Digitalising energy production and asset management increases efficiency – machine learning improves decision-making

Future proofing large plants with an operating model based on machine learning and highly skilled expertise

Maintaining competitiveness while also focusing on improving sustainability and protecting the environment is a familiar challenge for today’s energy producers and material asset owners. Conventional energy production has some inherent inefficiencies, such as shift variations and an inability to react quickly to changing prices and demand. A McKinsey study* estimates that 44% of work in the energy sector can be automated, but new ways of working are clearly needed to achieve this.

Although more environmentally friendly behaviour often goes hand in hand with cost efficiency because resources are being used more efficiently, there are also other opportunities available to help businesses develop a competitive edge.

Automation and machine learning are increasingly being recognised as invaluable tools for working more intelligently, improving reliability and efficiency, minimising waste and cutting costs. Linking these with highly skilled experts monitoring and operating sites, generates a unique operating model and new way of working.

Plugging in to a sophisticated digital operations and maintenance platform provides the opportunity to digitalise both production and offsite operations. Based on centralised data and analytics, the platform gathers all the data needed to support smart business decisions, bringing significant synergy benefits along with direct cost savings. The sites are operated by advanced machine-learning algorithms, backed up by skilled experts who monitor the sites 24/7. The plant automation system maximises the energy and material efficiency, and optimizes the operations, reducing OPEX and emissions. Not only can this improve processes, performance and profitability, it can also reduce costs, allowing resources to be focused on proactive, predictive maintenance during office hours, improving both reliability and availability.