Optimization of Industrial-Scale Assemble-To-Order System
We provide insights and algorithms to improve inventory control in industrial-sized Assemble-To-Order (ATO) systems. We seek base-stock levels for components that minimize the sum of holding costs and product-specific backorder costs. Our initial focus is on first-come first-serve (FCFS) allocation of components to products. By developing a novel stochastic programming (SP) formulation for this setting, we compute solutions that are within one percent of the lower bound for realistically sized systems. We then answer the following questions for such systems: How do common heuristics used in practice compare to our performance, and how costly is the FCFS assumption? For the first question, we investigate the effectiveness of ignoring simultaneous stock-outs, a heuristic that has been used by companies such as IBM and Dell to optimize inventory levels. We find that the performance of the heuristic, when compared to the optimal FCFS base-stock policy, increases as the average newsvendor (NV) service level increases. For the second question, we adapt the SP formulation of Doğru, Reiman and Wang (2010), yielding an upper bound on the benefit of optimal allocation over FCFS. We find that FCFS performs surprisingly well for many practical cases, and that its performance improves, again, with increasing average NV service levels.
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