1. Client Snapshot

A Fortune-500 metals conglomerate operating three alumina refineries and their captive power plants—high-temperature, 24 × 7 operations where every minute of downtime hits production targets and energy efficiency KPIs.

2. Operational Headaches

Challenge

Real-World Impact

Disconnected CMMS Logs

Maintenance history scattered across spreadsheets, legacy CMMS, and paper binders—making root-cause analysis nearly impossible.

High Unplanned Downtime

Critical equipment (kilns, pumps, gas turbines) suffered unexpected failures, raising overtime bills and safety risks.

Manual Report Preparation

Compliance teams spent weeks compiling inspection evidence for ISO 55001, ISO 45001, and internal audits.

Lack of Asset Visibility

No single dashboard showed asset health, backlog trends, or risk hotspots across all three plants.

3. Project Objectives

  1. Cut unplanned downtime by at least 30 % through data-driven maintenance.

  2. Create a single source of truth for every asset—history, inspections, photos, and documents.

  3. Automate audit readiness so that evidence packs are exportable in minutes, not weeks.

  4. Deploy with zero disruption to ongoing refining and power operations.

4. Digisailor Solution

Pillar

Key Components

Asset IQ Platform

Multi-tenant SaaS, modular micro-services architecture.

QR / NFC Tagging

22,000 assets encoded with unique IDs—printed on high-temperature-rated tags.

Guided Mobile Inspections

Role-based checklists with photo capture, anomaly thresholds, and offline sync for remote areas inside the refinery.

Predictive Analytics Engine

Machine-learning models detect trends (vibration, temperature, pressure) and flag impending failures.

One-Click Audit Exports

Generates ISO, OSHA, and corporate templates—complete with digital signatures and time-stamped evidence.

REST / OData Integrations

Bi-directional sync with SAP PM and the legacy CMMS; data pushed to Power BI for executive dashboards.

5. Implementation Roadmap

Phase

Duration

Milestones

Asset Master Clean-Up

3 weeks

Deduplicated 28 k+ asset records, validated hierarchies, mapped location codes.

Tagging & Baseline Surveys

6 weeks

Field teams tagged equipment, uploaded photos, and captured first-run condition readings.

Mobile Pilot

4 weeks

25 inspectors across one refinery; iterative tweaks to checklists and anomaly thresholds.

Full Roll-Out

5 weeks

All three plants live; integration to SAP and CMMS activated; user training completed.

6. Measurable Impact

Over a 12-month post-deployment benchmark across all refineries and power units, the system showed strong results. Unplanned downtime dropped significantly, falling from an average of 24.5 hours per month to just 14.7 hours—a 40% reduction that directly improved reliability and productivity.

The time spent on report preparation also improved dramatically. What previously required about 50 person-days per year was reduced to only 20 person-days, saving teams 60% of their effort and freeing them to focus on higher-value tasks.

For compliance, the audit preparation cycle was shortened from 11 days to just 6, a 45% improvement. This made audits smoother, faster, and far less resource-intensive.

Finally, data accuracy in work orders saw a major boost. Accuracy levels rose from around 70% to over 98%, a 28-point improvement. This ensured that decisions were based on reliable, high-quality data, strengthening both operations and compliance confidence.

7. Why It Worked

Success Factor

Detail

Field-First Design

Inspectors scan a tag, the app surfaces only the relevant checkpoints—no scrolling through irrelevant fields.

Offline Capability

Refinery dead-zones no longer block data capture; sync occurs automatically once connectivity returns.

Predictive Alerts

ML models flag anomalies (e.g., bearing temperature drift) 7–10 days before critical thresholds—allowing planned shutdowns.

Tight ERP Links

Work orders are auto-generated in SAP PM, eliminating double entry and ensuring bill-of-materials accuracy.

Instant Evidence Packs

One click bundles photos, signatures, and checklist results into auditor-ready PDFs.

8. Client Quote

“Asset IQ has turned maintenance into a science. We now see failure trends before they bite and walk into audits with confidence—all from a single dashboard.”

— Head of Reliability Engineering

9. Looking Ahead

  • Condition-Based Lubrication – Integrating online grease analysis sensors to refine predictive models.

  • Digital Twin Integration – Streaming real-time asset data into a 3-D plant model for immersive troubleshooting.

AI-Driven Spare-Parts Forecasting – Linking failure probability to inventory planning to cut carrying costs.

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