{"id":14211,"date":"2026-07-31T11:19:40","date_gmt":"2026-07-31T11:19:40","guid":{"rendered":"https:\/\/www.jstconsultancy.cz\/?p=14211"},"modified":"2026-08-01T18:36:06","modified_gmt":"2026-08-01T18:36:06","slug":"how-to-do-ai-in-practice-part-i-introduction","status":"publish","type":"post","link":"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/en\/how-to-do-ai-in-practice-part-i-introduction\/","title":{"rendered":"How to do AI in practice, part I – Introduction"},"content":{"rendered":"

Thomas Wingens<\/strong> published an interesting article on the topic of \u201cTHE AUTONOMOUS THERMAL PLANT: HOW AI AND ROBOTICS ARE CHANGING HEAT TREATMENT\u201d. For those who don\u2019t know Thomas, he is a man who was born in a hardening plant and has held managerial and executive positions in companies in the metalworking and heat treatment industries during his working life, including Bodycote, Ipsen, Tenova Group, Seco\/Warwick, ThyssenKrupp and Voestalpine Group. I have known him personally for years, we worked together at Bodycote, and in my time he was responsible for acquisitions. (www.wingens.com<\/a>).<\/p>\n

\"\"<\/a>\"\"<\/a>\"\"<\/a><\/p>\n

When I read it, the content intrigued me, but I couldn’t imagine how to do it practically. Although I fully agree with the content of his article, it is not a guide. So I started to research what can be done with it. How can AI actually intervene in the life of a tempering plant from receipt to invoicing.<\/p>\n

The first thing I learned was that AI<\/strong> in a heat treatment plant is not like AI<\/strong> like Copilot<\/strong> or ChatGPT.<\/strong> They communicate with us, have a language model, understand text, and respond. But AI<\/strong> in a heat treatment plant usually doesn’t talk, doesn’t write texts, doesn’t communicate with people, doesn’t have a personality, doesn’t have consciousness, and doesn’t ask anyone questions.<\/p>\n

The core of industrial AI<\/strong> in a heat treatment plant is usually not a conversational model, but an analytical or predictive<\/strong> model. It evaluates data, classifies states, and predicts results. In addition, however, Document<\/strong> AI<\/strong> or a language model can be used to read RFQ<\/strong>s, technical documents, and prepare explanations or protocols.<\/p>\n

AI<\/strong> in the heat treatment shop is a silent mathematical module that: reads data, looks for patterns, detects deviations, predicts failures, recommends parameters, checks preparation, generates protocols.<\/p>\n

AI<\/strong> takes: furnace logs, batch preparation photos, photos from batch loading, CHD results, hardness, microstructure, order history, vacuum pump and fan vibrations, flow rates, temperature gradients. And it \u201casks\u201d: What does the normal state look like<\/strong>? What does the deviation look like<\/strong>? What is likely to happen next<\/strong>? What parameter is optimal? Is the preparation correct? Is the fan OK? Is the pump OK? Is the CHD within tolerance?<\/strong><\/p>\n

AI<\/strong> does not ask people. AI<\/strong> asks history and looks for patterns in historical, reference and current data<\/p>\n

What AI \u201csees<\/strong>\u201d: T1, T2, T3, pressure, vacuum, acetylene flow, diffusion time, temperature gradient, CHD results from past batches.<\/p>\n

What AI \u201cdoes<\/strong>\u201d: compares the current batch with 500 previous ones, finds deviation, assesses risk, recommends correction.<\/p>\n

What the AI \u200b\u200b\u201csays<\/strong>\u201d (in practice, in the form of a report): \u201cDiffusion is slower than usual. I recommend extending the time by 3 minutes.\u201d<\/p>\n

The AI<\/strong> \u200b\u200bdoes not ask a person. The AI<\/strong> \u200b\u200basks a data pattern. And how to imagine it simply?<\/p>\n

ChatGPT = \u201cAI that talks, writes, communicates\u201d, AI in the heat treatment plant = \u201cAI that calculates\u201d. Both are AI, but of a completely different type.<\/strong><\/p>\n

So what is AI<\/strong> in the heat treatment plant technically?<\/p>\n

AI<\/strong> in the heat treatment is: a prediction model<\/strong> (time to ventilator failure), a deviation detection model<\/strong> (poor preparation), a recommendation model<\/strong> (optimal diffusion), a classification model<\/strong> (correct part orientation), a regression model<\/strong> (prediction of CHD), a segmentation mode<\/strong>l (recognition of parts on a preparation). These are mathematical functions, not personalities.<\/p>\n

Now it’s getting interesting. AI<\/strong> in the heat treatment plant is:<\/p>\n

a query engine<\/strong> (asks for data),
\nan analytical engine<\/strong> (evaluates),
\na prediction engine<\/strong> (predicts),
\nan advisory engine<\/strong> (recommends).<\/p>\n

So how do I start with AI<\/strong>? I am not an \u201cIT guy<\/strong>\u201d but a metallurgist. That is why I have to realize that ERP<\/strong> does not ask the data warehouse, but this query is automatically raised by AI,<\/strong> connected to ERP<\/strong> via API<\/strong>, which communicates with the data warehouse. ERP<\/strong> only asks AI<\/strong> in the form of a JSON<\/strong> query.<\/p>\n

ERP<\/strong> is the \u201cquery submitter\u201d, AI<\/strong> is the \u201cquery resolver\u201d. The data warehouse is the \u201cdata source\u201d. AI<\/strong> provides technological analyses, predictions and recommendations, but ERP<\/strong> remains a system of official records, workflow management and approved business and production data. And what it could look like is in the following infographic.<\/p>\n

\"\"<\/a><\/p>\n

One of the possibilities to test and use AI<\/strong> is the bidding process after receiving an RFQ<\/strong>. This is one of the few areas where they do not need to have connected furnaces and monitor data from IoT<\/strong>. On the other hand, it is also one of the few areas where we need predictive and analytical functions of AI<\/strong> to help us guess what we normally do not see during the bidding process. If we receive CAD<\/strong> data as part of the request, then AI<\/strong> will perform the entire risk analysis for us, can prepare the entire APQP<\/strong>, PPAP,<\/strong> Control Plan<\/strong> process, based on prediction functions, it will propose Routing<\/strong>, batch preparation, and estimate energy consumption and total costs so that we can propose the correct price, giving us sufficient margin.<\/p>\n

There is one limit, however. All data for this phase comes primarily from ERP<\/strong>. If we don’t have this data in ERP<\/strong>, AI<\/strong> has nothing to deal with. This needs to be realized before we start. In the area of \u200b\u200baccounting data, all ERP<\/strong> systems are essentially the same, but the fundamental difference is in the production module. Before implementing AI<\/strong> in the heat treatment plant, we must first consider whether the ERP<\/strong> provides us with the right data at the right time in the right structure. And since I have implemented several ERP<\/strong> systems (Altus Vario, Navision, Dynamic AX 2012, QI) in my 40 years of experience, I have worked for companies that use SAP,<\/strong> I can say with certainty that this phase will be as important as the AI<\/strong> \u200b\u200bimplementation phase.<\/p>\n

In the past, all the development steps we took in ERP<\/strong> were focused on improving traceability and definability functions, with the aim of making our work easier and satisfying the customer or legislation. We never did it to satisfy AI<\/strong>. But looking back, our path was exceptionally correct. I can say this because I spent the last 5 years at Bodycote doing just this, developing the global manufacturing module.<\/p>\n

Anyone who starts today without this preparation has to start from the end. What to do in ERP<\/strong> to satisfy AI.<\/strong> And only if they solve this in ERP<\/strong>, then they can think about AI<\/strong>.<\/p>\n

=======================================================================================================<\/p>\n

Abbreviations used::<\/span><\/strong><\/span><\/p>\n

AI<\/strong>:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 A<\/strong>rtificial I<\/strong>ntelligence
\n<\/span><\/em>API:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>Application Programming I<\/strong>nterface: definovan\u00e9 rozhran\u00ed a soubor pravidel, podle kter\u00fdch si dva syst\u00e9my p\u0159ed\u00e1vaj\u00ed data a p\u0159\u00edkazy. Webov\u00e9 API m\u016f\u017ee b\u00fdt dostupn\u00e9 prost\u0159ednictv\u00edm jedn\u00e9 nebo v\u00edce URL adres, ozna\u010dovan\u00fdch jako endpoints.
\n<\/strong><\/span><\/em>JSON: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>JavaScript O<\/strong>bject N<\/strong>otation: textov\u00fd form\u00e1t pro strukturovan\u00fd p\u0159enos dat. Vznikl v prost\u0159ed\u00ed JavaScriptu, ale dnes je nez\u00e1visl\u00fd na programovac\u00edm jazyce.
\n<\/span><\/em>Middleware: \u00a0 <\/strong>integra\u010dn\u00ed vrstva, kter\u00e1 propojuje syst\u00e9my, transformuje data, \u0159\u00edd\u00ed komunikaci, zabezpe\u010den\u00ed a auditn\u00ed z\u00e1znamy.
\n<\/span><\/em>AI model: \u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>matematick\u00fd nebo statistick\u00fd model, kter\u00fd prov\u00e1d\u00ed klasifikaci, predikci, detekci odchylek nebo doporu\u010den\u00ed.
\n<\/span><\/em>OPC UA<\/strong>: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Open Platform Communications nebo OPC Unified Architecture
\n<\/span><\/em>AI vrstva: \u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>\u0159e\u0161en\u00ed sestaven\u00e9 ze standardn\u00edch platforem, konektor\u016f, pravidel a individu\u00e1ln\u011b nakonfigurovan\u00fdch nebo vyvinut\u00fdch model\u016f.
\n<\/span><\/em>Document AI: <\/strong>Technologie vyu\u017e\u00edvan\u00e1 k\u00a0extrakci vyu\u017e\u00edv\u00e1 k\u00a0extrakci informac\u00ed<\/a>\u00a0z ti\u0161t\u011bn\u00fdch i\u00a0digit\u00e1ln\u00edch dokument\u016f<\/a>, jako jsou obr\u00e1zky, texty, znaky
\n<\/span><\/em>DMS:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 D<\/strong>ocument M<\/strong>anagement S<\/strong>ystem
\n<\/span><\/em>FQ<\/strong>: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 R<\/strong>equest F<\/strong>or Q<\/strong>uotation
\n<\/span><\/em>APQP:<\/strong> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 A<\/strong>dvanced P<\/strong>roduct Q<\/strong>uality P<\/strong>lanning
\n<\/span><\/em>PPAP:<\/strong> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 P<\/strong>roduction P<\/strong>art A<\/strong>pproval P<\/strong>rocess
\n<\/span><\/em>ERP:<\/strong> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 E<\/strong>nterprise R<\/strong>esource P<\/strong>lanning<\/span><\/em><\/p>\n

=======================================================================================================
\nAre you solving a similar problem? I will help you with the analysis\u2026<\/strong>
\n\"✔\"\u00a040+ years of experience in the field
\n\"✔\"\u00a030+ years of experience\u2026 HT-PROGRES, Bodycote, Galvamet
\n\"✔\"\u00a0cooperation\u2026 V\u0160B, Czechimplant, ECM Technologies, TAV Vacuum Furnaces, GHC Invest
\n\"✔\"\u00a012+ years of expert activity
\nWant to ask for a solution or want a non-binding consultation? Click on this link, I will usually respond within 24 hours.\u00a0Contact email<\/a>
\n========================================================================================================
\nJi\u0159\u00ed Stanislav, Ing. CSc.
\n<\/strong>Consultant and forensic expert
\n========================================================================================================<\/p>\n

31\/7\/2026<\/p>\n","protected":false},"excerpt":{"rendered":"

Thomas Wingens published an interesting article on the topic of \u201cTHE AUTONOMOUS THERMAL PLANT: HOW AI AND ROBOTICS ARE CHANGING HEAT TREATMENT\u201d. 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