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Generative AI Powered Intelligent Maintenance Assistant Platform
  1. case
  2. Generative AI Powered Intelligent Maintenance Assistant Platform

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Generative AI Powered Intelligent Maintenance Assistant Platform

neoteric.eu
Automotive
Manufacturing

Production Downtime & Maintenance Efficiency

ZF Group experiences production downtime due to equipment malfunctions, often requiring external service calls and resulting in delays and inefficiencies. Diagnosing the root cause of these malfunctions can be time-consuming, leading to extended downtime and increased costs. Lack of readily accessible and easily searchable internal knowledge hinders quick problem resolution.

About the Client

ZF Group is a global technology company supplying systems for passenger cars, commercial vehicles, and industrial technology. They have a significant global presence with numerous production locations.

Project Goals

  • Reduce production downtime by enabling faster and more accurate equipment failure diagnosis.
  • Improve efficiency of maintenance teams by providing quick access to relevant troubleshooting information.
  • Streamline internal knowledge sharing related to equipment maintenance across ZF's global operations.
  • Reduce reliance on external service calls for common equipment issues.
  • Enhance employee skills and reduce the time required to resolve technical issues.

System Functionality

  • Natural language query interface for asking maintenance-related questions.
  • AI-powered analysis of user queries to identify potential root causes.
  • Retrieval of relevant information from internal databases and documentation.
  • Summarization of complex information for easy understanding.
  • Citations/links to original information sources to improve trust and transparency.
  • Ability to handle structured and unstructured data.
  • Support for multiple languages and data formats (future enhancement).

Technology Stack

GPT-3.5 (or equivalent large language model)
Azure Cognitive Services
Azure Cloud Platform
Cognitive Search
GPT3.5 embeddings

Data Source Integrations

  • Internal maintenance databases (across multiple plants)
  • Internal documentation repositories (e.g., SharePoint, Confluence)
  • Historical failure logs
  • Potentially other industrial IoT data sources (future)

Non-Functional Requirements

  • Scalability to handle increasing data volume and user load.
  • High availability and reliability to ensure continuous operation.
  • Security to protect sensitive maintenance data.
  • Performance for quick query response times.
  • User-friendly interface for ease of adoption.

Expected Business Impact

The implementation of the AI Maintenance Assistant is expected to significantly reduce production downtime, resulting in increased operational efficiency and reduced maintenance costs. Improved access to knowledge will empower maintenance teams to resolve issues faster, minimizing disruptions to production schedules. The system will also contribute to a more skilled and self-sufficient workforce.

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