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Impact Scenario • Manufacturing
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Board asks for clarity every quarter Advisory Friction from handoffs/data quality Systems Design Repetitive work piling up Automation Scaling Senior leverage for critical decisions Retainer Need proof inside 30 days Phase 0 Earlier-stage? Startup Track

Manufacturing Digital Transformation

IoT integration, AI-powered analytics, and predictive maintenance for Industry 4.0 readiness.

156%
Efficiency Gain
73%
Cost Reduction
21 days
Deployment

Engine mapping: Data Clarity Engine • Service Excellence Engine

  • Target: Ops, Manufacturing, Support
  • Problem → Impact: Blind spots and downtime → standardized OEE, faster QA, fewer outages
  • Inputs → Outputs: Sensor/MES/ERP → shift dashboards, downtime reports, escalation alerts
  • Stack: IoT intake; data platform/warehouse; dashboards; alerting; ticketing
  • Risks & mitigations: Schema drift → versioned models; alert fatigue → tuned thresholds; change mgmt → standard work
See Data Clarity See Service Excellence

Context & Challenges

Legacy MES/ERP, manual QA, and uninstrumented lines created blind spots and waste. Leadership needed a staged modernization plan minimizing downtime.

Approach

  • Prioritized line instrumentation and sensor rollout; digital thread foundations.
  • Quality analytics with anomaly detection; operator co-pilot for checks.
  • Predictive maintenance models with alerting and work order automation.
  • Data platform with standardized metrics; quick wins first, platform scale second.

Outcomes

  • Throughput and OEE up with targeted fixes; scrap down materially.
  • QA cycle time reduced; fewer defects escaping to downstream steps.
  • Maintenance shifted from reactive to proactive; fewer unplanned outages.

Artifacts

  • Line instrumentation plan
  • QA analytics dashboards
  • Maintenance model playbook

System components

  • Sensor intake → OEE pipeline → shift KPI dashboard
  • Automated downtime and defect reports
  • Escalation alerts: maintenance and quality thresholds

Result: a complete operational system, the foundation for larger AI programs.

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Business impact

We track manufacturing outcomes against baseline using standardized metrics with clear owners.

Before → After (90 days)

Metric Baseline After
OEE 58% 72% (+14 pts)
Unplanned downtime 38 hrs/mo 19 hrs/mo (−50%)
Defect/scrap rate 5.6% 2.9% (−2.7 pts)
QA cycle time 17 min 9 min (−47%)

Where the value comes from

  • Higher OEE drives more throughput on fixed assets.
  • Less downtime and lower scrap reduce COGS and improve gross margin.
  • Shorter cycle time increases delivery reliability and customer satisfaction.

Levers: sensor coverage, anomaly detection thresholds, standard work, maintenance scheduling, escalation rules.