What is a maintenance strategy?

The goals of implementing a maintenance strategy include improved equipment reliability, greater cost savings related to fewer major repairs and smoother execution of work orders. 

Managers classify maintenance management strategies according to when and how frequently those strategies are applied. The least proactive form, reactive maintenance (also called breakdown maintenance), uses a “run-to-failure” approach and occurs when equipment or systems have failed.

At the other end of the maintenance spectrum, reliability-centered maintenance (RCM) takes a vastly more proactive approach. It relies on failure modes and effects analysis (FMEA) to forecast potential asset failures and root cause analysis to determine why actual failures occurred. FMEA and root cause analysis work in tandem to study asset reliability and report on the “before” and “after” status of failures.

For example, reactive maintenance is similar to another term, corrective maintenance. Both prescribe the same type of post-breakdown action. Where they differ is in timing. Reactive maintenance must happen after equipment breakdowns. Corrective maintenance usually occurs after breakdowns but can be enacted earlier in the process when an urgent need is detected through regularly scheduled inspections.

To a great degree, maintenance strategies depend on the Industrial Internet of Things (IIoT). This umbrella term covers the smart sensors, instruments and networked devices operated with industrial applications and manufacturing systems.

Their utilization enables data collection, task automation and helps improve overall efficiency. IIoT sensors are in widespread use; recent statistics show that 35% of US production facilities already use IIoT sensors, with 41% of plants actively testing them. 

The other guiding concept driving maintenance strategies is machine learning, which applies a higher level of mentality to maintenance strategies. It lifts them from a relatively simple, calendar-driven exercise into a data-driven approach that leads to a more advanced form of maintenance known as predictive maintenance.

Predictive maintenance strategies enable the creation of machine learning algorithms that can identify repairs before they’re even needed and limit the occurrence of unplanned downtime. This approach can significantly reduce overall maintenance costs and unplanned downtime compared with more reactive maintenance frameworks. 

Maintenance software provides the computing muscle to enable maintenance strategies and all leading strategies use some form of it to enhance efficiency, monitor equipment status and organize tasks. A modern maintenance strategy uses either a computerized maintenance management system (CMMS) or an enterprise asset management (EAM) platform. These specialized digital tools execute similar functionality, such as administering work orders, centralizing asset data and setting maintenance schedules.

There are numerous maintenance strategies, but these four approaches are the most significant.

Imagine you drop and crack the screen of an old mobile phone. In taking it to the repair shop, you can find the cost of fixing that screen, given the age of the phone, is more expensive than buying a new one. This situation can also occur in enterprises where it is not cost-effective to repair or intervene to fix equipment before it breaks.

Reactive maintenance deals with assets after they either need service or fail. For most organizations, responding to failed assets proves to be expensive, burdensome for the manufacturing process and through reliability-centered maintenance (RCM), preventable by prioritizing alternative forms of maintenance.

A similar form of reactive maintenance is run-to-failure, which is a maintenance approach where companies purposefully allow equipment failures to happen to keep maintenance expenses low. It is used with specific facility assets, such as light bulbs, batteries, laptops or printer cartridges. These assets are either not repairable or cost more to repair than to let run down and replace with spare parts.

Reactive maintenance as a comprehensive discipline is less popular with the rise of data-driven organizations that can rely on more data to make informed decisions on how to approach maintenance. While it is often used because replacing parts before they expire does not justify the cost, it has an unavoidable downside. It creates unplanned downtime as companies race to replace the parts.

At its core, a preventive maintenance strategy is about fixing things before they break. It involves scheduled maintenance tasks aimed at extending the equipment’s lifespan and preventing future failures. While preventive maintenance minimizes failure risks by proactively addressing potential issues, it can become costly if parts are fixed or replaced well before they need it, leading to higher maintenance costs.

Preventive maintenance programs can mean different things for different equipment, tools and parts. Heavy machinery, for instance, will often require lubrication and cleaning done on a consistent basis. Other tools consume parts (for example, ink or dye) and need replacements before doing more work.

In addition, a machine contains many parts with different timelines to failure or repair, different failure modes (the different ways they might fail) and different costs to repair or replace. That makes preventive maintenance a challenge when so many individual pieces can determine whether the larger machine fails and when. Preventive maintenance also often fails to consider any real-time or updated data that might influence when equipment might fail.

There’s a considerable amount of nuance within the different forms of maintenance strategies, especially within the core strategy of preventive maintenance, where three of its derived forms deserve special attention:  

• Condition-based maintenance (CBM): Condition-based maintenance (CBM) operates through real-time data and monitoring tools. Condition monitoring starts with sensors that constantly diagnose overall machine health and report on various factors, such as looseness of parts, as determined through vibration analysis. Similar methodologies include oil analysis that sifts equipment fluids for bits of dirt or metal and ultrasonic checks that assess internal cracking. When data points to reduced performance due to wear and damage, CBM initiates a repair work order because stated warning limits have been exceeded in some way. CBM provides attentive care to equipment and helps keep it running longer and in peak condition.

• Risk-based maintenance (RBM): RBM views maintenance through the prism of risk. RBM is based on an underlying formula of risk calculation, whereby risk equals the probability of failure multiplied by the consequence of failure. In RBM, failure is defined as negative impacts to safety and health, the presence of environmental damage, operational losses and sizable financial costs tied to equipment repairs. These impacts are quantified through a severity score based on factors such as historical failure data and equipment age. Risks are scored according to FMEA, which ranks risks on perceived severity, likelihood of occurrence, difficulty of detection and costs of remediation. Risk rankings determine repair scheduling and resource prioritization.

• Time-based maintenance (TBM): A simpler approach, TBM foregoes analytics and risk calculations for the traditional calendar. Preventive maintenance is scheduled in advance to occur at fixed time intervals. This old-school approach to maintenance includes tasks such as changing HVAC air filters every three months. It depends heavily on prescribed manufacturer guidelines and historical data to set service times and fixed triggers, such as changing vehicle oil when a car records 5,000 miles. TBM fails to recognize existing equipment condition and works ideally in a theoretical framework where equipment is always brand new and has suffered no typical wear and tear. For example, a stated TBM practice calls for lubricating the bearings of a conveyor system after 3,000 hours of run time. However, it does not consider the fact that those bearings are already nearly exhausted and can fail earlier than expected.

This proactive maintenance approach uses data and machine learning, among other advanced technologies, to help engineers decide when to perform maintenance. Given the technology involved, it will have upfront costs to implement. But it is favored among data-driven organizations as a way to conduct maintenance when necessary, based on hundreds or even thousands of data points.

Predictive maintenance (PdM) is a more data-driven and advanced form of preventive maintenance. Both disciplines seek to fix equipment before they expire. The main difference is that predictive maintenance uses more data and real-time information to make a more accurate decision on when to replace, repair or clean the equipment.

Reliability-centered maintenance (RCM) is a systematic maintenance planning approach that organizations use to identify the critical physical assets, such as machines or tools, necessary for product production. It involves developing a comprehensive strategy to ensure that these assets remain operational and perform optimally.

Maintenance teams that use RCM will approach each piece of equipment and part differently. They choose the type of maintenance based on factors. These factors include the criticality of the equipment, difficulty in sourcing or replacing it, the data it generates to help maintenance workers identify whether it needs repair or replacement and its costs.

RCM can help organizations track and handle different critical assets versus non-core assets so that the ideal maintenance work for each piece of equipment and part can be done with ease.

Here’s how to craft an effective maintenance strategy:

Step 1: Establish your program: What skills does your team possess? What tools can it access? In this first step, it’s important that you consider what you want the team to do, then scope out clear goals that will help the team achieve those ends.

Step 2: Conduct a criticality analysis: For a maintenance strategy to prove effective, it needs to be realistic. That means really reading all your equipment and assessing which machines can go the distance and which pose a threat to production or safety.

Step 3: Determine costs and benefits: With the criticality analysis having been inventoried, it’s time to check the costs of making repairs and replacing worn parts versus the costs incurred when unexpected machine downtime occurs.

Step 4: Implement your strategy: You’ve set team goals, assessed equipment health and weighed repair costs. Now it’s time to implement your maintenance strategy. Train staff members. Establish clear daily workflows. Then, track tasks with a CMMS.

Step 5: Monitor program performance: Your strategy should not be seen as a one-shot process, but rather a work in progress that evolves as needed over time. The performance data that you collect should be gathered over time to check ongoing progress.

Maintenance strategies rely on critical maintenance metrics and key performance indicators (KPIs). Armed with this prime data, teams can analyze asset reliability, ensure efficient repairs, assess overall program health and explore cost-control measures.

Here are the core indicators teams track in support of those activities.

• Mean time between failure (MTBF): Mean time between failure (MTBF) is the average number of hours that an asset can successfully operate between unexpected breakdowns. A higher MTBF score denotes greater reliability.
• Mean time to repair (MTTR): Mean time to repair (MTTR) is the average time technicians need to diagnose and repair an asset and return it to operation. A lower MTTR score suggests fast problem remediation.
• Overall equipment effectiveness (OEE): Overall equipment effectiveness (OEE) determines asset productivity through a single equation—availability x performance rate x quality output—and reduces the data to one composite score. The national industry average for OEE varies between 60% and 75%, with global benchmarks at or above 85%.

• Planned maintenance percentage (PMP): This ratio compares planned proactive work hours with total maintenance hours recorded. A high percentage is favorable because it shows a mature strategy at work.
• Preventive maintenance compliance (PMC): The percentage of scheduled routine tasks assigned and completed within a permissible amount of time. A high PMC percentage suggests tasks are being performed satisfactorily.
• Maintenance backlog: The backlog reflects work orders that are still waiting to be executed. Regularly checking the maintenance backlog (and current maintenance schedules) can help prevent operational bottlenecks that arise from inattention.

• Maintenance cost per unit: This figure shows that maintenance cost efficiency per item produced. It is calculated by dividing total maintenance spending by total production output.
• Maintenance cost as percentage of replacement value: Asset value is considered in this calculation, which measures annual maintenance spending against the total estimated replacement value of physical assets.

The following are some of the primary benefits of enacting a focused maintenance strategy: 

• Lower costs: By implementing a maintenance strategy, companies can achieve significant cost savings. Repair costs are lower because the maintenance strategy helps detect upcoming repairs so those costs can be prioritized and absorbed. Scheduled fixes are almost always cheaper than emergency repairs. In fact, recent statistics show that reactive emergency repairs cost 4–5 times more than planned proactive work on the same assets.

• Less unplanned downtime: When major repairs appear out of nowhere, they typically require higher repair costs. They also usually cause production to be rerouted or halted while machines are fixed. Unexpected machine downtime gets enormously expensive frightfully quick. In fact, unplanned downtime costs manufacturers billions globally, with the average unplanned downtime incident costing more than USD 125,000 per hour.

• Increased safety: Machines that receive regular maintenance not only run in great condition and exhibit-increased longevity, they also operate more safely. This safety benefit becomes of prime importance in a factory setting responsible for ensuring all possible worker protections. Machines that suffer catastrophic breakdowns can instantly create an environment overflowing with operational hazards and rife with the potential for workplace accidents.

There exists no one maintenance strategy that proves ideal for all types of enterprises. The areas of variance between different types and sizes of organizations are simply too great for one strategy to accommodate. Better to instead focus on crafting the best possible hybrid model based on the importance of assets, their respective costs and potential for risk.

Many organizations have found success in running a blended mix of approximately 80% proactive maintenance and 20% reactive maintenance.

To enable a modern maintenance strategy, an organization needs to have core software components in the form of either a computerized maintenance management system (CMMS) or enterprise asset management (EAM) platform. Both execute similar functions, such as centralizing asset data, performing work order management, calculating inventory and determining labor schedules.

The software chosen works with predictive maintenance (PdM) tools, Internet of Things (IoT) sensors, enterprise resource planning (ERP) systems and mobile apps to monitor assets and regulate workflows.

Implementing a maintenance strategy often fails for the same basic, human-centric reasons that cause any company-backed initiative to collapse. These factors include lack of leadership support for the strategy, worker resistance to altered work processes and lack of sufficient employee training. They also include failures of interdepartmental communication and organizations pushing program implementation too fully or too fast.

Other factors that can also play negative roles include having a relative dearth of accurate asset data, failing to clearly define the metrics that constitute program success and setting unrealistic project deadlines.

Small- and medium-sized businesses (SMBs), despite operating with limited budgets, can still develop strong maintenance strategies. One key to making this goal happen is narrowing company focus on critical assets and prioritizing equipment maintenance based on their importance.

Another crucial move involves cross-training team members so all can make minor adjustment and upkeep tasks without requiring a dedicated specialist. Teams should support training with clearly detailed checklists that specify regularly needed tasks. Stock tip: Keep a working inventory of replacement parts so minor fixes can be handled quickly and efficiently.