Data mining for improvement of product quality

Abstract

The assemble-to-order strategy delays the final assembly operations of a product until a customer order is received. The modules used in the final assembly operation result in a large product diversity. This production strategy reduces the customer waiting time for the product. As the lead-time is short, any product rework may violate the delivery time. Since quality tests can be performed on the stocked modules without impacting the assembly schedule, the quality of the final assembly operations should be the focus. The data-mining approach presented in this paper uses the production data to determine the sequence of assemblies that minimizes the risk of producing faulty products. The extracted knowledge plays an important role in sequencing modules and forming product families that minimize the cost of production faults. The concepts introduced in the paper are illustrated with numerical examples.

Keywords:

• Assemble to order
• Quality
• Data mining
• Mass customization
• Production strategy

Acknowledgements

The authors wish to acknowledge the support of the Natural Science and Engineering Research Council of Canada (NSERC). The research project has also been supported by the Fonds Québecois de la Recherche sur la Nature et les Technologies (FQRNT).