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setting through artificial intelligence: a comparative study of artificial neural networks and decision trees (2000)

Markham, Ina S. ; Mathieu, Richard G. ; Wray, Barry A.

Bradford : Emerald

Integrated manufacturing systems 11 (2000), S. 239-246

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ISSN: 0957-6061

Source: Emerald Fulltext Archive Database 1994-2005

Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Economics

Notes: 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.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1108/09576060010326230

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Neural network identification of critical factors in a dynamic just-in-time kanban environment (1997)

WRAY, BARRY A. ; RAKES, TERRY R. ; REES, LOREN PAUL

Springer

Journal of intelligent manufacturing 8 (1997), S. 83-96

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ISSN: 1572-8145

Keywords: Kanban ; inventory ; artificial neural networks

Source: Springer Online Journal Archives 1860-2000

Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics

Notes: Abstract Prior research has examined the proper number of kanbans to be used in various just-in-time environments, but relatively little work has been done in exploring which factors internal and external to a shop in a given time period are critical in determining the necessary number of kanbans to be specified for the next period. The research reported here examines the identification of shop factors in a dynamic and stochastic just-in-time environment. In particular, three questions are addressed: does information from a prior period help in setting the kanban level in the current period? If so, which endogenous and exogenous factors considered individually help the most? And finally, what grouping of individual factors is most important in deciding the number of kanbans? The methodology employed is to use artificial neural networks to fit simulated shop data to learn the relationship between prediction factors and overall shop performance. Appropriate non-parametric statistical tests are then used to answer the questions. The answers obtained, although shop specific, may also be generated by firms willing to follow the procedure presented here for conditions specific to their particular operation.