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Research Papers

A Complexity Model for Assembly Supply Chains and Its Application to Configuration Design

[+] Author and Article Information
Hui Wang1

Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109johnwang@umich.edu

Jeonghan Ko

Department of Industrial and Management Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588

Xiaowei Zhu, S. Jack Hu

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109

1

Corresponding author.

J. Manuf. Sci. Eng 132(2), 021005 (Mar 30, 2010) (12 pages) doi:10.1115/1.4001082 History: Received April 30, 2009; Revised January 14, 2010; Published March 30, 2010; Online March 30, 2010

A complexity measure for assembly supply chains has been proposed based on Shannon’s information entropy. This paper extends the definition of such a measure by incorporating the detailed information of the supply chain structure, the number of variants offered by each node in the supply chain, and the mix ratios of the variants at each node. The complexity measure is then applied to finding the optimal assembly supply chain configuration given the number of variants offered at the final assembler and the mix ratios of these variants. The optimal assembly supply chain configuration is theoretically studied in two special scenarios: (1) there is only one dominant variant among all the variants offered by the final assembler, and (2) demand shares are equal across all variants at the final assembler. It is shown that in the first scenario where one variant dominates the demand, the optimal assembly supply chain should be nonmodular; but in the scenario of equal demand shares, a modular supply chain is better than nonmodular one when the product variety is high. Finally a methodology is developed to find the optimal supply chain with/without assembly sequence constraints for general demands.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 4

Three modular assembly supply chains with six nodes in the most upstream echelon and one node in the intermediate echelon

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Figure 5

The example used to illustrate the methodology of finding the optimal assembly supply chain

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Figure 6

There are five configurations to connect the four nodes in the most upstream echelon to the final assembler

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Figure 7

There are more than one supply chain candidates for the same configuration

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Figure 8

Iterative decomposition algorithm to generate all possible supply chain candidates and × stands for the infeasible candidates, which will be discussed in Sec. 5

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Figure 9

A set of assembly sequence constraints

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Figure 10

Feasibility check of the intermediate supply chain candidates after each decomposition

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Figure 11

Feasibility check criteria for an intermediate assembly supply chain

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Figure 12

One nonmodular assembly supply chain and one modular assembly with only one intermediate subassembler

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Figure 1

Manufacturers move from nonmodular to modular assembly supply chains

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Figure 2

A general assembly supply chain and relationships of variants’ demand share

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Figure 3

The example used to illustrate the calculation of assembly supply chain complexity

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