Forecast accuracy as a driver of excess inventory reduction in ERP-centric supply networks

The other long-term structural inefficiency of the ERP-based supply networks despite the massive implementation of the advanced planning systems and analytics is the excessive inventory. Even though the accuracy of forecast has been a statistical aim, its practical importance has been anchored on how forecast indicators are transformed into replenishment decisions, safety stock computation and multi-echelon coordination models. The recent research suggests that the predictive improvement would not be commensurate savings in inventory especially when imbedded within mutually coherent systems of governance, the regular incentive plans and data structure. This review synthesizes theoretical and empirical evidence on how forecast accuracy influences excess inventory performance under the ERP based planning rationale. The discussion is related to the opinions of inventory control theory, organizational forecasting studies, digital transformation, and AI-based planning systems. These mediating processes are decision-translation fidelity, safety stock parameterization and information sharing across the nodes as one of the key determinants whether the improvement of the forecasts will result in reduced working capital and obsolescence risk. The last part of the review is the description of the future research agenda on the opportunities of AI integration and digital twins, sustainability indicators, and resilience modelling in the multi-echelon ERP contexts.