Predictive Maintenance & Vision QA Across 6 Plants
Illustrative reference architecture. This is a composite engagement template built to show how Mopshy structures enterprise work. It is not a real Mopshy client, and every figure, KPI, and quotation on this page is illustrative rather than measured client data.
Fleet-wide predictive maintenance, computer-vision quality inspection, and shift-level AI copilots deployed across 6 discrete-manufacturing plants.
−63%
Unplanned downtime
−48%
Scrap rate
$22M
Annualized value created
The bottleneck
Unplanned downtime cost $310/minute per line, scrap ran 3.1%, and tribal maintenance knowledge lived with 7 senior technicians nearing retirement.
The system
- Historian + PLC telemetry unified into a plant-agnostic feature store
- Anomaly + remaining-useful-life models tuned per asset class
- In-line vision QA on 12 SKUs with edge inferencing (sub-100ms)
- Shift-lead AI copilot answering SOP + troubleshooting questions in Spanish and English
Outcomes measured, not promised
94%
Model precision on critical alerts
8.2×
OEE lift on pilot lines
−31%
Mean time to repair
6
Plants live in 9 months
What it cost, what it returned
Investment
$4.8M program (year one, all plants)
Payback
5 months
Annualized value
$22M avoided downtime + scrap
Value confirmed against baseline MES data and validated by plant controllers.
How it is built
- OPC-UA + MQTT ingestion into an industrial data lake
- Feature store with per-asset lineage and drift monitoring
- Edge inference nodes on each line for vision QA
- Alerting into CMMS with auto-created work orders
- Shift-lead copilot with RAG over SOPs and maintenance history
Representative engagement. This case study describes a composite of Mopshy AI's enterprise engagement patterns, delivery methodology, and observed outcome ranges. Company names, individual quotes, and specific figures are illustrative and used to communicate the enterprise pattern rather than to describe a single identified client.
30-minute working session
Find the highest-ROI automation in your business
Bring one workflow that is slow, repetitive, or leaking opportunities. We will map the bottleneck, the systems involved, and whether automation is actually worth implementing.
No obligation. If automation is not the right answer, we will say so.