A forecast-driven tactical planning model for a serial manufacturing system

Abstract

Our work is motivated by real-world planning challenges faced by a manufacturer of industrial products. We study a multi-product serial-flow production line that operates in a low-volume, long lead-time environment. The objective is to minimize variable operating costs, in the face of forecast uncertainty, raw material arrival uncertainty and in-process failure. We develop a dynamic-programming-based tactical model to capture these key uncertainties and trade-offs, and to determine the minimum-cost operating tactics. The tactics include smoothing production to reduce production-related costs, and segmenting the serial-flow line with decoupling buffers to protect against variance propagation. For each segment, we specify a work release policy and a production control policy to control both the work-inprocess inventory within the segment and the inventory in the downstream buffer. We also optimize the raw material ordering policy with fixed ordering times, long lead-times and staggered deliveries. We test the model on both hypothetical and actual factory scenarios. The results confirm our intuition and provide new managerial insights on the application of these operating tactics. Moreover, we compare the performance predictions from the tactical model to a simulation of the factory, and find the predictions to be within 10% of simulation results, thus validating the model.

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