A Proposed Supply Chain Analytics Framework for Improving Forecasting Accuracy and Reducing Bottlenecks

Accurate demand forecasting and the mitigation of supply chain bottlenecks remain persistent challenges for large-scale enterprises navigating volatile global markets. This study proposes a comprehensive supply chain analytics framework designed to improve forecasting accuracy and streamline operational efficiency through advanced data-driven methodologies. The framework integrates predictive, prescriptive, and real-time analytics to facilitate proactive decision-making across procurement, production, logistics, and distribution processes. By leveraging big data, artificial intelligence (AI), and machine learning (ML) algorithms, the model identifies demand patterns, predicts disruptions, and optimizes end-to-end supply chain performance. The proposed framework comprises four interconnected layers: the Data Acquisition Layer, which consolidates heterogeneous data sources from enterprise resource planning (ERP), warehouse management systems (WMS), and external market indicators; the Analytical Processing Layer, where AI and ML models perform demand forecasting, risk assessment, and inventory optimization; the Decision Support Layer, which provides visualization dashboards and scenario simulations for managers; and the Optimization Layer, which applies prescriptive analytics to propose corrective actions for potential bottlenecks. Through these layers, the framework ensures transparency, traceability, and adaptability within complex supply networks. A key innovation of the model is its hybrid analytical engine, which combines statistical forecasting (e.g., ARIMA, exponential smoothing) with deep learning approaches (e.g., LSTM and reinforcement learning) to improve prediction reliability under uncertain conditions. Additionally, dynamic bottleneck analysis is embedded to detect flow constraints in real time, allowing decision-makers to prioritize resource allocation, adjust lead times, and maintain service levels efficiently. The framework’s implementation potential is illustrated through simulated scenarios showing measurable improvements in forecast accuracy, order fulfillment rate, and supply chain resilience. By promoting integrated analytics-driven decision-making, the proposed framework supports enterprises in achieving sustainable competitive advantage, reducing costs, and enhancing responsiveness to market fluctuations. This research contributes to the ongoing discourse on intelligent supply chain management by offering a scalable, technology-driven solution for forecasting and operational optimization. Future work will focus on empirical validation through industrial case studies and integrating carbon footprint analytics for sustainable supply chain performance.