How Much Data Is There in an Electronics Factory?

One of the most common assumptions in electronics manufacturing is that collecting comprehensive machine data at factory scale is prohibitively expensive. Modern SMT lines generate enormous volumes of measurements, traceability records, inspection results, and process parameters, so the concern seems reasonable.

But the economics have changed dramatically over the last decade.

Advances in compression and low-cost cloud object storage have made long-term storage of factory machine data surprisingly practical and inexpensive. In most cases, the limiting factor is no longer storage cost. It is the work required to build scalable factory data workflows that can collect, contextualize, and operationalize the data effectively.

To understand why, it helps to look at what an SMT line actually generates in practice. The reason this matters is that many of the signals most useful for root cause analysis and process optimization exist at the measurement level, not in summarized production reports.

An SMT line is one of the larger data-generating segments of an electronics factory, so it’s a useful place to anchor the analysis. Consider a standard six-up panel (six PCBAs, approximately 500 total components, 5,000 total pads) running through a typical line configuration: printer, SPI, pick-and-place, reflow oven, and AOI.

If you capture the full set of machine-level measurements and traceability data available from each process, here’s roughly what you’d collect per panel:

Printer: Relatively little. A set of process parameters (printing pressure, stroke speed, and similar), a barcode, and a program name. Maybe 10 numerical values total, well under a kilobyte.

SPI: This is where volume starts to climb. With three measurements per pad (height, area, volume), registration offsets, min/max limits, and a pass/fail result per board, you’re looking at roughly 75,000 numbers per panel. Stored as an uncompressed JSON or XML file, that’s approximately 2 MB per panel.

Pick-and-place: Somewhere in between. Data is captured per component rather than per pad, which reduces volume somewhat. But if you’re storing full traceability, including pickup error, placement offsets, vacuum pressure, feeder and nozzle serial numbers, and reel barcodes, you’re looking at several thousand strings and roughly 5,000 process parameters. Approximately 100 KB per panel, though not all PNP machines can report all of these fields.

Reflow oven: Minimal. Set points and actual measurements per zone, on the order of 10 to 30 numbers. Negligible storage footprint.

AOI: AOI: Similar in structure to SPI but generally smaller unless raw image retention is included. Structured inspection results are roughly 1 MB per panel.

Summed across the line: approximately 4 MB of uncompressed data per panel.

At a 30-second cycle time running 24/7, a single SMT line produces around 2,880 panels per day. That works out to roughly 12 GB per day, per line. Scale to 10 lines and a one-month retention period, and you’re looking at approximately 3.5 TB, enough that you’d want to think carefully about your storage architecture before simply loading everything into a relational database.

That’s the number that tends to give people pause. But it assumes uncompressed data, which is not how modern factory data systems actually operate.

FIGURE 1: Factory Data Workflows in an SMT Line

Modern SMT lines generate gigabytes of structured machine data every day, but advances in compression have dramatically reduced storage costs. The challenge now is building factory data workflows that can connect, contextualize, and operationalize that data across the factory.

Modern numerical compression algorithms (which became widely available and practical around 2015) are highly effective on the kinds of data SMT machines generate: repetitive numerical measurements, structured strings, and bounded ranges. Applied to the data described above, compression reduces ~4 MB per panel to approximately 93 KB. These are real figures drawn from measurements across roughly 500 SMT lines.

That changes the storage picture considerably. At 93 KB per panel, a single SMT line running continuously generates about 8 GB per month, roughly equivalent to two DVDs. At current cloud storage pricing, storing a full year of machine data for an entire 10-line factory costs on the order of $22 per year.

The practical conclusion is that measurement data (while the highest-volume data type in an SMT line) is also the most amenable to cost-effective storage. Modern compression, combined with the decreasing cost of cloud storage, has largely eliminated the infrastructure barrier that previously made comprehensive machine data collection impractical.

This matters because measurement data is arguably the most operationally valuable the line produces. Per-component placement offsets, per-pad solder volume measurements, and per-cycle process parameters are exactly the kinds of signals that enable defect root cause analysis, feeder health monitoring, and process drift detection.  

The limiting factor in modern factory data workflows is no longer storage cost or capacity. It is the ability to contextualize machine-level data and transform it into operational intelligence that improves quality, throughput, and decision-making in real time.