# Manufacturing

> In manufacturing, the clearest AI gains are in quality control, planning and document-heavy work. Corrplus builds camera-based defect detection, demand forecasts from past sales and orders, assistants that answer from technical documents, and automations that end manual data transfer between the ERP and other systems, for manufacturers in Türkiye and companies that source from them.

URL: https://corrplus.net/industries/manufacturing/
Updated: 2026-10-22
Corrplus Team

## Challenges

- Visual quality checks depend on operator attention; the risk of missed defects rises toward the end of a shift.
- Production plans rely on experience; stock either piles up or orders wait.
- Purchasing, order and shipping data moves by hand across Excel, the ERP and email.
- Finding the right instruction in machine manuals, procedures and ISO documents takes time.
- Supplier invoices, delivery notes and certificates are checked by hand.

## Where it helps

- **Visual quality inspection**: A system that flags scratches, stains or missing parts from a line camera and shows suspect items to the operator.
- **Demand forecasting**: A model that forecasts demand per product from order history, seasonality and promotions and gives the planner a recommendation.
- **Technical document assistant**: An assistant used on a tablet or phone on the shop floor that answers from maintenance manuals, work instructions and quality procedures with sources.
- **Delivery note and invoice matching**: A workflow that compares Turkish e-invoices and e-delivery notes with purchase orders and alerts purchasing to differences.
- **ERP data transfer**: An n8n or RPA workflow that posts dealer orders, shipping details and production records into Logo, Mikro, Netsis or SAP Business One.

## Where does AI help on the factory floor?

The most concrete gains come where human eyes and memory tire. An operator watching the same product on the line for hours, a technician searching a long maintenance manual for the right instruction, a planner building the weekly schedule from experience. Each of these jobs has a fitting tool.

The second large area is moving data. Entering orders into the ERP, matching delivery notes with invoices and compiling reports by hand take the most office time in manufacturing firms. These are often solved with well-built automation, without any AI at all.

Many manufacturers in Türkiye run Logo, Mikro, Netsis or SAP Business One, with a large number of Excel files living alongside. Those files usually point to a need the ERP does not cover, so understanding them first shows which process to tackle first.

## Which problem, which method?

| Problem | Method | Data source |
|---|---|---|
| Missed visual defects | Visual quality inspection | Line camera |
| Excess stock or waiting orders | Demand forecasting model | ERP sales and order history |
| Searching instructions and procedures | RAG document assistant | Manuals, work instructions, ISO documents |
| Invoice and delivery note mismatches | Document comparison workflow | e-invoice, e-delivery note, purchase order |
| Manual data entry between systems | n8n or RPA workflow | ERP, Excel, email |

Which row to start with depends on the cost of errors and whether the data is ready. The fastest results usually come from document and data transfer; visual inspection needs more preparation.

## How do we work?

We walk the plant and the office with you and trace the path of one order from entry to shipping. Then we list the manual steps and where the data lives. The first project is usually limited to a single line or process and expanded once it proves itself.

Production data is often a trade secret. During design we decide together whether models run on local servers or in the cloud, and which data may leave the site. For workflows that include employee data, we review KVKK and GDPR obligations with your legal counsel.

To begin, the [AI discovery sprint](/ai-discovery-sprint/) produces a prioritised list and a written proposal for the first project in two weeks.

## Frequently asked questions

### How much data does visual inspection need?

It depends on the defect types and imaging conditions. We first collect sample images from your line and run a short trial to see whether they are enough. For rare defects we set up a human-approved approach.

### Can we get data from older machines?

Often, yes. PLC, SCADA or files the machine exports can be used. If none exist, adding an external sensor or camera is an option.

### Can it run without data leaving the plant?

Yes. Vision and language models can be deployed on local servers. We weigh cloud against on-premises during design for cost and security.

### Can you support a European company with production in Türkiye?

Yes. We work in English and Turkish and can connect your plant in Türkiye to group systems and reporting while respecting both KVKK, Türkiye's data protection law, and GDPR.