Manufacturing lead time estimation using data mining

Ozturk A., Kayaligil S., Ozdemirel N. E.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol.173, no.2, pp.683-700, 2006 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 173 Issue: 2
  • Publication Date: 2006
  • Doi Number: 10.1016/j.ejor.2005.03.015
  • Journal Name: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.683-700
  • Keywords: production, lead time estimation, knowledge-based systems, data mining, regression trees, RULE INDUCTION, DUE-DATES, JOB-SHOPS, SIMULATION, MANAGEMENT, DISCRETIZATION, INFORMATION, ASSIGNMENT, PREDICTION, SELECTION
  • Middle East Technical University Affiliated: Yes

Abstract

We explore use of data mining for lead time estimation in make-to-order manufacturing. The regression tree approach is chosen as the specific data mining method. Training and test data are generated from variations of a job shop simulation model. Starting with a large set of job and shop attributes, a reasonably small subset is selected based on their contribution to estimation performance. Data mining with the selected attributes is compared with linear regression and three other lead time estimation methods from the literature. Empirical results indicate that our data mining approach coupled with the attribute selection scheme outperforms these methods. (c) 2005 Elsevier B.V. All rights reserved.