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- W1811713946 abstract "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." @default.
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- W1811713946 date "2003-01-22" @default.
- W1811713946 modified "2023-09-27" @default.
- W1811713946 title "Artificial neural networks for predicting the optimal number of kanbans in a JIT manufacturing environment" @default.
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- W1811713946 doi "https://doi.org/10.1109/ijcnn.1999.836216" @default.
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