Data Driven Prediction and Control of Bullwhip Effects in Multi-Echelon Supply Chains Using Integrated Machine Learning and Operational Transaction Records

Abstract

The bullwhip effect remains one of the most persistent and costly inefficiencies in multi-echelon supply chains, driven mainly by demand uncertainty, delayed information flows, and limited forecasting accuracy. Recent advances in data availability and machine learning techniques have created new opportunities to address this phenomenon through data-driven decision-making. This study proposes an integrated predictive and control framework for mitigating the bullwhip effect in multi-echelon supply chains using machine learning models trained on operational transaction records. The research leverages high-dimensional transactional data, including historical order quantities, inventory levels, shipment records, and sales information, to develop demand prediction models capable of capturing nonlinear patterns and temporal dependencies. Multiple machine learning approaches are incorporated into the proposed framework to enhance forecasting accuracy across different echelons of the supply chain. These predictive outputs are then embedded into a coordinated control mechanism that adjusts replenishment policies dynamically, reducing demand amplification across upstream stages. Unlike traditional analytical or simulation-based studies, this research emphasizes empirical analysis grounded in real operational datasets drawn from manufacturing and distribution systems. The proposed framework enables systematic comparison between conventional forecasting-based replenishment policies and machine learning driven strategies in terms of demand variance propagation, inventory stability, and order synchronization. By integrating predictive analytics directly with operational control decisions, the study provides a structured pathway for translating data-driven insights into tangible supply chain performance improvements. The findings demonstrate that machine learning based demand prediction significantly reduces demand distortion across multiple echelons, leading to measurable attenuation of the bullwhip effect. Furthermore, the results highlight the critical role of data granularity and model selection in achieving stable and robust performance improvements. From a managerial perspective, the study offers practical guidance for supply chain managers seeking to exploit operational data and advanced analytics to enhance coordination, resilience, and efficiency in complex supply networks. The proposed approach contributes to the growing literature on data-driven supply chain management by presenting an empirically validated framework that bridges predictive modeling and operational control.