Intelligent CIO Europe Issue 47 - Page 76

THE USE OF AI ALLOWS MANUFACTURERS TO PREDICT WHEN OR IF FUNCTIONAL EQUIPMENT WILL FAIL SO THAT MAINTENANCE AND REPAIRS CAN BE SCHEDULED IN ADVANCE .
INDUSTRY WATCH biggest challenges in manufacturing , as well as other industries including but not limited to logistics , healthcare , insurance , finance and audit .
Taking advantage of just the technology isn ’ t enough – there is also a wider aspect of people and cultural change that needs to be addressed . Stakeholders and end-users must also be convinced of the insights regarding the reliability of the data generated with AI . For instance , even when people are aware that the inventory recommendations for raw materials or deliverables are accurate , they feel more comfortable holding a little extra stock or to be a little protective in the supply chain . Therefore , incorporating human heuristics becomes a challenge .

THE USE OF AI ALLOWS MANUFACTURERS TO PREDICT WHEN OR IF FUNCTIONAL EQUIPMENT WILL FAIL SO THAT MAINTENANCE AND REPAIRS CAN BE SCHEDULED IN ADVANCE .

Lastly , based on our experience , selecting the right sponsor for the project is another challenge . Manufacturing is a complex area in which the choice of the sponsor is vital to gaining the trust of the stakeholders , especially when it comes to the adoption of new Digital Transformation technologies .
What are the benefits of digital twins in manufacturing ?
In the manufacturing sector , digital twins are increasingly being used to improve quality control , supply chain management , predictive maintenance and customer experiences . It is these amazing potentials that will enable digital twins to reap all the benefits of AI in manufacturing .
In a recent implementation , with the assistance of UST ’ s AI experts , our client was able to overcome its manufacturing challenges with AI-based Vision solutions using a digital twin-based approach .
Vision Box analysed UST ’ s factory CCTV camera network in real time and identified problem areas based on AI analyses and vision intelligence ( AIVI ). As IoT implementations enable greater access to Big Data and vast digital ecosystems , creating and maintaining high-fidelity digital twins gets easier and easier .
How can digital twins transform manufacturing processes ?
Digital twins work by essentially evolving profiles of past and current behaviours of physical objects or processes that can be effectively analysed to optimise business performance . These can be used in a variety of ways to improve manufacturing operations and help engineering , production , sales and marketing to work together using the same data , making better decisions .
The digital twin also helps transform the manufacturing process as part of quality management to identify variances in every part of the process , as well as the use of better materials or processes . For example , supply chains , fleet managers and route efficiency are measured with digital twins .
A digital twin shows variances in equipment or manufacturing processes that indicate a need for maintenance before something major happens . In fact , UST has learned that the ease of access to operational data through digital twins facilitates collaboration , improved communication and faster decision-making . p
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