Lead Time Forecasting with Machine Learning Techniques for a Pharmaceutical Supply Chain

Maiza Biazon de Oliveira, Giorgio Zucchi, Giorgio Zucchi, Marco Lippi, Douglas Cordeiro, Núbia Rosa da Silva, Núbia Rosa da Silva, 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.

Paper Citation

in Harvard Style

Biazon de Oliveira M., Zucchi G., Lippi M., Cordeiro D., Rosa da Silva N. and Iori M. (2021). Lead Time Forecasting with Machine Learning Techniques for a Pharmaceutical Supply Chain. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8, pages 634-641. DOI: 10.5220/0010434406340641

in Bibtex Style

@conference{iceis21,
author={Maiza Biazon de Oliveira and Giorgio Zucchi and Marco Lippi and Douglas Cordeiro and Núbia Rosa da Silva and Manuel Iori},
title={Lead Time Forecasting with Machine Learning Techniques for a Pharmaceutical Supply Chain},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={634-641},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010434406340641},
isbn={978-989-758-509-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Lead Time Forecasting with Machine Learning Techniques for a Pharmaceutical Supply Chain
SN - 978-989-758-509-8
AU - Biazon de Oliveira M.
AU - Zucchi G.
AU - Lippi M.
AU - Cordeiro D.
AU - Rosa da Silva N.
AU - Iori M.
PY - 2021
SP - 634
EP - 641
DO - 10.5220/0010434406340641