{"id":1637,"date":"2020-05-25T13:10:53","date_gmt":"2020-05-25T13:10:53","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=1637"},"modified":"2020-05-25T13:10:53","modified_gmt":"2020-05-25T13:10:53","slug":"optimising-forecasting-models-for-inventory-planning","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2020\/05\/25\/optimising-forecasting-models-for-inventory-planning\/","title":{"rendered":"Optimising forecasting models for inventory planning"},"content":{"rendered":"

Nikolaos Kourentzesa, Juan R. Trapero, and Devon K. Barrow, 2020. International Journal of Production Economics. https:\/\/doi.org\/10.1016\/j.ijpe.2019.107597<\/a><\/p>\n

Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter.<\/p>\n

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