A mid-size factory usually already has plenty of documents and data. Machine SOPs, work instructions, maintenance logbooks, QC reports and production data in the ERP. The trouble is finding the right piece of information when it is needed. A night-shift operator who forgets a machine setting sequence has to call a senior technician. The maintenance lead builds the monthly report from handwritten logbook entries. The QC team types up reports that look almost the same every week.
AI for manufacturing is most useful at exactly these points. It helps teams find and summarise information that already exists, while technical decisions stay with the people responsible for them. This article covers four realistic use cases for mid-size manufacturers, the data involved, the risks to manage and how to begin.
SOP search for operators
SOPs and work instructions often live in physical binders, shared folders or PDFs with inconsistent file names. New operators rarely read them end to end. They ask a colleague, and the answer depends on who happens to be on shift.
With RAG (retrieval-augmented generation), the AI finds the relevant part of an SOP and answers with a citation. Operators can ask from a tablet on the shop floor, for example, "What is the standard temperature for the type B mould on injection machine 3?" The answer names the document, version and page so the operator or supervisor can check it directly.
For anything involving workplace safety, the AI should quote the SOP as written and never improvise steps. If the documents do not contain the answer, it should say so and suggest the operator contact a supervisor.
Summarising maintenance logs
Maintenance notes are usually brief and full of shop-floor shorthand, sometimes mixing Indonesian, Javanese and English technical terms. They contain useful history, but nobody rereads them because there is too much.
AI can summarise the logbook per machine or per period. The output is a list of recurring faults, the spare parts replaced most often and the machines with the most downtime. The maintenance lead can use this to plan preventive maintenance and justify spare part purchases.
If the logbook is still handwritten, the first step is to photograph it or move it to a simple digital form. AI can help read the photos, but the results need checking because handwriting from the production floor is often hard to read.
Drafting quality reports
Weekly QC reports and non-conformance reports (NCRs) tend to follow the same format. Findings, reject quantities, suspected cause, containment action and the corrective plan. A lot of time goes into writing sentences and copying numbers from spreadsheets.
AI can prepare a draft report from inspection data and the QC team's notes. The numbers come straight from the system, and the AI writes the narrative in your company format. Root cause analysis and decisions on corrective action remain with QC and production. The AI simply helps the report get done faster and keeps the format consistent.
Asking questions of ERP data
Production heads and plant managers often need quick answers. How much of a given raw material is in stock, which work orders are late, or how line 2's output this week compares with target. Usually they have to ask an admin for a report or click through several ERP menus.
AI can be connected to the ERP to answer questions like these in everyday language. Access should start as read-only, with permissions that follow job roles. A line supervisor sees only their line's data, while the plant manager can see everything. If production data is still spread across many spreadsheets, tidying the flow through our data and analytics service may be a sensible step before AI.
One thing that is easy to overlook is how numbers are defined. Production and QC may count "output" or "rejects" differently. Agree on the definitions first and write them down, so the AI's answers match the figures discussed in production meetings.
Risks and how to start with AI for manufacturing
A few things to put in place before AI is used in the plant:
- Make sure only the latest version of each SOP is searchable. Old versions still in circulation are the most common source of errors.
- Agree that the AI gives no safety instructions beyond the official documents.
- Start ERP access as read-only. Data changes still go through the usual procedure.
- Set access per team, especially for cost data, supplier prices and employee data.
- Test the AI with real questions from operators and technicians, including the shop-floor terms they actually use.
- Log questions that were not answered well, and use them to improve the documents or the system setup.
Begin with one area, for example SOP search for a single production line. Collect the questions operators ask over two weeks, then test whether the AI answers them correctly and cites its source. Once supervisors trust the answers, extend it to other lines or to the next use case.
Talk it through with Averoa
Averoa helps companies connect AI to internal documents and data, including ERP, with answers that cite their sources and access set per team. If you would like to discuss which area of your plant makes the most sense to start with, you can read more on our Company AI page.
