t cht lk and information from IoT sensors , digital twins can accurately depict physical objects and systems such as motor vehicles , factory equipment , entire assembly lines and , as mentioned earlier , whole cities .
t cht lk and information from IoT sensors , digital twins can accurately depict physical objects and systems such as motor vehicles , factory equipment , entire assembly lines and , as mentioned earlier , whole cities .
Using machine learning and other AI technologies , the performance and behaviour of these virtual models can then be simulated online . In turn , analytics tools can be applied to derive insights that are critical for decision-making . For example , digital twins can show when a certain piece of factory equipment will break down , enabling decisionmakers to conduct upkeep when necessary and reduce hours spent on routine maintenance .
Similarly , businesses can simulate a whole assembly line , production process or supply chain to identify bottlenecks and make the necessary adjustments . Actual products , from household appliances to massive aeroplanes , can also be simulated , allowing engineering teams to identify design flaws or optimise performance without having to manufacture a life-sized model of the said product .
Challenges in implementing digital twins for businesses
However , organisations aiming to leverage this technology must overcome multiple obstacles : the absence of essential infrastructure like sensors and IoT devices , the quality and accuracy of collected
data used for digital twin generation and updating , the cost of data collection , the scarcity of professionals possessing the expertise to effectively utilise digital twin technologies and the ability to successfully extract insights from the digital twins .
Without the required infrastructure , organisations cannot guarantee the accuracy and currency of their digital twins , thus increasing the risk of decisionmaking errors . Merely possessing IoT infrastructure is just an initial step towards success , as data from various sources must be integrated to be useful .
The question of technical expertise is also important as the successful implementation and management of digital twins call for expertise in data analytics , modelling , simulation and IoT . Hiring or training skilled personnel can prove challenging for some organisations .
Meanwhile , data and analytics play a crucial role in unleashing the complete potential of digital twins . The true value of digital twin use cases lies in the ability of organisations to extract accurate insights and predictions from these systems . This necessitates the implementation of advanced analytics tools that enable effective data visualisation , predictive modelling , and self-service analytics .
Lastly , implementing and maintaining digital twins can be a costly endeavour , involving investments in hardware ,
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