Digitalization & New Technologies

Companies in the manufacturing and process industries, as well as the energy and utilities sectors, face growing challenges in optimizing their processes, operations, and decision-making. Luckily, they are also sitting on a goldmine of data, which could help with solving the challenges. This article presents five ways Industrial AI and custom AI agents can help companies achieve key business goals.

Most industrial companies have no shortage of data. A constant stream of information feeds operational systems, such as ERP and MES systems, as well as SCADA, which perform their own well-defined roles within the enterprise architecture. Yet, Etteplan’s Matti Partanen says that companies are missing opportunities to make operational data actionable, derive business value, and become more data- and AI-driven simultaneously. AI pilots are started, but few solutions end up in production. What is the right way forward?

The energy and utilities sectors face growing complexity, regulatory demands, and rising expectations for reliability and efficiency. From managing distributed energy systems to maintaining critical water infrastructure, performance and cost control are constant challenges. When supported by high-quality data, AI technologies such as machine learning and advanced analytics enable predictive maintenance, demand forecasting, smart asset management, and much more. These capabilities offer a powerful path to improve efficiency, reduce risk, and unlock new value across operations, says Etteplan’s Artur Mroczkowski.

In the face of the dynamic growth of the smart metering market, integrated lifecycle management of meters becomes crucial for companies facing new challenges.

In the last decade or so, the energy landscape has shifted drastically undergone significant changes, transitioning from a centralized model dominated by corporations to a decentralized one, where prosumers play pivotal role. Today, energy production has become much more complex due to the variety of different energy – generating assets – ranging from traditional, fossil-fuel-powered sources to modern renewable sources. Moreover, individual prosumers, as well as smaller enterprises, are producing energy and feeding it into the grid. How do we navigate energy management in this new paradigm?

The EU’s new taxonomy regulations oblige industries to thoroughly examine their climate and environmental impact. This may sound challenging and raise concerns. However, an agile company with a mature R&D process can remain calm and align with sustainability requirements. In addition, it pays off to do lifecycle assessments with the help of AI.

Manufacturers often have the golden opportunity to increase sales with existing products if they could be updated to fit emerging needs. When internal research and development (R&D) efforts lean towards innovation rather than lifetime extensions for existing products, companies can easily miss potential revenue that could be made with moderate effort.

Artificial intelligence (AI) is not new in the manufacturing industry — many companies have been leveraging AI and machine learning for over two decades. Now, generative AI promises to do even more, which has piqued the interest in industrial research and development (R&D).

Research and Development units in industrial companies face increasing pressure to identify new ways to improve business results – the faster, the better. Unsurprisingly, artificial intelligence, AI, is a hot topic due to its promise to improve efficiency. But what are the other key trends in industrial R&D?