{"id":1514,"date":"2018-06-26T14:31:48","date_gmt":"2018-06-26T14:31:48","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=1514"},"modified":"2018-06-26T14:31:48","modified_gmt":"2018-06-26T14:31:48","slug":"incorporating-macroeconomic-leading-indicators-in-tactical-capacity-planning","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2018\/06\/26\/incorporating-macroeconomic-leading-indicators-in-tactical-capacity-planning\/","title":{"rendered":"Incorporating macroeconomic leading indicators in tactical capacity planning"},"content":{"rendered":"<p>Yves R. Sagaert, Nikolaos Kourentzes, Stijn Du Vuyst, El-Houssaine Aghezzaf and Bram Desmet, 2018, International Journal of Production Economics.<\/p>\n<p>Tactical capacity planning relies on future estimates of demand for the mid- to long-term. On these forecast horizons there is increased uncertainty that the analysts face. To this purpose, we incorporate macroeconomic variables into microeconomic demand forecasting. Forecast accuracy metrics, which are typically used to assess improvements in predictions, are proxies of the real decision associated costs. However, measuring the direct impact on decisions is preferable. In this paper, we examine the capacity planning decision at plant level of a manufacturer. Through an inventory simulation setup, we evaluate the gains of incorporating external macroeconomic information in the forecasts, directly, in terms of achieving target service levels and inventory performance. Furthermore, we provide an approach to indicate capacity alerts, which can serve as input for global capacity pooling decisions. Our work has two main contributions. First, we demonstrate the added value of leading indicator information in forecasting models, when evaluated directly on capacity planning. Second, we provide additional evidence that traditional metrics of forecast accuracy exhibit weak connection with the real decision costs, in particular for capacity planning. We propose a more realistic assessment of the forecast quality by evaluating both the first and second moment of the forecast distribution. We discuss implications for practice, in particular given the typical over-reliance on forecast accuracy metrics for choosing the appropriate forecasting model.<\/p>\n<p>Download <a href=\"http:\/\/kourentzes.com\/forecasting\/wp-content\/uploads\/2018\/06\/ijpev2-incorporating-macroeconomic.pdf\">paper<\/a>.<\/p>\n<div class=\"SPOSTARBUST-Related-Posts\"><H3>Related Posts<\/H3><ul class=\"entry-meta\"><li class=\"SPOSTARBUST-Related-Post\"><a title=\"Forecasting Forum Scandinavia &#8211; first workshop!\" href=\"https:\/\/kourentzes.com\/forecasting\/2020\/09\/20\/forecasting-forum-scandinavia-first-workshop\/\" rel=\"bookmark\">Forecasting Forum Scandinavia &#8211; first workshop!<\/a><\/li>\n<li class=\"SPOSTARBUST-Related-Post\"><a title=\"Elucidate structure in intermittent demand time series\" href=\"https:\/\/kourentzes.com\/forecasting\/2020\/05\/25\/elucidate-structure-in-intermittent-demand-time-series\/\" rel=\"bookmark\">Elucidate structure in intermittent demand time series<\/a><\/li>\n<li class=\"SPOSTARBUST-Related-Post\"><a title=\"Optimising forecasting models for inventory planning\" href=\"https:\/\/kourentzes.com\/forecasting\/2020\/05\/25\/optimising-forecasting-models-for-inventory-planning\/\" rel=\"bookmark\">Optimising forecasting models for inventory planning<\/a><\/li>\n<\/ul><\/div><!-- AddThis Advanced Settings generic via filter on the_content --><!-- AddThis Share Buttons generic via filter on the_content -->","protected":false},"excerpt":{"rendered":"<p>Yves R. Sagaert, Nikolaos Kourentzes, Stijn Du Vuyst, El-Houssaine Aghezzaf and Bram Desmet, 2018, International Journal of Production Economics.<!-- AddThis Advanced Settings generic via filter on get_the_excerpt --><!-- AddThis Share Buttons generic via filter on get_the_excerpt --><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[5],"tags":[27,72,87,86],"_links":{"self":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/1514"}],"collection":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/comments?post=1514"}],"version-history":[{"count":1,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/1514\/revisions"}],"predecessor-version":[{"id":1516,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/1514\/revisions\/1516"}],"wp:attachment":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/media?parent=1514"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/categories?post=1514"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/tags?post=1514"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<!-- WP Super Cache is installed but broken. 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