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：本文旨在系统识别并比较分析最先进的供应链（SC）预测策略与技术。文章提出了一种整合大数据分析于供应链管理的新框架（包括问题识别、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），并探讨其对人力资源、库存及整体供应链的预测影响。首先，本文讨论了根据供应链策略收集数据的必要性及具体收集方法。随后，文章阐述了根据周期或供应链目标进行不同类型预测的需求。为优化最佳性能模型，本文推荐了供应链关键绩效指标（KPI）与误差测量体系。文中还阐述了虚拟库存对预测的不利影响，以及管理决策对供应链KPI的依赖性——这些KPI用于确定模型性能参数，并改进运营管理、透明度与规划效率。框架内的循环连接基于后处理KPI引入预处理优化，从而优化整体控制过程（库存管理、劳动力规划、成本、生产与产能规划）。本研究的贡献在于提出了标准的供应链流程框架、推荐的预测数据分析方法、预测对供应链绩效的影响、所遵循的机器学习算法优化过程，并为未来研究指明了方向。