A Hybrid DDMRP-OUTL Inventory Policy with Defect Prediction for Resilient Supply Chains
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Keywords
Demand-Driven Material Requirements Planning (DDMRP), Order-Up-To-Level (OUTL), defect prediction, Hybrid inventory model, inventory strategy, random forest regressor
Abstract
The high variability of consumer demand makes the development of inventory strategies crucial, especially regarding operational inventory resilience. Combining defect prediction with inventory strategies is crucial amidst uncertainty related to quality. Conventional Demand-Driven Material Requirements Planning (DDMRP) strategies are sensitive to shifts in consumer demand based on buffers for replenishment. However, this strategy has the disadvantage of not considering losses due to defective production output quality. This study develops a hybrid inventory model by combining DDMRP with Order-Up-To-Level (OUTL) replenishment management, and defect rate prediction. Production output is assessed from estimated defect rates converted into yield factors. OUTL is used for conditional quantity setting by determining the amount of excess replenishment. Defect rate prediction uses a manufacturing defect dataset along with production volume, supplier quality, maintenance hours, time percentage, and worker productivity. The initial predictive element in inventory simulation using a random forest regressor configuration achieved an R² value of 0.7208. Numerical experiments to evaluate the inventory model used 24 scenarios over a 200-day daily review period. Scenarios were conducted by integrating various demand patterns, production process conditions, and production capacity limitations. The DDMRP-OUTL hybrid strategy model can reduce the Bullwhip Effect Ratio (Ratio of Echelon Logistics - REL) compared to conventional DDMRP for various scenarios, and the most significant reduction is close to 24% under intermittent demand. Furthermore, it demonstrates a higher average inventory increase as a trade-off between replenishment stability and inventory load. Stockout events are not consistently reduced across all scenarios, although the integration of defect rate prediction and the DDMRP-OUTL hybrid model leads to replenishment stability, and inventory load and service reliability must be balanced when implementing this policy.
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References
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Funding data
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Universitas Telkom
Grant numbers 331/LIT06/PPM-LIT/2026