How Data Can Enhance Maintenance Operations

How Data Can Enhance Maintenance Operations

In our world of information overload, it’s surprising how few of our maintenance-related decisions are based on quality data. Many manufacturers are still operating in a reactive maintenance mode – responding to equipment only when it fails to work properly – or relying on the intuition of experienced maintenance managers and technicians who are working from tribal knowledge.

The reality is that our marketplace will continue to grow more competitive; the only way to keep up with the new, innovative businesses will be to rely much more heavily on accurate data in order to optimize various aspects of maintenance operations. This means a rapid shift from reactive maintenance to proactive and predictive maintenance strategies, which must be built on accurate, high-quality data and employ sophisticated data analytic tools.

Relevant, Quality Data Must Be the Foundation

The old saying “garbage in, garbage out” aptly applies to the first, most important step of leveraging data to enhance maintenance operations. The most advanced data analytic tools are useless if the information they are analyzing is incomplete or just plain wrong.

Improving the accuracy and quality of data can be done in two ways. First, modern sensors and internet of things (IoT) devices can gather real-time information about the condition of equipment. Second, maintenance technicians must understand the importance of creating clear records of any work they have performed on particular assets as they complete work orders. The latter often requires training and culture change, which can be the most challenging part of collecting quality data.

Collected data should include equipment performance metrics, historical maintenance records, sensor readings, and operational data, all of which can enable maintenance teams to monitor asset performance and anticipate potential issues.

Predictive Maintenance is the Gold Standard

Reactive maintenance has historically been the starting place for maintenance departments. Many have then transitioned a proportion of their time to preventive maintenance, which is based on time or usage records of particular pieces of equipment. Although preventive maintenance may reduce the number of unexpected failures and downtime, it can also lead to unneeded maintenance or simply not “catch” everything that indicates potential failures.

Predictive maintenance, on the other hand, uses sensors, historical data and advanced analytics to predict when equipment is likely to fail and schedule maintenance just-in-time. It is considered the “gold standard” today. Here are the three key components of predictive maintenance:

• Condition Monitoring: Predictive maintenance tools such as IoT sensors can immediately alert maintenance teams if a key measurement falls out of an acceptable range. Real-time data on vibration, temperature, water levels, humidity, and other relevant factors that could indicate a problem are continuously monitored. This condition monitoring enables timely interventions and adjustments to prevent failures and ensure optimal equipment performance.

• Data Analytics: Data analytics tools play a crucial role in extracting valuable insights from the collected data. Advanced algorithms and machine learning models can identify patterns, correlations, and anomalies in the data that might not be immediately apparent to human operators. These insights can help maintenance teams make informed decisions and prioritize tasks effectively.

• Maintenance Schedules: Both condition monitoring and data analytics then leads to better scheduling of repairs or replacements, minimizing downtime and reducing the likelihood of costly emergency repairs. These predictive maintenance schedules are now responding to the actual condition of the equipment and not based on a more arbitrary schedule of time or usage. This prevents unnecessary maintenance and ensures that resources are allocated where they are needed the most.

Benefits of Leveraging Data in Operations

Perhaps the most significant benefit in leveraging data to improve maintenance operations is reducing unplanned downtime. With the average hour of downtime costing manufacturers $260,000 and the average number at 800 hours per year, this can be a significant savings. Using data to predict potential failures and taking preemptive action means that organizations can minimize unplanned downtime and keep operations running smoothly. This leads to increased productivity and customer satisfaction.

Another key benefit is that by embracing data analytics, organizations can optimize their spare parts inventory, reducing costs of carrying too much inventory while at the same time ensuring they have key parts on hand. By analyzing historical maintenance data and failure patterns, manufacturers can identify which parts are most frequently needed and ensure they have an adequate supply on hand. This prevents delays in repairs due to unavailable parts or spending exorbitant amounts on overnight shipping. 

Continuous Improvement

Data-driven maintenance is not a one-time endeavor. It requires continuous monitoring, analysis, and refinement as information is collected and markets change. Organizations should regularly review their data collection processes, analysis methods, and maintenance strategies to adapt to changing conditions, competitor behavior, and new technologies.

Conclusion

Incorporating data-driven strategies into maintenance operations can lead to significant improvements in efficiency, cost savings, and equipment reliability. By leveraging data analytics, predictive maintenance, and condition monitoring, organizations can transition from reactive to proactive maintenance approaches. This shift not only reduces downtime and unexpected costs but also enhances overall operational performance. As technology continues to advance, embracing data-driven maintenance will become increasingly vital for organizations aiming to remain competitive in their respective industries.