# Modeling uncertain forecast accuracy in supply chains with postponement

LeBlanc, Hill and Harder emphasis the uncertainty in forecast accuracy in their 2009 paper. And therefore address a huge gap in current SCRM research.

##### Model

In the model of LeBlanc et al. exist two uncertainties: Uncertainty over forecast accuracy and demand uncertainty.

There are two decision points where the manager can decide if / how much he wants to produce: A and B. At time C the finished goods are shipped. Restrictions to the production quantity at A an B can be applied.

LeBlanc et al. address three questions with this model.

- How much should the firm pay to improve the accuracy of the forecast?
- How much should the firm pay to achieve strategic changes, such as reducing the cost of incurring each shortage or the cost of delaying procurement and production until time B?
- For different forecast accuracies, shortage and holding costs, postponement add-on percentages, etc., what percentage of the forecast should a manufacturer postpone until time B, instead of producing at time A?

##### Solution

The solution is done in using dynamic programming. The random variables have overlapping sums, the Forecast is calculated by FC = X_1 + X_2; the actual demand is calculated by AD = X_2 + X_3.

Using this technique it is easy to simulate different forecast accuracies: If X_2 is low compared to X_1 and X_3, forecast accuracy is low as well, and vice-versa.

##### Result

After implementing this model in a computer, it is possible to answer the above mentioned questions, and especially quantify the benefit of better forecasting accuracy.

LeBlanc, L. J., Hill, J. A., & Harder, J. (2009). Modeling uncertain Forecast Accuracy in Supply Chains with Postponement Journal of Business Logistics, 30 (1), 19-31

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