FINAL WORD 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 .
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 .
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
Customised LLMs , unlike their generic counterparts , are fine-tuned for specific industrial enterprises .
Digital twins , defined as virtual replicas of physical entities or processes , enhances this data ecosystem
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