Abstract
This paper focuses on forecasting of products with sporadic demand. The demand for such products is not continuous but diffused seemingly at random, with a large proportion of zero values in the analyzed time series. The sporadic character of demand patterns actually means that the information available on the demand for previous selling periods is patchy, resulting in lower quality of data available. Under such circumstances demand forecasting is a challenging task. We present the results of a case study, where forecasting practice of a pharmaceutical wholesaler firm –we call it Pharma– is analyzed and developed. We present state-of-the-art knowledge related to demand forecasting of sporadic products and test suggestions related to them. We show that these suggestions can only partly be backed. We extend therefore the suggested product classification scheme and recommend using the concept of demand data aggregation. This will reduce sporadicity and result in higher quality forecasting. Aggregation also helps to specify the recommended forecast period, the length of time recommended to calculate the forecast for. The managerial consequences of these suggestions are also discussed, and future research directions are highlighted.
Highlights
Inventory management and forecasting are traditionally linked to each other and represent a major research area in operations and logistics management
The sporadic character of demand patterns means that the information available on the demand of previous selling periods is patchy, resulting in lower quality data
We analyzed its sales data and developed a classification scheme for its product portfolio based on the two relevant demand characteristics, using the coefficient of variance and the average interdemand interval of products
Summary
Inventory management and forecasting are traditionally linked to each other and represent a major research area in operations and logistics management. According to our analysis based on long term and real-life time series data, it was found that the new methods proposed really can lead to higher forecast accuracy in certain cases, when basic (daily) demand data is aggregated in time. This aggregation allows an appropriate forecasting period to be determined (working days, weeks, months or even quarters). As will be discussed in the paper, our application- oriented work has resulted in new observations, given us new insights and resulted in generalizable suggestions related to the state-of-the-art knowledge in this field These new insights raise new questions and open new research directions in the field of forecasting and inventory management
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