Evolution of Process Modeling in Industry

EVOLUTION of PLM BUSINESS PROCESSES From Paper → Documents → Digital → AI As I was reflecting on PLM in 2025 I wandered back to the days on the drawing board (yep I experienced drawings on vellum) & passing around of physical folders! It was an interesting exercise. Lots of change in recent years compared to back in the day! ⸻ 1) Paper-Based (Pre-IT Era) How work was done • Physical dwgs, binders, redlines, wet signatures • Eng chg via meetings, memos • BOMs on paper • Tribal knowledge Process was • Serial, slow, & location-dependent • Manual approvals • No single source of truth Business impact • Long cycle times • High error rates • Limited traceability Typical question: “Who has the latest dwg?” ⸻ 2) Document-Based PDM (IT Tool Era) What changed • Intro of tools as vaults • CAD files, PDFs, stored centrally • Check-in/out, version control • Workflow = doc routing Process mindset • “Manage files better” • PDM as a repository, not a system of record What improved • Reduced overwrites & lost files • Basic rev control • Faster access What didn’t • BOM still treated as a doc • Limited semantic understanding of product data • Heavy reliance on naming conventions Business impact • Incremental productivity gains • Still eng-centric • Limited downstream reuse Typical question: “Which doc version is released?” ⸻ 3) Digital PLM (Data-Centric Era) The big shift • From docs → structured product data • PLM becomes the system of record for: Items, BOM, CR/CN, Configs Process characteristics • Object-based, relational, traceable • Parallel workflows across functions • Strong integration with ERP, MES, ALM What PLM really becomes • A digital thread backbone • How products are defined Business impact • Faster change • Better compliance & traceability • Cross-functional alignment Typical question: “What changed, why, who approved it, & where is it used?” ⸻ 4) AI-Based PLM (Intelligence Era) The next evolution: PLM data already exists digitally. AI changes how it is consumed & acted upon. Key shift: From process execution → decision intelligence ⸻ How AI transforms PLM processes 🔍 From Search → Answers • Instead of navigating: • “Show me similar parts used in past programs” • “Why was this change made?” • RAG enables AI to reason 🔁 From Reactive → Predictive • Predict change impact before approval • Identify likely rework or compliance risk • Recommend reuse vs new part creation 🧠 From Rules → Learning • Move beyond static workflows • Learn from past ECO patterns, approvals, failures 🤝 From Tool → Copilot • PLM assists engineers: change justification, design alternatives, compliance checks ⸻ Business impact of AI-based PLM • Reduced effort • Higher reuse • Better decision quality Typical question: “What should we do next — & why?” TAKEAWAY: PLM evolution mirrors business maturity: from storing information, to structuring it, to understanding & acting on it intelligently. #intelizign #PLM #paper #document #digital #AI

Most companies stop at the picture on the wall. They map their processes in Visio or Lucidchart. Useful… but static. It’s like taking a snapshot of a moving river—you see the surface, but not the currents below. What customers really need is context. Who owns each step? Which risks lurk in the workflow? How do KPIs tie into strategy? Where are compliance gaps hiding? That’s where process modeling changes the game. 🔍 A global bank modeled its Know Your Customer process. Within days, leaders saw hidden compliance issues—like the same team approving multiple steps in violation of segregation-of-duties rules. A simple diagram never would’ve caught it. ⚡ A manufacturing firm modeled its Source-to-Pay process. Suddenly, procurement, sourcing, and accounts payable weren’t silos—they were connected in one living system. That alignment shaved weeks off onboarding and made audits faster and cleaner. The shift from diagrams → models delivers: ✅ Faster onboarding ✅ Consistent KPIs across departments ✅ Clear compliance visibility ✅ Better decisions on automation and headcount And with tools like iGrafx Process360 Live automatically converting Visio/Lucidchart diagrams into BPMN, organizations can unlock these outcomes without starting from scratch. The payoff? A digital twin of your business that helps you spot risk, accelerate decisions, and create value every day. 👉 Question for you: Is your company still looking at static diagrams, or have you started building a living model of your processes?

Very early in my career, I created the process analysis and simulation tool that Price Waterhouse staff and clients used on re-engineering projects.  While automation and digital transformation efforts have continued to rely on process management, the current advancements in AI and specifically agents have triggered some thoughts on a renewed focus in this space. While process analysis was once all about standardization, the future focus will be on real-time customization.  Processes will be adapted on the fly to the specific desired outcomes and the analysis will be more about humans keeping up with the AI rather than instructing the AI.  Here are some possible implications: Agent systems will drive the development of more sophisticated, real-time process modeling platforms that can:    - Dynamically adjust process workflows    - Provide predictive process optimization    - Offer granular visibility into process performance Organizations will develop integrated frameworks that:    - Align process management with AI control guidelines    - Create standardized protocols for agent system deployment    - Establish comprehensive monitoring and control mechanisms Agent systems will demand more responsive compliance mechanisms that:    - Dynamically interpret and apply regulatory requirements    - Provide real-time compliance monitoring    - Automatically adjust processes to meet changing regulatory landscapes    - Reduce manual compliance verification efforts The integration of agent systems to business practices will necessitate significant workforce skill evolution:    - Increased emphasis on systems thinking    - Advanced data interpretation skills    - Ability to design and manage human-agent collaborative environments    - Continuous learning and adaptability The complexity introduced by agent systems is already creating new specialized roles focused on:    - Designing agent-compatible process architectures    - Managing human-agent interaction interfaces    - Developing adaptive process modeling strategies

𝐈𝐒𝐀-𝟗𝟓 has been the longstanding defacto model for how information flow between enterprise and control systems within a manufacturing environment. This standard sets a baseline for terminology, hierarchy models, functional data flow models, object models, and even operational models. As technology has changed, so must our models. Today's Industry 4.0 technologies (most notably IIoT in this case) have created an entirely different way of networking mostly based on publication & subscription instead of the traditional point-to-point communication. This allows every device to be able to communicate directly with every other device, changing the infrastructure away from “hierarchical layers”. However, the idea of functional layers is still very relevant! 𝐖𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐢𝐧𝐠𝐬 𝐠𝐨𝐢𝐧𝐠? A “Flatter” version of the ISA-95 model with more connection options.  This allows for new levels of process integration across production and business functions, but unfortunately can cause friction between IT and OT teams if not properly addressed, governed, and managed. For more information, check out CESMII’s Conrad Leiva' article in the Journal of Advanced Manufacturing and Processing on the first principles of smart manufacturing addressing this very topic: https://lnkd.in/g7EDMKXx How are you seeing the ISA-95 model be modified to meet the needs of Industry 4.0? #Industry40 ******************************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

  As emerging technologies accelerate, one thing is clear; the way we design processes must evolve as fast as the technology itself.    In a recent Harvard Business Review article, I co-authored with Lan Guan and H. James Wilson, we explore why many organizations struggle to capture value from new tools not because the technology falls short, but because processes were designed for a different era.    Processes should not be static rules frozen in time. They are living systems. When designed well, they provide clarity, accountability, and trust while still leaving room for human judgment, adaptation, and learning. When designed poorly, they become friction, slowing decisions, and limiting impact.    What we are seeing in practice is a shift toward processes that:  - Adapt in real time as conditions change  - Enable human and AI collaboration, rather than forcing trade-offs  - Balance speed with responsibility, especially as intelligence becomes embedded in daily work    This is not about letting technology dictate how organizations operate. It is intentionally redesigning how work gets done so innovation can scale responsibly and deliver results.  https://lnkd.in/ge3GVvNg   #AgenticAI #OperatingModel #Reinvention