🤖 AI Summary
To address fragmented forecasting strategies, weak data-driven capabilities, and the absence of decision闭环 in supply chain forecasting, this paper proposes a KPI-driven big-data prediction management framework. Methodologically, it integrates multi-source heterogeneous data collection, phantom inventory impact modeling, and hierarchical periodic forecasting strategies, coupled with XGBoost/LSTM ensembles, Bayesian hyperparameter optimization, and dynamic preprocessing. This enables a closed-loop workflow spanning problem identification, modeling, and feedback. The key contribution is a novel KPI-oriented paradigm that tightly couples preprocessing, prediction, and decision-making—marking the first integration of inventory, workforce, and capacity KPIs into both feature engineering and feedback-driven model refinement. Empirical results demonstrate an 18.3% average reduction in MAPE for mid-to-long-term demand forecasting, a 22% improvement in inventory turnover ratio, and a 35% reduction in planning response cycle time, significantly enhancing forecast transparency and operational decision agility.
📝 Abstract
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.
