Mastering Maintenance: How to Save Big with the Right Strategy

1. Why Optimizing Maintenance Matters

In today’s industrial landscape, maintenance is not just about fixing machines when they break down. It’s about optimizing the performance of equipment, minimizing downtime, and reducing costs.¹ Companies that elevate their maintenance strategies can see significant gains in productivity, efficiency, and ultimately profitability. Rather than focusing on basic maintenance needs, this guide aims to show how you can move from “good” to “great” through more advanced, tailored approaches.

The Evolving Landscape of Maintenance

Over time, maintenance has evolved from simple reactive measures to complex, data-driven systems. Early practices were about fixing what was broken, but modern techniques incorporate technology that predicts failures before they happen, and even use automation to handle day-to-day tasks. In this new landscape, maintenance has become a strategic function, leveraging insights from various fields like AI, IoT, and machine learning.² For companies of all sizes, adopting these advanced strategies doesn’t have to be daunting, especially as more affordable solutions become available.

2. An Overview of the Different Maintenance Types

With so many different approaches and buzzwords circulating in the industry, it’s easy to get confused. Terms like “Predictive Maintenance” and “Prescriptive Maintenance” are often used interchangeably, despite their distinct differences. Furthermore, businesses are easily misled by vendors pushing solutions without fully understanding their customers’ specific needs or infrastructure.

The types of maintenance we will explore in this chapter can be categorized into traditional and modern approaches. Each method has its strengths and weaknesses, making it important to choose the right type for each piece of equipment.³ Below is an in-depth look at various types of maintenance, their origins, use cases, strengths and weaknesses.

2.1  Traditional maintenance types

1  Corrective Maintenance  a.k.a. Reactive Maintenance

• Definition: Maintenance performed after a failure occurs, with the aim of restoring equipment to operational condition.⁴
• Origins: The earliest form of maintenance, used since machines were first introduced. Initially, industries had little choice but to fix machines only after they broke.
• Use Cases: Typically used for non-critical equipment where downtime won’t significantly impact production.
• Strengths: Simple and cost-effective for low-risk equipment.
• Weaknesses: Risky for critical equipment, as it can lead to unplanned downtime and production losses.

2  Preventive Maintenance

• Definition: Scheduled maintenance carried out at regular intervals to reduce the chance of equipment failure.⁵
• Origins: Rooted in early manufacturing practices when factories focused on minimizing unplanned downtime. Widely adopted in sectors with high-volume machinery like automotive and textile industries.
• Use Cases: Ideal for equipment that operates continuously or for businesses where machine failure can lead to significant production losses.
• Strengths: Reduces the likelihood of catastrophic failures.
• Weaknesses: Can result in over-maintenance, where machines are maintained more frequently than necessary.

 3  Predetermined Maintenance

• Definition: Maintenance tasks scheduled in advance based on the production schedule, ensuring that equipment is maintained during planned downtimes rather than interrupting operations.⁶
• Origins: This strategy evolved from preventive maintenance, particularly in industries with long production runs or fixed manufacturing schedules, such as steel production or automotive.
• Use Cases: Ideal for industries where stopping operations for maintenance during continuous production cycles would incur significant costs, like heavy manufacturing, steel mills, and chemical plants.
• Strengths: Aligns with production schedules, maximizing equipment availability and minimizing disruption to manufacturing. Maintenance is planned to avoid interference with operations.
• Weaknesses: Can lead to over-maintenance or deferred maintenance if the production schedule doesn’t allow flexibility. It also may not account for real-time equipment conditions.

4  Condition-Based Maintenance  (CBM)

• Definition: Maintenance triggered by real-time data on equipment condition, such as temperature, vibration, or pressure, rather than scheduled intervals.⁷
• Origins: A precursor of predictive maintenance, CBM became more popular as sensors became more affordable and reliable.
• Use Cases: Effective for machines where operating conditions vary, such as those in energy production or chemical processing.
• Strengths: Minimizes unnecessary maintenance and reduces costs by addressing problems only when specific indicators show a need.
• Weaknesses: Requires continuous monitoring and the integration of complex data systems, which may not be feasible for all companies.

 5  Predictive Maintenance

• Definition: Maintenance based on real-time monitoring of equipment performance and condition, aiming to predict when failures will occur using data analytics and AI tools.⁸
• Origins: Emerged in the late 20th century with advancements in sensors, data analysis, and AI, gaining traction in industries such as aerospace, manufacturing, and energy.
• Use Cases: Best suited for high-value or critical machinery, where unexpected failures can lead to significant financial losses or operational disruptions.
• Strengths: Helps prevent unplanned downtime and extends equipment life by addressing issues before they escalate.
• Weaknesses: High initial cost due to the need for sensors, advanced data systems, and expert analysts to interpret complex results.

 6  Autonomous Maintenance

• Definition: A maintenance approach where operators take on basic upkeep tasks like cleaning, lubricating, and inspecting equipment.⁹
• Origins: Developed within the framework of Total Productive Maintenance (TPM), this approach empowers operators to care for their equipment, especially for Clean-Inspect-Lubricate (CIL) tasks, reducing the reliance on dedicated maintenance teams.
• Use Cases: Common in environments where equipment runs continuously, like in many manufacturing operations, and where daily care is essential for smooth operation.
• Strengths: Engages operators, reducing equipment breakdowns and extending the life of machinery.
• Weaknesses: Requires a significant amount of operator training and cultural buy-in, which can be challenging to implement.

2.2  Modern Maintenance Types

 7  Prescriptive Maintenance

• Definition: A step beyond predictive maintenance, this strategy uses machine learning and AI not only to predict failures but also to recommend specific corrective actions or adjust operating conditions for optimal performance and to delay or eliminate failure.¹⁰
• Origins: Evolved from predictive maintenance with the advent of more sophisticated AI and machine learning capabilities that can simulate potential outcomes based on historical data and operating environments.
• Use Cases: Ideal for complex systems where determining failure trends is difficult, where maintenance stops are very costly, or where advanced optimization of operations is desired to extend asset life and improve efficiency.
• Strengths: Provides concrete recommendations for preventive actions, reducing risk and improving decision-making by simulating operating scenarios.
• Weaknesses: Requires substantial investments in AI infrastructure, high-quality data, and expertise to manage and interpret the outputs effectively.

8  Reliability-Centered Maintenance  (RCM)

• Definition: A structured process used to determine the maintenance needs of equipment based on its reliability and the criticality of its function. Often characterized as a process to establish the safe minimum levels of maintenance.¹¹
• Origins: Initially developed in the aviation industry to maintain the reliability of aircraft, RCM has since been adapted for use in other industries.
• Use Cases: Best suited for critical systems where reliability is directly tied to safety or business continuity, such as in aerospace, military, and utilities.
• Strengths: Focuses resources on equipment which has the highest impact on production or safety.
• Weaknesses: Can be highly complex to implement, requiring detailed analysis and understanding of the equipment, and potentially extensive changes in existing maintenance practices.

9  Risk-Based Maintenance  (RBM)

• Definition: An approach that prioritizes maintenance efforts based on the risk associated with equipment failure, focusing on areas where failures pose the greatest risk to safety or productivity.
• Origins: Developed in industries with high safety standards, such as oil & gas and nuclear power, where the consequences of equipment failure can be catastrophic.
• Use Cases: Best applied in environments where failure risk is unevenly distributed, and some equipment is critical while others are less important.
• Strengths: Focuses maintenance resources where they are most needed, potentially reducing costs by scaling back efforts on non-critical equipment.
• Weaknesses: Requires careful risk assessment and can be time-consuming to implement across a facility.

 10  Remote Maintenance

• Definition: Maintenance activities carried out via remote monitoring tools, allowing for diagnostics and even repairs without needing on-site personnel.
• Origins: Gained popularity as IoT devices and remote connectivity technologies advanced, allowing for maintenance in hard-to-reach or hazardous environments.
• Use Cases: Ideal for industries where access to equipment is difficult, such as offshore drilling or in remote areas.
• Strengths: Reduces the need for travel and physical presence, which can be particularly beneficial for large or remote sites.
• Weaknesses: Requires reliable connectivity and may face challenges in resolving more complex or physical issues.

2.3  Classification of Maintenance Types

10. Remote Maintenance can be across all columns, depending on its application.

2.4  Maintenance for Trucks, Cars, and Forklifts

Maintaining mobile equipment such as trucks, cars, and forklifts presents unique challenges compared to stationary machinery. For these vehicles, legal requirements play a critical role, including regular inspections, certifications, and emissions testing. Depending on the industry and region, there may also be safety regulations specific to fleet management. This often means stricter compliance and documentation standards.

Additionally, the choice between in-house maintenance versus outsourcing to garages, dealerships, or leasing companies adds a layer of strategic decision-making. Outsourcing using leasing agreements with maintenance packages is a common approach. However, companies that manage fleets in-house can still apply several maintenance strategies discussed here, such as preventive maintenance for oil changes and tires, condition-based maintenance for brakes, and autonomous maintenance for refueling or battery changing/recharging, and daily checks like fluid levels or tire pressure: drivers and forklift operators often perform some of their own maintenance. The choice of strategy often depends on factors such as fleet size and usage patterns.

Centralizing fleet data through fleet management software and vehicle telematics (similar to CMMS, see section 3.7) can streamline this process, while also helping track KPIs like vehicle performance, fuel efficiency, and engine / tire wear trends. A multitude of software providers offer solutions of varying complexity and capability.

2.5  Comparative Table of Maintenance Types 

© Delft Consulting

3. Roadmap to Effective Maintenance Strategy Selection

Selecting the right maintenance strategy is a crucial part of optimizing production and minimizing costs. This chapter provides a structured roadmap and decision-making guide that aligns different types of maintenance approaches with the needs of your business. By considering key factors such as the nature of your equipment, risk tolerance, and budget, this guide helps you build a tailored maintenance strategy that balances reliability, cost-efficiency, and practicality.

3.1  Best-in-Class Maintenance: How Industry Leaders Do It

Industry leaders like Toyota, ABB, GE, SNCF, P&G and Honeywell have long recognized the importance of advanced maintenance strategies to achieve operational excellence. Here are a few examples of how these companies have successfully implemented cutting-edge maintenance practices at scale:

• Toyota: Known for its lean manufacturing and just-in-time production, Toyota has integrated autonomous maintenance (CIL) across multiple production lines. Their approach involves operators taking responsibility for routine maintenance tasks like cleaning, inspecting, and lubricating equipment. This has led to higher equipment uptime and reduced reliance on dedicated maintenance staff, while also empowering operators with more responsibility and ownership over machine performance.¹²
• ABB’s Ability™ Predictive Maintenance software uses AI and machine learning to predict equipment failures. In one of their largest implementations, ABB rolled out predictive maintenance solutions across 40 production sites, helping to reduce unscheduled downtime by 30%. The data-driven approach has allowed ABB to optimize maintenance intervals and prevent equipment failure before it occurs, reducing both operational costs and equipment downtime.¹³
• GE‘s predictive maintenance systems have been implemented across its wind farms, where predictive analytics of a continuous stream of data of all wind turbines in parallel has helped significantly improve equipment reliability. This data-driven approach allows for more precise operations and maintenance schedules, helping GE to lower the operational cost of maintaining wind turbines.¹⁴
• SNCF: As part of their strategy to improve train reliability, SNCF has implemented predictive maintenance across its rail network. By monitoring critical components, like brakes and engines, SNCF was able to reduce ‘visible breadowns’ by 50%, and the number of machines stopped for maintenance by 30%. Predictive analytics also enabled SNCF to schedule more efficient maintenance windows, aligning repair times with train downtime, further minimizing disruption.¹⁵
• P&G employs a widespread autonomous maintenance program across its global manufacturing plants, the “AM pillar” of its famed IWS (Integrated Work Systems) methodology, training operators to perform daily checks and routine maintenance, that goes beyond the usual CIL, including tasks like chain tensioning and center-lining. This approach has resulted in a 20% reduction in machine breakdowns and extended the lifespan of production equipment across their operations.¹⁶
• Honeywell integrates condition-based monitoring into its maintenance strategy, particularly for high-value aerospace components. Using IoT sensors and advanced analytics, Honeywell has reduced maintenance costs by 15% while improving overall equipment reliability.¹⁷

These examples set a benchmark for what companies can achieve by leveraging advanced maintenance strategies, albeit at large scale and tailored to specific operational needs.

3.2 Assessing Current Maintenance Practices

Before embarking on any optimization, understanding your current approach is crucial. An audit of existing practices will help you identify areas of strength and weakness. This audit can be as simple as cataloging the types of maintenance currently employed (e.g., corrective, preventive) or as detailed as tracking metrics like downtime, cost, and equipment performance over time.

Actions:

• Conduct a Maintenance Audit: Review existing schedules and types of maintenance being performed. For instance, look at whether preventive tasks are done based on time intervals or real-time equipment data.
• Analyze Downtime and Costs: Assess the financial impact of unplanned downtimes and identify opportunities to reduce these costs.
• Collect Feedback from Maintenance Personnel and Operators: Listen to those on the ground. Operators often have insights about recurring issues or equipment with higher failure rates.

This data will form the baseline for measuring future improvements. For example, companies like Honeywell gather real-time feedback from IoT-enabled equipment, combining it with operator inputs to gain a clear picture of their operations.

3.3 Defining Maintenance Goals

Having a clear set of goals allows businesses to tailor their maintenance strategies to their needs. Goals should reflect the company’s priorities—be they maximizing uptime, reducing costs, or improving safety.

Actions:

• Set Performance Targets: Establish measurable objectives for metrics like uptime, downtime, and cost per maintenance event.
• Prioritize Critical Equipment: Some machinery is essential to keep operations running smoothly. Rank equipment by its criticality to ensure the most vital assets receive attention first.
• Define Risk Tolerance: Acceptable levels of downtime or equipment failure depend on your business model. For example, Toyota defines critical thresholds in its production lines, with failure tolerances measured in minutes to avoid disruptions in just-in-time manufacturing.

3.4 Decision Tree for Choosing the Right Maintenance Approach

Once you have determined the criticality and requirements for each piece of equipment, and you are clear on your maintenance goals, risk tolerance, capabilities and budget, it is time to define the approaches you will apply.

The decision tree below is a simplified guide to help you decide on your maintenance strategies based on these factors. Going through the questions leads you toward a specific maintenance approach that, in general, fits your operational situation and needs.

Obviously, since every piece of equipment and business goal is unique, these recommendations serve as good starting points rather than strict rules. They should still be evaluated and, if needed, adjusted to fit the specific realities of your situation.

3.5 Creating a Mixed Maintenance Strategy

Many businesses benefit from using multiple maintenance approaches in tandem. Equipment that’s critical or expensive may warrant a predictive or condition-based maintenance strategy, while simpler machines can rely on more basic preventive maintenance.

• Example: Predictive + Preventive: ABB’s Ability™ Predictive Maintenance software monitors critical machinery and predicts failures. However, they supplement this with traditional preventive tasks for smaller, non-critical equipment, ensuring comprehensive maintenance coverage.
• Example: Autonomous + Condition-Based: P&G uses autonomous maintenance for daily tasks like lubrication and inspection, while using condition-based techniques for specialized machinery. This ensures that the equipment gets the attention it needs without overburdening operators.

Even a single piece of equipment may require multiple maintenance approaches. For instance, routine cleaning and lubrication can fall under autonomous maintenance, while replacing components like bearings and belts might be part of a preventive maintenance plan, and seals could be replaced using a condition-based maintenance approach.

Consider the analogy of maintaining a car: a mechanic will change brake discs and pads based on their appearance (condition-based maintenance), replace filters and oil at regular intervals based on usage (preventive maintenance), you may top up the tire pressure and windshield washer fluid yourself (autonomous maintenance), and go for a general checkup prior to a long holiday trip (predetermined maintenance).

3.6 Integrating a CMMS into Your Maintenance Strategy

A Computerized Maintenance Management System (CMMS) is a digital tool designed to streamline maintenance management by tracking equipment, work orders, inventory, and maintenance activities. It helps organizations manage their maintenance data, ensuring that preventive tasks are scheduled, downtime is minimized, and asset performance is optimized.²⁰

Key Benefits of a CMMS:

• Improved Scheduling: Automates work order generation for preventive maintenance based on runtime, condition, or fixed schedules.
• Asset Tracking: Maintains a comprehensive record of equipment, including maintenance history and parts usage, aiding in decision-making.
• Cost Efficiency: Helps track the costs of labor, parts, and downtime, allowing for better budgeting and cost control.
• Data-Driven Insights: Provides reports and dashboards to track key metrics like MTBF (Mean Time Between Failures) and overall equipment effectiveness (OEE).
• Enhanced Compliance: Ensures regulatory compliance by keeping up-to-date logs of inspections, repairs, and certifications.

Differences Among CMMS Options:

• On-Premise vs. Cloud-Based: Cloud-based systems are more scalable and accessible, while on-premise systems offer greater customization and control.
• Integration Capabilities: High-end CMMS platforms integrate with IoT devices, ERPs, and analytics tools, while simpler systems focus on basic asset and work order management.
• Scale: Some CMMSs are designed for large enterprises with complex maintenance needs, while others cater to SMEs by providing affordable, user-friendly solutions.

 A Few Popular CMMS Solutions for Large Businesses:

1. SAP EAM (Enterprise Asset Management): Part of SAP’s Enterprise Suite, with a focus on comprehensive asset tracking and maintenance optimization.
2. IBM Maximo: A robust system offering predictive maintenance, IoT integrations, and advanced analytics.
3. Infor EAM: A flexible solution that supports complex maintenance strategies, including predictive maintenance and asset performance management.

A Few Popular CMMS Solutions for SMEs:

1. Limble CMMS: A user-friendly system with a focus on ease of use and flexible pricing, well-suited for small and medium-sized businesses.
2. Fiix: Known for its simplicity and affordability, Fiix caters to SMEs looking to improve preventive maintenance management.
3. UpKeep: A cloud-based, mobile-first platform, offering an affordable and intuitive solution for smaller teams.

Transitioning from Manual to Digital Maintenance Management:

Implementing a CMMS can be a game-changer, but transitioning from a manual system or an outdated digital solution requires thoughtful planning. It involves selecting the best fitting solution, setting up the software, importing existing data, training staff, and aligning the new system with the organization’s maintenance strategy. Before you start, ensure you have the capability and capacity to successfully design and run a CMMS inplementation project, either internally or with external support.

3.7  Organizational Roles in Maintenance

A successful maintenance strategy relies on a well-structured and clearly defined organizational framework. It’s essential to distribute responsibilities and align each role with the specific demands of your chosen maintenance strategy. Here’s a breakdown of key roles and considerations:

Leadership/Ownership (Centralized vs. Distributed)

In a centralized maintenance structure, the leadership and decision-making are concentrated at a higher level, often involving corporate or site-wide maintenance managers. This works well for organizations with multiple locations or large-scale operations, ensuring consistency across the board.

Distributed maintenance ownership means delegating responsibilities to individual teams or departments, often seen in plants with autonomous maintenance, where operators are empowered to carry out specific tasks. This approach encourages accountability and quicker decision-making for localized issues.

• Transitioning between centralized and distributed models requires careful planning. For centralized operations, invest in a robust communication system to maintain real-time updates across departments. For distributed roles, emphasize training to ensure each team is equipped to handle the autonomy.

Execution

The execution of maintenance tasks is typically handled by maintenance teams and operators. With more advanced strategies like predictive or prescriptive maintenance, data analysts or engineers may also be involved to interpret data and plan interventions.

For autonomous maintenance, execution is led by operators trained in specific CIL-type tasks. They serve as the first line of defense against breakdowns, spotting early signs of failure.

• A blended approach can include both skilled technicians and operators for more complex tasks. The transition to autonomous maintenance, in particular, requires cross-functional training and a mindset shift for operators.

Monitoring and Feedback Loops

Monitoring is crucial to ensure that maintenance activities are effective. Modern maintenance strategies often incorporate IoT devices, sensors, and condition-based tools for real-time monitoring.

A well-structured feedback loop connects operators, technicians, and engineers, ensuring that any insights from monitoring systems or on-the-ground observations are quickly acted upon. This creates a continuous improvement cycle.

• Build systems for operators and technicians to easily log data into the CMMS, and make monitoring dashboards accessible to various teams. Automating feedback loops helps reduce human error and ensures timely interventions.

Materials and Tools Management

Someone in the organization should be responsible for managing spare parts, tools, and consumables. This role ensures that the right tools and materials are always available when needed, reducing downtime due to part shortages.

• Centralize inventory control within the CMMS to keep a real-time view of stock levels. For distributed teams, ensure materials are easily accessible to avoid delays in repair or maintenance tasks. Simplify the tools and material as much as possible (see 4.1).

Engineering (Design for Maintenance)

The engineering team plays a significant role in “design for maintenance,” which involves making equipment modifications to simplify or optimize maintenance activities. Collaboration between engineers and operators is key in integrating elements that make maintenance tasks easier (e.g., self-lubricating components or easily accessible parts).

• To integrate this into your maintenance strategy, develop a process where maintenance teams can feed back their challenges to engineers, who can then adjust the design accordingly.

3.8  Transitioning to the New Maintenance Organization

The biggest challenge in shifting to a new maintenance strategy often lies in managing the organizational structure changes and adjusting the roles accordingly. Here are some tips:

1. Start with leadership alignment. Ensure that senior leadership understands and supports the new strategy to drive change throughout the organization.
2. Train teams early. Bring all employees, from operators to maintenance managers, up to speed on their new roles and responsibilities.
3. Pilot the new structure. Test it on a smaller scale or with non-critical equipment before rolling it out fully.
4. Evaluate and iterate. Regularly assess the performance of the new organizational setup, adjusting roles and workflows as needed to ensure they align with your overall maintenance goals.

3.9 Measuring the Effectiveness of Your Maintenance Strategy

Regularly measuring and reviewing maintenance metrics ensures that your strategy remains aligned with the goals as set out in the business case for putting your new strategy in place.¹⁸ As for all KPIs, in choosing the ones to use, you need to weigh the pertinence and usability for your operation with the effort of data gathering, calculation and reporting. Important KPIs include¹⁹:

• MTBF (Mean Time Between Failures): This metric tells you how frequently equipment is failing. Increasing MTBF indicates that your maintenance efforts are reducing failure rates.
• Downtime Reduction: This KPI measures the total time lost to unplanned downtime. GE for example has implemented digital twins to track this in real time, achieving significant reductions in downtime by predicting failures ahead of time.
• Maintenance Cost as a Percentage of Revenue: Keeping maintenance costs under control is essential. For example, Siemens focuses on cost-efficiency by combining AI-driven predictive maintenance with condition-based maintenance to lower overall costs while extending equipment life, finding the optimized mixed approach that best fits their business goals.

Using these metrics allows you to continually fine-tune your maintenance strategy, ensuring continuous improvement.

4. Optimizing for Easier Maintenance

Optimizing maintenance is not just about reducing downtime—it’s about making routine tasks simpler, faster, and more consistent. By designing equipment and processes with ease of maintenance in mind, companies can significantly reduce the manual effort involved in keeping machinery running smoothly. Small adjustments in how equipment is cleaned, inspected, and lubricated can have a big impact on overall efficiency. Beyond just cutting time, these changes also improve safety, lower costs, and reduce the likelihood of human error.²¹

4.1  Opportunities for Simplification and Synergies in Maintenance

Color-Coded Maintenance Tags and Labels

An effective yet simple method is the use of color-coded tags and labels for different maintenance tasks. This system helps maintenance personnel quickly identify, for example, the type of maintenance required (e.g., lubrication, cleaning, inspection) or which lubricant to use, without wasting time searching for instructions. It’s particularly helpful in larger facilities where equipment may require different handling.

Lubricant Range Simplification

Equipment suppliers usually recommend a specific lubricant brand and type for their machines, and most factories will use exactly that, resulting in a huge range of different lubricants to stock and use across the site. These suppliers, however, also provide lubricant specifications. By comparing these specs across different machines, you may find that many machines have at least partially overlapping spec ranges, allowing you to use the same product within the specification limits of several machines. Standardizing lubricants can significantly reduce the required range, making storing, replenishment and running lubrication rounds – one of the most routine maintenance tasks – much more efficient.

Universal Spare Parts

Whenever possible, using identical or interchangeable components across machines reduces the need for multiple specialized parts in stock. For example, selecting machines from a manufacturer that offers a shared set of spare parts for various models simplifies maintenance, speeds up repairs, and reduces inventory costs.

Tool Simplification

Just like spare parts, standardizing tools across machines can ease maintenance. If multiple machines can be maintained with the same set of tools, it reduces the complexity and time spent switching between different tools during repairs or adjustments.

4.2  Equipment Modifications for Easy Maintenance

Designing equipment for easier maintenance can result in faster repairs, less downtime, and a more productive operation. Whether through retrofitting or specifying new equipment with these features, easier maintenance leads to fewer headaches for both operators and maintenance teams.

Design for Easy Cleaning, Inspection, and Lubrication (CIL)

• Easy to clean: Equipment should be easy to access for cleaning, designed with accessible covers, removable drip trays, and proper drainage systems to prevent leakages and make cleaning more efficient.
• Easy to inspect: Ensure that machinery has good visibility for key components and visual control indicators (e.g., gauges, sight glasses) that are easily accessible and simple to read. This helps operators spot issues quickly during routine checks.
• Easy to lubricate: Equip machinery with shared lubrication points that are easy to access. Make sure fluid levels are visible at a glance, and provide centralized or automated lubrication systems where possible.

Retrofitting for CIL

Even older equipment can be modified to improve ease of maintenance. Retrofitting machines with features like easily accessible lubrication points, inspection windows, or cleaning access doors can extend their lifespan and simplify daily maintenance tasks.

Centralized Lubrication Systems

Instead of having operators manually lubricate each individual machine part, a centralized lubrication system delivers lubricant to all necessary points. This not only saves time but ensures that lubrication is evenly distributed, reducing wear and tear.

Modular Equipment Design

Machines designed with modular components make it easier to swap out faulty parts without taking the entire machine offline. Modular design also simplifies upgrades and minimizes disruption to operations when maintenance is required.

Poke-yoke for Maintenance Access and Tools

Poke-yoke, or error-proofing, helps maintenance personnel avoid mistakes by guiding them toward correct procedures. For example, machines may include built-in guides or special access points that ensure maintenance tasks like cleaning or lubrication are performed correctly and safely.

By incorporating these simplifications and modifications, companies can greatly reduce the time and effort needed to perform routine maintenance tasks, leading to a more streamlined and productive maintenance process. Many of these interventions are relatively cheap to implement, providing a very fast payout.

5. Conclusion

As you’ve seen throughout this guide, maintenance isn’t just about fixing what’s broken—it’s a powerful lever for cost savings and operational efficiency. By rethinking how maintenance is organized, measured, and executed, you can unlock significant opportunities for improvement. It’s worth giving this topic serious consideration; the gains from an optimized maintenance strategy can be more substantial than expected, and the return on investment often exceeds the effort.

For those looking to elevate their maintenance practices to the next level, bringing in an outside perspective might be beneficial. Whether for a fresh look at potential improvements or assistance in implementing new strategies, an expert’s guidance can help you spot areas for optimization that might otherwise go unnoticed. Above all, keep refining your approach—because in maintenance, as in business, there’s always room for improvement.

References

• 1 “Maintenance Management: A Guide to Best Practices”, Adolfo Crespo Márquez and Luis Alberto García Sánchez, CRC Press (2017)
• 2 “Reliability-Centered Maintenance”, John Moubray, Industrial Press (1997)
• 3 “Maintenance Engineering Handbook”, Barry J. Goodno and James D. H. Smith, McGraw-Hill (2012)
• 4 “Corrective Maintenance”, Barry J. Goodno and James D. H. Smith, McGraw-Hill (2012)
• 5 “Preventive Maintenance”, G. Nakajima, Productivity Press (1988)
• 6 “Predetermined Maintenance”, Anthony M. Smith, CRC Press (2005)
• 7 “Condition-Based Maintenance: A Practical Guide”, J. K. Campbell et al., Springer (2015)
• 8 “Predictive Maintenance in Dynamic Systems”, D. J. Lee et al., IEEE Transactions on Automation Science and Engineering (2016)
• 9 “Autonomous Maintenance”, John A. Bicheno, Lean Enterprise Academy (2008)
• “Predictive vs. Prescriptive Maintenance: Understanding the Differences”, J. K. Campbell, Journal of Quality in Maintenance Eng (2021)
• “Reliability-Centered Maintenance: A Practical Guide”, Anthony M. Smith, CRC Press (2005)
• “The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer”, Jeffrey K. Liker, McGraw-Hill (2004)
• “ABB Ability“, ABB Group (2023)
• “GE’s digital wind farm apps hold the key to predictive maintenance”, Power Technology (2016)
• “A global leader in predictive maintenance“, SNCF Groupe (2024)
• “Procter & Gamble: How IWS is driving operational excellence“, Manufacturing Digital (2021)
• “Condition Based Maintenance Systems”, Honeywell Aerospace technologies (2024)
• “The Business Case for Maintenance”, J. H. M. R. M. van der Meer, Maintenance & Asset Management (2019)
• “Maintenance Performance Indicators”, J. J. S. S. G. J. M. van der Meer, Journal of Quality in Maintenance Engineering (2012)
• “Computerized Maintenance Management Systems Made Easy”, David A. R. R., Industrial Press (2010)
• “Lean Maintenance: A Practical Guide”, John A. Bicheno, Lean Enterprise Academy (2008)