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Artificial neural networks for predicting the optimal number of kanbans in a JIT manufacturing environment

Current techniques for predicting the number of kanbans needed at a workcenter typically use only efficient factory data to develop a predictive model that maps relationships between inputs (shop operating conditions) and a desired output (number of kanbans). The paper presents a methodology that uses autoassociative neural networks to determine if a proposed number of kanbans will result in a starved, efficient, or saturated factory, based on a given set of factory conditions.