AI in manufacturing, planned around your ERP, your MES and your quality team

Where does AI in manufacturing pay back for a mid-size plant?

It pays back where skilled people spend hours reading, retyping and chasing information. Predictive maintenance and machine vision get the headlines, but most plants under 1,000 staff do not yet have the sensor history those projects need. The office around the floor is a different story: buyers, planners, quality engineers and customer service reps handle documents all day, and language models are good at reading documents.

When we walk a plant, the same time sinks keep appearing. A quality engineer writes up a nonconformance from photos and a shift note. A buyer pastes three supplier quotes into a spreadsheet to compare lead times and unit prices. A planner answers the same 'where is my order' email by opening the ERP, finding the work order and checking routing status. Each of these is a candidate.

• Quality: drafting NCRs and corrective action summaries from inspection notes, photos and past CAPAs.
• Purchasing: reading supplier quotes and RFQ replies into one comparison table for the buyer.
• Order entry: extracting line items from customer purchase orders into the ERP for review.
• Shop floor knowledge: an assistant that answers operators from your SOPs and work instructions, with citations.
• Supplier scouting: finding and screening alternate sources against your approved vendor rules.
• Reporting: weekly OEE and scrap commentary written from MES exports, checked by the plant manager.

Which plant systems does an AI project need to connect to?

Usually the ERP, the MES or its spreadsheets, and wherever SOPs and drawings live. Common ERPs at this size include NetSuite, Epicor, SAP Business One and Plex, and some plants still run a homegrown Access database. Quality records may sit in a QMS, in SharePoint folders or in a shared drive of Excel NCR logs. Maintenance history lives in a CMMS or a clipboard.

We map these during the review because integration effort, not the model, drives most of the cost. A use case that only reads from SharePoint is quick to stand up. One that writes back to the ERP needs an API user, field mapping, a review queue and a rollback plan, so it moves later in the roadmap even if its value is high.

How do we rank AI use cases on a manufacturing roadmap?

We score each candidate on four things and plot it on a value and ease matrix. Value is hours per month multiplied by who does the work, plus the cost of the errors it prevents (a missed spec on a PO costs more than a typo in a report). Ease is data availability, integration depth and how clearly someone owns the outcome.

Top-right items go first. In plants we typically see order entry, quote comparison and the SOP assistant land there, while anything that touches machine data or scheduling logic needs more groundwork. The output is a sequenced plan with an estimated effort, a success measure and an owner for each item, so the operations leader can defend the order to the owner or the board.

What governance does AI in manufacturing need?

The main risks are wrong numbers reaching an order, confidential drawings leaving your control, and quiet drift in output quality. Governance for a plant is practical: who approves, what is logged, and which data a model may see.

We write rules your quality manager can audit. Extracted PO lines carry a confidence score, and anything under the threshold goes to a human-review queue instead of the ERP. Customer drawings and specifications covered by NDA stay in your own tenant, processed through enterprise or API tiers that do not train on your data. Every prompt and output is logged with the record it touched. Each use case has a small eval set of real past documents that we rerun whenever a prompt or model changes.

• No AI write to a work order, BOM, routing or price without human approval.
• Cost caps per task and per month, with alerts to the owner.
• A written fallback: if the tool is off, the old process still works.

What did supplier scouting look like for a sourcing client?

For a sourcing and supplier quality consultancy, we built two tools: one finds candidate suppliers for a part or material, the other applies the client's own screening rules automatically. The client reported an 85% time saving and a 91% cost saving on supplier scouting. You can read the detail in our case studies.

The lesson for plants is that the screening rules came from the client's buyers, not from us. A good AI in manufacturing project encodes the judgment your team already uses, then frees those people for negotiation and supplier visits.

How does a manufacturing AI engagement run?

It starts with a free review call, then a short discovery where we interview the people doing the work and pull sample documents: a month of NCRs, recent POs, a stack of supplier quotes. From that we deliver the ranked roadmap and a fixed price for the first use case.

If you want us to build it, we do, with your team testing against real documents before go-live. If you have an internal developer or an integrator, we hand over the specification and stay on as advisors. Either way, you own the accounts, the code, the prompts and the data.