PLM news, announcements and industry trends.

durolabs.co · Oct 20, 2026

PLM Software · CAD, CAE & Simulation

Shaun Kennedy October 13, 2026 TABLE OF CONTENTS • Key Takeaways • What Is PCB Design for Manufacturability (DFM)? • The Stages of DFM in a PCB Program • The Practical PCB Design for Manufacturability Checklist • Build PCB Design for Manufacturability Into Your Workflow • PCB Design for Manufacturability FAQs • About the Author PCB design for manufacturability helps engineers find problems that could make a board difficult, costly, or impossible to fabricate and assemble at scale. It checks the design against a fabricator’s capabilities and a contract manufacturer’s assembly processes, and the requirements of the released product configuration. Engineers can resolve these problems before they delay a prototype build or production run. Manufacturability also depends on whether the selected components can actually be sourced. About 64% of electronics manufacturers report limited component and material availability or extended lead times. An unavailable or obsolete component can force package changes, footprint updates, or even a board respin after routing is complete. For that reason, PCB DFM should account for both the physical board and the parts required to assemble it. This article explains how DFM differs from design for assembly and design rule checking. It follows the DFM process from product concept through production ramp. The practical checklist covers layout and stackup, component selection, BOM accuracy, revision control, assembly, testing, and production release. Key Takeaways • PCB design for manufacturability covers more than a final design rule check. A board can pass DRC and still exceed the fabricator’s process limits or create assembly problems. • Component lifecycle status, availability, and second-source risk can determine whether a finished layout reaches production or requires new footprints and another board spin. • A controlled BOM keeps MPNs, approved alternates, EBOM and MBOM data, and sourcing information aligned across engineering, procurement, and manufacturing. • Assembly and test requirements can affect spacing, panelization, component orientation, and test-point access. These requirements must inform the layout before release. • Every file in the manufacturing package must correspond to the same approved revision. Mismatched BOMs, drawings, and production files can delay builds or produce the wrong board configuration. What Is PCB Design for Manufacturability (DFM)? PCB design for manufacturability (DFM) is the practice of designing a printed circuit board (PCB) so a fabricator and contract manufacturer can produce it with their available materials, equipment, and processes. It considers the physical board, component selection, BOM, and manufacturing data before release. The goal is to prevent design choices that cause redesigns, production delays, yield loss, or manual factory workarounds. DFM vs DFA vvs. DRC: What’s the Difference Design for Manufacturability (DFM), Design for Assembly (DFA), and Design Rule Checking (DRC) address different production risks. DFM checks whether the bare board can be fabricated within the manufacturer’s process limits. DFA checks whether components can be placed, soldered, inspected, and reworked. DRC compares the layout with the electrical and physical rules configured in the ECAD tool. Teams sometimes use DFM as an umbrella term that includes assembly and testing. When discussed separately, DFM usually refers to bare-board fabrication. DRC supports both reviews, but it only checks encoded rules. A board can pass DRC and still require changes if those rules don’t reflect the selected manufacturer’s capabilities. | Approach | Core focus | Key factors | Main goal | | DFM | Bare-board fabrication | Stackup, materials, copper weight, trace spacing, drill sizes, aspect ratios, solder mask, and panel constraints | Produce the bare board reliably within the fabricator’s capabilities | | DFA | Component assembly | Footprints, spacing, orientation, solder access, thermal profiles, inspection access, and panelization | Place and solder components with fewer defects and manual adjustments | | DRC | Compliance with configured ECAD rules | Trace widths, clearances, annular rings, vias, holes, keepouts, and layer restrictions | Find layout violations before generating manufacturing files. | See how Duro streamlines PCB design for manufacturability. The Stages of DFM in a PCB Program DFM develops alongside the PCB rather than beginning after layout is complete. Each stage adds new manufacturing information, while early decisions limit later changes. • Product conceptualization: The team defines expected production volumes, cost and performance targets, and the intended manufacturing approach. These decisions establish the boundaries for the PCB design. • Schematic entry: The circuit architecture and early component choices begin to shape manufacturing risk. Engineers can identify sourcing, package, and test constraints before the design reaches layout. • Layout design: The schematic becomes a physical board with defined placement, routing, and stackup. Fabricator and contract manufacturer feedback can now inform the design before release. • Pre-production release DFM sign-off: Engineering, procurement, quality, and manufacturing review the release package and address open DFM issues. The approved data becomes the baseline for prototype or pilot production. • Mass production/ramp stage: Production data reveals how the design performs under actual equipment, process, and volume conditions. Teams use yield results, defects, and supplier feedback to determine whether the board needs a controlled revision. The Practical PCB Design for Manufacturability Checklist A strong DFM process checks more than the final board layout. It should follow the PCB from physical design through production release, catching issues before they turn into fabrication delays, assembly defects, or revision problems. 1\. PCB Layout and Stackup Layout and stackup choices define the geometry and materials the fabricator must reproduce. Because fabrication limits vary by stackup, copper weight, and process, teams should confirm these constraints before routing begins. • Confirm the layer stackup, laminate system, dielectric thicknesses, and relevant material properties with the fabricator before routing. • Size traces for the required current and permitted temperature rise. Confirm that the fabricator supports the specified copper weights in its available stackups. • Set trace-to-trace, trace-to-pad, and pad-to-pad clearances at or above the fabricator’s limits. Use standard values where space permits instead of targeting absolute minimums. • Validate finished hole sizes, drill tolerances, annular rings, and via aspect ratios. Remove overlapping drill hits and define slots correctly. • Use design rules to enforce the fabricator’s minimum trace width, finished-hole diameter, annular ring, and copper-to-edge clearance. • Check solder mask sliver widths and mask expansion around fine-pitch pads against the fabricator’s registration limits. • Remove silkscreen from pads, holes, and exposed copper. Keep polarity marks and reference designators clear and legible. • Maintain balanced copper distribution and a symmetric stackup where the electrical design permits. This reduces the risk of bow and twist during fabrication. 2\. Component Selection Component approval must account for lifecycle status, availability, and second-source risk, not only electrical fit. Parts selected without current sourcing data can create BOM problems and force footprint changes after layout. • Avoid packages with unusual lead pitches, uncommon terminal finishes, or specialized reflow requirements unless the design requires them and the contract manufacturer (CM) confirms support. • Check stock across authorized distributors, lead times, and lifecycle status before approving a component. Avoid end-of-life (EOL) and not recommended for new designs (NRND) parts unless the program has a documented sourcing plan. • Qualify at least one form-fit-function alternate for each critical or single-source component where possible. Document a mitigation plan when no true alternate exists. • Use verified symbols, footprints, pin mappings, and package data from a controlled component library. Compare each record with the manufacturer’s datasheet. • Confirm that the selected packages and land patterns fit the CM’s placement, stencil, soldering, inspection, and rework capabilities. Duro helps teams keep these sourcing checks connected to engineering data by linking ECAD and PLM component records with current supply chain information. Through its Altium 365 integration, engineers can review availability, pricing, compliance, and lifecycle data while there is still time to adjust the design. 3\. BOM Accuracy and Structure The PCB BOM connects engineering intent with procurement and manufacturing. Engineering defines the required parts and quantities, while procurement uses the sourcing data to place orders. The CM relies on the same record to load components, manage substitutions, and assemble the correct board configuration. • Record an MPN for every purchased component. Keep distributor part numbers as separate sourcing fields because they identify a seller’s listing, not the component itself. • Maintain a controlled relationship between the EBOM and MBOM. The MBOM may add production items or restructure the EBOM, but each change should remain traceable. • Store approved manufacturers and alternates in the controlled BOM or a linked approved manufacturer list. Don’t leave substitution decisions in email threads. • Check each purchased component against current inventory, lead times, lifecycle status, and required build quantity before release. Use authorized sources where possible. • Flag long-lead-time and single-source components early. Record the approved alternate or mitigation plan beside the affected item. • Manage the BOM in one controlled system. Avoid parallel spreadsheets that procurement, engineering, and manufacturing can update independently. 4\. Revision Control and Change Management As a PCB approaches release, each revision must connect the schematic, layout, BOM, fabrication data, and assembly files to one approved configuration. An auditable revision management process records what changed and prevents the CM from building with mismatched files. • Assign formal revision identifiers to the schematic, PCB layout, BOM, and release package. Don’t rely on filenames such as “final\v2” to identify approved data. • Route changes through a documented ECO process after the design reaches a controlled baseline. Record the affected items, reason, impact, reviewers, and approval. • Confirm that engineering, procurement, the fabricator, and the CM use the same released revision. Remove or clearly mark superseded files from shared production locations. • Preserve a revision history that identifies what changed, why it changed, who approved it, and when the change takes effect. Include side-by-side comparisons where possible. 5\. Design for Assembly (DFA) A DFA review checks whether the CM can place, solder, clean, coat, inspect, and rework each component with the planned equipment. A layout can meet electrical rules and still slow assembly if tools or soldering equipment can’t reach the required areas. • Provide enough clearance around hand-soldered and selective-soldered parts for the iron, nozzle, fixture, and rework tools specified by the CM. • Identify electrostatic discharge (ESD)-sensitive parts in the BOM and assembly documentation, then specify handling controls. Mark conformal-coating and keep-out zones on the assembly drawing. • Coordinate panelization with the fabricator and CM for small or irregular boards. Account for rails, tooling holes, fiducials, breakaway features, and depanelization stress. • Make pin 1, polarity, and rotation clear across the footprint, silkscreen, assembly drawing, and centroid data. Don’t rely on silkscreen markings alone. 6\. Testability and Testing Design for testability reserves access for inspection, programming, and electrical testing before routing is locked. The test strategy should reflect production volume, required fault coverage, and fixture cost. Flying probe testing often suits prototypes and lower volumes, while bed-of-nails in-circuit testing (ICT) supports faster testing at higher volumes. • Place automated test points on one accessible side where practical. Confirm the preferred side with the CM or fixture designer instead of assuming the top side. • Size test pads, spacing, keep-outs, and solder mask openings for the selected probe system. Bed-of-nails fixtures and flying probes have different access requirements. • Export a dedicated test-point report and the required netlist or intelligent manufacturing data. A centroid file describes component placement, not test access. • Define the required mix of ICT, flying probe, automated optical inspection (AOI), X-ray, boundary scan, and functional testing early enough to influence layout and fixture access. • Document coverage targets, accessible nets, test limits, expected results, fixture requirements, and the applicable board revision in the manufacturing package. 7\. Manufacturer Handoff and Production Release Manufacturer handoff turns the approved design into the release package that the fabricator and CM will build. Depending on their requirements, the package may include Gerber or ODB++, NC drill data, the released BOM, pick-and-place data, fabrication and assembly drawings, stackup details, manufacturing notes, and test documentation. Every file must correspond to the same approved PCB or PCBA revision. Duro publishes the eBOM and manufacturing outputs into a controlled product record. It keeps the files, revision, release status, and change history connected, so manufacturing receives one approved dataset instead of files collected from separate folders or email threads. See how Duro streamlines PCB design for manufacturability. Build PCB Design for Manufacturability Into Your Workflow PCB design for manufacturability begins before routing and continues through production release. Fabricator capabilities should guide the stackup and layout, while CM requirements should shape assembly and test access. Current sourcing data and approved alternates reduce component-related redesigns. A controlled BOM and revision process keeps every manufacturing file tied to the same approved board configuration. Duro gives engineers a shared product record for PCB BOMs, sourcing data, revisions, and released files. Its ECO workflows connect each approved change to the affected product data. Manufacturing receives the correct release package without teams reconciling separate spreadsheets, folders, or email attachments. The result is fewer BOM errors, avoidable late-stage ECOs, and delays caused by outdated or mismatched files. See how Duro streamlines PCB design for manufacturability. PCB Design for Manufacturability FAQs What is the 20H rule in PCB design? The 20H rule says an internal power plane should sit back from the ground plane edge by 20 times the dielectric spacing between the planes. It was intended to reduce edge radiation, but measurements and models have found it unreliable and sometimes counterproductive. Engineers should treat it as a historical rule of thumb and verify plane geometry through power integrity and EMC analysis. What are DFM, DFA, and DFT? DFM checks whether a fabricator can produce the bare PCB within its material and process limits. DFA checks whether the CM can place, solder, inspect, and rework the components. DFT plans how the board will be inspected and tested, including test points, fixture access, programming access, and coverage targets. What are the different types of PCB testing methods? PCB testing includes bare-board electrical testing, assembly inspection, electrical testing, functional testing, and reliability testing. Bare-board tests use fixtures or flying probes to find opens and shorts before assembly. AOI checks visible placement and solder defects, while X-ray or AXI inspects hidden joints under packages such as ball grid arrays (BGAs). ICT and flying probe testing check assembled circuits for opens, shorts, and incorrect component values. Boundary scan tests digital interconnects with limited physical access. Functional circuit testing powers the board and checks its expected operation.

openbom.com · Oct 20, 2026

PLM Software

Every day brings another example of software being built with AI. People are creating new applications, adding AI capabilities to familiar tools, and rebuilding existing workflows around their own needs. For anyone looking to become more productive, the question is increasingly practical: what could I automate, improve, or build myself? Engineering and product development offer many opportunities for this kind of improvement. An engineer can automate a repetitive task, prepare a report, create a tool for a specific calculation, automate CAD or simulation work. These improvements matter. They can remove friction from work people do every day. As engineering change management work moves between engineering, purchasing, suppliers, and manufacturing, the coordination problems become especially painful. A faster engineering task can leave the team waiting for a supplier’s clarification, an approval, or confirmation that the manufacturing partner is working from the applicable revision. Each person can improve their own process while the product still waits between them. I came across the article “ The Next Engineering Giant Might Sell Parts, ” by Mark Burhop. He points to a practical opportunity for engineering software. Here is the passage that caught my attention: “For a product manager, the practical opportunity is the work between applications. Consider a customer approving a change to a housing. Someone must identify affected manufacturing instructions, make sure the correct revision reaches suppliers, and determine which inspection evidence needs updating. A faster model edit helps. Carrying the change through those steps could solve a larger problem.” The large problem is the one that makes total sense to me – Mark describes a customer approving a housing change and the work that follows: identifying affected manufacturing instructions, getting the applicable revision to suppliers, and determining which inspection evidence needs updating. I found it important because it connects directly to the problem we see with our customers every day. Engineering, planning, procurement, supply chain, contractors – the communication and alignment is hard. At OpenBOM, we are exploring the same problem in our OpenBOM Launch Master. We see applications and people often belong to different companies. Following a change through production means coordinating people whose responsibilities, systems, and commitments extend beyond your own organization. A change becomes consequential when someone else has to act on it. The supplier needs to understand what to manufacture. The contract manufacturer needs to prepare the assembly process. Quality needs to establish which evidence supports acceptance. The person responsible for the build needs to know whether their combined answers still support the required quantity and date. A change travels through several companies Consider an illustrative example. A manufacturer is preparing a build of 50 assemblies. Each requires one housing at revision D, available at the contract manufacturer by November 30. Engineering has revised a connector opening and approved the updated definition. The housing supplier previously committed to November 26. But that commitment covered revision C. Revision D requires a programming update and a review of the fixture. The supplier has received the new package, but has not yet confirmed its effect on delivery. Meanwhile, purchasing still has the original date in its records. The contract manufacturer has its own preparation work. Someone must determine whether the change affects assembly instructions or tooling. The quality team must review which inspection requirements changed and whether existing evidence remains applicable. Any housings already made to revision C need an explicit disposition for this build. Engineering has completed its approval. The supplier has acknowledged the files. The purchasing record contains a delivery date. Yet the build owner still lacks a confirmed answer about 50 revision D housings at the assembly location by November 30. The same pattern applies to a material change, component substitution, revised quantity, or new customer requirement. It also runs in the other direction. A supplier can report a shortage or a longer lead time that requires engineering to evaluate an alternative. Each change can affect an agreement made elsewhere. Communication needs a clear product reference “We are on schedule” is useful information when everyone understands what that schedule covers. In the housing example, the statement needs a part identity, applicable revision, quantity, and a date whose meaning is clear. Arrival at the assembly location and dispatch from the supplier describe different events. Company boundaries make this harder. The supplier may use its own part number. Its quotation can reference a particular drawing, while the purchase order refers to an internal Item. Connecting these references allows people to see whether they are discussing the same requirement. The response itself also has stages. Acknowledging receipt establishes that a package arrived. Technical acceptance establishes agreement on what is required. A delivery confirmation establishes a commitment under stated conditions. Each stage answers a different question about the work. Launch Master could help make those questions specific. For example: Does your November 26 commitment cover 50 housings at revision D, available at our contract manufacturer? If it depends on a fixture decision, who must make that decision, and by when? Supplier collaboration needs shared product context This coordination needs a common reference that participants can return to as their work changes. In Who Is Holding Your Product Launch Together? , I described a launch commitment through its configuration, quantity, milestone, and date. That commitment gives the surrounding information a practical purpose: determining what supports the build and what requires attention. OpenBOM already provides a foundation for this work by connecting Items, BOMs, files, revisions, and sourcing information. Our approach to supplier collaboration supports sharing selected product information across companies, with permissions and views appropriate to the participants. Companies can also use familiar Excel, PDF, and ZIP exchanges as part of their process. A supplier can continue using its own manufacturing systems while sharing the evidence relevant to an agreed handoff. The OEM can retain control of the engineering definition. The contract manufacturer can contribute its requirements and responses. The information owner determines what is shared, and missing information remains an explicit question for the responsible participant. The Product Context Graph connects that product information with the work around it: questions, findings, decisions, and dated commitments. For the housing, the useful connection is between the required revision, the supplier’s response, the affected build, and the conditions behind its promised date. How Launch Master could coordinate the work Launch Master remains a proposed application direction. Our initial focus is engineering and sourcing alignment and material readiness for a defined build. Manufacturing preparation and inspection coordination describe a broader direction we want to explore as the supporting information and checks become available. For the housing example, I would want Launch Master to help the build owner work through the following steps: • Define the commitment. Identify the applicable configuration, required revision, quantity, and material need date at the assembly location. • Connect the evidence. Bring together the approved definition, supplier quotation or response, purchase reference, and any supplied preparation requirements. Confirm identities and relationships when they are ambiguous. • Expose the unresolved dependency. Show that the recorded supplier date covers revision C while the build requires revision D. Keep the revision D commitment unconfirmed until the supplier provides an applicable answer. • Prepare the next action. Help the buyer ask for a revised commitment and identify any engineering decision the supplier needs. Assign responsibility and a due date for the follow-up. • Reassess the build. When a reviewed response arrives, compare its quantity and usable arrival date with the requirement. Keep other open dependencies visible. • Verify closure. Recheck the condition using current evidence. A completed task records progress; closing the finding requires evidence that its specific condition has been resolved. Software can perform explicit comparisons and quantity calculations. AI can help interpret supplied information, investigate a finding, explain its implications, and prepare a focused question. Engineers approve technical changes, suppliers confirm their commitments, and the build owner makes the delivery decision. Agreements need to be preserved for the next change Suppose the supplier confirms that revision D housings can reach the assembly location on December 3. The response resolves one uncertainty and exposes a date conflict. The build owner now has a specific issue to discuss with engineering, purchasing, and the contract manufacturer. The team might investigate expediting the work, changing the build sequence, or approving a limited use of existing revision C housings. Each option needs its own evidence and authorization. An exception accepted for this build needs a clear scope so that a later program does not inherit it accidentally. This is also where Product Memory becomes useful. Preserving the supplier’s response, the engineering disposition, and the reason for a decision helps the next person understand the plan. If the quantity increases or the required revision changes again, Launch Master could identify which earlier confirmations need review. The coordination continues after an update is distributed. A new response changes what is known. A missed decision date changes the available options. The shared picture needs to reflect those developments so the build owner can act while there is still time. Measure the work completed between companies Mark’s article asks how much of the customer’s work engineering software could carry through dependably. For Launch Master, I would measure progress through the time needed to obtain an applicable supplier confirmation, the unresolved dependencies found before a material deadline, and the effort people spend reconstructing the current situation. A practical place to begin is one build, one engineering change, and one supplier handoff. Establish the requirement, connect the available evidence, and see whether the team can reach a clear answer with less chasing and fewer assumptions. Our build readiness direction starts with this kind of defined job. At OpenBOM, we are exploring how Launch Master can help the people responsible for these commitments coordinate the work across applications and companies. If you manage engineering changes, supplier relationships, or product builds, I would like to hear where that coordination becomes difficult for your team. When engineering approves a change, who confirms that the commitments across your suppliers and manufacturing partners still hold? At OpenBOM we help companies to coordinate their work across multiple disciplines, departments, organizations, suppliers and contractors. REGISTER FOR FREE and explore how OpenBOM can help you. Best, Oleg

Aras · Oct 20, 2026

PLM Software · Customer Stories

ニュース → 製品ライフサイクルマネジメント（PLM）ソリューションおよびエンジニアリング AI 向けデジタルスレッド・ソリューションのリーダーである Aras（本社：米国マサチューセッツ州、CEO：レオン・ローリセン、日本法人：アラスジャパン合同会社、本社：東京都千代田区、日本法人社長：久次昌彦）は本日、骨導聴覚ソリューションのリーダーであるオティコン・メディカル（Oticon Medical）が、新たな PLM ソリューションとして Aras Innovator® を採用したことを発表しました。本導入により、製品情報の管理と追跡に高い制御性とトレーサビリティをもたらす、製品中心のデジタル基盤を構築するとともに、医療機器業界で求められる厳格な規制要件への対応を支援します。 オティコン・メディカルは、皮下に埋め込むインプラントと磁石で保持するサウンドプロセッサーを組み合わせた能動経皮型聴覚システム「Sentio™ システム」をはじめ、骨導聴覚の製品ポートフォリオの拡充を進めています。同社は、複雑化する製品およびライフサイクルデータをより効果的に管理するため、SaaS ベースの新しい PLM 環境の構築に着手しました。 Aras は、医療機器業界における豊富な実績が評価され、オティコン・メディカルの従来のドキュメント管理および PLM システムに代わるソリューションとして選定されました。Aras Medical Device Accelerator は、設計管理、リスクマネジメント、品質プロセスに関する組み込み型の機能を提供し、規制コンプライアンスを日常の製品開発に組み込むことで、監査対応力を強化します。 オティコン・メディカルのプロダクトケア ディレクターであるクリスター・ヨハンソンは、次のように述べています。 「製品データの管理方法は、効率的な業務運営と高度に規制された業界の要求への対応を支える基盤です。Aras Innovator により、チームが依拠する情報の一貫性と信頼性を高め、より製品中心の業務プロセスへの移行を実現できます。」 Aras Innovator は、オティコン・メディカルのエンタープライズ環境全体にわたって製品情報を接続し、システム間の手作業による受け渡しを削減してデータの一貫性を向上させます。また、製品変更とそのライフサイクル全体への影響の可視性を高めることで、変更管理と部門横断的な連携を強化します。 導入の一環として、オティコン・メディカルのレガシーシステムから関連する製品データとドキュメントが Aras Innovator へ移行され、過去の情報へのアクセスを維持しながら従来システムの廃止を可能にします。 Aras の CEO であるレオン・ローリセンは次のように述べています。 「医療機器メーカーにとって、効果的な PLM はデータ管理にとどまりません。高度に規制された製品を市場に届けるために関わる多くのチームとプロセスを支える構造を構築することが重要です。オティコン・メディカルは、業界が求める厳格さを維持しながら、部門間の壁を取り払い、組織全体でより統合的な業務の仕組みを構築しようとしています。」 初期導入では、ドキュメント管理、変更管理、製品開発に重点を置きます。オティコン・メディカルは今後、組織全体のより多くの基幹プロセスの支援と接続に向けて、Aras Innovator の利用を拡大していく計画です。 Oticon Medicalについて オティコン・メディカルは、あらゆるライフステージの人々に音の力を届けることを使命とする、埋込型聴覚ソリューションのグローバル企業です。10 年以上にわたり、医師、聴覚専門家、患者にとっての治療をよりシンプルにすることで、骨固定型聴覚システムの普及に貢献してきました。 患者と聴覚専門家が、患者の治療過程のあらゆる段階で最適なソリューションを選択できるべきだと当社は考えています。これを「Freedom of Choice（選択の自由）」と呼び、オティコン・メディカルの事業の根幹をなしてきました。そのため、当社のソリューションは可能な限り互換性を持つよう設計されています。オティコン・メディカルのインプラントは、生涯にわたるサポートへの揺るぎないコミットメントの証です。 当社はプロフェッショナルと協力し、ユーザーのニーズを第一に考えたソリューションの開発に取り組んでいます。人々の生活の質を高め、今も、そしてこれからも、人生を最大限に豊かにするための革新的なソリューションとサポートを提供することに強い情熱を持っています。音がどれほど大切かを知っているからです。 Aras について Aras は、製品ライフサイクルマネジメント（PLM）とエンジニアリング AI 向けのデジタルスレッド・プラットフォームを提供しています。Aras のテクノロジーは、AI ネイティブかつローコード開発プラットフォーム上に構築され、柔軟なソリューションの迅速な提供を可能にします。Aras は、製品ライフサイクルおよび拡張バリューチェーン全体を通じて、部門や職能を横断するチームを、重要な製品データおよびエージェント型 AI へと繋ぎます。Aras の情報については、公式ウェブサイトをご覧ください。また、SNS では、Aras に関する最新情報をご確認いただけます。 主要顧客：川崎重工業、クボタ、デンソー、日産自動車、富士フイルム、三菱重工業、村田製作所、ルネサス エレクトロニクス、Airbus、Audi、Microsoft 等 ウェブサイト : https://www.aras.com/ja ソーシャルメディア : LinkedIn : https://www.linkedin.com/company/aras-japan YouTube : https://www.youtube.com/user/arasplm X : https://x.com/ArasJapan Facebook : https://www.facebook.com/ArasJapan/

Beyond PLM · Oct 20, 2026

PLM Software · AI in Product Development

AI-generated summary: AI could make PLM applications easier to build and replace without eliminating manufacturers’ need for reliable product knowledge, change control, traceability and supplier coordination, argues Beyond PLM author Oleg Shilovitsky. His central thesis is that PLM’s operational importance may grow while its established business models face pressure. Drawing on two MicroConf talks by Rob Walling, he identifies eight potential competitive defenses based on trusted responsibilities rather than reproducible features. Shilovitsky discloses that he is OpenBOM’s co-founder and CEO; the article presents his analysis, not an announced product roadmap. AI-built alternatives illustrate both the threat and its current limits. PhotoCraft describes itself as early alpha, not a replacement for daily professional Photoshop work, while CADCraft’s pre-alpha roadmap acknowledges workflow and file-preservation gaps. Nevertheless, a challenger needs only enough functionality to solve a particular problem. Competition could come from customers, open-source developers, implementation partners, adjacent CAD or ERP platforms, and agents spanning multiple applications. Manufacturers might retain PLM records while needing fewer people to search, reconcile BOMs and coordinate handoffs. Free distribution is not new: Dassault Systèmes’ DraftSight attracted Bobst through its free application and paid Premium Service Pack, while Aras Innovator combined free software availability with subscriptions for support and upgrades. These examples show the value of dependable operations, but AI could also reduce implementation and integration effort, pressuring service revenues. The first two defenses are **industry trust and authoritative Product Memory**. Recommendations, responsive support, practical education and successful implementations create credibility that a new application cannot immediately reproduce. Product Memory extends formal records—structures, revisions, configurations and approvals—with decision context, rejected alternatives, accepted exceptions and subsequent outcomes. Replacing such a system requires preserving relationships and meaning, not merely importing files. Shilovitsky explicitly argues that this memory belongs to customers: defensibility should come from maintaining its integrity, accessibility and usefulness, not restricting portability. The third and fourth defenses concern **delegated action and dependable calculations**. An authorized agent could prepare an engineering-change package, check it, obtain approval, distribute the correct revision, collect acknowledgments and escalate exceptions. Its advantage would depend on reliable permissions, timing and accountability, with engineering decisions remaining assigned to responsible people. Similarly, trusted BOM cost and quantity calculations require correct configurations, consistent units, valid supplier prices and explicit assumptions—not simply arithmetic. Outputs should remain traceable and explainable even when users cease independently recalculating every result. The fifth and sixth defenses are **compliance assurance and active collaboration networks**. Regulated uses may require controlled records, approvals, traceability, testing evidence and validation support. The article cites the FDA’s February 2026 software-assurance guidance as an example of risk-based confidence in automation, while cautioning that serving regulated customers does not establish blanket approval or suitability. Collaboration across OEMs, suppliers, contractors and procurement can also make replacement difficult because participants must change shared practices together. However, dormant portal accounts offer little protection: a genuine network effect requires additional relevant participants to improve existing participants’ experience. The seventh defense is **learning from engineering and manufacturing outcomes**. In a hypothetical machined-housing change, the platform records the drawing revision, supplier clarification requests, approved interpretations, acknowledgments and production feedback. Recurring omissions, such as ambiguous surface-finish requirements, could inform earlier checks on future packages. Acknowledgment alone does not prove technical correctness; production results provide stronger evidence. Such learning must respect confidentiality and agreed rights, and qualification or field-performance history takes time to accumulate. The eighth defense, **maintained BOM and service information**, concerns ongoing reconciliation, updates, permissions and integration upkeep. Unlike an authoritative record’s role in establishing what is official, this service keeps information usable; it need not consolidate every source into one database. Commercially, Shilovitsky sees faster development, charging for agent use and delivering completed work as compatible approaches. Supplier-package preparation and approved distribution could become bounded services rather than tools requiring multiple licensed operators. Subscriptions, usage allowances, transaction fees or hybrids could support them, provided acceptance criteria and exception-handling costs are clear. He concludes that incumbents possess valuable records, relationships and experience, while entrants can reorganize these responsibilities. Neither has guaranteed protection: defensibility depends on what customers continue entrusting to the platform when alternative applications become easier to build.

Ansys · Oct 19, 2026

CAD, CAE & Simulation

Ansys Automotive Software Solutions Navigate Design, Development, and Homologation Challenges Faster Transform vehicle development using AI-enabled simulation and safety solutions that can reduce time, cost, and complexity across the entire lifecycle. Visit us at Ansys Transportation Summit Saint John's Resort - Plymouth, MI on October 13-14, 2026 Complexity is growing. Timelines are shrinking. The good news? Ansys simulation is helping automotive engineers cut through the disruption to bring world-class vehicles to market faster. Accelerate Time-to-Market Shorten development cycles through simulation-powered optimization. Innovate Faster Quickly test assumptions and make discoveries on the way to new products. Reduce Cost Increase engineering efficiency and productivity through rapid iterative testing. Ansys solutions connect individual tools and solvers to match how automotive teams actually work. No fumes here. Every day, original equipment manufacturers (OEMs), start-ups and tier suppliers from around the globe look to Ansys' industry-leading tools and solvers to solve big challenges. 40 % Efficiency gain “FreeFlow's seamless integration within a unified Ansys simulation suite streamlined our process and minimized data handling challenges across different tools. ” “Schaeffler's expertise in scalable automotive manufacturing, special machinery, and life cycle services addresses the hardest barriers to adoption, positioning us well in the humanoid and physical AI spheres. ” “Without the tool, one light guide design and simulation might take approximately 30 minutes to an hour, producing six to eight combinations per day. With optiSLang software, we can analyze up to 300 light guide designs in just one day. ” Key Industry Partners Predicting Battery Failure with Ansys Simulation Battery packs are an integral part of the electrification and electric vehicle revolution. Battery crash is an important event with respect to battery design and crash response, which plays a crucial role. An electric car can catch fire ... Safety is an important functional requirement in the development of large-format, energy-dense, lithium-ion (Li-ion) batteries used in electrified vehicles. Many automakers have ... Learn from leading automotive organizations and Ansys experts how engineering simulation drives accelerated innovation. Redefine automotive safety and performance with MBSE by bridging software and hardware, integrating safety, and accelerating innovation ... Automotive Design: How AI is transforming the art of simulation HPC Utilization for Automotive Engineering Powering up Automotive Simulation: Why Migrating to the Cloud is a Game Changer Driving Speed and Reliability in Automotive Systems Engineering AUMOVIO Tests its Product Reliability in a Fraction of the Time Using Ansys Sherlock, AUMOVIO accomplished its goal of evaluating and identifying a superior adhesive material before building the prototype. Read full case study Ansys Software Boosts Andes' Automotive-grade IP Development To streamline ISO 26262 verification, Andes Technology used Ansys medini analyze software to automate the safety analysis process. Read full case study Elausa Uses Ansys in Automotive Interior Lighting Projects Elausa conducts thermal optimization in automotive interior lighting projects with help from Ansys solutions. Featured In Person Digital Engineering for Automotive Automotive Safety and Security Driven by Simulation What is automotive simulation? What is automotive simulation? Automotive simulation uses virtual engineering software to model, test, and validate vehicle systems before physical prototypes are built. Engineers use automotive simulation to evaluate structural performance, crash behavior, aerodynamics, thermal management, electronics, powertrain systems, sensors, embedded software, and safety-critical functions. With Ansys simulation solutions, automotive teams can accelerate vehicle development, reduce testing costs, and improve safety, reliability, and performance across the product lifecycle. How does simulation improve vehicle engineering? How does simulation improve vehicle engineering? Simulation improves vehicle engineering by enabling teams to analyze complex vehicle systems early and often throughout the design process. Instead of relying only on physical testing, engineers can use simulation to study structural mechanics, fluids, thermal behavior, electromagnetics, electronics, software, and system-level performance in a connected digital environment. This helps automotive manufacturers reduce development time, identify design issues sooner, optimize vehicle performance, and bring safer, more efficient vehicles to market faster. What types of vehicle systems can be simulated with Ansys? What types of vehicle systems can be simulated with Ansys? Ansys supports simulation across a broad range of vehicle systems, including body structures, chassis, crashworthiness, aerodynamics, thermal systems, batteries, electric motors, power electronics, electrified powertrains, embedded software, sensors, semiconductors, electronics cooling, and connected vehicle technologies. These multiphysics simulation capabilities help engineers evaluate how mechanical, electrical, thermal, fluid, and software systems interact in modern vehicles. How is simulation used in electric vehicle development? How is simulation used in electric vehicle development? Electric vehicle simulation helps engineers design and validate batteries, battery management systems, power electronics, electric motors, fuel cells, and integrated electrified powertrains. By using simulation, EV engineering teams can improve range, efficiency, thermal performance, safety, durability, and system integration. Simulation also helps reduce dependence on costly physical prototypes and enables faster design iteration for electric and hybrid-electric vehicles. Why is simulation important for ADAS and autonomous vehicle development? Why is simulation important for ADAS and autonomous vehicle development? ADAS and autonomous vehicle systems require extensive validation across complex real-world driving scenarios. Simulation enables engineering teams to test sensors, perception systems, control algorithms, embedded software, human-machine interfaces, and safety-critical vehicle behavior in virtual environments. Ansys simulation supports physics-based sensor modeling, safety-by-design workflows, and safety-by-validation approaches that help manufacturers develop advanced driver assistance systems and autonomous vehicles more efficiently. How can automotive simulation reduce physical testing costs? How can automotive simulation reduce physical testing costs? Automotive simulation reduces physical testing costs by allowing engineers to evaluate many design scenarios virtually before building prototypes. Simulation can be used to study crash performance, battery safety, aerodynamics, thermal behavior, structural durability, electronics reliability, and system integration. By identifying issues earlier in development, automotive companies can reduce the number of physical tests required, shorten validation cycles, and make better design decisions before committing to manufacturing. How does simulation support automotive safety engineering? How does simulation support automotive safety engineering? Simulation supports automotive safety engineering by helping teams analyze, verify, and validate vehicle systems against safety requirements and industry standards. Engineers can use simulation to evaluate crashworthiness, battery safety, embedded software, functional safety, sensor performance, and autonomous system behavior. Ansys safety analysis and embedded software solutions support automotive standards such as ISO 26262 and AUTOSAR, helping teams meet safety goals more efficiently. What role does multiphysics simulation play in next-generation vehicle design? What role does multiphysics simulation play in next-generation vehicle design? Multiphysics simulation is essential for next-generation vehicle design because modern vehicles combine mechanical systems, electronics, software, sensors, batteries, thermal systems, communications, and advanced materials. Multiphysics simulation helps engineers understand how these systems interact under real-world operating conditions. For automotive applications, this can improve vehicle performance, safety, energy efficiency, connectivity, reliability, and time to market.

Dassault Systèmes · Oct 19, 2026

Customer Stories · PLM Software

Dassault Systèmes announced that Safran Electronics & Defense Australasia (SEDA) has selected its ENOVIA and CATIA applications, powered by its 3D EXPERIENCE platform, to support the design, development and production of high-technology equipment for Australia's aerospace, defence and space sectors. Dassault Systèmes was selected for its best-in-class Product Lifecycle Management (PLM), Computer-Aided Design (CAD) and Model-Based Systems Engineering (MBSE) capabilities and standards-compliant systems engineering environment, combined with established local expertise in aerospace and defence engineering. SEDA designs, develops and produces equipment and services for civil and military customers operating on land, at sea, in the air and in space. The deployment supports SEDA's objective of providing equipment designed and engineered for Australian government requirements, strengthening the nation's sovereign defence capability, as Australia's 2026 National Defence Strategy and Defence Industry Development Strategy place renewed emphasis on self-reliance and the resilience of its sovereign defence industrial base. This marks a new deployment of: • 3D EXPERIENCE platform and ENOVIA for PLM, providing a single environment for managing product data, configuration and change across the engineering lifecycle • CATIA for MBSE, supporting requirements capture, system design, verification and validation in a single, traceable model • CATIA for detailed design and engineering of the product Integrated into SEDA's engineering environment, the 3D EXPERIENCE platform is expected to support product data management, cross-team collaboration, engineering traceability, configuration management and faster development cycles. The platform enables early error detection, streamlined compliance and improved collaboration across engineering teams as SEDA develops equipment to local requirements. The deployment positions SEDA to support the Australian Department of Defence on future sovereign capability programs, with the potential to expand workforce, programs and local production capacity as its engineering capability scales. “Sovereign engineering capability is central to how we design and deliver equipment for Australia's defence needs. Deploying Dassault Systèmes’ ENOVIA and CATIA applications strengthens our ability to manage complex product data and systems requirements with the rigour Australia's defence programs demand, as we build capability designed and engineered specifically for Australia,” said Peter Harris, Chief Executive Officer, Safran Electronics & Defense Australasia. “SEDA is building engineering capability that meets the exacting standards of Australia's aerospace and defence sector. Our technology will support SEDA as it strengthens product lifecycle management and systems engineering disciplines, positioning it to contribute to Australia's sovereign capability ambitions well beyond this deployment,” said Rashpal Singh, ANZ Consulting and Sales Director, Dassault Systèmes. About Dassault Systèmes Dassault Systèmes is a catalyst for human progress. Since 1981, the company has pioneered virtual worlds to improve real life for consumers, patients and citizens. Through the 3D EXPERIENCE platform, AI-powered, science-based virtual twins help 390,000 customers of all sizes, in all industries, collaborate, imagine and create sustainable innovations that drive meaningful impact. For more information, visit: www.3ds.com Download the press release

Aras · Oct 17, 2026

PLM Software · Customer Stories

ニュース → Aras ® , the next leader in enterprise Product Lifecycle Management (PLM) software, today announced that National Electric Vehicle Sweden AB (Nevs), a Swedish holding company that develops electric vehicles and sustainable mobility services, has selected Aras Innovator® as the global PLM backbone. Nevs will implement the PLM backbone to manage vehicle configurations and variants from conceptual phase through product development, manufacturing and service. Nevs selected Aras based on the following criteria: • Solution functionality to serve as the system of record for productinformation and integrated change management across the lifecycle. • Platform flexibility to address complex engineering businesschallenges, especially coordination of electronic, software andmechanical development. • Ability to securely integrate with existing systems for design,production and service. • Ease of customization and upgrades provides a resilient approach to PLMto drive product innovations today and into the future. “Nevs is both developing innovative electric vehicles and transforming their business processes at the same time,” said Peter Schroer, CEO of Aras. “It’s an opportunity to rethink the business of engineering starting with a clean sheet for the design and development of electric vehicles.” “Nevs demonstrates further proof that the world’s most innovative product developers recognize the need for a new approach to PLM for improved results,” stated Leon Lauritsen, VP at Minerva. “We are proud to be the implementation partner for Nevs and believe that this deployment will set the benchmark for a highly optimized PLM solution.” About National Electric Vehicle Sweden (Nevs) National Electric Vehicle Sweden (Nevs) provides global customers with mobility services that provide a truly engaging electric vehicle (EV) experience. With headquarters in Trollhättan, Sweden, facilities include a highly efficient factory offering engineering work to both self-owned product lines and external global customers. In cooperation with the Chinese shareholders, Nevs has production and R&D joint ventures in Tianjin Bin Hai High-tech Zone, as well as a brand experience center in Beijing. http://www.saabcars.com/ About Minerva Founded in 1996, Minerva specializes in the development and implementation of Enterprise solutions in both PLM and ERP. Established as Europe’s largest Aras implementation partner, Minerva offers a full range of PLM services, productivity tools, integrations and complete industry solutions. The company works closely with its customers, challenging ineffective modes of operation and providing insight and expertise to drive results and help customers reach their business objectives. For additional information visit www.minerva-plm.com/aras/home About Aras We offer the best Product Lifecycle Management (PLM) software for global businesses with complex products and processes. Resilient PLM platform technology makes Aras more scalable, flexible and upgradable for the world’s largest organizations, and a full set of applications provide complete functionality for the business of engineering. Customers include Airbus, GE, Hitachi, Honda, Kawasaki, Motorola and XEROX. Aras is privately held with global headquarters in Andover, Mass. More at www.aras.com and Twitter @aras_plm

Siemens Digital Industries Software · Oct 17, 2026

AI in Product Development · CAD, CAE & Simulation

In July 2026, HD Hyundai – the world’s largest shipbuilder – announced it had selected Siemens as its software partner to establish an AI-powered digital shipyard to modernize U.S. shipbuilding. Combining HD Hyundai’s shipbuilding expertise with Siemens’ leadership in industrial AI, ship engineering, manufacturing, simulation and operational software, the companies aim to help shipbuilders accelerate production readiness, improve quality, reduce rework and create a connected digital thread across the shipbuilding lifecycle. This news isn’t just great for Siemens and HD Hyundai; it creates a blueprint for next-generation digital shipbuilding, benefiting both commercial and naval shipbuilders. Many shipbuilders around the world will be watching this transformation with interest. How do you establish a completely digital, secure and collaborative environment that enables controlled data sharing, configuration management, digital approvals, production visibility and lifecycle traceability? That’s what the next-generation digital shipyard aims to find out. AI-driven design and engineering tools help create a bridge between today’s somewhat disconnected, linear engineering workflows and tomorrow’s connected, optimized and innovative processes. And it should be on every shipbuilder’s radar. Understanding the industrial shift from ‘smart machines’ to ‘cognitive partners’ in ship engineering There’s been a lot of change in the manufacturing sector since 2010, dubbed “the fourth industrial revolution,” which brought about connected smart machines and the Internet of Things. Now we’re at the cusp of the “fifth industrial revolution,” and adapting to artificial intelligence, robotics, cognitive systems and improved connectivity. In the shipyard of the very near future, AI is not a replacement for the engineer; it is a force multiplier for human expertise. In a ‘human-in-the-loop’ model, AI can handle the iterative heavy lifting – calculating thousands of permutations for hull design, weight stress and fluid dynamics in less time. This allows lead engineers to focus on higher-level strategy and radical innovation. Barriers to AI adoption are real but temporary We can’t talk about this shift without addressing the reality of AI adoption and understanding the barriers. While AI generates great interest across nearly every industry, including marine, implementation remains a challenge for most companies. • ROI : 75% of executives report that they have not yet seen a clear return on investment from AI initiatives. Many companies are still in the early stages of applying AI effectively. (Source: BCG 2025 ) • Quality : Nearly half of organizations cite poor data quality and concerns about bias as obstacles. If data is incomplete, inconsistent or difficult to access, the quality of AI-driven insights will inevitably suffer. ( Source : IBM 2025 ) • Data readiness : More than 60% of organizations report that they are not adequately prepared from a data perspective. When company data is spread across multiple systems, documents and databases, it’s difficult to create the trusted foundation that AI requires. (Source: Gartner 2025 ) • Employee acceptance : 70% of companies have not yet trained their employees to work effectively with AI. Like any new technology, people need help understanding how to use it and integrate it into workflows. (Source: BCG 2025 ) • Trust : Almost half of organizations report a lack of confidence in AI systems. Users want to understand where answers come from and whether results are reliable. (Source: KPMG 2025 ) • Isolation : Despite all the excitement, many AI initiatives remain as isolated pilots or departmental experiments rather than fully integrated, enterprise-wide deployments. (Source: McKinsey 2025 ) The encouraging message for the marine industry is that these barriers are not permanent. They are challenges that can be addressed through better data management, stronger digital foundations, targeted training and carefully selected use cases that deliver measurable value. Companies that take this approach will be in the strongest position to benefit from AI as adoption continues to mature. Siemens Designcenter™ software provides the engine for practical AI applications Siemens’ Designcenter software exemplifies how advanced AI and generative design are being deployed in marine engineering projects to enable rapid design iteration. • AI copilots help users find documentation, commands, best practices and workflow guidance within Designcenter using natural language questions. By providing contextual assistance directly within the engineering environment, copilots help reduce time spent searching for information and enable engineers to focus more on design, problem solving and innovation. • AI-enabled capabilities help engineers work faster and more efficiently – such as command prediction, component selection, voice command assistant, molded part design – just to name a few. This does not replace engineers; it simply removes low-value administrative work so experts can focus on more complex problems. • AI-assisted engineering workflows help ship designers automate repetitive engineering tasks and accelerate design creation, helping teams deliver projects faster with greater consistency. But there’s even more to come. To achieve the full vision of a transformed shipyard, general-purpose AI is not enough. A shipyard needs engineering-aware AI, which learns and understands the specific challenges, physics, materials and regulatory constraints of the maritime world. That’s at the core of Siemens’ new Intelligence Center X . By bridging the gap between advanced AI models and real-world engineering data and requirements with a human in the loop, Intelligence Center X can help ensure that every AI-generated suggestion is not just innovative, but manufacturable, safe and cost-effective. Siemens’ vision is not for AI to replace expertise in engineering, CAD or simulation. Instead, AI can enhance the engineering tools and knowledge that already exist. For marine organizations, it creates the opportunity to accelerate design cycles, reduce repetitive work, improve knowledge reuse and help engineers focus more of their time on innovation. Riding the AI tide to transformation The maritime AI market is growing 23% annually and is rapidly being adopted across vessel operations, with AI‑enabled systems already improving optimization of resources, workloads and maintenance processes throughout the industry. The future of ship design that’s being propelled by the partnership between HD Hyundai and Siemens is an invitation for others to innovate. “AI is not merely a tool for improving productivity, but a transformative technology that is reshaping how ships are designed, built and operated,” stated H.K. Kim, president and CEO of HD Korea Shipbuilding and Offshore Engineering. This transformation will model how shipbuilders can establish a secure, collaborative operating environment that enables controlled data sharing, configuration management, digital approvals, production visibility and lifecycle traceability while maintaining design authority and execution responsibility. Are you ready to navigate the future of maritime engineering? Download our latest e-book, The artificial intelligence tide .

Siemens Digital Industries Software · Oct 16, 2026

CAD, CAE & Simulation

Siemens today announced Simcenter™ SCADAS™ ES hardware, a new member of its next-generation data acquisition (DAQ) family designed to help engineering teams modernize physical testing and address the growing complexity of product development. Built on the same underlying modular technology as Simcenter™ SCADAS™ RS hardware used for data acquisition in rugged outdoor environments, the new offering provides a scalable, connected approach to data acquisition that supports laboratory, field and mobile testing environments. S imcenter SCADAS ES is a modular, scalable data acquisition system that expands from a few channels to thousands, delivering synchronized, AI-ready data for faster setup, earlier validation and smarter engineering decisions. As engineering organizations face increasing pressure to accelerate development cycles while maintaining confidence in results, traditional testing approaches can create workflow bottlenecks, data quality challenges and barriers to collaboration. Simcenter SCADAS ES addresses these challenges through a modular architecture that enables remote access, multi-user operation and seamless connectivity across testing environments. S imcenter SCADAS ES is a modular, scalable data acquisition system that expands from a few channels to thousands, delivering synchronized, AI-ready data for faster setup, earlier validation and smarter engineering decisions. "Physical testing remains a critical foundation for engineering confidence, but the way organizations perform testing is evolving rapidly," said Bruno Massa, vice president, Simcenter Testing Solutions, Siemens Digital Industries Software. "With Simcenter SCADAS ES, we're helping customers move beyond traditional, isolated testing workflows toward a more connected, scalable approach that delivers trusted, AI-ready data for efficient product verification, while supporting Digital Twin validation and engineering automation initiatives." Designed to support both current testing requirements and long-term transformation goals, Simcenter SCADAS ES and Simcenter SCADAS RS , both part of the next-generation data acquisition family, help organizations reduce setup effort, improve data quality and streamline collaboration across distributed engineering teams. Engineers can remotely configure, monitor and validate measurements while maintaining access to trusted, synchronized measurement data. By capturing synchronized multiphysics data in real-world conditions to enhance durability, reliability and operator comfort. Key benefits of the next-generation SCADAS ES and RS data acquisition systems include: • Modular scalability that enables organizations to start small and expand across channels, applications, teams and test environments. • Onboard, license-free software allows for remote multi-user collaboration that improves engineering productivity and resource use, • Trusted, AI-ready data with synchronized, annotated and traceable measurements. • Open architecture that supports integration with existing workflows and third-party ecosystems. • Edge-to-expert workflows for deeper engineering insights when extended with Simcenter Testlab analysis software. The new DAQ systems help organizations protect existing investments by supporting coexistence with established testing environments while providing a pathway toward more connected and automated workflows. Simcenter SCADAS ES and Simcenter SCADAS RS both support Test Connect, a new open hardware-to-software data exchange interface that extends the value of next-generation data acquisition systems beyond the hardware itself. With Test Connect, customers can develop applications that interface directly with acquisition systems, stream measurement data to external software environments and integrate third-party technologies into testing workflows. Next-generation Simcenter SCADAS ES & RS systems protect your existing data acquisition investments through backward compatibility with previous Simcenter SCADAS measurement systems. As part of the Siemens Xcelerator portfolio, Simcenter SCADAS ES helps connect physical testing activities with broader digital engineering processes. By delivering trusted test data for simulation correlation, Digital Twin validation and AI-assisted engineering workflows, the solution helps bridge physical and digital domains while supporting faster and more informed decision-making. To learn more about Simcenter SCADAS ES, visit https://www.siemens.com/en-us/products/simcenter/physical-testing/scadas/es/ Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens' software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today's ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation. Note: A list of relevant Siemens trademarks can be found here . Other trademarks belong to their respective owners. Contacts for Press Siemens Digital Industries Software PR Team press.software.sisw@siemens.com Further Reading Press Release all Siemens accelerates physical testing with Simcenter Testlab September 28, 2026 Press Release all Siemens recognized as a Leader in Gartner Magic Quadrant for Global Industrial AIoT Platforms September 21, 2026 Press Release all Simple Energy expands Siemens Xcelerator adoption to scale electric vehicle engineering August 4, 2026

Dassault Systèmes · Oct 16, 2026

Digital Thread & Digital Twin · AI in Product Development

At NVIDIA GTC Berlin from Oct. 20-22, 2026, Dassault Systèmes will demonstrate how its industrial AI architecture brings trusted intelligence into the physical world. Building on its long-standing collaboration with NVIDIA announced earlier this year , Dassault Systèmes will showcase how Virtual Twins, Industry World Models and Virtual Companions are transforming engineering, manufacturing, science and AI infrastructure, at booth 3006: • Long-running Virtual Companions: Dassault Systèmes’ Virtual Companions AURA, LEO and MARIE demonstrate how agentic AI can reason, plan and execute complex industrial tasks over time, while keeping human expertise at the center. • AI-powered engineering: Virtual Companions work alongside engineers from requirements to final design, to evaluate alternatives and expand possibilities. The engineer stays in control while AI expands the design space at machine speed. • Physical AI for manufacturing: Manufacturers can explore a Virtual Twin of a production system with a fully automated, end-to-end process with Dassault Systèmes’ DELMIA applications. • AI for science: MARIE supports scientific workflows in an interactive laboratory experience, combining Dassault Systèmes scientific knowledge with NVIDIA BioNeMo Agent Toolkit. BioNeMo Agent Toolkit turns any agent or software platform into a life sciences agent. It equips general agents, scientific agents, and in-house platforms with ready-to-call tools for biology, chemistry, and drug discovery –along with skills for using tools and interpreting results— moving agents from reading science to actually doing it. • Engineering AI Factories: Virtual Twins, model-based systems engineering and LEO can be used to design, simulate and validate AI Factories based on the NVIDIA DSX reference architecture. Scheduled sessions with Dassault Systèmes executives include: • Florence Hu-Aubigny, Executive Vice President, Research and Development, Dassault Systèmes, on the technologies behind virtual twin factories: “Virtual Companions: Industrial AI Agents at Work,” Oct. 21, 15:30-16:10. • Karine Gosse, vice president R&D, DELMIA, Dassault Systèmes, and Chris Whiting, vice president R&D, SIMULIA, Dassault Systèmes, on the benefits of using industrial knowledge and physics-based models: “Automotive’s Agentic Era: from vehicle design to the factory floor with virtual twins,” Oct. 22, 11:00-11:40. For more information: https://blog.3ds.com /