How maintenance optimization improves safety and reliability

Traditionally, most organizations viewed maintenance as a necessary cost of asset management. Today, new technologies and tools such as computerized maintenance management system (CMMS) platforms, Internet of Things (IoT) sensors and artificial intelligence (AI) make it possible to collect data faster. They also support greater data volumes than ever before and enable faster data analysis. This shift has made maintenance strategy a potential differentiator for companies willing to invest time and resources into maintenance optimization.

Modern maintenance optimization strategies vary, but they typically combine aspects of preventive maintenance, predictive maintenance, condition-based maintenance (CBM), corrective maintenance and reactive maintenance.

Rather than treating each maintenance task the same, effective maintenance optimization helps maintenance teams consider factors such as asset criticality, failure rate, operating condition and historical performance when planning maintenance activities.

Maintenance optimization also helps organizations make their processes and operating conditions safer by enabling teams to identify which assets and failure modes pose the greatest risk. Real-time CBM can help teams detect problems early and take corrective action before they lead to hazardous equipment failures.

Maintenance optimization and reliability-centered maintenance (RCM) are closely related concepts. People often use the terms interchangeably, but they are complementary practices.

RCM is a structured methodology for determining what maintenance is required to ensure that an asset continues to perform. It also often incorporates failure mode and effects analysis (FMEA), a structured framework for identifying potential failures in a design, manufacturing process or product.

While maintenance optimization often includes RCM, it applies other analytical techniques to determine correct maintenance intervals, resource allocation and costs.

While no universal process exists for maintenance optimization, organizations typically follow these seven steps and adapt them to their needs.

The first step in optimizing maintenance processes is to understand which assets are most important to an organization. This process is known as an asset criticality assessment.

During asset criticality testing, maintenance teams identify assets whose failure can disrupt core business processes, endanger worker safety or threaten environmental or regulatory standards. These assets are designated as “critical assets” and receive top priority in the organization’s asset hierarchy.  

Next, organizations examine how assets labeled “critical” are most likely to fail and what happens when they do. This step usually involves RCM and FMEA to help maintenance teams identify potential failure modes.

Historical work order records and maintenance data provide further evidence of recurring failures and help teams identify patterns. The goal of this step is to gain a deeper understanding of the relationship among asset condition, failure rate, consequences and intervention.

Next, organizations review the maintenance strategies they currently use and identify gaps and areas for improvement.

For example, preventive maintenance suits some assets, while predictive or CBM is more effective for others. Some low-risk assets are even suitable for planned reactive maintenance or run-to-failure strategies to reduce costs.

Reliable maintenance data is critical to maintenance optimization.

Most modern organizations rely on a computerized maintenance management system (CMMS) to capture data on work orders, labor, spare parts, equipment failures and completed maintenance tasks. IoT sensor systems can provide valuable data on temperature, vibration, pressure, electrical characteristics and other indicators of equipment condition. Over time, this information can refine maintenance schedules and identify opportunities for proactive maintenance.

When an organization creates an asset hierarchy and collects enough maintenance data, it can evaluate whether its existing maintenance intervals and tasks are appropriate or need refinement.

Some organizations use advanced techniques to determine optimal maintenance intervals and resource allocation, such as operations research, linear programming, quadratic programming and other optimization models. Others don’t require this level of sophistication and rely on a straightforward analysis of work order history to make their determination.

In this step, maintenance teams implement the new maintenance schedules and work instructions from Step 5.

Modern maintenance operations often rely on a CMMS to generate recurring work orders, assign maintenance tasks and document completed work. During this stage, maintenance technicians should also receive any necessary training, instruction or tools required to implement the new processes effectively.

Treat maintenance optimization as an ongoing process and track key performance indicators (KPIs) such as mean time between failures (MTBF) and unplanned downtime. 

Operating and environmental conditions can change over time, introducing new patterns and production requirements that affect equipment performance. Organizations should regularly review asset data and adjust maintenance strategies based on new evidence.

Maintenance optimization has many benefits. Here are some of the most common:

• Safer working conditions: Maintenance optimization helps create safer working conditions for technicians and machine operators by preventing deterioration and reducing the likelihood of catastrophic equipment failure. Equipment deterioration can create hazards ranging from mechanical failures and electrical faults to leaks, fires and uncontrolled energy releases. Maintenance optimization helps teams identify critical failure modes early and prioritize maintenance activities to address them.
• Increased equipment reliability: Maintenance optimization improves equipment reliability by enabling teams to target interventions at the failure modes deemed most critical to business processes. An optimized maintenance program applies reliability engineering principles to identify which maintenance activities will reduce the probability or consequences of failure and focuses maintenance efforts accordingly.
• Reduced unplanned downtime: Unplanned downtime is expensive. According to a recent report from MaxGrip, it costs industrial manufacturers USD 50 billion annually, with an average hourly cost of approximately USD 260,000. Maintenance optimization uses predictive maintenance and CBM to reduce downtime by providing early warnings of deteriorating equipment, giving maintenance teams a chance to intervene before failure.  
• Lower maintenance costs: Maintenance optimization reduces costs by focusing resources on the assets and activities that deliver the most value. For example, it can help maintenance teams identify equipment that can safely operate until failure and apply predictive or condition-based maintenance to address problems before they lead to a breakdown.
• More efficient use of resources: Maintenance optimization helps teams allocate resources based on cost, risk and business priority, so they can focus more time on critical assets (rather than low-risk equipment). Using resources more efficiently improves both uptime and operational efficiency without hiring more workers.

Maintenance optimization spans many industries, though specific objectives and techniques vary by use case. Here are several common use cases:

• Manufacturing: Manufacturers rely on maintenance optimization to minimize disruptions to core business processes and improve production equipment reliability. IoT sensors monitor vibration, temperature and other indicators of changes in operating conditions, while a CMMS can link maintenance data to work orders and schedules. Manufacturers often use RCM and FMEA to identify critical production assets and apply predictive or preventive maintenance to reduce the likelihood of equipment failure.
• Energy and utilities: Energy and utilities companies rely on large, often expensive critical assets whose failure can affect worker safety and service continuity. Maintenance optimization helps operators prioritize maintenance for turbines, transformers, pumps, substations and other critical infrastructure based on condition and the consequences of failure.
• Transportation: Airlines, rail operators and fleet managers must balance reliability, safety and costs to keep transportation networks running smoothly and avoid service interruptions. Maintenance optimization uses historical failure data, inspections and condition monitoring to determine when to inspect, repair or replace components. In aviation, for example, maintenance teams use reliability data and FMEA to identify significant failure modes when optimizing preventive maintenance intervals.
• Healthcare: Hospitals and other healthcare facilities depend on the availability of advanced medical equipment and building systems, including imaging systems, ventilators, sterilization equipment, elevators and emergency power systems. Maintenance optimization helps modern healthcare facilities prioritize critical assets for proactive maintenance and apply less intensive, reactive strategies to less critical equipment.
• Oil and gas: Oil and gas facilities depend on complex networks of pumps, compressors, pipelines, valves, electrical systems and processing equipment. Maintenance optimization strategies for oil and gas facilities combine RCM, condition monitoring, inspection data and CMMS records to identify and prioritize assets that require proactive intervention. This approach helps reduce the likelihood of equipment failures, enhance safety and compliance and minimize environmental risk.