How to Use Technology for Traceability

Your AI pipeline is only as strong as the paper trail behind it Picture this: a critical model makes a bad call, regulators ask for the “why,” and your team has nothing but Slack threads and half-finished docs. That is the accountability gap the Alan Turing Institute’s new workbook targets. Why it grabbed my attention • Answerability means every design choice links to a name, a date, and a reason. No finger pointing later • Auditability demands a living log from data pull to decommission that a non-technical reviewer can follow in plain language • Anticipatory action beats damage control. Governance happens during sprint planning, not after the press release How to put this into play 1. Spin up a Process Based Governance log on day one. Treat it like version-controlled code 2. Map roles to each governance step, then test the chain. Can you trace a model output back to the feature engineer who added the variable 3. Schedule quarterly “red team audits” where someone outside the build squad tries to break the traceability. Gaps become backlog items The payoff Clear accountability strengthens stakeholder trust, slashes regulatory risk, and frees engineers to focus on better models rather than post hoc excuses. If your AI program cannot answer, “Who owns this decision and how did we get here” you are not governing. You are winging it. Time to upgrade. When the next model misfires, will your team have an audit trail or an alibi?

One photo from a customer almost destroyed years of trust... I still remember the feeling. My heart dropped into my stomach. Attached was a photo from a consumer. It was one of our yoghurt cups. The problem? It was expired. The Board didn't just want an apology, they demanded an explanation. "How did this leave the factory?" We mobilized the team. We tore through the dispatch logs. We checked the manual inventory cards. We interviewed the drivers. The result? Silence. We couldn't trace it. According to our paperwork, that yoghurt didn't exist. But there it was, on a consumer's table. That was the day I learnt the hard truth about manual traceability: If it’s not digital, it’s not defensible. We relied on paper logs that allowed for human error, "ghost" pallets, and dispatched units that slipped through the cracks. How do we prevent this? 1️⃣ Enforce FEFO (First Expired, First Out) digitally: Don't let a forklift driver choose which pallet to pick. Let the system lock the others out. 2️⃣ Scan-to-Dispatch: If the barcode doesn't match the active batch, the gate doesn't open. 3️⃣ Mock Recalls that "Fail": Don't just trace the easy batches. Trace the "ghost" scenarios to find your blind spots. A product on a consumer's table is the ultimate truth. Your paperwork is just an opinion. #food #dairy #foodsafety #qualityassurance #dairyindustry

IT/OT integration is how you de-risk growth.     If the top floor can’t see the shop floor in real time, quality slips, downtime grows, and batch release slows. In our world of compliance and complex supplier networks, blind spots turn into audit findings and missed delivery windows.     Here’s the core move I see working. Combine the real and digital worlds across product and production so horizontal data flows become routine. Think engineering models, test results, materials, building processes, automation code, and performance data moving between teams. Then connect the vertical path. Executives, planners, and operators sharing the same context so decisions line up with actual conditions. That’s where you get predictive maintenance instead of unplanned stops, data‑centric supply chain adjustments instead of last‑minute expedites, energy transparency that feeds credible sustainability metrics, and stronger cybersecurity plans that account for both IT and OT exposure.     Pharma adds constraints, but the pattern still holds. IoT devices can read modern and legacy equipment, extending the digital thread into your supplier ecosystem so logistics, production timing, and potential disruptions show up early. A closed loop between development, production, and optimization tightens traceability and speeds corrective action. Digital twins let engineering teams iterate quickly on both process and line design without risking validated operations.     Pick one high‑stakes decision and wire it end to end. For many, that’s batch release. Map the horizontal data you need across quality tests, materials, and line performance. Then build the vertical connection so insights reach the teams that plan, schedule, and approve. Keep the scope small, include cybersecurity from day one, and define the single source of truth for that decision. When it works, scale to the next decision. 

Engineering Data is everywhere: distributed across tools, documents, and platforms. But if we want AI to truly understand our products and support development, we need to link that data, create traceability, and make it accessible and modifiable. At Accenture together with Vlad Larichev and many other colleagues, we see 3 powerful approaches to unlock engineering data with AI: 1️⃣ Retrieval Augmented Generation (RAG) Aggregate your distributed engineering knowledge into a Vector Database. This enables semantic search and document-level Q&A across tools and documents. When paired with a Language Model (LM), relevant context is retrieved based on similarity to your prompt, perfect for engineering Q&A and documentation support. 2️⃣ Graph Retrieval Augmented Generation (GraphRAG) Link your engineering data across domains using a Knowledge Graph. Capture relationships between requirements, CAD, simulation, test data, etc. Enabling traceability and holistic V-model understanding for your LM. Essential for typical cross-domain tasks, such as impact analysis and E2E configuration management. 3️⃣ Model Context Protocol (MCP) Why move your data at all and store it in additional databases? With MCP, AI agents can access and modify data directly inside your tools without data ingestion and storage efforts. It’s an agentic interface to your engineering stack, which enables cross-domain data access, retrieval and generation, making it suitable for E2E ECR processing. These aren’t just technical solutions. They’re a paradigm shift in how we will interact with engineering data and develop complex products. These methods only unlock their full potential when high-value use cases are identified and applied in a goal-oriented way. Interested in how this can work for your organization? Let’s talk. — Dr. Matthias Ziegler | Dr.-Ing. Tobias Guggenberger | Arne Breitsprecher | Georg Brutzer | Florian Böhme #EngineeringIntelligence #DigitalEngineering #ProductDevelopment #Accenture

When you're executing missions with $1M to $500M on the line, you can't afford to guess where a part has been or what's been done to it. I constantly see teams tracking their parts, testing, and operations across 10+ different systems. Spreadsheets for purchase orders, different tools for inventory and work orders, test results in emails, and assembly records in binders. When something goes wrong or you need to prove compliance, you're playing detective, calling people, cross-referencing timestamps, and hoping nothing fell through the cracks. That's not traceability. That's archeology (and yes, it might be digital, but there's a better way). Real traceability means pulling up any part and seeing its complete timeline: when it was ordered, who touched it, what tests were run, which build it went into, etc. Timestamped and linked. You also need a blamelist (i.e. who did what, when they did it, and if there were any issues reported.) At Epsilon3, all of that is automatic. Parts and work orders are tracked in real time, and you can see what's happening as it unfolds. If you're in Florida and I'm in Los Angeles, I can see your work the moment it happens. I don't need to refresh anything or call to ask if you're done. That changes how teams operate. You're making decisions instead of chasing updates and catching issues early because you can see what's already been done. In high-stakes work, traceability is what makes trust possible. What challenges have you run into with tracking work across your team? I'm curious to hear what slows you down.

Managing requirements in a MedTech project feels manageable, until you count them. Design inputs, process validation specs... one user need can easily generate 10 to 15 distinct requirements across different documents. This happens for a clear reason: Medical devices bring together every stakeholder group (clinical, regulatory, manufacturing, servicing, safety), each with valid concerns, and each concern needs to be captured somewhere. The real challenge has nothing to do with the number.  Structure makes or breaks a requirement set. A flat list of 300 requirements in a spreadsheet becomes almost impossible to review, maintain, or trace.  The structure of your requirement set should reflect how users actually work, organised by goals and user types, not by the order requirements were written. Three things that make a requirement set manageable:  1) Every requirement: correct, complete, consistent, verifiable, and traceable  2) The set as a whole: realistic and concise, no requirements conflict with each other, nothing duplicated across documents  3) The structure: hierarchical, user needs at the top, system requirements in the middle, detailed specifications at the lower levels, each linked Here are some tips I use to manage this type of project: ✓ A single monolithic RTM covering everything adds little value. Explicit trace chains for safety-related and regulatory-critical requirements do: hazard → risk control requirement → design element → test case (it’s an example).  That chain must stay readable, stable, and maintained under change control. ✓ Every change request should trigger an impact analysis based on that chain: which requirements, design elements, tests, and risk controls are affected, what needs to be updated, and what needs to be re-verified. ✓ The ID scheme matters more than the tool. Define it early (e.g. CLIN-XXX, BIOCOMP-XXX, SAFETY-XXX), keep it stable from your User Requirements Document through your specifications and test protocols (for you V&V tracability). × Changing IDs mid-project breaks traceability silently, and that kind of gap tends to surface during audits. Getting this right early saves a significant amount of rework later, especially when a design change arrives mid-project and you need to know exactly what it touches. What are your best tricks for keeping a large requirement set under control, without the whole project turning into a mess?

Why AI-Native Systems Engineering Is the Next Frontier - and Why It Matters Now As systems grow ever more complex - spanning automotive, aerospace, medical devices, and advanced software - traditional tooling and manual processes simply can’t keep up. The result? Fragmented requirements, siloed data, costly rework, compliance risk, and slow innovation cycles. But we’re at a turning point. AI is no longer an “add-on” feature - it’s becoming the foundation of next-gen systems engineering workflows. Instead of stitching automation onto legacy platforms, we now have tools built from the ground up with AI at their core - enabling engineers to shift from labor-intensive coordination to strategic problem solving. One standout example is Trace.Space (https://www.trace.space/) – AI‑Native Requirements & Systems Engineering Platform - a platform that demonstrates what this new paradigm looks like in practice: AI-Driven Traceability & Risk Detection: AI continuously maps relationships between requirements, tests, designs, and changes - identifying broken links, gaps, and compliance risks before they become costly issues. Structured Collaboration at Scale: By ingesting data from PDFs, JIRA, Git, Confluence, and more, the platform creates a living trace graph that keeps teams aligned and version history transparent - hardware, software, and systems engineers working in sync. Augmentation, Not Replacement: Rather than replacing engineers, AI suggests and supports - proposing links, surfacing blockers, flagging missing coverage, and enabling engineers to focus on high-value decisions. The result? Faster cycles, stronger compliance, fewer surprises, and better outcomes - from electric vehicles to satellites and regulated software systems. This is more than automation - it’s AI-augmented engineering intelligence. If your team is still wrestling with static requirements docs, siloed data, or manual trace matrices, it’s worth asking: Is your tooling enabling your engineers to lead, or is it slowing them down? #AI #SystemsEngineering #RequirementsEngineering #DigitalEngineering #EngineeringTools #Innovation Janis Vavere, Trace.Space

Sustainability requires robust, verifiable data. As Liz Larkin from JD Sports shared at NRF earlier this year, transparency is essential in avoiding greenwashing and ensuring decisions can be made on quantifiable evidence. With RFID and digital identification solutions, brands can now trace raw materials right back to source, track product utilization, and even quantify waste for reprocessing. This level of insight allows companies to not only optimize material use but also close the loop and so turn what would have been potential wastage, into a resource for future production. At Avery Dennison, we’re enabling brands to take control of their sustainability journey by embedding intelligence into every product. Real data. Real impact. Real change. How is your business using data to drive sustainability forward? #Sustainability #SupplyChainTransparency #RFID #CircularEconomy

#Automation has reduced human touching during #solar cell #manufacturing. However, process analysis tasks such as troubleshooting and defect diagnosis still rely on experienced engineers. Wafer tracing is often the first step. At Canadian Solar Inc. we have built a powerful manufacturing execution system (#MES) for our advanced heterojunction (#HJT) fab, capable of tracing individual wafer movement at every process station. Each wafer is assigned with a unique virtual ID (a digital “ID" without physical markings) upon initial loading. Programmable Logic Controllers (PLC’s) then build associations between this virtual ID and the wafer locations in machines and tooling, their quality data, processing time log and recipe. This database now enables #traceability for more than 90% of wafers in our solar cell lines. Why is MES with individual wafer traceability important? Here are examples. When we discover scratches on solar cells through photoluminescence (#PL) imaging after a wet chemical process, we can correlate such defects with wafer cassettes. Within minutes, we can pinpoint and replace the specific cassette causing the scratch. In the past, such a diagnosis could take hours even if possible. Another example is the deposition of nano-silicon layer. When we find defects with PL imaging after this process, we can correlate the defects with the wafer location inside the deposition chamber, therefore identify the root cause. With all these new tools, our HJT fab achieves solar cell efficiency above 27.2% and production yields above 99%, the highest in industry. We are busy implementing #AI tools to our new workshop. Stay tuned. #SolarManufacturing #Efficiency #YieldImprovement #FutureOfSolar #AdvancedManufacturing

What You Need in a Digital Product Passport Tool DPPs are not a future thing. They’re a now thing - and if you’re in fashion, they’re about to change how your products get made, sold, and trusted. First, what is a DPP? It's a scannable, structured data file that travels with every product - from fiber to finish, from store to after-sale. It holds info like: - Material origin and certifications - Environmental footprint (CO₂, water, recyclability) - Supplier & manufacturer transparency - Care, repair, reuse, and end-of-life instructions - Compliance data - Customer feedback - And even your brand’s accountability story By 2030, every textile product in the EU will need one. And for larger brands? The DPP clock starts ticking in 2027. Why it matters right now: - The EU’s Ecodesign for Sustainable Products Regulation (ESPR) will require DPPs for all textile products. - Brands that can’t create this data will lose shelf space or get fined. - Retail buyers are already asking for traceability proof. - Consumers want transparency. - Greenwashing penalties are getting serious. What to look for in a DPP tool: If you’re looking at tools or platforms to help you build DPPs, here’s what you SHOULDN'T compromise on: ✅ Flexible digital link capability - DPPs must work for web, physical, and retail channels. ✅ Built-in lifecycle & impact data - CO₂, water, recyclability, and more - not as an afterthought, but baked into the product creation. ✅ Supplier input capability - Because you can’t trace what your suppliers can’t contribute to. ✅ Consumer-friendly interface - A scannable QR code is useless if the info isn’t legible, trustworthy, and beautiful on the front end. ✅ Editable, scalable, and secure - Because products change, and your tool needs to keep up - across 10 SKUs or 10,000. Portia was built for this Most PLMs and ERP systems weren’t designed for circularity or compliance. They’re rigid, messy, and built for the old fast-fashion model. Portia was designed by industry people - for industry people, from day one to help brands build responsible products - and communicate their sustainability clearly, cleanly, and credibly. - Instant DPP creation per product - with structured, open digital links - Sustainability fields for CO₂, water, repairability, recyclability - Supplier portals for direct data contribution - Scannable QR code generation for every product - Integrated care, warranty, repair, and end-of-life info - Centralized data hub so all department teams work in sync Portia doesn’t just help you “comply” - it helps you stand out. It shows: → Where product came from → What it’s made of → Its impact → How to care for and extend its life → Get feedback to improve products → Why it’s worth believing in That’s a competitive edge. That’s the future. That’s Portia. Make #DPPs your differentiator We want to see a future where transparency is standard, not a selling point. We’d love to show you how it works. 👉 Link below