{"id":14258,"date":"2026-07-31T15:26:01","date_gmt":"2026-07-31T15:26:01","guid":{"rendered":"https:\/\/www.jstconsultancy.cz\/?p=14258"},"modified":"2026-08-01T18:32:35","modified_gmt":"2026-08-01T18:32:35","slug":"how-to-do-ai-in-practice-part-iv-data-flow","status":"publish","type":"post","link":"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/en\/how-to-do-ai-in-practice-part-iv-data-flow\/","title":{"rendered":"How to do AI in practice, part IV &#8211; Data Flow"},"content":{"rendered":"<p>So how about the RFQ process, the commercial offer? It&#8217;s a few steps that a salesperson, a receptionist or a technologist or metallurgist, the quality department, do for us for now.<\/p>\n<p><span style=\"color: #993300;\"><strong><span style=\"font-size: 14px;\">AI a RFQ (Request for Quotation)<\/span><\/strong><\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">AI \u2013 Basic data validation process (Customer, Product, Routing, Control plan exists?)<\/span><\/li>\n<li><span style=\"color: #000000;\">AI &#8211; supported Geometry Risk Screening<\/span><\/li>\n<li><span style=\"color: #000000;\">AI &#8211; supported Heat Treatment Risk Screening<\/span><\/li>\n<li><span style=\"color: #000000;\">AI &#8211; supported Load and Fixturing Design<\/span><\/li>\n<li><span style=\"color: #000000;\">AI \u2013 supported Costing Model (Offer Costing)<\/span><\/li>\n<li><span style=\"color: #000000;\">AI \u2013 supported Quotation Data Management and Traceability (Quotation storage)<\/span><\/li>\n<\/ul>\n<p><strong>Document AI<\/strong> helps us by retrieving all the data from the request, drawing, <strong>CAD<\/strong> model, checking and proposing a solution. It takes into account that the <strong>RFQ<\/strong> can be received in various forms, for example as an <strong>email<\/strong>, <strong>PDF,<\/strong> <strong>paper document, spreadsheet, XML or EDI message<\/strong>. The integration layer first identifies the document type. For structured formats, it performs mapping and rule validation, while for unstructured documents, it uses <strong>OCR<\/strong> and<strong> Document AI<\/strong> to extract technical and business data.<\/p>\n<p>The result is a single <strong>RFQ<\/strong> data object, independent of the original format. Each extracted data is associated with a source, a level of certainty and an audit trail. The <strong>ERP<\/strong> then creates an official offer record and starts a <strong>Contract Review<\/strong>. Unclear, conflicting or low-trust data must be confirmed by an authorized person before further processing. The <strong>Contract Review<\/strong> can take on the statuses \u2013 <strong>Feasible, Feasible with Conditions, Trial Required, Process Development Required, Not Feasible<\/strong>.<\/p>\n<p>So, if we have the assignment, a unified data object for the <strong>RFQ<\/strong>, we can start validating the data.<\/p>\n<p><span style=\"color: #800000;\"><strong>The validation system via API verifies:<\/strong><\/span><\/p>\n<ul>\n<li>customer exists,<\/li>\n<li>product exists,<\/li>\n<li>material is clearly defined,<\/li>\n<li>production process exists,<\/li>\n<li>control process exists,<\/li>\n<li>qualifying production resources exist,<\/li>\n<li>valid specifications are available,<\/li>\n<li>customer and OEM requirements are known,<\/li>\n<li>there are no conflicts between requirements.<\/li>\n<\/ul>\n<p>AI simultaneously creates a list of missing information, for example:<\/p>\n<ul>\n<li>The initial material state is not specified.<\/li>\n<li>It is not clear whether the effective or total layer depth is required.<\/li>\n<li>There is no consistency between the layer properties and the proposed measurement method<\/li>\n<li>A revision of the customer specification is missing.<\/li>\n<li>A matching product or process was not found<\/li>\n<\/ul>\n<p>During validation, the <strong>Product\/Process Classification<\/strong> is also determined: <strong>Existing Product, New Product, Existing Process, Process Adaptation Required<\/strong> or <strong>New Process Development Required<\/strong>. This is not an <strong>RFQ<\/strong> status, but a case classification for subsequent workflow.<\/p>\n<h2><span style=\"color: #993300;\"><strong><span style=\"font-size: 14px;\">RFQ Intake Status<\/span><\/strong><\/span><\/h2>\n<p>Once the data is loaded and validated, the offer will receive a<strong> Data Completeness Status<\/strong>:<\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>Data Complete<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>Data Incomplete<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>Conflict Detected<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>Customer Clarification Required<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>Manual Data Review Required<\/strong><\/span><\/li>\n<\/ul>\n<p>What needs to be explained must be explained and only then can we move on.<\/p>\n<p><span style=\"font-size: 14px; color: #993300;\"><strong>AI-supported Geometry Risk Assessment<\/strong><\/span><\/p>\n<p>In the next step, AI can help us with risk assessment. From a photograph, drawing, or CAD file, an orientation screening of critical areas can be performed. Accurate assessment of dimensions, radii, thicknesses, and spatial geometry requires a calibrated image, technical drawing, or CAD model. In addition to AI, a specialized geometry engine is used to analyze CAD data. However, we can get information about:<\/p>\n<ul>\n<li>sharp cross-section transitions,<\/li>\n<li>notches and local stress concentrators,<\/li>\n<li>missing or too small radii,<\/li>\n<li>blind holes and deep pockets,<\/li>\n<li>thin walls,<\/li>\n<li>significant thickness differences,<\/li>\n<li>holes near the edge,<\/li>\n<li>part asymmetry,<\/li>\n<li>areas prone to uneven heating or cooling,<\/li>\n<li>potentially problematic orientation during preparation,<\/li>\n<li>risk of oil retention or restricted quenching gas flow.<\/li>\n<\/ul>\n<p>But since the demand only includes simple data such as the weight of the piece and the number, <strong>AI<\/strong> can also calculate the areas we need for the correct management of a process dependent on the surface area, such as <strong>LPC, nitriding, coating<\/strong>.<\/p>\n<p>The person who prepares the offer must respond to the risks identified by AI. Alone or in cooperation with a metallurgist, technologist, or quality department.<\/p>\n<p><span style=\"color: #993300;\"><strong><span style=\"font-size: 14px;\">AI-supported Heat Treatment Risk Assessment<\/span><\/strong><\/span><\/p>\n<p>AI will also prepare an analysis of the risks arising from heat treatment<\/p>\n<ul>\n<li>Risk of cracks arising from geometry<\/li>\n<li>Risk of deformation<\/li>\n<li>Risk of uneven carburization or nitriding<\/li>\n<li>Risk during gas quenching<\/li>\n<li>Risk during oil quenching<\/li>\n<li>Risk of poor-quality washing<\/li>\n<\/ul>\n<p>The impact is the same, the risks must be assessed by the responsible person. However, the advantage of any internal or external negotiation is that we still have full traceability.<\/p>\n<p><span style=\"color: #993300;\"><strong><span style=\"font-size: 14px;\">AI-supported Load and Fixturing Design<\/span><\/strong><\/span><\/p>\n<p>Based on a photo, drawing or <strong>CAD model<\/strong> and a database of ovens and fixtures, <strong>AI<\/strong> can also determine a fixturing plan, select suitable grates, and even calculate the required quantity per batch. This, of course, requires a database of fixtures and their marking.<\/p>\n<p><a href=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/01a-qr-code-tags-returnable-pallets-2.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-14247\" src=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/01a-qr-code-tags-returnable-pallets-2-300x248.webp\" alt=\"\" width=\"300\" height=\"248\" srcset=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/01a-qr-code-tags-returnable-pallets-2-300x248.webp 300w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/01a-qr-code-tags-returnable-pallets-2.webp 800w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<ul>\n<li>proposes product orientation,<\/li>\n<li>selects available fixture,<\/li>\n<li>suggests part layout,<\/li>\n<li>calculates theoretical number of pieces,<\/li>\n<li>takes into account weight, thermal and hardening limits,<\/li>\n<li>determines recommended number of pieces in batch,<\/li>\n<li>estimates fixture time,<\/li>\n<li>calculates furnace utilization,<\/li>\n<li>transfers data to cost model.<\/li>\n<\/ul>\n<p>The output is approved by the technologist or other authorized person, especially for:<\/p>\n<ul>\n<li>new product,<\/li>\n<li>new fixture,<\/li>\n<li>new orientation method,<\/li>\n<li>critical hardening,<\/li>\n<li>parts with a high risk of deformation.<\/li>\n<\/ul>\n<p>For existing products, the system can gradually use the actual results of previous batches and refine the recommended number of pieces, optimize processes or propose changes to the procedure, resources.<\/p>\n<p><span style=\"color: #993300;\"><strong><span style=\"font-size: 14px;\">Offer Costing and Pricing<\/span><\/strong><\/span><\/p>\n<p>Using<strong> AI<\/strong> to calculate the costs of the bidding process is also an interesting task for <strong>AI<\/strong>. Usually, a rate matrix is \u200b\u200bused for this purpose according to <strong>Machine ID<\/strong>, or <strong>Machine Group ID<\/strong>, and <strong>Labour grade<\/strong>. This cost matrix also includes all overhead costs. Every company has it differently, but the model of calculating overhead costs for workers (<strong>Labour<\/strong>), overhead costs for equipment (<strong>Assets<\/strong>) and overhead costs for the production area (<strong>Floor<\/strong>) has proven to be successful. It is then easier to distinguish between the costs of operations with a higher consumption of personnel costs, costs for equipment with a higher acquisition value, or covering a larger area.<\/p>\n<p>The cost model distinguishes between direct costs (energy, gases, fixtures, consumable) and indirect costs in the form of overheads. For this cost calculation, all that is needed is to know the cycle time, the <strong>JobFillRate<\/strong> for batch utilization in %, the need for personnel costs for production and quality. It will therefore be good to have access to history, and <strong>AI<\/strong> has that.<\/p>\n<p>The pricing model then determines:<\/p>\n<ul>\n<li>recommended price,<\/li>\n<li>minimum price,<\/li>\n<li>target margin,<\/li>\n<li>expected profit,<\/li>\n<li>calculation confidence interval.<\/li>\n<\/ul>\n<p><strong>AI<\/strong> estimates uncertain costing inputs. The deterministic costing engine in the<strong> ERP<\/strong> then uses valid cost rates and calculates bid costs, based on <strong>AI<\/strong> estimated times and the production process it suggests. The resulting price, including margin, is set by the sales person or pricing model and approved by an authorized person according to the approval matrix.<\/p>\n<p><span style=\"font-size: 14px; color: #993300;\"><strong>Quotation Storage and Traceability<\/strong><\/span><\/p>\n<p>Finally, the <strong>ERP<\/strong> creates and stores the offer under a <strong>unique ID<\/strong>, including content, status and approval. The <strong>DMS<\/strong> (Document Management System) then archives the <strong>RFQ<\/strong>, drawings, specifications, <strong>GTC<\/strong> and attachments. In the audit repository, <strong>AI<\/strong> inputs and outputs, model versions, approvals, and finally in the data warehouse, analytical data for reporting and models<\/p>\n<p>Here our basic effort ends. The offer is almost ready and gets a status according to the subsequent activity:<\/p>\n<ul>\n<li><strong>Draft \u2013 Under review \u2013 Approved \u2013 Sent \u2013 Accepted \u2013 Rejected \u2013 Expired<\/strong><\/li>\n<\/ul>\n<p>Once the quote is in the <strong>Approved<\/strong> status, it can be sent to the customer. The advantage of the <strong>ERP\/A<\/strong>I combination is that even if we do not have qualified personnel, <strong>AI<\/strong> will largely help us deal with this handicap without reducing the quality of the quote process.<\/p>\n<p>Data from newly created quotes can be used for controlled retraining and validation of the next version of the <strong>AI \u200b\u200bmodel<\/strong> after quality control. However, the production model must not be automatically changed without approval and an audit trail. However, this means that we must have an <strong>AI model administrator<\/strong> who administers the content of the <strong>AI \u200b\u200blayer<\/strong> and brings new versions to life.<\/p>\n<p>This block, related to the bidding process, is, after the above modifications, a very good <strong>first independent use case<\/strong> for implementing <strong>AI<\/strong>. Direct control or data capture from the furnace is not needed here at all. The implementation risk is relatively small and the economic benefit is obvious. Risk reduction, reduced requirements for personnel qualifications, determination of the optimal selling price, optimal use of furnaces. But <strong>AI<\/strong> will also help us with strategy, because from the supply of bids we can better plan resources, investments, we can interpolate trends, etc.<\/p>\n<p>The next infographic provides an overview of what the ERP system must continue to do and what falls to AI.<\/p>\n<p><a href=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-12-51-52.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-14249\" src=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-12-51-52-300x200.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-12-51-52-300x200.png 300w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-12-51-52-1024x683.png 1024w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-12-51-52.png 1536w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>An interesting option is offered by using <strong>Power BI,<\/strong> which is an analytical and visualization layer. It is not a proprietary technological <strong>AI model<\/strong>, costing engine or quote creation system.<\/p>\n<p>If the data from the<strong> ERP<\/strong> can be securely accessed via <strong>API<\/strong>, database views, data warehouse, <strong>BI model<\/strong> or other supported connector, it can also be connected to <strong>Power BI.<\/strong><\/p>\n<p>The entire architecture would look like this:<\/p>\n<p><a href=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/dbaa3860-ea1e-4228-8305-4593adb1e6d6-kopie.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-14251\" src=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/dbaa3860-ea1e-4228-8305-4593adb1e6d6-kopie-300x225.png\" alt=\"\" width=\"300\" height=\"225\" srcset=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/dbaa3860-ea1e-4228-8305-4593adb1e6d6-kopie-300x225.png 300w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/dbaa3860-ea1e-4228-8305-4593adb1e6d6-kopie-1024x768.png 1024w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/dbaa3860-ea1e-4228-8305-4593adb1e6d6-kopie.png 1448w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>A common data warehouse would thus connect<\/p>\n<ul>\n<li><strong>business data from ERP, technological data, furnace process data, quality, actual consumption, maintenance, AI outputs.<\/strong><\/li>\n<\/ul>\n<p>From the <strong>ERP<\/strong> system, it is possible to obtain<\/p>\n<ul>\n<li><strong>customers, products, quotes, quote items, production orders, planned costs, actual costs, invoiced prices.<\/strong><\/li>\n<\/ul>\n<p>And then create <strong>Power BI<\/strong> dashboards, e.g.<\/p>\n<ul>\n<li>Quoted Cost vs. Planned Cost vs. Actual Cost<\/li>\n<li>Quoted Margin vs. Actual Margin<\/li>\n<li>Profitability by Customer<\/li>\n<li>Profitability by Product<\/li>\n<li>Profitability by Process<\/li>\n<li>Profitability by Furnace<\/li>\n<li>Quotation Lead Time<\/li>\n<li>Reasons for Cost Deviation<\/li>\n<\/ul>\n<p><a href=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-13-32-26.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-14253\" src=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-13-32-26-300x169.png\" alt=\"\" width=\"300\" height=\"169\" srcset=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-13-32-26-300x169.png 300w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-13-32-26-1024x576.png 1024w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/07\/chatgpt-image-27-7-2026-13-32-26.png 1672w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>=======================================================================================================<br \/>\n<span style=\"text-decoration: underline;\"><span style=\"font-size: 10px;\"><em><strong>Abbreviations used::<\/strong><\/em><\/span><\/span><\/p>\n<p><span style=\"font-size: 10px;\"><em><strong>AI<\/strong>:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>A<\/strong>rtificial\u00a0<strong>I<\/strong>ntelligence<br \/>\n<\/em><em><strong>API:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/strong>Application Programming\u00a0<strong>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<strong>.<br \/>\n<\/strong><\/em><em><strong>JSON: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/strong>JavaScript\u00a0<strong>O<\/strong>bject\u00a0<strong>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.<br \/>\n<\/em><em><strong>Middleware: \u00a0\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.<br \/>\n<\/em><em><strong>AI model: \u00a0\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.<br \/>\n<\/em><em><strong>OPC UA<\/strong>: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Open Platform Communications nebo OPC Unified Architecture<br \/>\n<\/em><em><strong>AI vrstva: \u00a0\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.<br \/>\n<\/em><em><strong>Document AI:\u00a0<\/strong>Technologie vyu\u017e\u00edvan\u00e1 k\u00a0extrakci vyu\u017e\u00edv\u00e1 k\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Information_extraction\">extrakci informac\u00ed<\/a>\u00a0z ti\u0161t\u011bn\u00fdch i\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Digital_document\">digit\u00e1ln\u00edch dokument\u016f<\/a>, jako jsou obr\u00e1zky, texty, znaky<br \/>\n<\/em><em><strong>DMS:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 D<\/strong>ocument\u00a0<strong>M<\/strong>anagement\u00a0<strong>S<\/strong>ystem<br \/>\n<\/em><em><strong>FQ<\/strong>: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>R<\/strong>equest\u00a0<strong>F<\/strong>or\u00a0<strong>Q<\/strong>uotation<br \/>\n<\/em><em><strong>APQP:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>A<\/strong>dvanced\u00a0<strong>P<\/strong>roduct\u00a0<strong>Q<\/strong>uality\u00a0<strong>P<\/strong>lanning<br \/>\n<\/em><em><strong>PPAP:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>P<\/strong>roduction\u00a0<strong>P<\/strong>art\u00a0<strong>A<\/strong>pproval\u00a0<strong>P<\/strong>rocess<br \/>\n<\/em><em><strong>ERP:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>E<\/strong>nterprise\u00a0<strong>R<\/strong>esource\u00a0<strong>P<\/strong>lanning<\/em><\/span><\/p>\n<p>=======================================================================================================<br \/>\n<strong>Are you solving a similar problem? I will help you with the analysis\u2026<\/strong><br \/>\n<img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"&#x2714;\" \/>\u00a040+ years of experience in the field<br \/>\n<img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"&#x2714;\" \/>\u00a030+ years of experience\u2026 HT-PROGRES, Bodycote, Galvamet<br \/>\n<img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"&#x2714;\" \/>\u00a0cooperation\u2026 V\u0160B, Czechimplant, ECM Technologies, TAV Vacuum Furnaces, GHC Invest<br \/>\n<img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"&#x2714;\" \/>\u00a012+ years of expert activity<br \/>\nWant to ask for a solution or want a non-binding consultation? Click on this link, I will usually respond within 24 hours.\u00a0<a href=\"mailto:stanislav.jirka@gmail.com\">Contact email<\/a><br \/>\n========================================================================================================<br \/>\n<strong>Ji\u0159\u00ed Stanislav, Ing. CSc.<br \/>\n<\/strong>Consultant and forensic expert<br \/>\n========================================================================================================<\/p>\n<p>31\/7\/2026<\/p>\n","protected":false},"excerpt":{"rendered":"<p>So how about the RFQ process, the commercial offer? 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