Production planning forecasting method selection in a supply chain: a case study

In modern supply chain, forecasting is critical for managers to ensure the availability of the proper amount of inventory to satisfy customers with minimum deviation. Without appropriate forecasting, it is very difficult to plan effectively and efficiently. After scanning the literature, it is to be noted that there is no recommendation of a specific method that is best for forecasting. This is also due to the availability of many alternative forecasting methods and criteria of preferences. Therefore, this paper proposes a ranking of forecasting methods for production planning in a supply chain. The proposed model is based on the analytical hierarchy process (AHP) since it has been proven useful in multi-criteria decision-making in many industrial and real life applications. It considers seven popular methods (decomposition, winter exponential, double exponential, single exponential, trend analysis, five months moving average and 12 months moving average) and three preference criteria (MAD, MSD and MAPE). The model is tested using a real case study. The sensitivity of the ranking was also tested and the ranges of criteria preferences are determined for robustness.