t cht lk to the product or service lifecycle , or the fluctuating productivity of processes .
With a Digital Twin , visibility is enhanced in industrial processes and the uncertainty is removed . These two key challenges , solved with a Digital Twin , improves predictive maintenance in manufacturing . Companies across a variety of industries including automotive and construction have reported that such advantages are only possible with a Digital Twin . This is because a digital copy of industrial process grants an unprecedented level of transparency into hardware , allowing users to proactively fix equipment before it breaks down , resulting in maximum productivity , minimised costs and improved revenue .
The Middle East has utilised the Digital Twin model in launching various successful Smart City projects .
For instance , Tata Consultancy Services ( TCS ) built a model for a Japanese client based on the Digital Twin technology . The customer highlighted a need to optimise the operating conditions for its coal-fired power plants with a model that could continuously monitor performance and raise any red flags . TCS developed a predictive Digital Twin model based on historic and realtime operating data that uses algorithms and Machine Learning to improve performance . The client recorded reduced fuel consumption resulting in lower costs but also identified an opportunity to optimise its boiler settings for reduced carbon emissions . Meanwhile , faults could now be detected in the plant 12 hours in advance with 85 % accuracy .
Bridging the gap between humans and machines
While smart manufacturing is run through technological processes , there is still the need for human presence to assist Machine Learning , which Digital Twins enable by connecting machines and humans . An employee sitting thousands of miles away from a physical machine can monitor its performance through the
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