Leveraging Comparables for New Product Sales Forecasting

Lennart Baardman, Igor Levin, Georgia Perakis, and Divya Singhvi, Leveraging Comparables for New Product Sales Forecasting, Production and Operations Management, Volume 27, Issue 12, 06 December 2018, Pages: 2340-2343. (First Prize of the 3rd POMS Applied Research Challenge)

This work develops an accurate, scalable and interpretable forecasting tool calibrated with our industry partners’ data. These characteristics are important to our two major industry partners, one being Johnson & Johnson Consumer Companies Inc., a consumer healthcare manufacturer, the other being a large fashion retailer. In building our tool we are motivated by an approach that has been used by industry practitioners: identify a set of products comparable to the new product, average their historical sales, and use this as a forecast. In line with this approach, we devise a model that uses analytics to jointly cluster products while estimating a regularized regression model for each cluster’s sales.... 

The joint cluster-while-regress model is formulated as a non-linear integer optimization problem that is proven to be NP-hard. However, we use the practical interpretation of our problem to devise a fast algorithm whose iterative steps mimic industry practice.... 

Working in collaboration with two large industry partners, we show that our algorithm results in a 20–70% MAPE improvement and 10–60% WMAPE improvement over several benchmarks used in practice.