Dynamic Loss Rate Prediction for Production Materials Based on AHP-Gray Markov

Accurately predicting the material loss rate is critical for MRP-driven delivery and inventory control. Inaccurate and delayed data acquisition leads to imprecise master data and poor knowledge of how machines are used in small- and medium-sized manufacturing enterprises (SMEs), but dynamic production environments and data latency are persistent challenges. This paper proposes a two-stage dynamic prediction framework integrating Time Decay Weighted Average (TDWA) and AHP-Gray Markov correction. TDWA establishes a baseline loss rate prioritizing recent process data, while the Analytic Hierarchy Process (AHP) quantifies eleven heterogeneous factors affecting material consumption. A Gray GM1,1 model predicts loss-rate overshoot, refined by Markov chain residual correction. Empirical validation using 42 months of production data from a hardware–sanitary ware manufacturer demonstrates a MAPE of 1.97%, outperforming standalone GM(1,1) by 38% and the three-year average by 66.5%. The method effectively balances delivery assurance and inventory costs, maintaining zero stockouts while limiting excess inventory to 1.7%. Unlike ARIMA, XGBoost, or LSTM, which suffer from overfitting or performance degradation with eight or fewer data periods, the proposed model maintains high accuracy and robustness with extremely small samples. Offering a low-threshold “small-sample master data governance” paradigm, this approach may be applied to aerospace spare parts support, cold chain logistics, and customized manufacturing, providing a viable pathway for digital transformation in resource-constrained environments.