Our point of view

Problem Statement:

While many firms are a wash in operational data, few firms are able use this data effectively to proactively predict issues, identify areas for improvement and optimise resource allocation.

POV:

Far from requiring fully operational data and analytics infrastructures, it is possible to run targeted Proof of Concepts (POCs) that not only drive greater understanding of the issues and tangible improvement opportunities but also prove in real terms the value of further investment in data and analytics. Using simple and accessible tools this can be done in timeframes enviable in traditional programme delivery environments.

 

Case Study

Problem:

The renewable energy generation portfolio companies of a major private equity firm were looking at ways to better use the operational data across their plant infrastructure.

Solution:

Using python models built on nodeRed and utilising simple machine learning techniques, rapidly rolled out a series of models that utilised the plants underlying SCADA management data to build views of optimal vs actual performance. These models were then used to identify and diagnose issues across the plant ( including predicting / optimising maintenance routines).

Result:

The sample data identified significant improvement and optimisation opportunities (15x + the cost of the POC ) and provided the tangible support needed for operationalising the wider rollout.

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