Jomthanachai Suriyan, Wong Wai Peng, Khaw Khai Wah
Faculty of Management Sciences, Prince of Songkla University (PSU), Songkhla, 90112 Thailand.
School of Information Technology, Monash University, Malaysia Campus, Selangor, Malaysia.
Comput Econ. 2023 Feb 1:1-52. doi: 10.1007/s10614-023-10358-7.
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.
在这项工作中，构建了一个机器学习应用程序，用于基于经济属性预测物流绩效指数。预测过程采用了线性和非线性机器学习算法。本研究使用了宏观经济面板数据集。此外，它还与通过数据包络分析方法获得的微观经济面板数据集相结合，以评估金融效率。该程序在六个东盟成员国中实施。人工神经网络的非线性算法在这六个国家的集体实例的复杂模式上表现最佳，其次是岭回归方法的惩罚线性算法。由于每个国家的训练数据量有限，人工神经网络预测程序仅适用于新加坡、马来西亚和菲律宾的数据集。岭回归适用于印度尼西亚、泰国和越南的数据集。结果提供了精确的趋势预测。宏观经济因素正在推动2020年越南的物流绩效指数上升。马来西亚的物流绩效受物流企业金融效率的影响。国家层面的结果可用于跟踪、改进和改革该国的短期物流和供应链政策。这可以为国家物流和供应链能力带来显著提升，也有助于支持全球贸易合作，促进该地区的长期发展。
