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Luminoso Leverages Natural Language Understanding Software to Improve Business Productivity with DENSO

Luminoso, the natural language company that provides AI-powered analysis of unstructured data, announced that it is working with DENSO, a global manufacturer of automotive parts, in root cause analysis for upstream improvements in its application of knowledge bases at its production sites.

With over 130 production facilities around the world, vast quantities of knowledge are generated through daily production and improvement activities, but these valuable collections of knowledge are spread around the world in disparate formats and expressions, limiting their use to only sharing information within constrained search ranges. DENSO is initiating improvements in business productivity through sharing and application, beyond the borders between business units or countries, of various reusable knowledge base entries collected with AI technology.

Luminoso is working with DENSO to deploy a sharing system of knowledge accumulated within the company, allowing, for example, staff responsible for facility improvement to instantly view past notes and repair information across over one million documents to find information useful to creating a highly productive facility. Luminoso’s AI-powered application understands not just individual words, but also concepts found within DENSO’s maintenance notes. With no manual annotation or training requirements, Luminoso’s AI comprehends unique automotive parts and DENSO-specific terms, such as equipment names and engineering shorthand (eg. “RB” for “robot”).

Moreover, the company plans to use Luminoso's text analysis solution to identify the root causes of facility breakdowns from decades of institutional knowledge inside DENSO to contribute to the development of nonstop facilities and identify those that require long hours for recovery through matching the vast Factory IoT data (for example: conditions of facilities or workers) and natural language data, as well as to derive insight into optimization of the frequency of scheduled facility maintenance.

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