Boring AI for manufacturing & logistics — use-case card
Manufacturing and logistics is the most honest AI deployment environment there is: either OEE went up or it didn't, scrap rate went down or it didn't, the route was shorter or it wasn't. The use cases that pay are the ones pointed at a clearly defined operational objective, in an environment where data is clean, the process is documented, and measurement is in place before the AI arrives.
The manufacturing & logistics boring-AI list
| # | Use case | What it replaces | Prerequisite |
|---|---|---|---|
| 1 | Vision-based quality inspection | Human inspector checking each unit; accuracy degrades at end of shift | Cameras on the line; defined defect standard |
| 2 | Predictive maintenance | Reactive repair after unplanned breakdown | Sensor data (vibration, temp, pressure) being collected and accessible |
| 3 | Route and delivery optimization | Dispatchers approximating sequences manually | Structured stop/delivery data; defined time windows and constraints |
| 4 | Inventory and demand reconciliation | Manual forecasting; stockout and overstock firefighting | Reliable real-time inventory counts |
| 5 | Safety monitoring (PPE compliance, restricted-zone detection) | Periodic safety walks and inspections | Existing facility cameras; documented escalation protocol |
| 6 | S&OP data assembly and demand forecasting | Staff manually pulling and reconciling data across systems before planning meetings | Connected ERP/MES data; historical sales data |
| 7 | Supply chain visibility and anomaly alerting | Manual tracking of shipments; late-to-learn on delays | Real-time inventory and shipment data in a connected system |
| 8 | OEE reporting and bottleneck identification | Manual assembly of shift and line performance data | MES collecting OEE data by shift and line |
| 9 | Assembly verification (computer vision) | Visual inspection confirming parts are present and correctly seated | Consistent part positioning; camera at station |
| 10 | Field documentation and hazard reporting (voice-to-text) | Workers filling in paper or manual forms mid-task | Mobile device access on the floor; structured form templates |
Map your top 3 loss drivers
Name the three losses — scrap, unplanned downtime, route inefficiency, inventory error, rework, or other — that cost the most per month. Then match each to a use case above and assess your data readiness.
| Loss driver | Est. monthly cost ($) | Matching use case (#) | Data available and clean? | Measurable baseline exists? |
|---|---|---|---|---|
| 1. ______ | Y / N / Partial | Y / N | ||
| 2. ______ | Y / N / Partial | Y / N | ||
| 3. ______ | Y / N / Partial | Y / N |
Data readiness gate
Before scoping any manufacturing AI pilot, pull the OEE report for your three highest-revenue production lines, by shift, for the last 90 days. If it takes more than a day to produce that report, fix the data infrastructure first. The AI is the last mile; the data pipeline is the road.
- ☐ 90-day OEE report by shift is available in under one day
- ☐ Sensor or inspection data for the target process is clean and accessible
- ☐ Baseline metric (scrap rate, downtime cost, route miles) is documented
- ☐ Alert-to-action protocol is defined — someone will act on what the AI flags
My pilot: ______ | My baseline metric: ______ | My 90-day target: ______
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