Competing on Predictive Maintenance

Intelligent algorithms are transforming Organizational Reliability Programs from being just a support tool into a strategic weapon in today’s competitive business market. With the proliferation of knowledge, firms emulate rival products’ cutting-edge features easily and adopt industry best practices for their production processes leading to a sector-wide homogeneity in Operations. 

Reliability Programs and Asset Management Plans are among the last remaining points of differentiation between companies.

How then does your Reliability Team become a competitor, contributing to the business’s bottom line? Use Smart Analytics! Start with Industrial Internet-of-Things (IIoT) enabled data-collection from your assets and perform sophisticated analysis to wring out every possible reliability improvement action. Don’t just monitor the performance of the equipment; predict the failure modes and mitigate the risks of unplanned downtime.

When a prediction is made using predictive analytics, execute appropriate maintenance inventions and measure the overall impact or “lift” of the prediction. This will serve as feedback to the algorithm and justify its usage in decision-making within the team.  There will be the occasional return to the age-old debate of analytics-vs-instinct when predictions go against the subject matter experts who had been operating machinery for years. Both are neither correct all the time. Analytics-driven Reliability Engineers must decide when to go with estimates and when to go with the guts.

Vitally important is the use of the right technology to make the right predictions. The accuracy of these estimates will depend on the data management, the model used, and the prediction range. If the operating conditions change in real time, then the model must be dynamic. Predictive Maintenance is, in essence, a combination of Predictive Analytics & Maintenance Management. Your team needs to have expertise in both for successful implementation.