7 manufacturing sites, 10 incompatible monitoring systems, headcount down from 75 to 40 technicians. One predictive maintenance and operator self-service agent — project lifecycle target cut from 180 days to ~90.
Company:
Fortune 500 Manufacturing Operations
My Role:
AI Product Manager, Enterprise Solutions (Unframe AI)
Year:
2026
Techstack
IoT Stream Processing · SCADA/Historian Integration · Predictive Maintenance Agents · Anomaly Detection · Escalation Routing · Siemens · Rockwell Automation · Schneider Electric · SAP · AVEVA · OSIsoft PI · Grafana · ServiceNow · Azure IoT Hub
A Fortune 500 manufacturing client had cut their maintenance technician headcount from 75 to 40. The machines weren't cut proportionally. Seven production sites running equipment from three different PLC vendors (Siemens, Rockwell, Schneider Electric) and two different SCADA historians (AVEVA, OSIsoft PI) — none of them talking to each other. A failure at Site 3 might mirror a pattern seen at Site 1 six months ago, but without unified data, no one would ever know.
With 40 technicians covering what 75 used to handle, every unplanned outage carried outsized cost. Target: cut project lifecycle from 180 days to ~90 days by catching failures early, resolving operator-resolvable issues without dispatching a technician, and routing only genuinely complex escalations to the right specialist with diagnostics pre-loaded.
STEP 1 — IoT Data Mapping Across 7 Heterogeneous Sites
The first problem wasn't predictive maintenance — it was normalization. Siemens, Rockwell, and Schneider Electric PLCs don't emit the same data shape. AVEVA and OSIsoft PI historians don't share a schema. Built a site-by-site mapping layer that translated every sensor stream, alarm code, and historian record into a common signal schema across all 7 sites — the same approach used in Outage Intelligence, applied to IoT at manufacturing scale.
STEP 2 — Predictive Maintenance Agent
With unified signal data, the predictive maintenance agent could pool failure histories across all 7 sites — surfacing early-warning patterns that were invisible when each site only saw its own data. A bearing failure signature that appeared at Site 1 three weeks before outage became a validated early-warning pattern across the fleet. The agent surfaced anomalies ranked by failure probability and estimated time-to-failure, giving the maintenance team a prioritized work queue instead of reactive firefighting.
STEP 3 — Operator Self-Service Layer
Not every alarm needs a technician. Floor operators knew which alarms they could resolve and which required escalation — but that knowledge lived in their heads, not in any system. Interviewed operators and supervisors across all 7 sites to map the real self-service vs. escalation decision boundary, then built that logic into an operator-facing agent. Operators could query the agent directly from the floor and either get resolution steps or a confirmation that an escalation had been routed.
STEP 4 — Escalation Intelligence
When an operator's query crossed the escalation threshold, the agent didn't just open a ServiceNow ticket. It created a work order pre-loaded with the full diagnostic context: which machine, which anomaly pattern, which site-specific failure history was relevant, and which certification level the technician needed. Technicians arrived on the floor knowing what they were looking at, not starting from zero.
RESULTS
Project lifecycle target: 180 days → ~90 days (50% reduction)
7 production sites unified · hundreds of machines · 10 underlying systems
Operator self-service resolves incidents without technician dispatch
Predictive maintenance: cross-site pooled failure data surfaces early-warning signals before downtime
Escalation routing: certification-matched ServiceNow work orders, pre-loaded with diagnostics
Engagement owned end-to-end — discovery through signed order form, direct line to CIO
