The Benefits of Managing MRO Systems with AI Instead of ERP
Bernhard Langefeld, Senior Partner at Roland Berger argues that the maintenance, repair and overhaul (MRO) industry needs serious overhaul.
He puts forward various points to support this in his blog, The lean MRO matrix: How to sustainably boost process and digital efficiency, on the company's website.
"The MRO industry keeps things moving," he explains. "Whether it is planes in the air or trains running on time, it covers all actions that keep a fleet item in working condition."
He goes on emphasise that safety requirements must be stringent: "After safety, the overarching goal of fleet operators is to maximise asset availability at minimum cost. In short, efficiency is king and MRO providers must run lean maintenance processes.
"Doing so makes good business sense – boosting efficiency only helps to improve profitability. Unfortunately, MRO companies are currently in need of some maintenance, repair and overhaul themselves."
The global industry is still in the midst of post-pandemic recovery, with expectations it will grow with a with a CAGR of around 5.5% from 2024 to 2031.
Otilia Ion, Global MRO category manager for WNS Procurement, believes MRO systems within manufacturing itself could do with an overhaul as well.
Below, she draws on almost 15 years of experience in BPO, e-auctions, contract negotiations, e-catalogue implementation, training, sourcing and category management to argue that manufacturers could better manage these systems through AI – instead of ERP.
Highlighting what successful deployment and integration of this would look like, Otilia shares how AI is fundamentally reshaping the MRO function in manufacturing.
How is AI being used to streamline manufacturing processes?
A core part of many global manufacturing inventories is managed through MRO (Maintenance, Repair and Operations), a system that covers the services and supplies required to maintain plant production. MRO programmes are vital for equipment upkeep, troubleshooting and addressing unforeseen breakdowns to ensure smooth operations.
Traditionally, organisations approach MRO management via traditional Enterprise Resource Planning (“ERP”) software applications.
However, these systems are only able to provide static tracking of vast inventories across multiple sites and often lack consistency.
Since ERP systems were not designed for MRO management, they fall short in meeting the dynamic needs of modern operations.
This is not an insignificant concern.
In fact, the International Society of Automation estimates that companies lose a combined 3.3 million hours annually to unplanned downtime, costing £668 billion—roughly 8 percent of their yearly revenue.
Now, as AI becomes increasingly prominent as a solution, an improved approach to managing MRO is available. With AI able to enhance inventory management, procurement and risk optimisation by identifying and prioritising urgent issues, it also improves efficiency.
With accurate and rapid inventory data and forecasting, AI helps streamline operations, cut excess stock and prevent unplanned downtime, further supporting smoother production processes.
What are the benefits of an AI-Powered approach to MRO?
Some of the most significant ways AI is reshaping the MRO function include applications in the areas of predictive maintenance, inventory management and tail spend management, among others:
- Predictive Maintenance
With AI proving itself an effective tool in predictive maintenance, traditional costly processes have been streamlined. By analysing data from sensors and historical failure patterns, AI can predict when maintenance is needed, ultimately reducing downtime, lowering maintenance costs and extending the lifespan of machinery.
- Inventory Management
Managing inventory is a persistent challenge for both stakeholders and procurement teams. However, AI can significantly enhance this process by identifying patterns and determining the optimum level of stock for any parts required. This can avoid overstocking and understocking, improves inventory visibility and frees up workers from performing laborious manual data analysis.
- Tail Spend Management
AI can make a significant impact on Tail Spend Management. Often neglected by MRO teams due to time constraints, tail spend can be optimised with AI. By improving product traceability and identifying redundant supplier accounts, AI helps reduce unauthorised transactions and the associated workload. This enables buyers to consolidate more volume with preferred suppliers, secure preferential rates and free up time for more strategic tasks.
Are there any significant challenges with Gen AI adoption?
While the excitement around AI’s potential for MRO is palpable, it is important to recognise and address legitimate concerns before launching any AI-driven initiatives.
Recent survey data underlines the varied adoption of Gen AI across industries. While 71 percent of respondents said their organisations deploy Gen AI in at least one business function, the manufacturing industry surprisingly lags behind, with only 13 percent of respondents in that space regularly using Gen AI. This gap highlights a stark difference between the potential of Gen AI in contrast with organisations' readiness to implement it.
The journey to adopting Gen AI may seem challenging, with leaders seeking external support in areas like strategy, prioritisation, infrastructure needs, algorithm development and optimisation.
How can Gen AI be deployed successfully?
Similar to previous technology-driven eras of change within manufacturing, Gen AI does not operate in isolation. Successfully guiding and launching its deployment within manufacturing requires strong management and specialised expertise, as well as a comprehensive, multipronged plan which should ideally address all key areas of implementation.
This is typically achieved by analysing past use cases in manufacturing and understanding their complexity, thereby identifying key initiatives. Through this approach, it becomes possible to evaluate the costs and benefits of pilot programmes.
As with all AI applications, a crucial first step is ensuring that the data is accurate, relevant and complete. Only with a well-integrated foundation, can the Gen AI journey become seamless, enabling smoother connections between business processes within an organisation.
Additionally, assessing the current talent levels within an organisation allows managers to develop in-house skills to meet the demands of AI, as it requires a multidisciplinary level of expertise for successful implementation and deployment.
Are there any other considerations for businesses?
Organisations must understand from the outset that, while Gen AI is part of the solution, it is not the solution. Gen AI should be considered a small component of a broader optimisation process, fitting into a larger agenda and working effectively alongside other systems, including human talent.
To unlock the full potential of Gen AI, it must complement human intelligence (HI). While AI excels at processing and analysing data, decisions often carry significant risks that require human oversight. Therefore, managers should adopt a ‘co-pilot’ model, using AI for data-heavy tasks while relying on human expertise to validate insights and make decisions.
By integrating AI and HI, MRO leaders can maximise value while minimising risk. Gen AI accelerates procurement and generates insights, while HI provides essential context and safeguards for informed decision-making. To implement AI effectively, managers should identify pain points, streamline manual tasks and determine where human oversight is crucial.
By clearly understanding their current and future needs, leaders can better determine how Gen AI can support teams, what training is needed and where it may have the most success.
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