spares.AI to increase asset availability while reducing inventory costs
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Increase critical asset availability while reducing inventory cost by up to 40%

spares.AI optimises maintenance, repair, and operations (MRO) inventory levels to minimise the cost of your inventory, and maximise the availability of your assets.

Wood’s inventory and materials management expert knowledge is packaged into models and deployed on your data to continuously optimise your asset over time.

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Increased accuracy in predictions

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Reduced inventory costs and associated working capital

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Increased asset availability through avoidance of stockouts

See how spares.AI can help you reduce inventory costs

Big data means traditional approaches to inventory level analysis and optimisation are no longer sustainable

Our research identified that the cost of holding inventory trends towards 30% of capital cost in the ongoing operational expenditure in capital-intensive industries. However, the availability risk on production plants due to stockout is substantial and requires strong reliability knowledge to manage.

This results in companies holding more inventory than required, while still being exposed to availability or reliability risks due to incomplete coverage.

Watch the video to understand how spares.AI works

Peter Carydias, Operations Manager, talks about trends in the industry, why Wood invested in spares.AI, how algorithms can help us predict the future, how we deploy spares.AI and the results we deliver.

Optimising inventory levels at scale

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spares.AI – domain expertise plus artificial intelligence

spares.AI can rapidly augment inventory data for an advanced and granular analysis based on a scalable, data-driven approach.

Make decisions today with your current data, while consistently optimising your inventory using live reliability information.

Wood has been optimising our clients’ inventories for 20+ years

spares.AI optimises inventory levels at scale. We’ve overhauled classic inventory models and optimised them based on years of R&D and real-life data.

The new models apply a semi-automated approach using data-driven models combined with human experience and proven maintenance management principles.

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Rapidly evaluates
larger datasets

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Brings operations, maintenance, and integrity insight to your inventory at scale, vetted by our engineers

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Forecasts accurate
spare holding levels

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Provides real-time updates of
inventory holding suggestions

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Uses advanced algorithms to infer reliable solutions, even with incomplete data

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Deploys inventory models to client cloud for dynamic inventory analysis

Case study:
$7B Water Corp


Optimise inventory and improve working capital


40% reduction in inventory value, while increasing critical spare materials to enable high asset availability (~8% of inventory lines)


We leveraged Wood’s data-driven models on reliability, lead time and cost to quickly improve inventory strategy

spares.AI outlook

Data-driven models are only scratching the surface of what is possible.

Backed by Wood and innovation funding, our team is always pushing to find better, faster, and more robust solutions. With the ability to build specialised teams from over 40,000 experts in 60 countries, our solutions have no boundaries.

Speak to a specialist

Related content

Asset management, operations and maintenance


Optimising Asset Performance

Be in the know
Peter Carydias
Operations Manager
Get in touch
United by our common purpose to unlock solutions to the world’s most critical challenges, we are future ready, now.
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