Optimal commonality in component design

Increased competition and more demanding customers have forced companies to offer a wide variety of products. Component commonality can help companies reduce the cost of providing product variety to their customers. However, determining the extent to which component commonality should be used is difficult. In this paper we present an approach to determine the optimal level of component commonality for end-product components that do not differentiate models from the customer's perspective. The work was inspired by and applied to a wire-harness design problem faced by a major automobile manufacturer. We model the component design problem as a mathematical program that considers production, inventory holding, setup, and complexity costs (the cost in indirect functions caused by component variety). We develop two approaches to solve the problem: a branch-and-bound algorithm that can solve small- and medium-size problems optimally, and a simulated annealing algorithm that can solve large-size problems heuristically. We apply both algorithms to the wire-harness design problem faced by the automobile manufacturer and to a number of randomly generated test problems. We show that an optimal design achieves high cost savings by using significantly fewer variants than a no-commonality design but significantly more variants than a full-commonality design. We apply sensitivity analysis to identify extreme conditions under which the no-commonality and full-commonality designs perform well, and we identify the key cost drivers for our application. Finally, we describe the impact of our analysis on the company's subsequent component design decisions.