Manufacturing
Manufacturers run ERP, shop-floor control, and supplier systems that were procured at different times for different purposes, and the operational reality on the line often diverges from what those systems report. The gap between what a spreadsheet says and what's actually happening on the floor is where cost hides.
01Industry Challenges
What defines this sector
- Data is fragmented across ERP, MES/shop-floor systems, and supplier portals that were never designed to talk to each other.
- Equipment on the floor is often older and lacks native connectivity, creating a persistent divide between operational technology and IT.
- Demand is volatile enough that spreadsheet-based forecasting consistently lags reality.
- Skilled labor shortages mean fewer people are available to manually reconcile the gaps between systems.
- OEM customers increasingly require traceability and quality documentation that spans the full production chain.
02Operational Problems
Where it shows up day to day
- Production decisions get made on weekly or monthly reports instead of what's happening on the line right now.
- Unplanned downtime is discovered only after it has already delayed a shipment.
- Supplier delays surface too late to reroute production around them.
- Inventory counts in the ERP drift from what's physically on the floor.
- Tracing a quality issue back to its source batch is a manual, time-consuming process.
03Engineering Opportunities
Where systems work helps first
- An OT/IT integration layer connecting PLCs and floor sensors to enterprise systems.
- An event-streaming architecture that moves shop-floor data in near real time instead of on a batch schedule.
- EDI or API-based integration with suppliers to replace manual purchase order and status entry.
- A unified data model spanning ERP, MES, and supplier systems, so the same part or order means the same thing everywhere.
- Time-series data infrastructure for equipment telemetry, sized for the volume and retention manufacturing sensors actually produce.
04AI Opportunities
Where applied AI fits specifically
- Predictive maintenance models trained on equipment telemetry to flag failure risk before a breakdown stops the line.
- Demand forecasting models that combine historical sales with external signals, replacing spreadsheet-based projections.
- Anomaly detection on sensor data to catch quality drift or defects earlier in the process.
- Computer vision at fixed inspection points to support visual quality checks alongside existing QA staff.
05Recommended Approach
What to solve first
- Get shop-floor data into a unified store before building any predictive model — the model is only as good as the data feeding it.
- Start predictive maintenance on the highest-cost failure modes first, not every asset on the floor at once.
- Treat OT/IT integration as the foundational project; forecasting and anomaly detection are built on top of it, not in parallel with it.
- Pilot computer vision inspection alongside the existing QA process before it replaces any manual check.
Let's talk about manufacturing
Tell us what's actually happening in your operations. We'll assess it against what's outlined here — honestly, including where it doesn't apply.
