From Breakdown Logs to Instant Repair Guidance

35% MTTR reduction with AI Maintenance Hub for Precision Manufacturers

Industry: Mechanical Engineering / Precision Manufacturing / Industrial Machinery

Users: Maintenance Engineers / Reliability Engineers

Solution Area: Predictive Maintenance, Knowledge Management, Troubleshooting Management, Asset Performance Management

🔍 The Challenge/ Client Context

A mid-sized precision manufacturing unit told us their maintenance engineers struggled with unstructured documentation—past maintenance reports, inspection logs, drawings, and machine manuals were scattered across formats. When a breakdown occurred, valuable time was lost in finding the correct service procedure or previous RCA notes.

💡 What we did with Dhimath

Greywiz team collected all their technical maintenance documents—PDFs, scanned forms, repair logs, drawings—and uploaded them into a dedicated maintenance knowledge base in Dhimath. Power Users from their engineering team were trained to structure this base and set up custom categories and tags (machine type, fault codes, intervention date, etc.).

Using Dhimath's chat interface, maintenance engineers were now able to ask real-time repair questions like “What was the fix for spindle motor failure in Machine 03 last October?”—and get exact instructions, images, and linked RCA reports in seconds.

Outcome Highlights:

• ⚙️ Reduced MTTR (Mean Time to Repair) by 35%

• 🧠 Enabled smart knowledge reuse from past breakdowns

• 📊 Consistent RCA documentation via guided templates

• 📱 Mobile-first access to troubleshooting support anytime

• 🔐 Controlled access to critical maintenance logs