Temporal Big Data for Tactical Sales Forecasting in the Tire Industry

Yves R. Sagaert, El-Houssaine Aghezzaf, Nikolaos Kourentzes, and Bram Desmet, Temporal Big Data for Tactical Sales Forecasting in the Tire Industry, Interfaces, Volume 48, Issue 2, March-April 2018, pp. 121–129.

We propose a forecasting method to improve the accuracy of tactical sales predictions for a major supplier to the tire industry. This level of forecasting, which serves as direct input to the demand-planning process and steers the global supply chain, is typically done up to a year in advance. The product portfolio of the company for which we did our research is sensitive to external events. Univariate statistical methods, which are commonly used in practice, cannot be used to anticipate and forecast changes in the market; and forecasts by human experts are known to be biased and inconsistent. The method we propose allows us to automate the identification of key leading indicators, which drive sales, from a massive set of macroeconomic indicators, across different regions and markets; thus, we can generate accurate forecasts. Our method also allows us to handle the additional complexity that results from short-term and long-term dynamics of product sales and external indicators. For the company we study, accuracy improved by 16.1 percent over its current practice. Furthermore, our method makes the market dynamics transparent to company managers, thus allowing them to better understand the events and economic variables that affect the sales of their products.

 Problem and solution:

Feature selection by Lasso in "Table 2. The Most Selected Types of Indicators in the LASSO Model Are Ranked by Percent Selected"