Forecasting Demand for New Products: Combining Subjective Rankings with Sales Data

Abstract

A major obstacle to wider adoption of the newsvendor model is the difficulty of obtaining its key input---the demand distribution forecast, specifically when the products are new and no historical data are available. In such cases, judgmental forecasting methods are a commonly suggested solution, in particular, the Sport Obermeyer approach which collects point forecasts of demand quantity from a panel of experts and uses the degree of disagreement between experts as a proxy for demand uncertainty. However, our attempt to implement this approach at fashion retailer Moods of Norway was a failure. We were not able to recruit a sufficiently large and diverse crowd because many potential experts found it difficult to provide quantity inputs. In response to this issue, we started asking the experts to rank the products within their respective categories. While this new type of input boosted participation, its conversion to quantities requires additional data and new methodology. To that end, we propose to use category-wise historical data, and we constructed a framework for this conversion based on a tripartite decomposition of the demand vector into total demand, ordered proportions, and ranking. We also propose several new evaluation metrics and test our framework on a dataset from Moods of Norway.