Prescriptive maintenance (RxM) is a maintenance strategy that not only predicts when a failure is likely to occur, but also recommends the best action to prevent that failure or minimise its impact. Where traditional systems mainly provide insight and warnings, RxM translates data into concrete decision proposals for planners, operators and maintenance teams.

RxM relies on AI, machine learning and IoT. Sensors in machines continuously collect data, algorithms detect patterns and risks, and on that basis the system proposes optimisations or interventions. This makes prescriptive maintenance the logical next step after predictive maintenance: not only predicting, but also prescribing what to do. Because the underlying models keep learning from new data and feedback from the field, RxM is scalable and adaptive, the more you use it, the smarter and more effective the maintenance process becomes over time.

Prescriptive maintenance is especially impactful in environments where failures are immediately felt in operations and results. It is highly relevant for manufacturing companies with a lot of unplanned downtime, where every breakdown leads to rescheduling, overtime, and pressure on delivery reliability. It also comes into its own in complex machine parks with different machine types, ages, and suppliers, because it helps you cut through the noise of all the signals and parameters to identify the real risks. In addition, it is valuable for organisations that struggle with root cause analysis: failures keep recurring, but the underlying cause remains unclear or is very time‑consuming to uncover.

This approach is a good fit for organisations with clear digital ambitions, or those that have already taken steps towards condition‑based or predictive maintenance. Prescriptive maintenance builds on the foundations already laid there: sensor data, historical failure information, and analytics capacity. Rather than replacing these investments, RxM actually enhances them. While predictive maintenance mainly provides insight into when something might go wrong, prescriptive maintenance adds a crucial extra layer: targeted recommendations on which action to take, so that earlier predictive steps deliver more return and strategic value.