Forecasting method selection in a global supply chain
In supply chains, forecasting is an important determinant of operational performance, although there have been few studies that have selected forecasting methods on that basis. This paper is a case study of forecasting method selection for a global manufacturer of lubricants and fuel additives, products usually classified as specialty chemicals. We model the supply chain using actual demand data and both optimization and simulation techniques. The optimization, a mixed integer program, depends on demand forecasts to develop production, inventory, and transportation plans that will minimize the total supply chain cost. Tradeoff curves between total costs and customer service are used to compare exponential smoothing methods. The damped trend method produces the best tradeoffs.
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