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Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand

Abstract : Supply chains are complex, stochastic systems. Nowadays, logistics managers face two main problems: increasingly diverse and variable customer demand that is difficult to predict. Classical forecasting methods implemented in many business units have limitations with the fluctuating demand and the complexity of fully connected supply chains. Machine Learning methods have been proposed to improve prediction. In this paper, a Long Short-Term Memory (LSTM) is proposed for demand forecasting in a physical internet supply chain network. A hybrid genetic algorithm and scatter search are proposed to automate tuning of the LSTM hyperparameters. To assess the performance of the proposed method, a real-case study on agricultural products in a supply chain in Thailand was considered. Accuracy and coefficient of determination were the key performance indicators used to compare the performance of the proposed method with other supervised learnings: ARIMAX, Support Vector Regression, and Multiple Linear Regression. The results prove the better forecasting efficiency of the LSTM method with continuous fluctuating demand, whereas the others offer greater performance with less varied demand. The performance of hybrid metaheuristics is higher than with trial-and-error. Finally, the results of forecasting model are effective in transportation and holding costs in the distribution process of the Physical Internet.
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https://hal-uphf.archives-ouvertes.fr/hal-03400323
Contributor : Mylène Delrue Connect in order to contact the contributor
Submitted on : Monday, October 25, 2021 - 8:06:20 AM
Last modification on : Thursday, November 11, 2021 - 3:07:31 AM

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Anirut Kantasa-Ard, Maroua Nouiri, Abdelghani Bekrar, Abdessamad Ait El Cadi, Yves Sallez. Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand. International Journal of Production Research, Taylor & Francis, 2020, pp.1-25. ⟨10.1080/00207543.2020.1844332⟩. ⟨hal-03400323⟩

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