Digitalisation has completely reshaped how industry manages its assets. In a market where margins are tight and reliability is critical, companies can no longer rely solely on reactive or calendar-based maintenance. The priority is to deploy a predictive maintenance plan tailored to Industry 4.0, where data and connectivity make it possible to anticipate failures. In this article, we outline the steps to develop a predictive maintenance plan, how to implement predictive maintenance in practice, and the organisational aspects that ensure it succeeds.
How to implement predictive maintenance
A predictive programme must be adapted to the reality of each organisation, blending technology and management. Below are the steps of predictive maintenance we recommend as a practical procedure for building an effective plan:
- Identify critical assets: Start by determining which machines or systems are most relevant for production, safety and cost. Not every item requires advanced monitoring, so prioritisation is essential.
- Define control parameters: Each asset needs a set of variables to monitor—e.g., vibration, temperature, pressure, noise or lubricant condition. ISO 17359 provides clear guidance on which parameters to analyse to ensure data are reliable and comparable.
- Use a reference methodology: The P–F curve is a key tool describing how a fault evolves from the point where it becomes detectable (P) to the point where it causes functional failure (F). By identifying this interval, you can intervene at the optimal moment, avoiding unnecessary costs and reducing risk.
- Select technologies: Install appropriate sensors and guarantee data connectivity. In Industry 4.0, it is advisable to use multiprotocol, multi-vendor platforms so equipment from different brands and generations can be integrated into a single analysis system.
- Implement predictive maintenance: Feed the collected information into a CMMS/GMAO. Configure alarms, define response protocols and establish action criteria. It is best to start with a pilot project in one area of the plant before rolling out broadly.
- Evaluate and drive continuous improvement: A predictive plan does not end at go-live. Measure performance using maintenance KPIs such as MTBF (mean time between failures), MTTR (mean time to repair), asset availability and reliability, reduction of unplanned stoppages, and spare-parts cost savings. These indicators allow you to tune the plan and ensure long-term returns.
Operational summary. If you need a minimal, actionable guide, the steps to develop a predictive maintenance plan every plant should follow are:
prioritise assets → select parameters → choose methodology → enable monitoring → pilot the implementation → measure and improve.
Successful implementation demands both technology and change management. The following points summarise how to implement predictive maintenance effectively in a modern plant:
- Leadership commitment. The strategy must align with business objectives, in line with ISO 55000 principles.
- Staff training. Technicians should build competence in data analysis and digital tools.
- Multidisciplinary collaboration. Maintenance, production and IT need to work as a single team.
- Cultural change. Many organisations still depend on preventive or corrective approaches; moving to predictive requires communication and trust.
Maturity stages in the roll-out
Not every company reaches the same level from day one. Typical stages are:
- Basic: monitoring limited to a single parameter, such as vibration.
- Intermediate: multiple techniques integrated and data consolidated in a central system.
- Advanced: application of artificial intelligence and digital twins to anticipate failures with greater precision.
- Prescriptive (Industry 5.0): the system not only predicts but recommends the optimal action, considering safety, cost and sustainability.
This gradual evolution allows scalable investment and alignment with each plant’s reality.
Common mistakes when building a predictive maintenance plan
Many plans fail due to avoidable errors:
- Trying to monitor every asset without prioritising critical ones.
- Failing to define clearly which parameters to track.
- Installing sensors without a robust analytics system behind them.
- Neglecting the training required for new digital competencies.
- Skipping KPI follow-up, which prevents a true view of value and ROI.
Avoiding these pitfalls increases the likelihood of success and ensures the strategy delivers sustained value.
Conclusion: how to create a predictive maintenance plan with impact
The steps to develop a predictive maintenance plan with real impact are to prioritise assets, measure what matters, roll out incrementally, and govern data with discipline. With well-defined steps and a structured approach to how to implement predictive maintenance, companies reduce costs, raise reliability and strengthen the safety of their operations.
Ultimately, a well-executed predictive maintenance plan turns maintenance into a driver of competitiveness and lays the groundwork for prescriptive practices in Industry 5.0.
