From Reactive to Proactive Supply Chain Planning: The Role of Intelligent Automation

Abstract

Supply chains are increasingly exposed to volatility, uncertainty, complexity, and ambiguity, rendering traditional reactive planning approaches inadequate for sustaining operational performance and resilience. While intelligent automation has been widely promoted as a transformative force in supply chain management, empirical evidence explaining how it enables a shift from reactive to proactive planning remains fragmented. This study addresses this gap by developing and testing a simulation-based primary research model that examines the performance implications of intelligent automation enabled proactive supply chain planning. Drawing on dynamic capabilities theory and resilience perspectives, the study constructs a scenario-based simulation framework comparing reactive planning systems with proactive planning architectures supported by predictive analytics, machine learning driven forecasting, and automated decision execution. Primary data are generated through repeated simulation runs under varying demand volatility and disruption scenarios. The results demonstrate that proactive planning systems consistently outperform reactive counterparts in terms of forecasting accuracy, lead-time stability, and disruption recovery speed. The findings further indicate that intelligent automation enhances sensing, seizing, and reconfiguring capabilities, enabling anticipatory decision-making rather than ex post corrective actions. By providing simulation-based evidence, this study contributes to supply chain theory by clarifying the mechanisms through which intelligent automation drives proactive planning and resilience. From a managerial perspective, the results offer actionable insights into sequencing automation investments and aligning digital capabilities with planning processes. The study advances the literature by positioning intelligent automation not merely as an efficiency tool, but as a strategic enabler of proactive and adaptive supply chain planning.