This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research.

翻译：本文旨在系统性地识别并比较分析最先进的供应链预测策略与技术。我们提出了一种将大数据分析融入供应链管理的新框架（涵盖问题识别、数据源、探索性数据分析、机器学习模型训练、超参数调优、性能评估与优化），并探讨了预测对人力资源、库存及整个供应链的影响。首先，讨论了根据供应链策略收集数据的必要性及收集方法。随后，针对不同时间段或供应链目标所需的预测类型进行了分析。研究推荐了供应链关键绩效指标与误差测量系统，以优化性能最优的模型。本文阐明了幽灵库存对预测的不利影响，以及管理决策如何依赖供应链关键绩效指标来确定模型性能参数，进而改进运营管理、透明度与规划效率。框架内部的循环连接基于后处理关键绩效指标引入预处理优化，从而优化整体控制流程（库存管理、人员配置、成本、生产与产能规划）。本研究的贡献在于提出了标准化供应链流程框架，推荐了预测数据分析方法，揭示了预测对供应链绩效的影响，实现了机器学习算法优化，并为未来研究指明了方向。