Solving Inventory Challenges in Industrial MRO Procurement

Industrial MRO procurement faces persistent inventory challenges that increase costs and risk downtime.

This article outlines practical, AI-driven strategies to improve spare parts availability and reduce excess stock.

Root Causes & Inventory Challenges

Demand variability and long lead times make forecasting difficult.

Large SKU counts, sporadic usage, and obsolete parts add complexity.

Poor data quality and siloed systems undermine decision making.

Excess safety stock ties up capital, while stockouts halt production.

AI Strategies to Optimize MRO Procurement

Use predictive analytics to forecast demand for low-frequency parts.

Apply classification models to identify critical SKUs and prioritize stocking.

Implement replenishment policies that combine dynamic safety stock and min-max rules.

Integrate condition-based signals from IIoT to trigger just-in-time orders.

Leverage supplier performance analytics to shorten lead times and enable consignment or vendor-managed inventory.

• Automate reorder workflows to reduce manual errors.
• Standardize part catalogs and use master data management.
• Adopt dashboards with KPIs: service level, carrying cost, fill rate, and obsolescence.

Start with a pilot focused on high-value or high-risk categories.

Measure ROI by tracking reductions in stockouts and carrying costs.

Scale improvements across sites once models prove accurate and workflows are adopted.

Call to action: Evaluate your MRO inventory with AI tools today to cut costs and improve uptime.

Contact your procurement or reliability team to design a pilot project.