Forecasting for Inventory Control with Exponential Smoothing

By Ralph D. Snyder, Anne B. Koehler, J. Keith Ord. 2002

77 citations recorded by the source corpus.

Exponential smoothing, often used for sales forecasting in inventory control, has always been rationalized in terms of statistical models that possess errors with constant variances. It is shown in this paper that exponential smoothing remains the appropriate approach under more general conditions where the variances are allowed to grow and contract with corresponding movements in the underlying level. The implications for estimation and prediction are explored. In particular the problem of finding the prediction distribution of aggregate lead- time demand for use in inventory control calculations is considered. It is found that unless a drift term is added to simple exponential smoothing, the prediction distribution is largely unaffected by the variance assumption. A method for establishing order-up-to levels and reorder levels directly from the simulated prediction distributions is also proposed.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a5142708d13cf074a300cd27a3ea36938271b453

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

The inventory performance of forecasting methods: Evidence from the M3 competition data (2019, methodology): “Snyder et al. (2002) focused on estimating the aggregate (lead-time) demand using exponential smoothing methods and evaluated the performance on the ﬁll rate.”
Determination of Exponential Smoothing Constant to Minimize Mean Square Error and Mean Absolute Deviation (2011, methodology): “Snyder et al. (2002) has shown that exponential smoothing remains appropriate under more general conditions, where the variance is allowed to grow or contract with corresponding movements in the underlying level.”
Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy (2014, methodology): “Vassian (1955) appears to be the first to apply the z-transform to an inventory control problem.”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, unknown): “The parametric bootstrapping procedure of Snyder (2002) and Snyder et al. (2002) should appeal to the practical forecaster because it is tailored to lead-time demand and can be used when the distribution of demand is non-normal, when the lead time is stochastic, and when demand is intermittent.”
Forecasting and operational research: a review (2008, methodology): “The method was claimed to be unbiased, however Syntetos and Boylan (2001) undermined this conclusion. Snyder et al (2002) and Shenstone and Hyndman (2005) have pointed out the inconsistency between Croston’s model (that assumes”
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011, methodology): “…smoothing methods, which are among the most widely used forecasting procedures in practice (Makridakis et al. 1982; Makridakis and Hibon 2000; Snyder, Koehler, and Ord 2002), have been shown to be optimal for a class of innovations state space models (Ord, Koehler, and Snyder 1997; Hyndman…”