Data-Driven Simulation and Optimization Approaches To Incorporate Production Variability in Sales and Operations Planning

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• 作者：Bruno A. Calfa ; Anshul Agarwal ; Scott J. Bury ; John M. Wassick ; Ignacio E. Grossmann
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2015
• 出版时间：July 29, 2015
• 年：2015
• 卷：54
• 期：29
• 页码：7261-7272
• 全文大小：519K
• ISSN：1520-5045

文摘

We propose two data-driven, optimization-based frameworks (simulation-optimization and bi-objective optimization) to account for production variability in the operations planning stage of the sales and operations planning (S&OP) of an enterprise. Production variability is measured as the deviation between historical planned (target) and actual (achieved) production rates. A statistical technique, namely, quantile regression, is used to model the distribution of deviation values given planned production rates. Scenarios are constructed by sampling from the distribution of deviation values and used as inputs to the proposed optimization-based frameworks. Advantages and disadvantages of the two proposed frameworks are discussed. The applicability of the proposed methodology is illustrated with a detailed analysis of the results of a motivating example and a real-world production planning problem from a chemical company.