Abstract
Manufacturing industry is constantly evolving technologically. To ensure that the workforce comply with pace of changing requirements, Learning Assistance Systems (LAS) can provide competency-based support for daily tasks. LAS provide personalized support to blue and white collars through modular and contextualized workflows, information and learning materials. This paper presents a LAS designed and implemented for maintenance personnel in semiconductor manufacturing. The implemented LAS employs a novel statistical learning algorithm to determine the required competencies per task, interlinking them with information from knowledge bases and Manufacturing Execution Systems. This leads to a reduced mean time to repair of 18% in the illustrated use case.
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