An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance

Suriyan Jomthanachai, Wai Peng Wong () and Khai Wah Khaw
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Suriyan Jomthanachai: Prince of Songkla University (PSU)
Wai Peng Wong: Monash University
Khai Wah Khaw: Universiti Sains Malaysia

Computational Economics, 2024, vol. 63, issue 2, No 11, 792 pages

Abstract: Abstract In this work, a machine learning application was constructed to predict the logistics performance index based on economic attributes. The prediction procedure employs both linear and non-linear machine learning algorithms. The macroeconomic panel dataset is used in this investigation. Furthermore, it was combined with the microeconomic panel dataset obtained through the data envelopment analysis method for evaluating financial efficiency. The procedure was implemented in six ASEAN member countries. The non-linear algorithm of an artificial neural network performed best on the complex pattern of a collective instance of these six countries, followed by the penalized linear of the Ridge regression method. Due to the limited amount of training data for each country, the artificial neural network prediction procedure is only applicable to the datasets of Singapore, Malaysia, and the Philippines. Ridge regression fits the Indonesia, Thailand and Vietnam datasets. The results provide precise trend forecasting. Macroeconomic factors are driving up the logistics performance index in Vietnam in 2020. Malaysia logistics performance is influenced by the logistics business's financial efficiency. The results at the country level can be used to track, improve, and reform the country's short-term logistics and supply chain policies. This can bring significant gains in national logistics and supply chain capabilities, as well as support for global trade collaboration, all for the long-term development of the region.

Keywords: Machine learning; Artificial neural network; Linear regression; Prediction; Data envelopment analysis; Logistics performance index (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10358-7

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