Evaluating demand forecasting models using multi-criteria decision-making approach

Badulescu, Yvonne (Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale ; University of Lausanne, Switzerland) ; Hameri, Ari-Pekka (University of Lausanne, Switzerland) ; Cheikhrouhou, Naoufel (Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale)

Evaluating appropriate error measures to determine demand forecast accuracy is essential in model selection, however there is no approach that simultaneously evaluates different model classes and several inter-dependent error measures. Furthermore, error measures may yield conflicting results making it more difficult to select the ‘best’ forecasting model when considering several error measures simultaneously. This paper proposes a novel process of evaluation of demand forecasting models using the analytical network process combined with the technique for order of preference by similarity to ideal solution (ANP-TOPSIS) which incorporates interdependence amongst error measures. The methodology is validated through an implementation case of a plastic bag manufacturer demonstrating that the use of the ANP-TOPSIS approach, avoided the selection of an inappropriate forecasting model due to conflicting error measurements. Moreover, a sensitivity analysis finds that the interdependence between the error measures is found to impact the relative closeness to the ideal solution, even though it plays a minimal role in the final ranking of the forecasting models.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
HEG - Genève
Institute:
CRAG - Centre de Recherche Appliquée en Gestion
Subject(s):
Economie/gestion
Date:
2021-02
Pagination:
40 p.
Published in:
Journal of advances in management research
Numeration (vol. no.):
To be published
DOI:
ISSN:
0972-7981
Appears in Collection:



 Record created 2021-02-18, last modified 2021-03-09

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