Automated MLOps Pipeline Implementation for Intelligent Procurement Replenishment: A Predictive Analytics Approach

Abstract

This article presents an advanced implementation of automated model retraining pipelines for procurement replenishment recommendations, leveraging Machine Learning Operations (MLOps) to enhance supply chain efficiency and decision-making accuracy. The integration of artificial intelligence and machine learning into procurement operations represents a paradigm shift from traditional rule-based systems to intelligent, adaptive frameworks capable of responding to market dynamics in real-time. Through multi-source demand forecasting, dynamic feature engineering, and multi-model ensemble approaches, the system achieves superior prediction accuracy compared to conventional methods. Real-time inventory monitoring, comprehensive supplier evaluation, and lead time variability tracking provide the foundation for data-driven procurement decisions. The containerized MLOps infrastructure ensures scalable, reliable deployment while automated data quality validation and model retraining maintain system accuracy over time. Smart reorder point calculations, dynamic safety stock optimization, and cost-benefit analysis automation deliver operational intelligence that optimizes inventory levels and procurement costs. The risk-adjusted recommendation engine incorporates vendor performance metrics and external risk factors to generate resilient procurement strategies, with real-time dashboards translating complex analytics into actionable recommendations for procurement teams.