A multi-stage production-inventory model with learning and forgetting effects, rework and scrap

This paper studies a serial production line where a proportion of defective items is produced at each stage. Defective units enter a rework process, which is imperfect as well. Twice defective items are scrapped. This paper also considers learning and forgetting in production and rework processes and studies how the number of shipments of a lot from a production stage to the next influences the overall performance of the system. A model for a multi-stage production-inventory system is developed and optimized against an aggregate performance measure of four partial measures that are based on production time, process yield, in-process inventory and shipment frequency. Each of these partial performance measures is weighed by the system’s decision maker in accordance to importance. The numerical results show how the values of learning rates, weights assigned to the partial performance measures and the number of production stages influence the overall performance of the system.

► We study a multi-stage supply chain producing defective items.
► Defective items are reworked or scrapped and learning occurs in production and rework.
► Synchronizing learning at the production stages smoothes the flow of items through the system.
► Fast learning at downstream production stages is better than fast learning at upstream stages.