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 obtained 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.

Lead time forecasting with machine learning techniques for a pharmaceutical supply chain / Biazon de Oliveira, Maiza; Zucchi, Giorgio; Lippi, Marco; Farias Cordeiro, Douglas; Rosa da Silva, Nubia; Iori, Manuel. - (2021), pp. 634-641. (Intervento presentato al convegno International Conference on Enterprise Information Systems (ICEIS) tenutosi a Online streaming nel 26-28 April, 2021) [10.5220/0010434406340641].

Lead time forecasting with machine learning techniques for a pharmaceutical supply chain

Giorgio Zucchi;Marco lippi;Manuel Iori
2021

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 obtained 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.
2021
International Conference on Enterprise Information Systems (ICEIS)
Online streaming
26-28 April, 2021
634
641
Biazon de Oliveira, Maiza; Zucchi, Giorgio; Lippi, Marco; Farias Cordeiro, Douglas; Rosa da Silva, Nubia; Iori, Manuel
Lead time forecasting with machine learning techniques for a pharmaceutical supply chain / Biazon de Oliveira, Maiza; Zucchi, Giorgio; Lippi, Marco; Farias Cordeiro, Douglas; Rosa da Silva, Nubia; Iori, Manuel. - (2021), pp. 634-641. (Intervento presentato al convegno International Conference on Enterprise Information Systems (ICEIS) tenutosi a Online streaming nel 26-28 April, 2021) [10.5220/0010434406340641].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1244256
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