A data and AI strategy to turn an equipment maker into a service business
An industrial equipment manufacturer sold reactively - spare parts and service only after something broke. We built the data and AI strategy that charts the shift to a proactive service business, with a roadmap to 2030 and targets like growing aftermarket revenue from €2M toward €16M.
The challenge
Our client built and sold quality equipment - but everything after the sale was reactive. Parts and service moved only once a machine had already failed, and a wealth of usage data sat unused.
A reactive aftermarket, lost to competitors
Across every business area, spare-part and service sales started only when the customer called, usually after a breakdown. By then customers often turned to a competitor, and the most profitable part of the business was left to chance.
Manual, slow, hard to scale
Sizing, design and quoting were manual and laborious. That tied experts up in routine work, invited errors, and made it hard to expand into new markets.
Data collected, never used
Valuable equipment and configuration data was already accumulating, but it was scattered and never put to commercial use or fed back into product development.
What we built
We built a company-wide data and AI strategy that reframes the business from selling products to selling outcomes - 50+ ideas gathered with the leadership team, narrowed to ten prioritized initiatives, and a concrete roadmap to 2030. Not a slide deck.
- 01Strategy
From product-centric to service- and value-based
We reframed the operating model around the customer's lifecycle: keep equipment running, sell outcomes, and turn the aftermarket into a predictable, recurring service business instead of firefighting.
- 02Tooling
AI-assisted design and quoting
A digital, AI-assisted sizing tool to automate quoting - simulating system options against real operating conditions and showing the lifecycle value of premium choices with data, instead of manual spreadsheets.
- 03Predict
Sensors and predictive maintenance
A plan for sensor-based monitoring feeding a central data platform, with machine-learning models that estimate failure probability and turn anomalies into sales leads - before the breakdown, not after.
- 04Roadmap
A digital marketplace and a roadmap to 2030
A digital storefront for equipment, parts and data services, plus a phased roadmap with prioritized initiatives, targets and the sequencing to get there.
Results
The strategy gave the client a board-ready path from a product business to a data-driven service business - with the targets and the sequencing to pursue it. The figures below are the goals the strategy sets, not delivered results.
The strategy charts a path to grow aftermarket revenue from about €2M toward €16M - roughly €11M of it gross margin - by turning reactive parts sales into proactive, predictive service.
The plan targets a tenfold increase in aftermarket operating profit, with parts margins doubled or tripled through more efficient direct sales.
A long-term goal of €50M in revenue from digital solutions and services, starting with a parts webshop targeting €1M in its first year.
Predictive-maintenance models in the strategy aim for 95% accuracy two weeks before a failure (50% at six months), generating service leads automatically - and targeting roughly 25% less monitoring workload for technical staff.
Beyond the numbers
- Smarter, proactively tuned systems also cut energy use and emissions - up to 15–30% energy savings in some applications
- Field failure and usage data feeds back into product development, closing the loop between how equipment is used and how it's designed
