Why maintenance data is the missing ingredient for accelerated innovation in manufacturing
Why maintenance data is the missing ingredient for accelerated innovation in manufacturing
The relationship between engineering and maintenance has always felt out of sync.
Product engineers are being asked to iterate on computer-aided design (CAD), finalize specs, and release designs faster and faster so new products can get to market. However, maintenance is rarely factored into this process. That means they often inherit the consequences of designs that look technically sound, but can fall apart in real-world conditions. They end up having to keep lines running while continually planning for new equipment specs, operating contexts, and edge cases.
This disconnect is getting harder to afford, reports. Product variations are exploding as customers demand more options and shorter lead times. Every new SKU impacts , parts management, and capacity planning. When maintenance isn鈥檛 factored into the design conversation, manufacturers pay later in downtime, scrap, rework, and delays.
In a recent conversation, Rush LaSelle, CEO of Fathom Manufacturing, discusses why bridging this gap will provide manufacturers with a big competitive advantage.
If manufacturers want to accelerate new product introductions (NPIs) while maintaining quality, we have to tear down the wall between product design and maintenance. Maintenance needs a seat at the table and to inform the design process from day one.
Key takeaways
- Quality is moving upstream to the design process. Maintenance needs to move with it.
- Maintenance data is design feedback. Work orders, maintenance logs, parts usage, and changeover notes should inform product and process decisions before design freeze.
- Maintainability is a speed strategy. If PMs, access, and changeovers are hard, reliability slips and your NPI timeline collapses under downtime, scrap, and rework.
- Connected data is essential. The best insights can鈥檛 stay trapped in technicians鈥 heads or scattered systems if you want them to influence design.
- AI helps turn messy maintenance history into usable signals.
Why maintenance data is a game-changing design tool for manufacturers
As Rush pointed out, quality is a competitive differentiator for today鈥檚 manufacturers.
鈥淨uality has to permeate all the way through [the NPI process],鈥 he said, 鈥渁nd I just think it鈥檚 moving further up the ideation channel than it鈥檚 ever been in the past.鈥
That鈥檚 exactly why maintainability can鈥檛 be treated as a downstream concern, especially as product variation accelerates. When you鈥檙e introducing more SKUs and more frequent design tweaks, you鈥檙e creating more opportunities for small design decisions to turn into big production problems.
And no one has a clearer view of those problems than maintenance.
Maintenance teams spend more time than anyone living with the consequences of product and process decisions, many of which never show up in the design process, like the failure that happens when humidity spikes or what happens when operators start making small adjustments. That experience isn鈥檛 just a collection of , purchase orders, and notes鈥攊t鈥檚 also valuable design feedback.
Product design is all about how reliably a product can be produced, how stable it stays as variations stack up, and how quickly you can recover when something drifts out of spec. Any advantage you have with the speed of product development is wiped away if failures chip away at production capacity, product quality, and on-time delivery.
That鈥檚 where maintainability becomes a quality lever. When you pull maintenance data into the ideation phase, especially for high-value or high-variation products, you鈥檙e not just making life easier for the people turning the wrenches. You鈥檙e building a production system that鈥檚 more predictable, more scalable, and less fragile as the pace of change increases.
How to incorporate maintenance data into the NPI process
Even if you have the intention to include maintenance in the NPI process, there鈥檚 a practical problem: the insight exists, but they鈥檙e often in no shape to be used quickly and effectively.
This is a fundamental roadblock because, as Rush put it, speed is king when introducing high-value products.
鈥淚f you go back 20 years, to iterate on a design took weeks, if not months,鈥 says Rush. 鈥淣ow 鈥 you can do that in hours and days. Winning in manufacturing is going to be all about speed.鈥
The most useful insights from maintenance teams are often scattered across work orders, maintenance logs, purchase orders, and changeover notes. If that data stays siloed in spreadsheets or, worse yet, the minds of technicians, it can鈥檛 be incorporated in the design process fast enough, or at all.
This is where AI can help accelerate the digitization of this data and translate it into useful insights for the NPI process. As Rush points out, 鈥淵ou鈥檝e got to get your data 鈥 using AI, ready to move through that [process] at an increasingly fast pace.
AI can help turn scattered maintenance data into actionable insights for design teams by:
- Prompting technicians and operators to submit clearer, context-rich notes
- Turning voice notes/photos into structured fields
- Summarizing technician notes and identifying common failure modes
- Finding edge case failures and root causes from , photos, and work orders
- Translating information in other languages so key details aren鈥檛 lost
- Generating procedures that track the right inputs for a valuable feedback loop
A real-world example of how to accelerate the NPI process in manufacturing
Rush outlined an interesting example of a manufacturer that has combined speed and quality to optimize its NPI process.
A medical devices manufacturer started with additive manufacturing to design products with complex fluidics. This allowed the team to 鈥渕ove really quickly, see changes, and see the characteristics of the new products鈥 during early testing, says Rush.
But additive wasn鈥檛 the end state鈥攊t was the learning engine. Rush noted the team used additive manufacturing for rapid iterations and to validate performance, then transitioned into a hybrid of injection molding and some CNC. In other words, the company prototyped to learn, then designed to endure.
Maintenance strengthens that handoff by capturing what early builds reveal: what wears out first, what drifts out of spec, and what makes changeovers painful so iteration cycles don鈥檛 turn into instability once product scales.
Maintenance is the missing piece to introducing high-value products in manufacturing
There is a massive shift in how products get to market. Speed is king, but speed without reliability is the surest way to wipe out any gains you build during the NPI process.
The most successful manufacturers of the next five years will be the ones who realize that the person closest to the machines that produce products is one of the best people to help design them.
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