Industrial track & trace: from machine to product

A serial number only tells you something if you can find the parts, operations and checks linked to it. That is what industrial track & trace is about: knowing where a product is and being able to reconstruct how it was made. For factories and machine builders, that starts with reliable records on the shop floor, not a dashboard.

Tracking and tracing: the difference

Tracking answers the question: where is this product and what is its current status? Tracing looks back: which material batch did it come from, which machine processed it and what were the measurement results? How current that information is depends on when it was recorded. A scan at dispatch does not automatically tell you exactly where a product is afterwards.

The level of detail needed varies by process. Sometimes batch-level records are enough; sometimes every part needs its own serial number. What matters is keeping the links intact: which raw materials went into which batch, which parts were assembled together and which products were delivered?

From identification to a useful product record

Barcodes and QR codes are simple ways to identify products, bins or packaging. They need a readable code and a clear line of sight. RFID can work without a direct line of sight, but needs testing with the actual materials and reading environment. Choose the technology to suit the process, not the other way round.

At each relevant process step, record at least the product or batch identifier, the timestamp, the machine or workstation and the result. Link material usage, process settings and quality measurements where needed. Record scrap, rework and replacement parts too. Otherwise, the record only shows the planned route, not what actually happened.

Connecting machines with OPC UA and MQTT

Manual scanning still has its place, but machine data can be collected automatically where possible. OPC UA can provide structured access to machine data. MQTT can publish events and measurements to other systems. The two can complement each other; neither makes data meaningful on its own.

That takes agreed definitions. What does ‘operation complete’ mean? Which serial number does the measurement belong to? Do the machines use the same time reference? Without that context, you have isolated signals, not reliable traceability. Include buffering for lost connections and processing that recognises duplicate messages, so a network outage does not silently create gaps or duplicate records.

A Unified Namespace (UNS) can make this information available through a shared structure, for example by factory, line and machine. That helps systems use the same names and context. A UNS does not replace a historical product record, though: you still need storage, retention periods and traceable corrections. Connections to ERP, MES and WMS then link production data to orders, materials and deliveries.

What it lets you demonstrate

If a material batch is suspect, you want to identify which products were made with it and where they ended up. Conversely, you want to trace a defective product back to its parts, process steps and checks. That supports quality investigations and helps define the scope of a potential recall, provided the records are complete and reliable.

Traceability also helps with audits and sector-specific requirements. These vary between food, pharmaceuticals and other manufacturing environments. Agree upfront which data you need to provide as evidence, who can access it and how long you must retain it. Collecting more data is not automatically better.

Start small and test the exceptions

We would start with a clearly defined product flow and a concrete question, such as: can we trace a delivered serial number back to every material batch used? Then test missing scans, batch splits, rework and network outages too. Involve operators and quality staff: they know where records risk drifting away from reality.

AI can later help search records or flag possible links between deviations. We keep the boundary clear: AI only reads, a human decides. The original records remain the source; an AI summary is not evidence and must not release a product or control a machine.

Want to explore where traceability starts in your production process, or where it falls short? Book a 15-minute conversation with Vlegel Technology in Genk, the Data, AI and Software Engineering team. We will discuss your product flow, existing machine connections and which first step makes technical sense.