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Design for Process Flexibility: Efficiency of the Long Chain and Sparse Structure

NUS Business School, National University of Singapore, Singapore
Nanyang Business School, Nanyang Technological University, Singapore
NUS Business School, National University of Singapore, Singapore
Antai College of Economics and Management, Shanghai Jiaotong University, Shanghai, China
bizchoum{at}nus.edu.sg
geoff.chua{at}gmail.com
bizteocp{at}nus.edu.sg
zhenghuan{at}sjtu.edu.cn

The concept of chaining, or in more general terms, sparse process structure, has been extremely influential in the process flexibility area, with many large automakers already making this the cornerstone of their business strategies to remain competitive in the industry. The effectiveness of the process strategy, using chains or other sparse structures, has been validated in numerous empirical studies. However, to the best of our knowledge, there have been relatively few concrete analytical results on the performance of such strategies vis-á-vis the full flexibility system, especially when the system size is large or when the demand and supply are asymmetrical. This paper is an attempt to bridge this gap.

We study the problem from two angles: (1) For the symmetrical system where the (mean) demand and plant capacity are balanced and identical, we utilize the concept of a generalized random walk to evaluate the asymptotic performance of the chaining structure in this environment. We show that a simple chaining structure performs surprisingly well for a variety of realistic demand distributions, even when the system size is large. (2) For the more general problem, we identify a class of conditions under which only a sparse flexible structure is needed so that the expected performance is already within optimality of the full flexibility system.

Our approach provides a theoretical justification for the widely held maxim: In many practical situations, adding a small number of links to the process flexibility structure can significantly enhance the ability of the system to match (fixed) production capacity with (random) demand.

Subject classifications: random walk; stochastic programming; production; flexible manufacturing; facility planning; design.
History: Received October 2007; revision received June 2008; accepted July 2008.