Clustering customers to forecast demand

This paper addresses the problem of forecasting irregular demand, balancing the tradeoff between forecast accuracy and cost of collecting information. The literature suggests the adoption of a clustering approach, however it is not clear under which conditions this method is actually beneficial. We consider three kinds of demand variability, namely structural (e.g. seasonality), managerial (e.g. promotions) and random (i.e. unpredictable), and we investigate their impact on the correlation of demand within clusters of customers and thus on the clustering approach effectiveness. We develop an analytical model of this relationship and test it with real data in the fresh food industry. Results show that while structural and managerial variability make the clustering approach feasible, random variability works in the opposite direction, providing guidelines on when this forecasting method can be adopted.