Why move from Reactive Maintenance to Predictive Maintenance

Have you heard about reactive maintenance and predictive maintenance? If you’re worked within industrial facilities and managed assets in plants/factories, then there’s a good chance that you must’ve not just heard these terms but also actively worked to move from reactive maintenance to predictive maintenance.

Reactive maintenance is when, inspections or observation, you discover a fault or anomaly in asset health or processes. You react and assign your engineers and technicians to assess and fix the issue.

There’s an inherent delay in these proceedings. There’s a time gap – T1 – between when the fault occurs and when it’s discovered. There’s a time gap – T2 – between discovery and assignment of the assessment/fixing job to the right person. The manager must ensure that the required spare parts are on-hand, and the person leading the job has the required skill-set.

Then there’s a time gap – T3 – between assignment and job completion. This depends on the complexity of the job, the supply chain (spare parts and consumables), the uptime of related machines/assets, the work order schedule for the shop floor engineer, etc.

Through this T3, the manager doesn’t always have clarity of the interim status of the job. They wouldn’t know if there’s been an issue or process barrier until the assigned person comes back with the info. This delay due to lack of visibility is a subset of T3.

The time taken with T1 + T2 + T3 (let’s say T0) rakes up quite a high cost. The main cost-factor is the downtime and opportunity cost of unutilized workforce and assets’ uptime. All this pushes the production cycle into delays, cascading into low productivity for the plant.

Enterprises look to reduce the T0 (time taken via reactive maintenance) by implementing workflows and processes that necessitate regular asset health checkups. The manager sets up cyclical asset inspections, designed to catch faults in their infancy. This helps reduce the T0 to increase overall machine uptime.

However, this still comes down to the unit of assessment, as in, it’s capped on the available time of the engineers for the inspections and their affinity to spot the fault quickly.

The above two paragraphs are an oversimplification of the preventive maintenance method. There are multiple processes to spot potential fault triggers (average downtime or output volume) that point towards possible anomalies within the machines. Many plants/factories have upgraded their asset management processes and have plateaued, now, at preventive maintenance.

Predictive maintenance is the zenith of Intelligent Asset Management achieved via a healthy mix of machine learning, AI-driven planning engine, and Internet of Things.

Let’s just think about the term ‘predictive’. What does it imply? Predictive abilities help you preempt a scenario and be fully prepared for the eventuality. As required, you can minimize or eliminate all faults beforehand.

In terms of asset management, the manager would be able to predict exactly when a fault would emerge and be prepared to line-up processes to nip it in the bud or fully avoid the possible fault/anomaly altogether.

This is enabled via a smart network of Internet of Things (IoT) enabled devices within the plant, handheld or stationary. They constantly capture accurate data. This is pushed through an intelligent platform like SAP Business Technology Platform (BTP) with the right planning and analyzing products (via industry-experts like Crave InfoTech).

The manager has the exact snapshot of the present, past, and future functioning of the assets. The future includes the projected uptimes as per analysis of historical, current, and situational information (processed via AI and machine learning).

This means that the manager would be able to align the supply chain (for the exact materials and parts) with the asset’s projected performance metrics. They would be able to ensure that the right person with the right skill-set and tools is ready with the right parts at the right time.

Assets, hence, function at their optimal level through their lifecycle perfectly in tandem with the production cycles and requirements. This maximizes effectiveness and productivity.

Crave InfoTech can analyze whether you’re at the reactive or preventive maintenance stage to chalk-up an apt plan for your enterprise. This plan would guide you through the IoT-enablement, platform intelligence-infusing, proper UX-optimized apps, intuitive workflows for fast adoption, etc. And all this, perfected for each industry.

We will streamline and execute everything you need to achieve predictive maintenance within your asset management. We have been the guide for multiple enterprises along their reactive to predictive journey. We know how to make it work.

Is predictive maintenance something that you want? Just drop in to check on the same.