Data Mining for Subassembly Selection

Abstract

The paper presents a model and an algorithm for selection of subassemblies based on the analysis of prior orders received from the customers. The parameters of this model are generated using association rules extracted by a data mining algorithm. The extracted knowledge is applied to construct a model for selection of subassemblies for timely delivery from the suppliers to the contractor. The proposed knowledge discovery and optimization framework integrates the concepts from product design and manufacturing efficiency. The ideas introduced in the paper are illustrated with an example and an automotive case study.