Top 4 KPIs to Track When Moving to Data-Led Maintenance

Data has become very important to make informed decisions in the day to day operations and maintenance of your assets. For any forward thinking organization, the shift towards data-led maintenance is no longer a choice but a necessity.

It is equally important to track your progress with the help of appropriate Key Performance Indicators (KPIs). This strategy is essential for gauging the success of your transition, determining the effectiveness of the shift, and ensuring you stay on the right trajectory.

In this article, we will examine the top four KPIs in this context, which serve as a guidance to direct organizations towards a seamless and efficient data-led maintenance.

What is Data-led Maintenance and Why is it Important?

Many organizations, until now, have relied on traditional maintenance methods such as reactive or preventative maintenance. However, for forward-thinking organizations aiming to embrace data-led maintenance, a departure from these traditional methods as well as from Excel sheets for data analysis is necessary. Despite their past effectiveness in reducing downtime and saving on breakdown costs, both reactive and planned preventative maintenance methods have limitations. Reactive maintenance can lead to unavoidable breakdowns and shorten the lifecycle of assets. On the other hand, planned preventative maintenance may overutilize resources, engineer time, and result in excessive maintenance of assets. These reasons alone underscore the importance of transitioning to data-led maintenance.

For a sustainable long-term growth strategy, organizations should make the shift to data-led maintenance. This approach provides a more deeper understanding of asset health and performance trends. Empowered by the data, organizations can optimize the usage and lifespan of their assets. The continuous monitoring and analysis of asset data make it possible to optimize maintenance schedules and practices, to ensure assets operate at their peak efficiency. In simple terms, data-led maintenance transforms traditional reactive techniques into proactive, data-driven strategies. This not only boosts the reliability and longevity of assets but also positions organizations to succeed in an increasingly competitive and technologically advanced business landscape. If you have already begun with data-driven maintenance, you must monitor your progress to ensure a success of your data-led maintenance strategy.

Important KPIs to Track When Moving to Data-Led Maintenance

To know how well your tools and strategies are working for your business, you must measure these four KPIs.

1. Corrective to Total Work Order Ratio

The Ratio of total corrective work orders to total work orders serves as a fundamental KPI to evaluate the effectiveness of data-led maintenance. Success is indicated by a steady rise in this percentage across the site or portfolio. The ideal scenario is to achieve a ratio of 1, indicating that maintenance efforts are primarily focused on solving identified issues rather than reactive responses. This KPI provides a reliable indicator of how proactive the maintenance plan is.

2. Percentage of Work Orders from Identified Opportunities

Measuring the percentage of work orders that result from identified opportunities is important in evaluating the accuracy of the tool or software's predictions that the O&M teams are using. A score of more than 60% is preferred, and eventually, one should aim for a score of about 85% in the long term. This KPI demonstrates how well the software transforms identified opportunities into actionable work orders, minimizing the time spent on false alerts or alarms. A lower percentage could suggest issues with the software's ability to distinguish actual issues from false alarms.

The capability to distinguish genuine issues from false alarms is the cornerstone of effective data-led maintenance. O&M teams frequently spend more time than necessary responding to false alarms or alerts, which is one of the common problems.?

3. Percentage of Opportunities Requiring Investigation

The most important KPI to monitor the progress of data-led maintenance is the percentage of opportunities that need further investigation. A steady decline in this number signifies progress. An investigation is necessary when there is insufficient knowledge about the likely cause to turn an opportunity into a work order. In contrast, a decline in this percentage indicates that the software is becoming more proficient at identifying and addressing issues without the need for additional investigation support.

4. Percentage Reduction in Planned Maintenance (Non-critical)

The percentage reduction in planned maintenance (non-critical) is a KPI which shows the impact of system-driven assessments on asset health. This indicator determines the number of planned preventive maintenance (PPM) tasks that could be avoided due to the insights given by the software for data-led maintenance. A higher percentage shows a successful reduction of unnecessary maintenance, resulting in cost savings and increased operational efficiency. This KPI emphasizes the software's role in optimizing maintenance schedules and ensuring that resources are allocated where they are needed most.

Moving Ahead With Data-Led Maintenance

Successful implementation of data-led maintenance demands a strategic focus on these key performance indicators. By keeping a close eye on these indicators, companies can evaluate the success of their strategy and make necessary changes and improvements over time. The transition to data-led maintenance involves more than just adopting new technology; it also entails using data intelligently to boost operations' overall efficiency, reduce costs, and improve maintenance practices. It is critical to track the progress of your digital tool or software solution against these KPIs in order to advance and reap the benefits of data-led maintenance.

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