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dc.contributor.authorSchmid, Philipp
dc.date.accessioned2022-10-14T14:19:01Z
dc.date.available2022-10-14T14:19:01Z
dc.date.issued2022-10-14
dc.identifier.citationAI+X Summit 2022en_US
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/1052
dc.description.abstractAt the junction where the digital, the physical, communication and people cross, is where Industry 4.0 comes forth: The automation that has long since become norm is being revolutionized, one could almost say, by automating automation – physical systems in logistics and production communicate with other systems whilst gathering data that is communicated to the relevant worker or consumer. AI would act as the ‘traffic light’ in this scenario, guiding data to the right places and turning that data into useful action-​items. With Industry 4.0 affecting both small and medium-​sized manufacturing enterprises by pushing them towards fast digital transformation, AI is the key competitive tool to survive and thrive in competitive markets. Applications would include using predictive maintenance to enable less machine and production line downtime, as well as overall lower operating costs through, e.g. autonomous product transport vehicles that get notified automatically by smart production lines. Data collection and data analytics enables promising solutions for industry, such as workers getting real-​time information via AR. However, it is usually not clear whether they enable sufficient value creation for the customers and value capture for the providers. Therefore, a smart service-​oriented approach helps finding a way towards viable solutions. Let’s unlock new ways for data-​based value creation in manufacturing by joining forces; in the AI+X Summit 2022 workshop, we will collect input on the challenges in the manufacturing sector, enriching a deeper understanding of the problem-​and-solution space and thereby deliberating possible approaches with practitioners and researchers.en_US
dc.language.isoenen_US
dc.subjectPredictive Qualityen_US
dc.subjectPredictive Maintenanceen_US
dc.subjectAIen_US
dc.subjectDeep Learningen_US
dc.subjectData Driven Innovationen_US
dc.titleData Driven Innovation - from Predictive Maintenance to Predictive Qualityen_US
dc.typeConferenceen_US
dc.type.csemdivisionsDiv-Ren_US
dc.type.csemresearchareasIndustry 4.0en_US


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