Slides

Abstract: Effective demand forecasting is critical for inventory management, production planning, and decision making across industries. One of the main challenges lies in understanding the origins of zeros in demand, which may happen naturally or due to anomalies such as stockouts and recording errors. Misinterpreting these zeroes can lead to the application of inappropriate forecasting methods, leading to poor decision making. To address this, we propose a two-stage model-based classification framework that identifies artificially occurring zeroes in the first step and then distinguishes between different types of demand into four categories: regular fractional, regular count, intermittent fractional, and intermittent count. The framework utilises statistical modelling and information criteria to classify demand into those categories. Simulations reveal that our approach significantly increases the accuracy of demand classification, while improving the performance of forecasting methods. The empirical study shows that the proposed classification increases the accuracy of the forecasting methods compared to those applied directly to the dataset without the two-stage framework.