The Key to Effective Maintenance is Data

“If used properly, this can direct maintenance focus and lead to higher performance,” says Bemis. “Unfortunately for most companies there has been a struggle in this area or a lack of understanding in how to use this information to drive maintenance.”

Understanding production stops

A dashboard showing the Key Performance Indicators (KPI) uses configurable targets to show the health of the alarm system in an “at-a-glance” display.Image courtesy of SmartSightsTracking minor stops provides visibility into their frequency, duration, and root causes. This awareness lets organizations identify patterns and make targeted solutions to minimize interruptions. “By collecting data on minor stops, organizations can analyze trends and make informed decisions regarding equipment maintenance, process improvements, and resource allocation. Capturing data from machine assets provides immediate insights for both people and systems, enabling them to make better, faster decisions, and drive automation,” says Bann.

Researchers studied more than 100 global manufacturing operations worldwide to benchmark performance and correlate over 20 manufacturing key performance indicators (KPIs). To determine a manufacturer’s competitive position, OEE was used as the top indicator of performance. Each manufacturer was ranked by OEE, and all other KPIs were viewed in context of this order. Some of the key findings include:

• Best-in-Class (top 25% OEE) and Average (middle 50% OEE) organizations exhibit an OEE more than two times over Laggards (bottom 25% OEE);
• Knowledge sets apart Best-in-Class performers over Laggards: only 0.5% of downtime reasons are unknown for Best-in-class versus 15.7% unknown downtime reasons exhibited by Laggards, a factor of more than 30 times;
• The poorest performing operations exhibit six times more minor stops per year than the best operations.

Results from this study can offer the industry strategic and operational direction for organizations to improve their competitiveness. All organizations, no matter how high they rank, always have areas to improve. Companies ranking on the lower end of the scale can immediately find where to start making improvements.

Preventative maintenance

Integrating third-party software to provide root cause analysis (RCA) identifies which asset issues led to underproduction or unplanned downtime. This process is vital in maintenance management as it leads to more reliable operations, reduces downtime, and saves costs in the long run. The software reports provide detailed data analysis, identify trends, and facilitate communication— which is crucial for effective root cause identification and resolution. Problems like downtime in secondary equipment, parallel operations, and process flows create challenges that require a holistic approach.

The goal of an effective maintenance and reliability program is to provide the right maintenance on the right assets at the right time. The goal of a maintenance program is to reduce the failures (rates/ frequency), to prolong the production uptime, and reduce production loss. However, many companies in the chemical process industries, for example, prefer to replace malfunctioning equipment with the latest technology instead of performing a critical evaluation of the maintenance plans.

Material starvation in chemical processing can indicate unoptimized maintenance intervals upstream as such that the buffer inventory system between upstream and downstream assets runs dry before the maintenance is complete, creating unplanned downtime.

“A good maintenance program is key to having a high performing production line,” says Bemis. “Maintenance [personnel] should not just keep the equipment in good and proper running order, [they] should also be used as equipment matter experts when the need arises from operations.”

Implementing a preventative maintenance plan with RCA prevents recurring issues from happening by eliminating their root causes and understanding the origin through insightful software reports, with 100% of the downtime captured including stoppages of less than 10 minutes, which is often missed by manual data collection. In fact, a report from Copia Automation, the “1st Annual State of Industrial DevOps Workforce Report: People, Process, and the AI-Powered Future,” finds that manufacturers lose an average of 45 hours every month to debugging tasks, largely due to reliance on manual tools like Excel for managing critical industrial code in 47% of organizations.

Predictive maintenance

While preventive maintenance relies on best practices and historical data, predictive maintenance (PdM) takes measurements from machine operations as they are occurring and uses this data to raise red flags when indications of a problem are noted. Like preventive maintenance, PdM is a proactive approach, and maintenance isn’t only performed on machines when it is necessary. McKinsey Global Institute reports that implementation of PdM practices across manufacturing will have a $240-$627 billion cost savings across the industry.

“With continued improvements of automation machines are beginning to be able to self troubleshoot and alert to possible downtime issues prior to them happening. If used properly this can greatly improve equipment uptime,” says Bemis.

SCADA systems serve as the backbone of predictive maintenance initiatives by providing real-time data acquisition, monitoring, and control capabilities across industrial processes. By harnessing the power of real-time data and predictive analytics, businesses can proactively address potential equipment failures before they escalate, thereby significantly reducing costly unplanned downtime. Seamlessly integrating third-party software that provide autonomous alerts, historical data analytics, and reports with SCADA systems can lead to smarter decision-making, improved processes, and a competitive edge. Using asset data helps predict when a failure may occur by catching asset malfunctions as early as possible. This helps avoid the need for more significant maintenance activities or lengthy and costly downtime.

“Leveraging aggregated data offers economies of scale for managers,” says SmartSights’ Bann. “With a summation of performance across all machines, larger performance inefficiencies become evident, and managers can develop a deeper understanding of their machine performance, people performance, and process performance.”