An AI-Enabled Predictive Procurement Model for Forecasting and Inventory Optimization
Abstract
This paper presents an AI-enabled predictive procurement model that integrates probabilistic demand forecasting, supplier lead-time prediction, and multi-echelon inventory optimization to secure target service levels while lowering working capital. The framework couples hierarchical time-series forecasting with gradient-boosted trees and lightweight sequence models, capturing seasonality, promotions, and regime shifts. It augments predictions with causal drivers including macroeconomic indicators, input price indices, marketing calendars, and policy shocks, producing calibrated distributions rather than point estimates. Operationally, the system ingests ERP and MRP transactions, purchase orders, invoices, catalog attributes, and supplier performance histories. Data quality pipelines execute de-duplication, unit harmonization, outlier handling, and late-arrival correction, while item-location-supplier embeddings transfer information across sparse series. Lead-time models use gradient boosting with monotonic constraints on distance and order size, and a compact LSTM captures intra-week receipt patterns. Forecast reconciliation ensures coherence from SKU to category to enterprise, and conformal prediction yields reliable uncertainty bounds. Optimization formulates a newsvendor-style objective that balances service penalties, holding, and ordering costs under budget and capacity constraints. A stochastic program solved on a rolling horizon outputs order quantities, reorder points, supplier splits, and dynamic safety-stock buffers aligned to service targets. A policy engine encodes guardrails shelf- life limits, ABC/XYZ policies, MOQ/pack constraints, and ESG exclusions and writes approved plans back to ERP via governed APIs and event streams with full traceability. In simulation and a limited field pilot, the approach reduced backorders and emergency expedites, lowered average on-hand inventory, and improved forecast bias and MAPE
Keywords
Predictive Procurement
Demand Forecasting
Inventory Optimization
Probabilistic
References
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