Complexity in New Product Development

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.

Auteur

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.

Texte du rabat

Jürgen Mihm 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.

Contenu

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.