Enhancing Operational Efficiency and Human-AI Interaction in Manufacturing through Time-Driven Costing and Predictive Analytics Integration in SAP ERP

In today's evolving manufacturing landscape, where operational efficiency is crucial for maintaining competitiveness, accurate and adaptable cost allocation methods are vital. Time-Driven Activity-Based Costing (TDABC) has proven valuable for dynamic cost allocation. Yet, its integration with real-time data and predictive analytics within ERP systems like SAP remains underexplored, limiting its application in complex, variable processes. This study investigates how a TDABC-based model, incorporating real-time data and predictive analytics, enhances production scheduling, resource allocation, and operational efficiency in manufacturing. Through a rigorous data collection and refinement process, including real-time validation and FMEA, the model improved cost accuracy, achieving prediction rates of 89% in the Hydrate department, respectively, while reducing discrepancies in automated processes. While the model significantly improved cost accuracy, ongoing variability in manual tasks highlights opportunities for further refinement and optimization. Findings support the potential of integrating TDABC with realtime data for data-driven AI solutions, advancing Industry 5.0 objectives for collaborative human-AI environments in manufacturing.