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Incorporating macroeconomic leading indicators in tactical capacity planning
Department of Industrial Management, Ghent University, Belgium / Department of Management Science, Lancaster University Management School, Lancaster, United Kingdom.
Department of Management Science, Lancaster University Management School, Lancaster, United Kingdom.ORCID iD: 0000-0003-0211-5218
Department of Industrial Management, Ghent University, Belgium.
Department of Industrial Management, Ghent University, Belgium.
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2019 (English)In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 209, p. 12-19Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 209, p. 12-19
Keywords [en]
Capacity planning, Forecasting, Inventory, Leading indicators, Industrial economics, Industrial engineering, Forecast distribution, Forecasting modeling, Inventory performance, Macroeconomic variables, Tactical capacity planning
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:his:diva-18235DOI: 10.1016/j.ijpe.2018.06.016ISI: 000464087900003Scopus ID: 2-s2.0-85049326644OAI: oai:DiVA.org:his-18235DiVA, id: diva2:1399241
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved

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Kourentzes, Nikolaos

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