AI Predictive Maintenance Cuts Equipment Failures and Unplanned Downtime — Global Manufacturing Case Study

Industry: Manufacturing, AI

Client

Global Manufacturing & Engineering Firm

Goal

To implement an AI-driven system that predicts potential equipment failures by analyzing error logs and technical documents, enabling proactive maintenance and reducing downtime.

Challenges

  • Automating the generation of daily market summaries for timely insights.
  • Integrating RNS feeds, economic release calendars, and price data into reports.
  • Providing accurate, comprehensive, and up-to-date reports for stakeholders.

Solution

Built a failure prediction system using error logs, integrated with LLM + RAG trained on manufacturer-specific documents.

Provided real-time assistance to engineers by generating support suggestions and predicting potential failures from analyzed data.

Integrated AI tools to deliver actionable insights on when and why equipment might fail, helping to prevent unplanned downtime.

Impact:

Reduced equipment failures and unplanned downtime through more accurate predictive maintenance.

Increased operational efficiency by equipping engineers with real-time predictive insights and recommendations.

Enhanced maintenance workflows, delivering cost savings and improving the reliability of manufacturing systems.

*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.