Economic Measures of Forecast Accuracy for Demand Planning: A Case-Based Discussion
Successful demand planning relies on accurate demand forecasts. Existing demand planning software typically employs (univariate) time series models for this purpose. These methods work well if the demand of a product follows regular patterns. Their power and accuracy are, however, limited if the patterns are disturbed and the demand is driven by irregular external factors such as promotions, events, or weather conditions. Hence, modern machine-learning-based approaches take into account external drivers for improved forecasting and combine various forecasting approaches with situation-dependent strengths. Yet, to substantiate the strength and the impact of single or new methodologies, one is left with the question how to measure and compare the performance or accuracy of different forecasting methods. Standard measures such as root mean square error (RMSE) and mean absolute percentage error (MAPE) may allow for ranking the methods according to their accuracy, but in many cases these measures are difficult to interpret or the rankings are incoherent among different measures. Moreover, the impact of forecasting inaccuracies is usually not reflected by standard measures. In this chapter, we discuss this issue using the example of forecasting the demand of food products. Furthermore, we define alternative measures that provide intuitive guidance for decision makers and users of demand forecasting.
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- Brown, R. (1967). Decision rules for inventory management. New York: Reinhart and Winston.Google Scholar
- Da Veiga, C. P., Da Veiga, C. R. P., Catapan, A., Tortato, U., & Da Silva, W. V. (2014). Demand forecasting in food retail: A comparison between the Holt-Winters and ARIMA models. WSEAS Transactions on Business and Economics, 11, 608–614.Google Scholar
- Hyndman, R. J. (2006). Another look at forecast-accuracy metrics for intermittent demand. Foresight, 4, 43–46.Google Scholar
- Kolassa, S., & Schütz, W. (2007). Advantages of the MAD/Mean ratio over the MAPE. Foresight: The International Journal of Applied Forecasting, 6(6), 40–43.Google Scholar
- Vasumathi, B., & Shanmuga Ranjani, S. P. (2013). Forecasting in SAP-SCM (Supply Chain Management). International Journal of Computer Science and Mobile Computing, 2(7), 114–119.Google Scholar