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How AI Enhances Aftermarket Part Planning for Manufacturers
Articles- Jul 26,25
As AI continues to transform the manufacturing landscape, it is also revolutionizing the aftermarket part planning segment. In this article, Emily Newton explores how AI is helping manufacturers enhance efficiency, predict demand, and optimize their supply chains for better aftermarket services.
Artificial intelligence (AI) has already made waves across manufacturing. It streamlines product design, reveals workflow inefficiencies, makes robots more flexible and enables proactive maintenance, but its potential still goes further. AI can also be transformative in the growing aftermarket part planning segment.
Why should manufacturers consider aftermarket support?
The need for aftermarket support is growing, and not just for companies specializing in replacement parts or third-party modifications. Many original equipment manufacturers (OEMs) have realized that the post-sale market represents a considerable underserved niche.
Demand is strong, with 98% of end users expecting their aftermarket budgets to remain steady or grow in 2025. Much of this need stems from a growing awareness around proactive maintenance and its benefits. End users are stocking up on official parts to enable faster repairs or give them more agility to adapt. As this trend continues, 94% of OEMs foresee growth in their aftermarket services over the next three years.
OEMs must pay attention to this shift because those that do not cater to this market may quickly fall behind. Customers may switch to other OEMs that offer more post-sale support. Alternatively, they may source machine components from third parties. In either case, failing to target this niche results in lost potential sales.
How AI improves aftermarket planning
As significant a value stream as aftermarket parts may be, capitalizing on it can be complicated. AI helps by improving the extensive planning phases of these services along several fronts.
Identifying aftermarket purchase trends
Demand forecasting is the most straightforward application of AI in aftermarket part planning. OEMs cannot feasibly maintain stock of every replacement part for every product — logistics aside, it would be needlessly expensive. However, they must also recognize which parts are the most important to have available to enable quick shipping and avoid stock-outs.
AI already has a well-documented track record in improving such demand forecasting. Organizations using it in this area reduce product unavailability by up to 65% and lower warehousing costs by 5% to 10%. By analyzing customer purchase trends, AI can recognize which aftermarket parts will see the most demand in the future, and can even specify predictions by region or audience for more in-depth planning.
Designing for repairability
Industry-leading aftermarket support also requires the original product to be repair-friendly. When designs are overcomplicated or machines require uncommon, specialized tools to maintain, aftermarket parts are less valuable, as the end user may be unable to perform their own fixes. AI can prevent such outcomes by helping teams design with repairability in mind.
AI design tools can highlight areas where a product may be too complex for quick repairs. Alternatively, they can suggest layouts or features to enable easier disassembly and reassembly. AI can also simulate these redesigns’ real-world performance to ensure they work without lengthy manual testing. Conventional alternatives often slow throughput and delay time to market, so such simulations can produce considerable savings.
Ensuring part compatibility and functionality
Similarly, OEMs can use AI to ensure new aftermarket parts fit within older designs and function as intended. Businesses can start this strategy by creating components for existing equipment that had no first-party replacements available previously. However, expanding or improving otherwise outdated machinery may be even more valuable.
Add-ons and upgrades that end users can install themselves are an excellent way to extend the service life of an older product. This drives customer loyalty and establishes a reputation for reliability. The same AI design tools that test and suggest adjustments for redesigns can help OEMs ensure these expansions are compatible with their older products without lengthy, expensive real-world trial and error.
Optimising supply chains
As with complete products, aftermarket parts require extensive supply chain planning to remain affordable and achieve fast shipping. These concerns may be even more pressing for organizations just starting their aftermarket services, as branching into this segment means additional material and logistics costs. Thankfully, AI can reduce these expenses and enable greater agility.
Increased visibility is the highest priority for digitalization among businesses today, and AI provides it by connecting data points from across a supply chain. Once it has gathered as much relevant information as possible, AI can reveal areas for improvement. These may include supplier diversification options, near-shoring opportunities or logistics alternatives. Predictive analytics can also help by warning of incoming disruptions before they occur, ensuring OEMs can adapt before such issues ripple downstream to their customers.
AI Is crucial to effective aftermarket part planning
Aftermarket part strategy is similar to planning any product’s supply chain operations, design and production workflows. As such, many of the same AI tools that have helped OEMs refine their primary equipment can do the same for their budding aftermarket services. The additional complexity and unique considerations these components carry raise the need for AI’s insight and speed even higher.
Demand for aftermarket services, especially from OEMs, will only rise in the future. Manufacturers must adapt now to serve this segment, and that means capitalizing on AI to keep the channel as profitable as possible.
About the author:
Emily Newton is a tech and industrial journalist and the Editor-in-Chief of Revolutionized magazine. Subscribe to the Revolutionized newsletter for more content from Emily.
Image Courtesy: www.freepik.com
Aftermarket Part Planning
Artificial Intelligence
Emily Newton
Demand Forecasting
Supply Chain Optimization
Manufacturing Efficiency
AI in Manufacturing
Part Compatibility
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