IBM: Why Manufacturers Need AI for MRO Inventory Management

Kendra DeKeyrel is vice president in IBM's automation and sustainability software division, specialising in solutions that drive efficiency, reduce environmental impact and create value for organisations around the globe.

Below she explains why manufacturers today need AI for MRO Inventory management.

In manufacturing, efforts to avoid asset failures and inventory issues are essentially efforts to predict the future: at what moment will that assembly robot slow down? When will that conveyor belt require a new motor?

While no manufacturer has a crystal ball, emerging technology is providing a viable alternative. When it comes to asset management, AI helps manufacturers shift from reactive and proactive maintenance to predictive maintenance. By analysing reams of asset data with AI models, manufacturers can unlock coveted, actionable insights about that assembly robot or conveyor belt. Run-to-failure and calendar-based maintenance now look quaint.

But predictive asset management is just one half of the equation. If a technician learns a motor is performing poorly, but doesn’t have a spare part to swap in, what good does that insight do her? There will still be costly downtime. Indeed, a surprising number of manufacturers still use pen, paper or manual spreadsheets to track spare parts. It's no surprise that a leading cause of downtime is an empty repair inventory.

The solution? Manufacturers need to thread predictive AI into their maintenance, repair, and operations (MRO) inventory management, as well. Predictive inventory management begins with data. With IoT tracking technology, manufacturers can gain unprecedented visibility into their inventory levels. Real-time and customisable dashboards can display how many motors they have at any given moment, and at which plants.

This data can be integrated with asset data, so the inventory side of the house knows when assembly robots, conveyor beltsand other machines will require repair. AI-powered software like predictive analytics and optimisation algorithms can automatically assess MRO inventory and adjust stock accordingly. Manufacturers set risk, replenishment and value thresholds for the AI, and then let automation take care of the rest. This approach prevents dreaded stockouts, but also enables a leaner inventory – no unnecessary surplus. Manufacturers don’t want to pay for or manage parts they don’t need.

The insights that these AI systems provide aren’t short-term, either. As data compounds, predictive AI can spot inventory trends and patterns that stretch across months and years. This long-term data can also be used for scenario simulations, when manufacturers believe a novel market moment or supply chain disruption is lurking around the corner.

Agentic AI is further enhancing predictive inventory management. Agentic AI is the latest evolution of generative AI – software that can act autonomously to pursue goals and complete complex tasks with very limited human supervision. Agentic AI is set to make MRO inventory management even more precise and automatic, freeing up technicians to do what they do best: rolling up their sleeves and fixing the machines. At my company, IBM, our approach to AI-powered inventory management has helped manufacturing clients reduce parts-related downtime by an average of 50%. Agentic AI will take this number even higher.

Of course, AI is a tool, not a panacea. Without a strategic approach, AI-powered inventory management can fail. One major challenge is poor-quality data. If manufacturers don’t collect sufficient inventory data, or structure it correctly, then inventory predictions will fall short. AI is only as useful as the data it’s built upon. It’s also crucial that manufacturers prioritise AI governance and security. Without the proper guardrails, the technology can lead to security vulnerabilities or undesired outputs.

These are hurdles to overcome, however, not reasons to eschew the technology. When manufacturers couple AI with inventory management, they ultimately achieve a leaner inventory without the risk of stockout. This translates to less waste, less downtime, and significant cost savings.

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