Understanding Supply Chain Visibility

📦 BMW had over $700M invested in returnable containers. And no idea where most of them were, until it implemented a simple passive RFID solution. Here is how the cycle works: 🏭 Suppliers fill containers with parts 🚛 Containers ship to the assembly line 🔧 Parts are consumed on the line ↩️ Empty containers return to a warehouse for cleaning 🔁 Then it repeats The problem? ✅ 10-15% of containers disappeared every year ✅ Replacements cost 3x the original price ✅ Roughly $300M in annual spend just to keep the cycle running ✅ Up to 30% were excess, sitting idle and invisible One senior manager found his own containers stacked above the walls of a competitor's plant. Not stolen. Just lost in a system with no visibility. The fix? RFID readers at the empties warehouse only. When a container did not return, BMW knew who had it and could charge for it. The mere threat of being charged established near-perfect compliance across the entire supplier network. Results: ✅ 30% reduction in total container inventory ✅ 75% reduction in reconciliation costs ✅ 65% reduction in substitute container costs ✅ 20% improvement in container turnaround time We designed and deployed this solution nearly 20 years ago. Total implementation cost: under $1M. The technology works. The ROI is clear. And there surely are lots of great success stories like this by now. Visibility is about making the right decisions, not about seeing everything, everywhere. 💬 What are your biggest supply chain visibility wins? #SupplyChain #RFID #Logistics #Innovation #Truckl

🎯 You can’t close the loop if you don’t know where the line begins. You can’t build a circular system without knowing what you’re buying. That’s why I’m obsessed with a solid Bill of Materials (BoM). Yes, it may sound boring. But this is where system change begins. Not just tracking the final product — but every raw material, pigment, and drop of glue. Because once you really know what something is, you can start to imagine what it could become. A strong BoM is your sustainability superpower. It enables circular thinking by answering: • What are we buying? • What’s it actually made of? • What could it become next? ✅ It connects purchasing to impact ✅ It turns “nice-to-know” into insight ✅ It transforms ESG from ambition into accountability Let’s be honest: • How can we talk recyclability if we don’t know what’s inside the product? • How can we phase out harmful substances if they’re buried five suppliers deep? • How can we report transparently if our data stops at the item name? 📌 Good sustainability starts with good data. And good data starts with a BoM that serves more than just sourcing — it serves the planet. Let’s stop flying blind. #Sustainability #RetailTransformation #CircularEconomy #DataDriven #BillOfMaterials #SupplyChainTransparency #ESG #CSRD #FlyingTigerCopenhagen

G7 cybersecurity agencies, including Cybersecurity and Infrastructure Security Agency, have released a “Software Bill of Materials for AI: Minimum Elements,” which provides a practical baseline for what organizations should expect in an AI SBOM. It is not mandatory and does not create new requirements, but it details recommended minimum elements to improve cyber and supply chain transparency for AI systems. Some of the recommended elements include: ✅ Model information - model name, version, producer, hash value/hash algorithm, license, training properties, and model description/known limitations. ✅ Dataset information - dataset provenance, sensitivity, license, hash, and dependency relationships. ✅ Security properties - security controls, compliance information, cybersecurity policy information, and vulnerability references. ✅ Infrastructure details - software and hardware dependencies needed to run and support the AI system. Why does this matter? Imagine your organization is using a third-party AI model in a customer support workflow. A new vulnerability or licensing issue emerges around one of the model’s dependencies, training datasets, or deployment frameworks. Without an AI SBOM, your team may not know whether you are exposed. With one, especially when tied into your asset inventory, model registry, third-party risk process, or vulnerability management workflow, the security team can quickly answer: ❓ Where is this model used ❓Which version is deployed ❓Who produced it ❓What datasets or dependencies are involved ❓What security controls are in place No Log4j-era guessing. It is the foundation for robust management of AI supply chain risk. Closely related is the important community work led by Helen Oakley , alongside Daniel Bardenstein and Dmitry R., on AI BOM implementation. At RSAC2025, Helen introduced an open-source tool for generating AI SBOMs for Hugging Face models using the CycloneDX format, with human-readable quality indicators. That community work is complementary to the CISA/G7 guidance, which gives us a public-sector consensus baseline for cyber and supply chain transparency. The SBOM for AI / AIBOM work on GitHub gives teams a practical path to implementation mapping fields to CycloneDX and SPDX AI Profile-compatible formats and integrating into engineering and risk workflows. The CISO takeaway: 💠 Use the CISA/G7 minimum elements as the baseline. Start asking AI vendors and internal AI teams for AI SBOMs. 💠 Use the AIBOM community work to understand how AI BOMs can be implemented in practice. 💠 And tie the output into third-party risk, model governance, vulnerability management, and incident response. AI supply chain transparency is a critical AI security control, not just a documentation exercise. Sources: https://lnkd.in/enH9vK3t https://lnkd.in/ebV-_2Hv https://lnkd.in/eydmfD98

From Predictive to Agentic: The Shift in Motion (Supply Chain Use Case) 🧠 Last week’s session on Agentic AI sparked incredible conversations in my inbox, especially around what this evolution looks like in practice. We’ve moved from: • Predictive AI: forecasting demand, spotting risks. • Generative AI: creating designs or reports. • Agentic AI: acting to solve problems autonomously. 💡 Let me share a Supply Chain example Imagine an AI agent that monitors global supplier data in real time. When it detects a delay in one region, it autonomously re-routes orders, adjusts inventory levels, and sends alerts and all this happens without a prompt. It doesn’t just predict disruption; it manages it. That’s Agentic AI in action, where data becomes the foundation for self-adjusting decisions. As data leaders, our challenge now is to ensure these systems act within trusted boundaries, guided by strategy, governance, and ethics. #AgenticAI #DataStrategy #SupplyChain #AI #Leadership #EmergingTech #FigureItOutWithFatema

Agentic AI in Supply Chain series - Day 14 — Supplier Risk Monitoring Your supplier risk process probably runs on a quarterly review and a gut feeling. An agent runs it every minute. Supplier risk monitoring is a near-perfect always-on agent. It watches what humans can’t watch continuously — across your entire supplier base, all at once - 365/24/7. What it does: → Continuously scans signals — delivery performance, financial health, news, weather, port delays. → Flags a supplier trending toward trouble before it becomes a disruption. → Triggers a play — alert the buyer, surface alternates, pause auto-reorder from that supplier. The shift is from reactive to proactive. From “the shipment didn’t arrive” to “this supplier is sliding, three weeks early.” It doesn’t replace the buyer’s judgment. It makes sure the buyer is looking at the right supplier at the right time. Risk doesn’t wait for your quarterly review. The always-on agent is the early-warning system you can’t staff by hand. How would you find out today if a key supplier was sliding toward failure — and how late? 👇 Another factor is our old friend bullwhip effect. Less than 25% of suppliers are digitally connected well enough to share updates. This pushes your envelope further. Time to become PROACTIVE!

Control Tower is the backbone of any supply chain business. Here’s how we built an army of AI agents to run Control Tower operations at Snitch. They don’t just alert but follow up till the issues are resolved. Traditionally, Control Tower teams rely on a large group of people constantly monitoring dashboards. This approach does not scale, especially in Q-commerce, where everything moves in minutes Here’s the Control Tower framework we built at SNITCH Quick, which helped us scale to nearly four-digit orders per day. 𝗢𝗿𝗱𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗱 𝘁𝗼 𝗛𝗮𝗻𝗱𝗼𝘃𝗲𝗿 𝗣𝗲𝗻𝗱𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁: Once a rider is assigned, the AI agent tracks whether the rider is actually moving toward the store. If there’s a delay, it alerts the team immediately. 𝗥𝗶𝗱𝗲𝗿 𝗥𝗲𝗮𝗰𝗵𝗲𝗱 𝗗𝗿𝗼𝗽 𝗯𝘂𝘁 𝗗𝗶𝗱 𝗡𝗼𝘁 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗔𝗴𝗲𝗻𝘁: If the rider is near the customer’s location but hasn’t completed the delivery within the defined time, the agent flags it. This helps us step in early and avoid RTOs or failed deliveries. 𝗢𝗿𝗱𝗲𝗿 𝗣𝗶𝗰𝗸𝗲𝗱 𝗨𝗽 𝗯𝘂𝘁 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗟𝗮𝘁𝗲 𝗔𝗴𝗲𝗻𝘁 : The agent calculates the expected delivery time based on distance and live movement. If the order is significantly delayed or detoured, it alerts the team so we can act fast and reduce in-transit losses. All of this gives our ops team real-time visibility into where things might go wrong, so they can jump in and fix issues quickly. And it all happens inside a WhatsApp group, because that’s where most business operations actually run. Beyond this, we built a full stack of AI agents that track orders, trigger inventory replenishment, ensure processes and SOPs are followed across the supply chain, and alert the team if something is about to slip. If you’re into supply chain product or operations, I would love to hear how you’re using AI in your day-to-day workflows. Drop a comment and let’s exchange notes.

BOM is a layered system that connects design, cost, production, and lifecycle execution. It’s not just a parts list, it’s the backbone that aligns engineering intent with factory reality, financial planning, and long-term product tracking. Here’s how the layers fit together: Layer 1: Basic BOMs (Engineering & Manufacturing Foundation) - EBOM (Engineering Bill of Materials) Defines the product from a design perspective. Created inside CAD and PLM systems, it reflects structure based on form, fit, and function - how engineers intend the product to exist. - MBOM (Manufacturing Bill of Materials) Translates design into production logic. It restructures components according to assembly sequence, routing, tooling, and shop-floor requirements. Layer 2: Costing BOM (Commercial & Financial Perspective) - Sales BOM Represents the product as sold to customers. It includes bundles, configurable options, and commercial packaging used for quoting and order entry. - Costing BOM Focuses on financial rollups. It calculates material, labor, overhead, and process costs to support pricing, margin analysis, and ERP integration. Layer 3: Execution BOMs (Operational Reality) - As-Built BOM Captures what was actually produced. It includes serial numbers, lot traceability, substitutions, and real-time shop-floor changes. - As-Maintained BOM Reflects the product’s condition in the field. Updated after repairs, upgrades, and replacements to support service and lifecycle management. Manufacturing clarity comes from BOM alignment. When EBOM defines, MBOM executes, Costing evaluates, and Execution BOMs record reality - the entire product lifecycle stays connected instead of fragmented. For a deep dive into PLM, MES, or CAD and to elevate your understanding of PLM, connect with us at PLMCOACH and Follow Anup Karumanchi for more such information. #plmcoach #plm #teamcenter #siemens #3dexperience #3ds #dassaultsystemes #training #windchill #ptc #training #plmtraining #architecture #mis #delmia #apriso #mes

Most supply chain professionals don't have a visibility problem. They have a reporting problem. A few months ago, I reviewed a logistics operation where the team spent hours every week pulling shipment data, checking OTIF performance, tracking delays, and preparing management reports. By the time the report was finished, the information was already outdated. So instead of creating another spreadsheet, I built a centralized Logistics & Supply Chain Dashboard that brings the most critical KPIs into one place. 📦 Total Shipments 🚚 Carrier Performance 📊 OTIF Tracking ⚠️ Delay & Exception Monitoring 🏭 Warehouse Performance 💰 Transportation Cost Analysis 🗺️ Regional Shipment Visibility The goal was simple: Turn scattered operational data into actionable insights. Now, decision-makers can instantly see: • Which warehouses are performing best • Where delays are occurring • Which regions need attention • Transportation cost trends • Service level performance across the network What I have learned from supply chain analytics is that dashboards are not about making reports look beautiful. They are about reducing reaction time. When operations teams can identify issues in minutes instead of hours, they make faster decisions, improve customer service, and protect margins. The most valuable dashboard is not the one with the most charts. It is the one that helps the business answer: "What needs my attention right now?" That is where real supply chain visibility starts. What KPI do you consider most important in a logistics dashboard? #SupplyChain #Logistics #SupplyChainAnalytics #DataAnalytics #DashboardDesign #ExcelDashboard #LogisticsManagement #WarehouseManagement #Transportation #BusinessIntelligence #DataDrivenDecisions #OperationsManagement #SupplyChainAnalyst #ExcelAutomation #Analytics

You have 50 supply chain dashboards, but your line still stopped today because a supplier was 3 days late and nobody knew. Dashboards are dead. By the time your planners log in, refresh the data, and realize a critical component is stuck in transit, the shift is already ruined. A mid-market manufacturer came to us recently with this exact nightmare. They had spent a fortune on a "modern data stack." They had spended hours building Power BI charts tracking inbound freight. But when a Tier 2 supplier missed a delivery window, nobody noticed until the floor operators stared at an empty bin. They spent the next four hours firefighting, re-routing lines, and authorizing premium air freight. When I asked the CIO why the dashboard didn't help, he said: "Nobody checks the inbound freight dashboard until Friday. The shortage happened on Tuesday." This is the reality for 90% of asset-heavy companies. You don't need another dashboard. You need a nervous system. Data is useless if doesn't change behavior. Here is how smart supply chains are redesigning their architecture: 1. Decouple the ERP from Reality     Your ERP assumes everything is fine until a human tells it otherwise. Stop relying on manual status updates. Map the exact gap between when a supplier misses a scan and when your planner finds out. That time gap is where your margin dies.     2. Define the Exception     Stop tracking what goes right. Only track what goes wrong. Define the exact tolerance for latency. If a supplier SLA is 48 hours and they hit 49 hours without an ASN scan, that is an exception. 3. Push the Alert to the Floor     Data must find the user, not the other way around. When an exception occurs, trigger an automated alert directly to the planner's workflow or the plant manager's screen. If a supplier slips, your planner sees it in hours, not on the day the line stops.     4. Tie the Exception to the P&L     Every open PO should carry a risk score, based on supplier history, lead time, and current status. Your planner should walk in every morning knowing which suppliers are likely to cause problems today. Not last week The alert shouldn't say "Truck is late." It should say "Line 4 will starve at 2 PM, risking $40k in unabsorbed labor."     If your data doesn't trigger an action, it is just expensive noise. The companies winning today aren't buying more BI licenses. They are closing the gap between data collection and operational action.

Logistics tech is entering an interesting phase. For years, the conversation was mostly about visibility. Where is the shipment? Where is the parcel? Where is the driver? Where is the inventory? And visibility still matters. But now logistics and supply chain teams are under pressure from many sides at once: • reduce costs • handle labor shortages • give customers faster updates • improve compliance • automate manual work • understand where AI can actually help Gartner predicts that SCM software with agentic AI capabilities will grow from less than $2B in 2025 to $53B by 2030. So it’s clear where attention and budgets are moving. But in logistics, AI is rarely the first step. Many teams still deal with fragmented operations: • shipment data in one system • warehouse data in another • carrier updates in separate portals • documents moving through emails • exceptions handled manually • decisions made in spreadsheets If data is disconnected and workflows are manual, AI has very little to work with. That’s why the real opportunity starts earlier. Before AI can support route planning, exception handling, document checks, forecasting, or customer support, companies need a stronger operational foundation: • connected ERP/WMS/TMS/carrier data • cleaner workflows • document automation • exception management • customer and partner portals • dashboards that help teams act, not just track For me, the next phase of logistics tech is not only about better visibility. It is about turning visibility into action. And the companies that will get the most value from AI will likely be the ones that first make their operations connected, structured, and ready for it 🙌