It is shown that the traditional choice for the initial smoothed statistics in general exponential smoothing leads to the same forecasts as the equivalent ARIMA model, provided that one uses zero starting values for the initial shocks. In addition, an initialization which uses 'backforecasts' as ini
The impact of exponential smoothing forecasts on the bullwhip effect
✍ Scribed by Frank Chen; Jennifer K. Ryan; David Simchi-Levi
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 351 KB
- Volume
- 47
- Category
- Article
- ISSN
- 0894-069X
No coin nor oath required. For personal study only.
✦ Synopsis
An important phenomenon often observed in supply chain management, known as the bullwhip effect, implies that demand variability increases as one moves up the supply chain, i.e., as one moves away from customer demand. In this paper we quantify this effect for simple, two-stage, supply chains consisting of a single retailer and a single manufacturer. We demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the use of a moving average forecast. We consider two types of demand processes, a correlated demand process and a demand process with a linear trend. We then discuss several important managerial insights that can be drawn from this research.
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