Deploy smart strategies and models to reduce planned maintenance by 20-40%, unplanned production trips and subsequent flaring by up to 30%, and achieve results in as fast as 12 weeks.
Assets these days are being challenged to dramatically reduce operating costs (~40%), site manning (~60-80%), emissions (zero), and safety (zero) while improving production throughput & availability (~10%). At the same time, ~30% of sanctioned value is lost in the industry on average due to production shortfall and unforeseen costs.
Our intelligent asset optimisation specialists work closely with clients to understand and address the main causes of these issues through operation; deploying smart, ‘low-touch’ strategies, IoT sensors and prescriptive equipment models to accelerate the transition to remote operations and automated maintenance management.
Human intelligence + machine learning + advanced analysis = better business logic for your assets
Minimise downtime with predictive maintenance strategies
Reduce operational costs and increase safety with a 'low-touch' maintenance approach
Create decarbonisation opportunities through smarter asset management
Many companies are grappling with supply chain disruptions and aggressive operational performance targets. An end-to-end perspective of maintenance operating and data models can vastly improve supply chain processes and operational performance, but optimisation initiatives often encounter these typical problem areas:
Strategies are generically derived with heavy reliance on equipment vendors, common sensor and data gaps, without utilising in-field equipment data and not optimised based on business targets and performance.
Prognostics are often conducted manually, critical failures are not detected and eliminated, and predictive maintenance approaches can lead to alarm flooding and false positives, reducing buy-in from the engineering technical authorities.
Execution challenges are exacerbated further with growing backlogs through queuing and risk prioritisation inefficiencies, maintenance compliance issues and ineffective maintenance resulting in defects on install.
With the increasing rate of learning and technical innovation pushing best practice and increased availability of large equipment data sets across the industry, traditional approaches to managing equipment maintenance come with high opportunity costs.
However, to unlock the value in each of these areas, your business, digital and engineering technical authorities will need to be comfortable that any risks associated with implementing AI are effectively managed.
Wood’s maint.AI models apply the latest in industry global best practice, lessons learned and value proofs, combining engineering first principles fundamentals and AI models to gain the confidence of your engineering function, to rapidly optimise your maintenance operating model. Our approach allows you to make decisions today with your current data while simultaneously mapping an improvement roadmap with you to achieve your performance targets.
maint.AI pairs AI with our codified domain expertise, built on your data and embedded in your processes to help your teams manage equipment maintenance and operations end-to-end
Apply globally vetted, best-in-class models, benchmarks and hypothesis-driven problem solving that we test on your data
Integrate and augment your maintenance processes, aligning with your meetings and key decision requirements
Work together with your key engineering technical authorities from day 1, finding the critical bottlenecks and risks preventing a decision
Fill your data gaps with our maintenance quality enrichment models, testing and triangulating multiple sources to search for the truth
Use an agile implementation framework designed to accelerate value realisation from operations transformation initiatives
For some of our clients, the pace of transformation can be exhausting – especially when fire-fighting reactive issues. To accommodate this, our models are deployed client-side and embedded in your organisation, along with training and performance measurement systems to track their effectiveness.
This puts you in the driver's seat to be able to complete the remaining changes at your pace. Alternatively, our domain experts are happy to help eliminate issues for good, as you focus on improving the overall system.
maint.AI key features:
- Rapid evaluation on large datasets and benchmarks against our global database and models
- ‘Low-touch,’ remote maintenance strategies defined based on failure mode to symptoms mappings
- Explainable equipment thermodynamics & dynamics physics models
- Sensor prognostics models
- High-performance anomaly detection and remaining useful life ML models (>98% recall, >80% specificity)
- Data quality enrichment models (CMMS, production delay, time-series data)
- Global equipment class MTBM, MTBF, MTTR statistics
- Mixed failure mode reliability growth population forecasts and modelling
- Failure histories and case/effects of individual equipment
- Historical equipment and strategy compliance
- Temporal maintenance execution assessments
- Failure mechanisms and maintenance types
- Standards and regulations
- Equipment criticality and redundancy Identification of existing and upcoming low-risk work that can be postponed in lieu of higher risk maintenance
- Identification of high-risk, overdue work for prioritisation
What you get
An optimised maintenance portfolio in 8-16 weeks, resulting in a 20-40% reduction in maintenance cost and 10% reduction in unplanned failures.
See if maint.AI works for you
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Wood has been optimising equipment maintenance and reliability of asset-intensive industries for 25+ years.
We have a rich history of innovation and first-principles understanding, having pioneered the field of equipment condition monitoring, working closely with clients and our partners in building sensing hardware and predictive maintenance software to troubleshoot and resolve critical equipment problems.
A recent example has seen our teams co-design our client's wireless vibration sensors, capable of high integration, accurate measurement, and data acquisition rates of 1-15 mins and co-deployed these sensors to 20 of 2,000 fin fans at one of their LNG sites. Our contribution accelerated development and deployments by ~2 years.
Based on extensive client knowledge and 10+years of operational data, we created and deployed explainable artificial intelligence models to production within four months.
We created a wireless sensor strategy assessment framework, capable of rapidly qualifying market sensors and building and developing an integrated architecture.
We have convinced our client's chief engineers to trust our transparent sensing analytics approach and architecture across all seven existing and new assets.
Human + machine: combining domain expertise with data-driven models
Data and analytics trends indicate that industry is increasingly using data fabric approaches to help address complexity and scale their data assets (Gartner, 2021). Data-driven models are only scratching the surface of what is possible.