Industrial software, AI and automation – explained properly.
Practical articles: how production data becomes usable, where AI genuinely helps on the shop floor, and what new regulation means for machine builders and mid-sized manufacturers. No buzzwords, sources included.
How to make manuals, datasheets, maintenance reports and standards machine-readable with AI – from PDF and OCR quality through RAG architecture and source attribution to on-premise operation. Technical depth instead of product promises, with an honest account of the limits.
OPC UA, MQTT, S7, MES, ERP and historians: how to get data out of legacy and new machines and into systems you can actually analyse. With decision matrices, fault diagnosis and reference architectures for brownfield plants.
OEE, availability, shop-floor and machine data capture, energy and quality figures: which KPI needs which data source, and how to turn that into a dashboard the shop floor actually trusts. With calculators, formulas and standards references instead of benchmark marketing.
The smallest sensible first step instead of a digitalisation vision: replacing Excel, digital shift handover, paperless production, goods receipt and document processing. Every article names the data source, the target system and the exceptions that make projects fail.
Before the project comes the decision: off-the-shelf or custom, requirement specifications, realistic cost structures for industrial projects and typical use cases in production and maintenance. Decision material that also says when off-the-shelf software wins.
The OEE formula is quickly explained. Whether the number holds up is decided by the time model and by where runtime, output and scrap come from - with a shift worked through minute by minute and an Excel layout.
The honest route from a document base to an answer you can verify: which document classes can actually be opened up, where layout parsing breaks down, and why source citation is an acceptance criterion.
Which shopfloor process should be automated first? A scoring grid built from frequency, media breaks, error costs and data availability, plus a checklist for a pilot that does not slide back into Excel after go-live.
The make-or-buy question is settled process by process, not company by company. A weighted scoring matrix, a five-year TCO logic and the tax side - plus a clear list of the processes where standard software simply wins.
A complete ISA-95 topic schema, naming rules with the reasoning behind them, an honest assessment of Sparkplug B and a grid for choosing a broker - vendor-neutral, including a section on when you do not need a UNS at all.
Brownfield is the normal case. Four escalation levels show how to reach the data inside an existing machine - and why capturing it is the smaller part of the work.
A credible price for custom industrial software exists only as a range. Which cost drivers occur only in manufacturing, what quotes routinely leave out, and how to calculate running costs over the life of the machine.
In October 2025 Germany's data protection authorities set out how they will judge a RAG system. This article turns the 18 pages into an ingest pipeline, permission filters, a deletion path and a 14-point checklist.
OPC UA models meaning and access at the asset; MQTT moves messages upwards through a broker. Which protocol carries which job, what Sparkplug B adds, and what the bridge between the two looks like.
The S7-1500's built-in OPC UA server or Snap7 on port 102? A decision table by PLC generation, the licence classes, the PUT/GET setting, optimized data blocks and a working python-snap7 example.
A language model in your own server room is a plant asset, not a subscription: the calculation from user count to GPU, why small models are what make on-premise operation economical, and which cases belong in an EU cloud.
Five integration patterns between MES and ERP compared — and then the part almost nobody writes about: confirmation logic, duplicate protection and what happens when one system goes down.
·20 min read
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