Analysis of Postponement Strategy by EPQ-based Models
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In this chapter we develop EPQ-based models with and without stockout to examine the impact of postponement. We formulate the total average cost functions of the two scenarios for producing and keeping n end-products in a supply chain, in which their demands are known and deterministic. Using standard optimization techniques , we show that postponed customization of end-products results in a lower total average cost in certain circumstances. We also find that two key factors that influence postponement decisions are variance of the machine utilization rates and variance of the backorder costs.
KeywordsSupply Chain Setup Cost Customer Demand Production Quantity Total Average Cost
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