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Predictive maintenance – towards the remotely operated, autonomous plant
maintAI analyses your operational data to increase uptime and reduce operating costs.
Industrial operators are under pressure to manage costs, risks and emissions while meeting production, reliability and safety targets.
Today’s assets generate vast amounts of data, but making sense and making the best use of it can be challenging. Information is often scattered across various systems, making it difficult to get a comprehensive view of workflows and optimise operations.
Wood combines cutting-edge technology, machine learning and industry expertise to optimise asset management. Our maintAI solution solves the problem of varied and diverse data to provide decision-makers with timely and actionable insights, enabling you to respond quickly to changing circumstances and identify opportunities for efficiencies.
Improve plant reliability
Target critical systems, equipment and failure modes through prescriptive recommendation models powered by our vast industry database
Reduce emissions
Predict and optimise power generation and gas-fired systems, avoid trip-related flaring and methane leaks
Elevate safety
Apply smart strategies and monitoring technologies to remotely diagnose and predict failures while reducing operational cost
Focus maintenance activities when and where they’re needed
maintAI empowers your decision-making today, streamlines your tasks for tomorrow and cuts maintenance costs now and in the future.
maintAI models apply the latest industry global best practices, lessons learned and digital technology, combining engineering first principles and AI models to rapidly optimise your maintenance operating model.
Our approach lets you make better decisions with your data and create an improvement roadmap to achieve and exceed your performance targets.
Optimise maintenance
10% savings target in annualised maintenance hours.
Maintenance optimisation can be a silver bullet to achieve operational efficiency.
With an optimised maintenance plan, asset owners and operators can reduce costs and extend asset lifespan to unlock the full productivity of their assets.
Wood will partner with you to optimise your operations by eliminating unnecessary maintenance tasks, prioritising capacity release and enhancing reliability and safety.
maintAI provides detailed recommendations to optimise your maintenance plans by analysing your CMMS data, including corrective maintenance history, planned work orders and asset database information.
Reduce backlog, free up resources
20% reduction in backlog with marginal change to facility risk.
Capacity and site staffing are a constant challenge affecting cost and profitability, especially with remote or offshore installations. Wood has helped numerous operators tackle this problem by assessing maintenance and asset data at a granular level to release capacity rapidly and safely.
Wood will work with you to analyse your maintenance backlog and identify maintenance spend that can be removed or re-prioritised.
Optimise spares and reduce waste
30% savings in inventory costs and reduced downtime.
Efficient inventory management is essential for a successful maintenance plan. Factors such as spares philosophy, obsolescence, supply chain insights, preservation, consumption rates and availability all contribute to effective maintenance execution.
Wood will work with you to optimise your supply chain, ensuring the right materials are available to complete the work or maintain critical equipment in the event of a failure. We partner with you to reduce downtime and manage inventory costs.
Linking material information with usage rates, our extensive experience library and industry standards, maintAI provides recommendations on holding requirements and waste reduction.
Capabilities
Backlog optimisation
Leverages AI, data processing and domain knowledge to recommend backlog removal and prioritise reliability improvements.
Predictive maintenance
Uses advanced data analysis and machine learning to identify equipment failures before they occur, enabling maintenance teams to perform repairs proactively and avoid unplanned downtime
Spares optimisation
Provides recommendations on holding requirements, linking material data with usage rates, our experience library and industry standards.
Maintenance strategy optimisation
Utilising CMMS data such as corrective maintenance history, planned maintenance work orders and asset database information, our models deliver recommendations to optimise maintenance plans.
maintAI applies AI and domain expertise to your data and processes to optimise equipment maintenance and operations from end to end
The process
1
Gain alignment
We understand your requirements and align with your processes to deliver solutions quickly and accurately, without impacting your current operations.
2
Configure the models
Models are optimised for performance and scalability and validated against industry standards.
We customise models to your unique data to advance your organisational goals.
3
Apply recommendations
maintAI outputs provide actionable insights with a data-driven foundation to help stakeholders make informed decisions.
Our models are agile and can adjust to align with facility changes.
4
Monitor results
Wood will work with you to extract value from the insights maintAI generates and create a plan for ongoing maintenance optimisation.
5
Get assurance
We provide transparency so you can trust the outputs from our models as a layer of validation and risk mitigation.
The results
Results visible in
8-16
weeks
Maintenance cost reduced by
20-40%
on average
Unplanned downtime
10%
reduction for typical clients
By applying our maintAI models, you will get:
- A clear path to smart maintenance, linking sparing to maintenance and facility health
- Optimised ‘low-touch’, smart maintenance strategy outcomes
- Increased critical asset availability
- Decreased maintenance cost without production loss
- Reduced production losses and trips, as well as trip-related emissions
- Reduced person-on-board requirements and a workforce focussed on reliability optimisation
- Real-time updates of strategy, equipment maintenance and backlog improvement suggestions
See if maintAI works for you
Get in touch with us, and one of our consultants will get back to you shortly to arrange a free consultation or demo.
Common questions
Yes. We know that every facility is different, and maintAI has been designed to work across a vast variety of data sets.
We analyse your data, technology and systems upfront to make sure to deliver results in a way that works for you.
maintAI works alongside your existing processes and systems. Our industry standard models and assumptions will be tailored to your existing processes and ways of working. If there is a specific feature you are looking for, we will work with you to develop and deploy it.
maintAI is designed to integrate with your existing tools and processes. We will work with you to identify the data sources required and will then connect the data feeds to our models.
Reporting and visualisations can be calibrated to combine inputs and output from our models into a single visualisation.
Yes, maintAI can help to predict failures before production is affected. Our predictive maintenance solution looks for leading indicators of declining equipment health and uses advanced data analysis and machine learning to identify potential failures before they occur.
Your maintenance teams will be alerted to perform repairs proactively and avoid unplanned downtime.
maintAI can tap into any relevant data sets your assets can provide. Computerised maintenance management system (CMMS) data such as work order history, bill of materials, asset hierarchies, etc, can all be used.
The inputs required are different depending on your industry and asset types. Our team will work with you to identify the right data to generate useful insights for your workflow.
Once all relevant data has been identified, it will be cleansed and analysed so any missing data is identified and accounted for in the model outputs.
maintAI evaluates current maintenance strategies, equipment health and inventory levels.
To provide recommendations on where to focus attention and prioritise spend. It also identifies areas where spending can be reduced, such as non-value-adding maintenance tasks or excess holding.
By shifting focus and budget to maximise returns, our clients can see significant savings in maintenance operations.
Instead of relying on industry standards and best practice assumptions, maintAI takes CMMS data directly from the assets, so recommendations and insights are based on your current facility data.
We can identify differences in equipment at your facilities and use this knowledge to evaluate where the maintenance spend can be optimised.
Yes, reports and insights from maintAI support decision-making from high-level strategic planning down to on-site tasks and technical recommendations.
By connecting operational data from various sources and extracting insights from current asset health, recommendations are designed to align with your strategic vision and will generate a roadmap to help you achieve them.
At Wood, data security and privacy are of utmost importance. We have implemented a secure cloud architecture and work closely with our customers to guarantee the safety and security of their data.
Contact us for more information.
Wood has been optimising equipment maintenance and reliability
of asset-intensive industries for 25+ years.
- Technology-enabled, independent engineers – combining domain expertise with data-driven models
- Superior in our capability offering – full complement of in-house expert knowledge and experience
- Problem solvers – combination of desktop simulation and field experts for optimum solution development
- Thought leaders – based on our strong commitment to research, innovation and knowledge sharing, backed by decades of experience
- First-class in our service – in-house software tools, resulting in improved accuracy, faster service and lower costs
- Consistent in our delivery – standard processes and quality control for inspections, simulations and risk evaluation
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