Forecasting demand from heterogeneous customers

Purpose – In many industrial contexts, firms are encountering increasingly uncertain demand. Numerous factors are driving this phenomenon; however, a major change that is spreading among different sectors is the ever-growing attention to customers. Companies have identified that customers are critical not only because they directly influence the success of specific products or firms, but also because they play a fundamental role in many internal processes. Although the role of customers in business processes has been deeply analysed, the issue of demand forecasting and the role of customers has not been fully explored. The present study aims to examine the impact of heterogeneity of customer requests on demand forecasting approaches, based on three action research cases. Based on the analysis of customer behaviour, an appropriate methodology for each case is designed based on clustering customers according to their demand patterns. Design/methodology/approach – Objectives are achieved by means of three action research case studies, developed in cooperation with three different companies. The paper structures a general methodology based on these three experiences to help managers in better dealing with uncertain demand. Findings – By means of proper analysis of customers' heterogeneity and by using simple statistical techniques such as cluster analysis, forecasting performance can significantly improve. In these terms, this work claims that focusing on customers' heterogeneity is a relevant topic both for practitioners and researchers. Originality/value – The paper proposes some specific guidelines to forecast demand where customers' differences impact significantly on demand variability. In these terms, results are relevant for practitioners. Moreover, the paper claims that this issue should be better analysed in future researches and proposes some guidelines for future works.