{"id":1260,"date":"2017-02-02T01:03:25","date_gmt":"2017-02-02T01:03:25","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=1260"},"modified":"2017-10-28T14:28:47","modified_gmt":"2017-10-28T14:28:47","slug":"temporal-big-data-for-tire-industry-tactical-sales-forecasting","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2017\/02\/02\/temporal-big-data-for-tire-industry-tactical-sales-forecasting\/","title":{"rendered":"Temporal Big Data for Tire Industry Tactical Sales Forecasting"},"content":{"rendered":"

Y.R. Sagaert, E-H.\u00a0 Aghezzaf, N. Kourentzes and B. Desmet, 2017, Interfaces. https:\/\/doi.org\/10.1287\/inte.2017.0901<\/a><\/p>\n

We propose a forecasting method to improve accuracy for tactical sales predictions at a major supplier to the tire industry. This level of forecasting serves as direct input for the demand planning, steering the global supply chain and is typically up to a year ahead. The case company has a product portfolio that is strongly sensitive to external events. Univariate statistical methods, which are common in practice, are unable to anticipate and forecast changes in the market, while human expert forecasts are known to be biased and inconsistent. The proposed method is able to automatically identify key leading indicators that drive sales from a massive set of macro-economic indicators, across different regions and markets and produce accurate forecasts. Our method is able to handle the additional complexity of the short and long term dynamics from the product sales and the external indicators. We find that accuracy is improved by 16.1% over current practice with proportional benefits for the supply chain. Furthermore, our method provides transparency to the market dynamics, allowing managers to better understand the events and economic variables that affect the sales of their products.<\/p>\n

Download paper<\/a>.<\/p>\n