Traditional approaches to asset management have failed to achieve the balance between commercial viability and appropriate customer service. The needs of the customer are driven by service and asset availability and any lack of availability inevitably leads to frustration and added cost, and reduced customer perception and revenue to the operator.
The use of linear planned processes, failing to commission or manage warranty processes and not understanding how an asset is used, leads to maintenance strategies which either over- or under- maintain assets resulting in unpredictable performance and waste.
To better understand the asset and its associated systems and components, we measure key attributes and create engineering feedback data flows which enables us to understand how each part of the asset is being used. With this new information we can identify the root-cause of non-planned performance and predict how assets will perform in service over time. This enables a far more predictive approach to maintenance and operational services. We use a number of mathematical, machine learning and artificial intelligence models to enable this capability.