{"id":1616,"date":"2019-07-09T20:29:19","date_gmt":"2019-07-09T20:29:19","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=1616"},"modified":"2019-07-10T14:56:13","modified_gmt":"2019-07-10T14:56:13","slug":"visit-at-universitat-politecnica-de-valencia","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2019\/07\/09\/visit-at-universitat-politecnica-de-valencia\/","title":{"rendered":"Visit at Universitat Polit\u00e8cnica de Val\u00e8ncia"},"content":{"rendered":"

I was recently invited to a workshop focused on forecasting and supply chain management at Valencia Polytechnic University. Many thanks to Ester Guijarro<\/a> for organising the workshop and helping to bring together forecasters and supply chain experts!<\/p>\n

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Left to right: Ester, Juan and me after our talks. You can guess how the weather is in Valencia from our colours!<\/p><\/div>\n

I presented on optimising forecasting model parameters for inventory management. You can find the presentation here<\/a>, and a working version of the paper here<\/a>. The paper is currently under review, so I would expect quite a few changes in the final version! Whether we are critical of the review process or not, in the vast majority of cases the it improves papers substantially and this will certainly be the case here. The view we take on this work with my co-authors is that we can integrate forecasting and inventory management more closely, and instead of optimising forecasting models to maximise fit on past sales, hoping that this will result in good inventory performance (and there are many good reasons for this to hold!), we can directly optimise so as to minimise deviations from the desired inventory performance. This seems to work quite well in our empirical evaluation.<\/p>\n

Juan R Trapero<\/a> presented a paper we have worked together with Manuel Cardos on calculating empirical safety stocks. You can find the presentation here<\/a>. It looks at using kernel density estimation and GARCH models to address different deficiencies of standard approaches. Namely, kernels are particularly good at handling asymmetries in the forecast error distributions (promotional forecasts I am looking at you) and GARCH for handling residual autocorrelations. Both cases are quite common in practice, as often our forecasting models are far from the underlying demand generating process. You can find the relevant published paper here<\/a>, as well as a follow up work here<\/a>.<\/p>\n

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