Digital Twin-Driven Production Planning in SAP S/4HANA: A Case for Predictive and Adaptive Supply Chains

Abstract

Increased supply chain variability has brought into sharp relief the need for predictive and adaptive planning mechanisms that go beyond conventional ERP-based decision-making. In this article, we present a new framework that integrates Digital Twin (DT) technology with SAP S/4HANA with the aim of raising real-time visibility, forecasting accuracy, and responsiveness in manufacturing environments. Although SAP S/4HANA provides an ideal foundation for enterprise resource planning and production planning, fixed-planning modules are prone to fall short when confronted with turbulent shop-floor disturbances or unexpected changes in demand. Incorporating the real-time digital representation of the production assets and processes within the SAP architecture enables the new DT-enabled architecture to support the formation of continuous closed loops between the physical and digital worlds. A case scenario demonstrates how the integration of DT with SAP enables proactive rescheduling, predictive maintenance acts, and adaptive material requirements planning (MRP), with lead times reduced and supply chain robustness enhanced. Simulation results uncover transformative improvements in forecasting accuracy (up to 18%) and downtimes slashed by 22% relative to legacy planning approaches. An implementation roadmap and conceptual architecture are provided for organizations that desire intelligent self-correcting supply chain networks. The research identifies the strategic potential for digital twins for enforcing Industry 4.0 thinking within enterprise-level platforms such as SAP S/4HANA.