Industrial Operations - Maintenance AI & Inspection OCR Implementation
A major chemical storage and logistics operator (~200 employees across Australasia) partnered with ELab following a merger to address critical maintenance coordination challenges. Through a three-phase programme spanning 10 months, ELab deployed a two-stage AI system for inspection form processing that achieved 95.5% accuracy across 73 inspection traces at 4 active sites, identified 39 defect references including critical safety hazards, and established a foundation for projected 60-70% reduction in coordinator processing time (from 71-97 hours to 20-30 hours monthly).
Industry: Chemical Storage & Logistics | Duration: 10 months | Services: AI Foundations, Lean Six Sigma, Maintenance AI Pilot, Inspection OCR, Managed Services
Following a 2020 merger that created their national footprint, the organisation faced significant operational integration challenges. Varied site-specific procedures, inconsistent documentation, and knowledge silos meant maintenance operations lacked standardisation. Two key coordinator positions were vacant, and the merger had created critical knowledge fragmentation across sites.
Key Pain Points
- Massive Administrative Waste: Maintenance coordinators spending 71-97 hours monthly processing 100-150 work orders, with only 23-29% of that time being value-added activity (Lean Six Sigma analysis)
- Processing Delays: Up to 2-month delays at one site due to batch scanning model, creating safety and compliance exposure
- Duplicate Work Orders: 30-40% of Follow-On Work Orders (FOWOs) were duplicates, wasting coordinator time and cluttering the maintenance management system
- Manual Data Entry Burden: 40+ hours per month per coordinator spent on manual data entry rather than technical analysis
- Paper-to-Digital Gap: Multiple system handoffs (Paper to Scanner to Email to SharePoint to MEX) creating errors and delays
Business Impact
| Challenge | Operational Impact | Risk Level |
|---|---|---|
| 71-77% non-value-added time | 5 coordinators spending most time on admin, not analysis | Critical |
| 2-month processing delays | Safety documentation backlog, compliance exposure | Critical |
| 30-40% duplicate FOWOs | System clutter, wasted rework time | High |
| 40+ hrs/month manual data entry | Coordinator capacity consumed by typing, not thinking | High |
| Fragmented procedures | Inconsistent maintenance quality across sites | High |
Lean Six Sigma Waste Analysis (8 Wastes)
| Waste Type | % of Total | Examples |
|---|---|---|
| Waiting | 30% | 2-month batch scanning delay; coordinator backlogs |
| Non-Utilised Talent | 25% | Coordinators doing data entry instead of technical analysis |
| Defects | 20% | Duplicate FOWOs, missed issues, sparse MEX entries |
| Motion | 10% | Multiple system interactions per work order |
| Extra-Processing | 10% | Manual typing of data already on forms |
ELab delivered a phased programme moving from discovery through pilot validation to production deployment, ensuring each phase built confidence and demonstrated value before the next.
Phase 1: Fundamentals & Exploration (April 2025 - AUD $7,500)
- Conducted stakeholder interviews across maintenance, operations, and dispatch departments
- Delivered 4-hour AI Fundamentals Workshop covering practical AI applications in industrial settings
- Completed current state assessment with detailed workflow documentation
- Produced AI Readiness Index Report and organisation-wide AI Enablement Roadmap
- Identified maintenance procedure standardisation as highest-priority pilot
Phase 2: Maintenance Manual AI Pilot (May-June 2025 - AUD $12,500)
- Developed comprehensive knowledge base from existing maintenance documentation
- Configured AI Coworker for maintenance procedures integrated into Microsoft Teams
- Conducted three validation workshops:
- Maintenance Team Workshop (27 May) - validated AI-generated procedures
- Maintenance Coordinator Workshop (27 May) - assessed workflow impacts
- Pilot Demo & Stakeholder Feedback (11 June) - demonstrated business case
- Positive feedback from all workshops supporting progression to Phase 3
Phase 3: Inspection OCR & AI Agent (November 2025 - January 2026)
Deployed a two-stage AI processing system within Microsoft Teams:
Stage 1: Document Management AI (30-60 seconds)
- Receives uploaded inspection forms/scans via Microsoft Teams bot
- Performs OCR on handwritten and printed forms (powered by Chunkr technology)
- Extracts metadata: work order number, asset ID, inspection date, inspector name, site location
- Applies standardised file naming and auto-organises to SharePoint by site/asset
- Validates document legibility and extraction confidence
Stage 2: Inspection Analysis AI (60-90 seconds)
- Reads inspection forms comprehensively including complex tables
- Extracts all defects and issues with criticality triage (P1-P4)
- Validates readings against normal ranges for specific products/assets
- Detects contradictions (e.g., "acceptable" marked but handwritten note indicating issue)
- Generates MEX-ready formatted output with structured defect classification and recommended FOWO actions
Solutions Deployed
- Microsoft Teams Bot: User-facing interface for form submission and AI responses
- Document Management AI: Automated OCR, filing, and metadata extraction
- Inspection Analysis AI: Defect identification, criticality triage, and MEX-ready output generation
- SharePoint Integration: Automated document organisation by site and asset
- Maintenance AI Assistant (Phase 2): Natural language access to procedures via Teams
Measured Results (Phase 3: November 2025 - January 2026)
Verified data from Langfuse trace analytics, quality scoring, and system logs
| Metric | Result | Evidence |
|---|---|---|
| AI processing traces | 73 | Langfuse trace data |
| Unique chat references | 36 | System logs |
| Work orders processed | 15 | MEX cross-reference |
| Active sites | 4 (Melbourne, Pelican Point, Corio, Geelong) | Usage analytics |
| Context correctness | 95.5% accuracy | Quality scoring |
| Defect references identified | 39 | AI analysis output |
| Processing time per form | 90-150 seconds total | System timing |
| Perfect quality scores | 2 sites at 1.000 (Geelong, Pelican Point) | Quality scoring |
Site-Specific Quality Performance
| Site | Quality Score | Context Correctness |
|---|---|---|
| Geelong | 1.000 (perfect) | 1.000 |
| Pelican Point | 1.000 (perfect) | 1.000 |
| Melbourne Site 3 | 0.957 | 0.914 |
| Melbourne | 0.925 | 0.975 |
Critical Safety Findings
AI-identified defects that demonstrate strategic asset intelligence capability
The system identified critical safety issues across sites, including findings that manual processes had missed:
| Finding | Asset | Priority | Significance |
|---|---|---|---|
| Missing earth strap (static hazard) | Tank C16, Melbourne | Critical | Explosion risk in chemical storage |
| Systemic radar failures (cluster pattern) | Radars C26, C30, C33, C34 | High | Pattern detection across sites suggests capital equipment issue |
| Actuated valve product leak | Tank T11, Melbourne | P1 Critical | Active leak requiring immediate action |
| Gate valve stem leak | Tank T49, Melbourne | P1 Critical | Active leak requiring immediate action |
| Body weep | Tank T33, Melbourne | P1 Critical | Active leak requiring immediate action |
| Safety shower defects | Melbourne Site 3 | Critical | Cracked heads, low flow rates |
Key Intelligence Capability: The AI identified a cluster of radar failures across C26, C30, C33, and C34, suggesting a systemic capital equipment issue rather than isolated maintenance events. This type of cross-site pattern detection only emerges with systematic data extraction and correlation.
Projected Efficiency Gains
From Lean Six Sigma analysis - PROJECTED, pending full rollout validation
| Metric | Current State | Projected Future | Improvement |
|---|---|---|---|
| Monthly coordinator time | 71-97 hours | 20-30 hours | 60-70% reduction |
| Time per work order | 40-60 minutes | 8-12 minutes | 75-80% reduction |
| Cycle time (P95) | 14-60 days | 1-3 days | 85-95% reduction |
| Duplicate FOWO rate | 30-40% | <5% | 85%+ reduction |
| Manual file management | 11 min per WO | 30 seconds | 95% reduction |
| Manual data entry | 40+ hrs/month | <5 hrs/month | 85% reduction |
Projected Annual Financial Impact (across 5 coordinators):
- Time savings: 50-70 hours/month per coordinator
- Annual savings at $50/hr loaded rate: $150,000-$210,000
- Note: These projections are from the Lean Six Sigma analysis and require full production rollout to validate
Challenges & Lessons Learned
December 2025 Incident: A third-party Power Automate connector update caused a silent failure mode where files were sent to the system but produced no response. This "black hole" had a significant impact on user trust, with key coordinators stopping usage entirely.
Resolution: Platform patched on 13 January 2026, restoring full functionality. Usage recovering but below potential as of late January 2026.
Key Lessons:
- System monitoring and proactive alerting are critical to prevent silent failure modes
- Users need visibility into system health status
- Early communication when issues are detected prevents trust erosion
- Third-party integration dependencies require additional monitoring layers
The engagement has transitioned to a Managed AI Services arrangement (AUD $1,000/month, 6 hours included) providing ongoing monitoring, maintenance, and optimisation support.
Immediate Priorities:
- Rebuild user confidence through hands-on demonstration sessions with coordinators
- Implement system health visibility indicator within Teams bot
- Deploy proactive alerting to prevent future silent failures
- Process backlog of digital contractor inspection reports (requiring no behaviour change from users)
Phase 4 Opportunity: MEX API Integration
- Direct automated data flow from AI to MEX system (eliminates copy-paste)
- Auto-generation of Follow-On Work Orders from AI defect classification
- Additional 20-30% time savings beyond current system
- Estimated investment: ~$20,000 (8-week sprint)
Phase 5 Opportunity: Digital Forms at Source
- Tablet/mobile data capture in the field, replacing paper entirely
- GPS/timestamp auto-population
- Addresses root cause vs interim digitisation
- Additional 40-50% efficiency gain
- AI Foundations Workshop
- AI Readiness Assessment
- Strategic AI Roadmap
- AI Policy & Governance
- Pilot Implementation
- AI Coworker Deployment
- Managed AI Services
- vCAIO Advisory
Document Control: v3.0 | Last Updated: February 2026 | Author: ELab
For more information, contact ELab at hello@elab.co.nz or visit www.elab.co.nz
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