The “as-a-service” model is spreading from software into industry. Large manufacturers — Rolls-Royce with “power by the hour”, Philips with “light as a service”, Caterpillar with flexible rental — convert equipment sales into outcome sales. You don’t sell a turbine; you sell generation hours with SLA. For the buyer: less capex, less risk. For the seller: recurring revenue and deeper customer relationship.
This article covers real models gaining traction, what makes them work, and which fail despite the hype.
The Existing Models
Equipment-as-a-Service (EaaS)
The customer rents equipment per use/time, manufacturer retains ownership. Maintenance included.
- Classic: Rolls-Royce “Power by the Hour” — pay per flight hour, they maintain.
- Chemical plants: cooling, compressed air, steam “as a service”.
- Manufacturing machinery: CNC, industrial robots per use.
- Construction: Kubota, Caterpillar offer flexible rental.
Outcome-Based Pricing
The customer pays for result, not product.
- Lighting: Philips charges per “lux × hour” delivered.
- Water: wastewater treatment, pay per m³ treated.
- Agriculture: yield per hectare with seed+fertiliser+services.
- Logistics services: charge per moved unit, not per vehicle.
Industrial Platform-as-a-Service
Digital platforms orchestrating distributed physical assets.
- Xerox: “managed print services” — pages printed.
- Hilti: power tools “fleet management” with tracking.
- ABB Ability: digital suite for industrial assets.
Maintenance-as-a-Service
Contract guaranteeing uptime in exchange for fee.
- Wind turbines: Siemens Gamesa, Vestas offer LTSA (Long-Term Service Agreement).
- Elevators: Otis, Kone, Schindler have premium “99.9% uptime” service.
- Industrial machinery: upgrades, parts, proactive operation included.
What Working Models Have in Common
Shared patterns:
- Deep telemetry: manufacturer receives asset data 24/7 to know real state.
- Clear SLAs: what counts as “delivered”, with measurable metrics.
- Structured financial model: who pays capex, how profits split.
- Aligned incentives: manufacturer wants the asset to work well, not fail.
- Risk assumed with skin in the game: not “I assure it’ll work”, but “I pay consequences if not”.
Without these five, as-a-service stays marketing.
Technical Requirements
To enable as-a-service:
- Advanced IIoT: sensors, reliable connectivity, cloud.
- Digital twins of installed asset.
- Predictive maintenance with ML over history.
- Field service management for quick interventions.
- Usage-based pricing and billing platform.
- OT security — the connected asset is an attack vector.
“As-a-service” is impossible without substantial prior digital investment.
Customer Benefits
From industrial buyer’s perspective:
- Capex → opex: improves balance, frees capital for other investments.
- Risk transfer: manufacturer carries operational risk.
- Update access: you don’t stay with 10-year-old equipment.
- Cost predictability: maintenance included, no surprises.
- Expertise access: manufacturer has aggregated data and know-how.
Manufacturer Benefits
- Recurring revenue: predictable ARR vs sales cycles.
- Superior valuation: SaaS multiples > manufacturing multiples.
- Deep relationship: constant upsell opportunities.
- Real usage data: continuous product improvement.
- Entry barrier: competitors can’t easily replicate.
Where the Model Fails
Lessons from unsuccessful attempts:
- Without real telemetry: you don’t know if the asset is being used per contract.
- Impossible SLAs: committing to 99.99% uptime when reality is 98%.
- Customer who doesn’t want the model: some prefer full control by buying.
- Poor financing model: manufacturer lacks balance to carry capex.
- Traditional sales mindset: sales team keeps selling units, not services.
- Organisational structure: separate “as a service” team from traditional sales.
Transition: How to Start
Pragmatic runbook:
- One pilot product: not the whole line initially. Something self-contained.
- Early adopter customers: 2-3 willing to experiment, not the biggest.
- Full instrumentation: sensors, data pipeline, operational dashboards.
- Detailed financial model: honest unit economics, usage scenarios.
- Dedicated operations: team understanding service, not just manufacturing.
- Iterate: first contract will have issues. Learn without breaking the relationship.
One year from pilot to second customer. Five years to being dominant model.
Sectors with Most Traction
By maturity order:
- Aerospace: pioneer since ’60s. “Power by the Hour” is standard.
- Heavy industrial: ABB, GE, Siemens, Schneider with mature offerings.
- Construction / Mining: Caterpillar, Komatsu.
- Renewable energy: LTSA standard in wind and solar.
- Precision agriculture: John Deere, Climate Corp.
- Logistics / fleet: truck management.
Sectors still nascent: small machinery, light chemical plant services, certain discrete industry.
Cases with Numbers
- Rolls-Royce: ~50% of civil aerospace revenue is services.
- Philips: “Light as a service” contracts >10 years with Schiphol, RAU.
- ABB: Ability generates >$5B/year in digital services.
- Hilti: fleet management grew to >30% of revenue.
- Schneider Electric: EcoStruxure services as recurring growth driver.
Risks and Difficulties
Realistic:
- Critical cybersecurity: connected asset is door to manufacturer.
- Complicated compliance: who’s responsible when problem occurs.
- Technological dependence: if manufacturer fails, your plant stops.
- Changing regulation: some sectors (health, energy) have evolving frameworks.
- Cultural resistance: traditional procurement departments don’t love growing OPEX.
Conclusion
The as-a-service model in industry is real and growing, but not universal. Working cases share ingredients: deep telemetry, honest SLAs, aligned incentives, solid financing. For industrial manufacturers, transition represents a fundamental business change — not just technological. Long-term benefits justify investment, but execution is where most fail. Starting small, with a product and customers wanting the model, is the pragmatic path.
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