FINAL WORD
Digital twins, defined as virtual replicas of physical entities or processes, enhances this data ecosystem by enabling industries to simulate, optimise, and predict outcomes without costly real-world errors. The latest version of the technology, called one intelligent twin, unifies the value chain from conceptual design and engineering through to project maturity into operations and optimisation for seamless, end-to-end operational visibility.
Digital twin technology helps French multinational TotalEnergies track greenhouse gas emissions in real time, and by addressing issues rapidly, engineers have helped save € 1.5 million and 64 days of downtime in a year.
We are familiar with LLMs, the AI systems reshaping business workflows by processing and interpreting vast amounts of data. Customised LLMs, unlike their generic counterparts, are fine-tuned for specific industrial enterprises, transforming proprietary business data into actionable insights, sort of a shortcut to success.
Simon Bennett, Director of Innovation and Incubation, AVEVA
At the moment, an industrial AI assistant can integrate operational data with an LLM to answer focused naturallanguage queries about system performance, like tracking offline turbines or comparing wind farm output.
Industrial robotics represents a fourth technology wave slowly breaking onto shopfloors. Whether drones, cobots or articulated arms, these machines are not here to replace workers but to augment their abilities, taking on repetitive or hazardous tasks while driving productivity gains. foundation of semi-automation. How do advanced technologies amplify human ingenuity?
Let us look at the four major digital technologies transforming industrial operations today by delivering benefits such as supply chain visibility, predictive warnings and operational recommendations.
IIoT can be compared to an industrial central nervous system. Industrial devices such as connected sensors, valves, or switches are now equipped with the capabilities to send data to HMI and SCADA systems or the cloud. When these data streams are aggregated into a single source of truth, AI and machine learning can easily analyse and contextualise them, delivering real-time operational oversight and enhanced human decisions thanks to predictive alerts.
For example, teams at Duke Energy used insights from 30,000 sensors to develop 10,000 models to identify plant failures before they occurred. With 385 predictive finds over three years, it saved $ 45 million.
In Italy, for example, the food packaging consultant and producer Livetech achieved up to 40 % savings and 50 % faster changeover time using robotic applications. Each of the above technologies drives industrial value on its own, as we can see from the examples.
Over the coming years, we will start to see them stacked atop each other to accelerate innovation, optimise operations and slash costs and carbon emissions.
For examples, robots, Boston Dynamics’ dog Spot comes to mind, can manoeuvre around a plant with a significant payload of sensors and devices for recording information from warehouses or shopfloors
Industrial robotics represents a fourth technology wave slowly breaking onto shopfloors.
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