Have you noticed that although there are many different approaches to modern maintenance, most of the enterprises usually incorporate elements of condition-based maintenance (CBM), reactive maintenance, predictive maintenance, corrective maintenance, and preventive maintenance?
This is where ‘maintenance optimisation’ helps maintenance teams plan maintenance activities by taking into account variables including asset criticality, failure rate, operating state, and past performance rather than treating every maintenance activity equally.
By helping maintenance teams to determine which assets and failure modes present the highest risk, maintenance optimisation also helps organisations improve the safety of their operations and procedures. Teams may identify issues early and take corrective action before they result in dangerous equipment breakdowns.
In this article, we explore a maintenance optimisation framework.
What is Maintenance Optimisation?
Maintenance optimisation is a process that modern businesses use to fine-tune their maintenance plans, increase equipment reliability, and save maintenance expenses.
In the past, the majority of businesses considered maintenance to be an essential expense of asset management. Faster data collection is now possible due to new tools and technology, including artificial intelligence (AI), Internet of Things (IoT) sensors, and computerised maintenance management systems (CMMS) platforms.
Additionally, they allow for faster data analysis and support larger data volumes than ever before. For businesses prepared to devote time and resources to maintenance optimisation, this change has made maintenance strategy a potential differentiator.
Key Takeaways
- Maintenance optimisation frameworks help businesses structure maintenance activities to improve equipment reliability and operational efficiency.
- Combining preventive maintenance, predictive analytics, and asset performance monitoring can reduce downtime and control maintenance costs.
- A data-driven maintenance strategy supports better resource allocation, extends asset lifespan, and promotes continuous operational improvement.
Framework of Maintenance Optimisation
When it comes to maintenance optimisation strategy, businesses usually follow these seven phases and modify them to suit their need.
Phase 1: Assess and Classify the Assets
Determining which assets are most crucial to a company is the first step in optimising maintenance procedures.
An asset criticality assessment is the name given to this procedure.
Maintenance teams use asset criticality testing to find assets that could jeopardise worker safety, interfere with essential business operations, or violate legal or environmental requirements. In the organisation’s asset hierarchy, these assets are given top priority since they are classified as ‘critical assets.’
Phase 2: Examine the Causes and Effects of Failures
Organisations then look at how assets classified as ‘critical’ are most likely to fail and what occurs when they do. RCM and FMEA are typically used in this step to assist maintenance teams in identifying possible failure modes.
Work order records and maintenance data from the past offer additional proof of recurrent failures and assist teams in spotting trends.
Gaining a better understanding of the connection between asset quality, failure rate, consequences, and action is the aim of this phase.
Phase 3: Analyse Current Maintenance Methods
Organisations then examine their present maintenance plans to find weaknesses and opportunities for development.
For instance, although predictive or CBM maintenance works better for some assets, preventive maintenance works better for others.
In order to cut expenses, some low-risk assets can even benefit from planned reactive maintenance or run-to-failure techniques.
Phase 4: Acquire and Review Maintenance Records
Maintenance optimisation depends on accurate maintenance data.
A computerised maintenance management system is used by the majority of contemporary businesses to record information on work orders, labour, spare parts, equipment failures, and finished maintenance activities. IoT sensor systems can offer useful information on pressure, temperature, vibration, electrical properties, and other equipment state indicators.
This data can eventually be used to improve maintenance plans and spot chances for preventative maintenance.
Phase 5: Improve the Schedule and Tasks of Maintenance
An organisation can assess if its current maintenance schedules and tasks are suitable or require improvement once it establishes an asset hierarchy and gathers sufficient maintenance data.
Some companies employ sophisticated methods, such as operations research, linear programming, quadratic programming, and other optimisation models, to find the best maintenance intervals and resource allocation. Some rely on a simple examination of work order history and do not need this level of intricacy.
Phase 6: Execute the Updated Plan
The updated maintenance schedules and work instructions from Phase 5 are implemented by maintenance teams in this context.
A CMMS is frequently used in modern maintenance operations to assign maintenance tasks, create recurring work orders, and record completed work. In order to successfully apply the new procedures, maintenance personnel should also acquire any required training, guidance, or equipment at this phase.
Phase 7: Set KPIs and Make Constant Improvements
Keep an eye on key performance indicators (KPIs) such as mean time between failures (MTBF) and unscheduled downtime, and approach maintenance optimisation as a continuous process.
Equipment performance can be impacted by changes in operating and environmental circumstances over time, which might introduce new patterns and production requirements.
Businesses should routinely examine asset data and modify maintenance plans in light of fresh information.
How Tigernix EAM Uses AI to Strengthen Enterprise Maintenance Optimisation
You can optimise enterprise maintenance now with Tigernix Enterprise Asset Management System, powered by AI and Industry 4.0 technologies. Its embedded AI models, real-time IoT monitoring, and predictive analytics help you identify potential failures, schedule proactive maintenance, and optimise asset performance.
With our software solution in place, you can reduce unplanned downtime, control maintenance costs, extend equipment lifespan, and improve operational efficiency through data-driven insights, automation, and smarter maintenance decisions across your enterprise.
Call for a free demo.
Tigernix-Digitalised Maintenance For Your Organisation
FAQs About Maintenance Optimisation
Maintenance optimisation entails enhancing maintenance plans, schedules, resources, and procedures to lower equipment downtime, manage expenses, increase asset longevity, and boost operational dependability.
Maintenance optimisation assists equipment to operate dependably while lowering unplanned breakdowns by identifying possible failures, prioritising important assets, improving servicing schedules, and guaranteeing prompt interventions.
Technologies such as AI, IoT sensors, predictive analytics, computerised maintenance management systems, and real-time monitoring aid in maximising equipment performance and maintenance scheduling.
Maintenance optimisation lowers maintenance costs, minimises unscheduled downtime, enhances resource allocation, prolongs equipment lifespan, boosts productivity, and facilitates more dependable industrial operations.
