A significant component of the global inventories of leading manufacturing companies is consolidated into a system called MRO (Maintenance, Repair, and Operations). It encompasses all the services and supplies necessary to sustain production in the plants. An MRO program is essential for managing equipment upkeep, troubleshooting issues, and addressing unexpected breakdowns to ensure uninterrupted operations. MRO involves procuring and maintaining an inventory of critical supplies, such as spare parts, tools, and consumables necessary for equipment maintenance, repair, and upkeep.
Over the years, manufacturers have been accustomed to managing the MRO function via traditional Enterprise Resource Planning (“ERP”) software applications. As such, these older software systems statically track spare part inventories, typically thousands of them across numerous sites—often inconsistently—with various information reflected in their inventory databases. These systems were never built for MRO management and are greatly flawed, falling short of what is needed.
Due to this inefficiency in managing such a large function, executives are continuously challenged to balance working capital and risk. The real objective is optimizing production and minimizing downtime. This is not a trivial concern. According to the International Society of Automation, “Cumulatively, Fortune Global 500 (FG500) manufacturing and industrial firms are estimated to lose 3.3 million hours a year to unplanned downtime. The financial cost of this downtime to those organizations is calculated at $864 billion, the equivalent of 8 percent of their annual revenues.”
Enter AI. AI brings a new technological approach to managing the MRO function. It helps provide clarity when optimizing inventory, procurement, and risk. AI-powered MRO systems have been shown to prioritize critical issues that demand immediate attention. AI has introduced renewed efficiency by enabling task monitoring and workload optimization through intelligent work queues and configurable layouts. Current market trends show that when manufacturers incorporate AI-powered MRO optimization of inventory and maintenance, these new technologies help improve margins, enhance routine maintenance needs, increase service levels, and minimize unplanned downtime—four crucial factors that can greatly influence a company's bottom line. With precise inventory data and forecasting capabilities, industrial production facilities can streamline processes, reduce excess inventory, and minimize stockouts.
Still, when it comes to the use of AI in manufacturing, the rate of adoption is low. According to the Business Trends and Outlook Survey (BTOS) of the U.S. CENSUS Bureau, from November 2023, only 3.8 percent of businesses reported using AI to produce goods and services. As expected, the highest percentage of adoption (13.8%) has been reported by the information sector, with a powerful increasing trend. So, what are the myriad ways that AI can assist and enhance the MRO function within manufacturing? We list below the various ways that AI is changing the paradigm of the MRO function.
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Predictive Maintenance has emerged as a highly effective area for AI application. AI can streamline this costly process by analyzing data from sensors and historical records of failure patterns to predict when maintenance is required. This can decrease downtime and maintenance costs and increase the lifespan of the machinery. According to a recent article from CIO magazine, companies like GE, Rolls-Royce, District of Columbia Water, and others are successfully using AI for predictive maintenance to identify problems and monitor system performance.
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Using AI for Virtual Testing and Simulation is becoming increasingly important in MRO. The wide range of scenario simulations allows technicians to practice and perfect their skills without the pressure and risks of real-world situations. Virtual simulation is effective when training new employees.
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Inventory Management is a constant challenge for stakeholders and procurement. AI algorithms can perform Inventory Management Data Analysis, identify patterns, and determine the precise minimum/maximum level of stock for each part. For companies with large inventories, this could positively impact multiple aspects like overstocking and understocking, inventory visibility, or avoiding human resources allocation to perform the laborious data analysis.
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Advanced analytics can be extended to Procurement Strategy and Supplier Relationship. AI can analyze market trends, prices, suppliers’ capabilities and indicate the most suitable sourcing strategies. This could help procurement teams stay updated with the market changes, gain fresh perspectives on buying strategy, and assess suppliers’ performance.
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Tail Spend Management can also benefit from AI. Tail spend is often overlooked by MRO teams due to time constraints. AI can enhance product traceability and identify redundant supplier accounts. By reducing tail spend, the number of unauthorized transactions, and the workload associated with it, buyers can consolidate more volume with preferred suppliers and dedicate the additional time to more strategic activities.
Even if the enthusiasm about AI’s potential is high, there are voices raising legitimate concerns, which should be carefully considered before planning any AI-related initiatives. Some of the most pressing concerns are:
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Data privacy and security, ensuring compliance with company’s policies and applicable legislation.
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Data quality, ascertaining that data used by AI is accurate, relevant, and complete, especially in maintenance-related processes, where errors might be costly.
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Integration complexity and assessment, recognizing the complexity and financial commitment needed to integrate AI with other systems.
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Skilled personnel, acknowledging the need for specialized expertise to define tasks for AI. They should also be able to assess and question the results generated by AI programs. All users working with AI programs should understand at least the basics of the process, the potential errors of the programs, and their consequences.
We should keep in mind that these technologies are evolving rapidly, so we expect sooner than later that they will extend their applicability and will become more user-friendly and transparent in the way they function. With the advent of AI, the time has come for manufacturers to take control of the MRO function. AI can combine and harmonize disparate material data across multiple enterprise systems, thereby reducing risk, optimizing working capital, and ensuring production uptime to meet customer needs—everything that any manufacturer wants.