McKinsey & Company
A predictive maintenance program for wind farm assets
When I saw the dashboard for the first time, I could see very clearly this could be a powerful tool for us as we move forward in trying to improve efficiency. I thought: yes, eventually we have got something that gives me an indication on the condition of those cables.
— Ryan Henderson, Executive Director, EDS group part of James Fisher & Sons
I led the project as an analytics translator. I developed the AI strategy for the client by running a data workshop, designing and developing the prototype of a data-driven application that helped the customer envision their long-term AI strategy.
Value to customer
Through data storytelling, I illuminated the impact of AI on James Fisher’s business by showing how to combine predictive and prescriptive models to generate cost savings and optimize the wind farms’ maintenance operations so to make them more efficient.
Addressing an AI use case from a storytelling perspective gave stakeholders the means to understand the business value provided by the ML models. the prototype reflecting the new vision for James Fisher illuminated the business outcomes that the clients could get by combining predictive and prescriptive models to optimize their maintenance operations and make them more efficient to increase cost savings.
I extracted the customer’s needs by leading UX interviews. In parallel, I conducted a data-driven workshop to conceptually understand the information that the business users at James Fisher wish they had access to in order to improve their decision-making process.
I analyzed the prediction data provided by the machine learning model implemented by the data scientists. I used that data to even expand the client’s vision and designed a future scenario that addressed James Fisher’s business challenges.
To learn more about the project, watch the video on the James Fisher & Sons and IBM collaboration.
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