- Case study
Turning an energy business into a predictive-maintenance powerhouse
How to keep crucial turbines running with more accurate forecasting
A leading original equipment manufacturer (OEM) that provides turbines, spare parts, and services to utility companies worldwide
We used machine learning (ML), artificial intelligence (AI), and advanced analytics to predict turbine-equipment issues and maintenance needs more accurately
To help its utility-company clients increase turbine availability using predictive maintenance
Challenge
This global energy leader provides turbine engines to large-scale utility companies around the world.
Typically, half of all parts failures occur unexpectedly at utilities' operations. And when problems arise, power generation suffers while companies wait for replacement pieces and field-worker repairs. Meanwhile, if there are turbine stoppages, utility companies may experience power outages that impact their commitments to their own customers.
Part of the problem was that until now, maintenance forecasting had been limited to using simple extrapolation models. To be effective, these models needed to interpret a diverse array of structured and unstructured data. But that was not an easy task.
But it's changing.
The energy firm knew it had to find a better, more comprehensive way to analyze its data. Its operational and inspection records, machine specifications, and equipment-imaging devices held a wealth of intelligence. Using such rich, science-driven information was the only way the company could boost customer satisfaction by accurately predicting parts failures to keep the turbines running. This data could even reveal new business opportunities, while also helping the company allocate maintenance resources at the right time and level of detail.
Solution
Genpact had already been working with businesses to improve maintenance predictability and created our unique Smart Event Forecasting solution. The technology combines process knowledge, digital technology, and predictive analytics to sharply improve the accuracy of parts-breakdown forecasts.
Smart Event Forecasting combines natural language processing (NLP), ML, neural networks, and advanced visualization to interpret a range of structured and unstructured data. After it deciphers all the factors influencing potential parts failure and machine breakdowns, it delivers predictive maintenance scenarios to decision makers. It also uses accessible visualization with charts and images so they can also understand the information quickly and easily.
The solution interprets and integrates data, such as inspection reports, operational information, specifications, and equipment images, and uses the following key components:
The system incorporates decades of reports and generates best-fit probability distributions based on techniques that Genpact has developed with leading universities. This makes the solution considerably more accurate than similar tools. In fact, it predicts future events with more than 90% accuracy, compared to about 80% using traditional modeling techniques.
Smart Event Forecasting can be scaled and used in any manufacturing process. But its remarkable versatility extends beyond manufacturing. In fact, it's effective in almost any operation, such as accounting or IT, where the parameters driving failure can be identified and have relevant data available. Its potential applications are vast.
Impact
By predicting events in manufacturing processes more accurately, our client has:
This global manufacturer is extending the benefits of Smart Event Forecasting by rolling it out at many of its production sites. It also has plans to extend these sharper insights and efficiencies throughout its operations.