Predictive maintenance from an economic perspective – a business case for energy production

Predictive maintenance is considered one of the most important levers for increasing efficiency, availability and sustainability in energy production. The basic idea is intuitive: plants should no longer be maintained at fixed intervals, but precisely when data indicates an impending fault. However, whether this approach actually creates added value can only be assessed through a well-founded business case validation. Technical feasibility alone is not enough to make an investment decision.

Technical vs. economic feasibility

Proof-of-concept projects often show that algorithms are well suited for recognising patterns in sensor data or suggesting anomalies. However, this only answers the question: Can the use case work technically?

The second question is at least as important: What economic effect does it have?

For business case validation, the basis of current maintenance is first assessed. This includes:

• Maintenance intervals for relevant components,
• Frequency and type of unplanned downtime,
• OPEX costs for materials, personnel and logistics, and
• Economic consequences of downtime, such as loss of revenue or availability penalties.

Only this transparency makes it possible to compare potential savings.

How predictive maintenance creates value

Once the baseline has been defined, hypotheses are formed about how predictive maintenance will affect operations. Many effects do not result from spectacularly avoiding major damage, but from the sum of small improvements in day-to-day business. These include:

• More targeted maintenance instead of blanket intervals,
• avoidable unplanned downtime, which is expensive and organisationally complex,
• optimised timing, such as maintenance during low wind or low generation phases, and
• longer service life of selected components.

In a second step, these effects are translated into financial terms – typically using key figures such as return on investment (ROI), net present value (NPV) and increases in availability.

The importance of plant availability

In almost all forms of energy production – wind, solar, hydro or conventional – plant availability is directly linked to economic success. Every unplanned hour of downtime not only reduces revenue, but often affects contractually defined minimum availability levels. Falling below these levels can result in deductions or penalty payments.

Predictive maintenance helps operators reduce these risks. Early detection of faults, more predictable deployments and the ability to avoid dangerous deterioration in condition are key factors in ensuring that plants run more reliably.

Conclusion

Predictive maintenance only reveals its full value when technical feasibility is combined with a clear economic assessment. A structured business case validation shows which savings, availability gains and operational advantages are realistic – and thus forms the basis for sound investment decisions in energy production.

At the same time, predictive maintenance is not an isolated use case, but a building block of modern service innovations. With our service innovation prototyping approach, we support energy companies in iteratively developing data-based services, testing them early on and securing their economic viability.