供应链管理中的智能预测模型:越南咖啡案例研究

IF 3.4 3区 经济学 Q1 ECONOMICS
Thi Thuy Hanh Nguyen, Abdelghani Bekrar, Thi Muoi Le, Mourad Abed, Anirut Kantasa‐ard
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引用次数: 0

摘要

预测是供应链管理的重要组成部分。准确的预测对供应链绩效有很大影响。不同领域和行业已经开发并采用了许多预测方法。然而,由于数据的特点和方法的优势,没有一种方法在所有情况下都是完美的。因此,我们提出了一种新的 ARIMAX-LSTM 混合预测模型,将 ARIMAX 和 LSTM 模型整合在一起,以提高捕捉时间序列中线性和非线性模式不同组合的能力。我们提出的模型在越南咖啡需求的案例研究中得到了验证。案例研究结果表明,在性能指标和关联度方面,我们提出的模型优于著名的单一模型和当前的混合模型。此外,为了证明该模型的稳健性,我们对泰国的农产品(菠萝、玉米和木薯)进行了测试和比较。计算结果表明,我们的混合模型在大多数实验中都更胜一筹。它具有预测复杂时间序列数据的强大能力。此外,我们提出的方法还提高了预测准确性,增强了供应链性能(以牛鞭效应、净库存放大和运输成本衡量)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward a smart forecasting model in supply chain management: A case study of coffee in Vietnam
Forecasting is a crucial part of supply chain management. Accurate forecasts have a strong influence on supply chain performance. Many forecasting methods have been developed and adapted in various domains and industries. However, none are perfect in all contexts due to the data's characteristics and the methods' strength. Hence, we propose a new ARIMAX‐LSTM hybrid forecasting model that integrates ARIMAX and LSTM models to improve the ability to capture different combinations of linear and nonlinear patterns in time series. Our proposed model is validated in a case study of coffee demand in Vietnam. The case study results show that our proposed model outperforms the well‐known single and current hybrid models regarding performance measures and degree of association. Moreover, to prove the model's robustness, we test and compare our proposed model to the previous study for Thailand's agricultural products (pineapple, corn, and cassava). Computational results demonstrate that our hybrid model is superior in the majority of experiments. It has a strong capability of predicting complex time series data. Furthermore, our proposed method increases forecasting accuracy and enhances supply chain performance (measured by the bullwhip effect; net‐stock amplification, and transportation cost.
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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