Leveraging Comparables for New Product Sales Forecasting

36 Pages Posted: 14 Dec 2017

See all articles by Lennart Baardman

Lennart Baardman

Massachusetts Institute of Technology (MIT) - Operations Research Center

Igor Levin

Johnson & Johnson - Johnson & Johnson Consumer Companies Inc.

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Divya Singhvi

Massachusetts Institute of Technology (MIT) - Operations Research Center

Date Written: December 11, 2017

Abstract

Many firms regularly introduce new products. Before the launch of any new product, firms need to make various operational decisions, which are guided by the sales forecast. The new product sales forecasting problem is challenging when compared to forecasting sales of existing products. For existing products, historical sales data gives an indicator of future sales, but this data is not available for a new product. We propose a novel sales forecasting model that is estimated with data of comparable products introduced in the past. We formulate the problem of clustering products and fitting forecasting models to these clusters simultaneously. Inherently, the model has a large number of parameters, which can lead to an overly complex model. Hence, we add regularization to the model so that it can estimate sparse models. This problem is computationally hard, and as a result, we develop a scalable algorithm that produces a forecasting model with good analytical guarantees on the prediction error. In close collaboration with our industry partner Johnson & Johnson Consumer Companies Inc., a major fast moving consumer goods manufacturer, we test our approach on real datasets, after which we check the robustness of our results with data from a large fast fashion retailer. We show that, compared to several widely used forecasting methods, our approach improves MAPE and WMAPE by 20-60% across various product segments compared to several widely used forecasting methods. Additionally, for the consumer goods manufacturer, we develop a fast and easy-to-use Excel tool that aids managers with forecasting and making decisions before a new product launch.

Keywords: Data-driven Operations, New Products, Forecasting, Clustering, Regularization, LASSO

Suggested Citation

Baardman, Lennart and Levin, Igor and Perakis, Georgia and Singhvi, Divya, Leveraging Comparables for New Product Sales Forecasting (December 11, 2017). Available at SSRN: https://ssrn.com/abstract=3086237 or http://dx.doi.org/10.2139/ssrn.3086237

Lennart Baardman

Massachusetts Institute of Technology (MIT) - Operations Research Center ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

Igor Levin

Johnson & Johnson - Johnson & Johnson Consumer Companies Inc. ( email )

410 George Street
New Brunswick, NJ 08901
United States

Georgia Perakis (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-565
Cambridge, MA 02142
United States

Divya Singhvi

Massachusetts Institute of Technology (MIT) - Operations Research Center ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

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