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Kanban setting through artificial intelligence: a comparative study of artificial neural networks and decision trees

Ina S. Markham (James Madison University, Harrisonburg, Virginia, USA)
Richard G. Mathieu (School of Business and Administration, Saint Louis University, St Louis, Missouri, USA)
Barry A. Wray (The University of North Carolina at Wilmington, Wilmington, North Carolina, USA)

Integrated Manufacturing Systems

ISSN: 0957-6061

Publication date: 1 July 2000

Abstract

Determining the number of circulating kanban cards is important in order effectively to operate a just‐in‐time with kanban production system. While a number of techniques exist for setting the number of kanbans, artificial neural networks (ANNs) and classification and regression trees (CARTs) represent two practical approaches with special capabilities for operationalizing the kanban setting problem. This paper provides a comparison of ANNs with CART for setting the number of kanbans in a dynamically varying production environment. Our results show that both methods are comparable in terms of accuracy and response speed, but that CARTs have advantages in terms of explainability and development speed. The paper concludes with a discussion of the implications of using these techniques in an operational setting.

Keywords

Citation

Markham, I.S., Mathieu, R.G. and Wray, B.A. (2000), "Kanban setting through artificial intelligence: a comparative study of artificial neural networks and decision trees", Integrated Manufacturing Systems, Vol. 11 No. 4, pp. 239-246. https://doi.org/10.1108/09576060010326230

Publisher

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MCB UP Ltd

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