- 5.2.3 Instantaneous broadcast of decisions among agents.- 5.2.4 Communication of preliminary information.- 5.2.5 Networks not fully connected.- 5.2.6 Equivoque.- 5.2.7 Robustness of model and 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.