A cost-based module mining method for the assemble-to-order strategy

作者：Tsai Chieh Yuan^*; Chen Chih Jung; Lo Yu Ting

来源：Journal of Intelligent Manufacturing, 2014, 25(6): 1377-1392.

DOI：10.1007/s10845-013-0736-z

• Assemble-to-order
• Modularity
• Data mining
• Module mining
• Cost analysis

The assemble-to-order (ATO) strategy is one of the most popular operations management approaches to achieve mass customized products while maintaining lower costs. In the ATO system, manufacturers keep inventory at the component and module level, and postpone product differentiation until the final stage of production. However, most research on modularity assumes that modules are already known in advance. In fact, in the ATO system, the determination of which components should be pre-assembled as modules mainly depends on the types and volumes of products ordered by customers. That is, module composition and volume should be derived dynamically from the product database based on updated customer orders. To bridge this gap, a two-stage cost-based module mining method for the assemble-to-order strategy is proposed. The first stage determines which sets of components can be formed (pre-assembled) as modules based on a list of customer orders. In the second stage, a cost-based selection approach is developed to evaluate the total cost of each module implementation project generated from the set of feasible modules. The module implementation project with the lowest cost is thus found and suggested to production managers.

• 出版日期2014-12