Authors:
Maiza Biazon de Oliveira
1
;
Giorgio Zucchi
2
;
3
;
Marco Lippi
4
;
Douglas Farias Cordeiro
5
;
Núbia Rosa da Silva
6
;
1
and
Manuel Iori
4
Affiliations:
1
Special Academic Engineering Unit, Department of Production Engineering, Federal University of Goiás, St. Dr. Lamartine Pinto de Avelar, 1120, 75704020, Catalão, Goiás, Brazil
;
2
Fondazione Marco Biagi, University of Modena and Reggio Emilia, Largo Marco Biagi 10, 41121 Modena, Italy
;
3
R&D Department, Coopservice S.Coop.p.A, Via Rochdale 5, 42122 Reggio Emilia, Italy
;
4
Dipartimento di Scienze e Metodi dell’Ingegneria, University of Modena and Reggio Emilia, Via Amendola 2, Pad. Morselli, 42122 Reggio Emilia, Italy
;
5
Faculty of Information and Communication, Federal University of Goiás, Campus Samambaia, 74690900, Goiânia, Goiás, Brazil
;
6
Institute of Biotechnology, Federal University of Goiás, St. Dr. Lamartine Pinto de Avelar, 1120, 75704020, Catalão, Goiás, Brazil
Keyword(s):
Lead Time Forecasting, Machine Learning, Pharmaceutical Supply Chain.
Abstract:
Purchasing lead time is the time elapsed between the moment in which an order for a good is sent to a supplier and the moment in which the order is delivered to the company that requested it. Forecasting of purchasing lead time is an essential task in the planning, management and control of industrial processes. It is of particular importance in the context of pharmaceutical supply chain, where avoiding long waiting times is essential to provide efficient healthcare services. The forecasting of lead times is, however, a very difficult task, due to the complexity of the production processes and the significant heterogeneity in the data. In this paper, we use machine learning regression algorithms to forecast purchasing lead times in a pharmaceutical supply chain, using a real-world industrial database. We compare five algorithms, namely k-nearest neighbors, support vector machines, random forests, linear regression and multilayer perceptrons. The support vector machines approach obtai
ned the best performance overall, with an average error lower than two days. The dataset used in our experiments is made publicly available for future research.
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