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

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.