This article explores the evolution of maintenance strategies, progressing from a basic reactive approach to more advanced prescriptive plans.

It outlines the hardware and software requirements at each level of readiness, starting with basic plug-and-play solutions for proactive maintenance and advancing to prescriptive maintenance, which relies on cutting-edge technologies like simulation models, AI, and machine learning.

Some of the main features of plug-and-play solutions, suitable for early readiness levels, include standard sensors with embedded 5G/LTE antennas for condition monitoring purpose, standard software for data gathering and reporting on equipment status, basic work order management capabilities.

While these solutions offer several advantages like ease of deployment, cost-effectiveness and scalability, in some cases more robust custom solutions for predictive maintenance offer competitive advantages. Tailored made machine learning algorithms, simulation and AI applied to critical equipments can better support the decision-making process over maintenance strategies and reduce downtime, ultimately leading to better opex.

On the other hand, custom solutions entail challenges like higher upfront costs and longer implementation timelines. Moreover, they require caution and deep expertise in the field to overcome design challenges and ensure success.

Finally, the paper contains a detailed business case regarding the implementation of an advanced prescriptive maintenenance concept in the energy field.

The solution required for prescriptive maintenance follows the traditional Automation Pyramid framework, integrating IoT, AI, and data analysis across various levels: from on-field sensors to cloud infrastructure, machine learning and simulation models, this paper showcase how to achieve the next level of operational efficiency, reduce downtime and optimize resource utilization to unprecedented levels.