Forecasting intermittent demand for inventory management by retailers: A new approach

Abstract The forecasting of intermittent demand is a complex task owing to demand fluctuations and interval uncertainty. Intermittent demand is essentially random demand with a high percentage of zero values. In the retail industry, there are many products which face intermittent demand and this poses a problem of inventory management. This study proposes a Markov-combined method (MCM) for forecasting intermittent demand, which takes into account the inventory status and historical sales of products. We divide the prediction process into two stages. In the first stage, the transition probabilities of the four basic states of demand and inventory are calculated. In the second stage, the corresponding and appropriate prediction method is selected according to the predicted state. Further, using two large datasets from the two biggest e-commerce companies in China, we verify our results and show that the MCM forecasts more accurately than the Single Exponential Smoothing (SES), Syntetos-Boylan Approximation (SBA), and Croston (CR) methods. The MCM can be as an alternative method for forecasting intermittent demand because it is easy to compute and typically more accurate than the classical forecasting methods.