Using Supply Chain Analytics to Enhance Agility

Tariff volatility is here. Can you adapt fast enough? Entering 2025 we are facing a radically altered trade landscape. Tariff proposals range from 10% to 60%.  🚢 Organizations must manage rising costs, sudden supply disruptions, and inflationary pressures, all while contending with fast-changing rules and potential retaliation from trading partners. Yet volatility also creates opportunities for organizations who are prepared. 🧭 𝗚𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗰𝗮𝗻 𝗽𝗿𝗼𝘃𝗶𝗱𝗲 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗶𝗻𝘁𝗲𝗿𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝘄𝗲𝗯 𝗼𝗳 𝘀𝘂𝗽𝗽𝗹𝗶𝗲𝗿𝘀, 𝘁𝗮𝗿𝗶𝗳𝗳𝘀, 𝗮𝗻𝗱 𝗹𝗼𝗴𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗿𝗼𝘂𝘁𝗲𝘀. Here's how: 1️⃣ 𝗠𝘂𝗹𝘁𝗶-𝗛𝗼𝗽 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 ↳ Map your entire supplier network as nodes and relationships in a graph.  ↳ Visualize dependencies several layers deep, often hidden in traditional systems. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗧𝗮𝗿𝗶𝗳𝗳 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 ↳ Add tariffs to the graph and then use graph algorithms to simulate alternate sourcing paths with lower duties or better resilience. ↳ This enables decision-makers to test “what-if” scenarios, minimizing guesswork when a sudden tariff spike occurs. 3️⃣ 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗥𝗶𝘀𝗸 & 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 ↳  Apply centrality and community-detection algorithms to find which suppliers or markets could cause cascading failures. ↳  Uncover clusters of high-risk exposure, allowing proactive adjustments rather than reactive damage control. Graph-based platforms help executives move beyond spreadsheets and siloed databases. They offer a living, interconnected view of all the moving parts, enabling better-informed decisions on pricing, sourcing, and expansion. 🚀 𝗔𝘁 𝗗𝗮𝘁𝗮2 𝘄𝗲 𝗵𝗮𝘃𝗲 𝗯𝘂𝗶𝗹𝘁 𝗼𝘂𝗿 𝗿𝗲𝗩𝗶𝗲𝘄 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗼𝗻 𝘁𝗼𝗽 𝗼𝗳 𝗡𝗲𝗼4𝗷 𝘁𝗼 𝗵𝗲𝗹𝗽 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲 𝘁𝗵𝗲𝗶𝗿 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗼𝗳 𝗴𝗿𝗮𝗽𝗵𝘀 𝗮𝗻𝗱 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. If your organization is concerned about how it can adapt to the new era of trade volatility, reach out and we can start the conversation. ♻️ Know someone who needs better visibility into their supply chain? Share this post to help them out! 🔔 Follow me Daniel Bukowski for daily insights about delivering value from connected data.

Dear My Network, I'm wrapping this series on Segmentation with the following key Takeaways: • ML and Agentic AI are powerful enablers of E2E supply chain segmentation by enhancing agility, automation, and intelligence across supply chain processes. • These technologies can dynamically adapt segmentation strategies based on real-time data, customer behavior, and changing market conditions. • It can identify profitable clusters, predict disruptions, and automate scenario planning across multiple supply chain models. • Agentic AI brings autonomy to processes—executing tasks, learning, and optimizing supply chain responses without constant human intervention. The insights for 4-part series are drawn from my chapter in our new book: https://lnkd.in/gVNSdWsW Lets close with another Example: Global Consumer Electronics Manufacturer - Context: A multinational consumer electronics company sells both premium and value-tier products across multiple channels—direct-to-consumer (DTC), big-box retailers, and e-commerce platforms. Each segment had distinct demand patterns, service expectations, and profitability margins. - Challenge: They were using a one-size-fits-all supply chain model, leading to: • Stockouts of premium products during product launches • Overstocking of slower-moving value-tier items • High logistics costs due to expedited shipments - E2E Segmentation in Action: 1. Planning Phase They used ML algorithms to profile and cluster customers and products based on buying behaviors, seasonality, margin contribution, and service requirements. 2. Implementation Phase They designed virtual supply chains: • One for high-margin flagship unpredictable products with make-to-order and expedited fulfillment • Another for value-tier SKUs using a low-cost, forecast-driven model with bulk shipments • A third for e-commerce with decentralized inventory and last-mile delivery partners 3. Sustain Phase Agentic AI systems monitored these segments in real time, dynamically adjusting planning parameters and alerting teams when service levels or cost thresholds were breached. - Results: • 15% reduction in working capital tied to inventory • 10% improvement in on-time delivery for premium products • Faster decision-making and fewer fire drills • Greater alignment between sales, supply chain, and finance This example reflects the core principles outlined in my book chapter on segmentation, showing how advanced technology and structured transformation can drive real business value. Now, How are you planning to use AI to enable e2E segmentation in your supply chain? Please share your thoughts in the comments!

Ever struggle with unpredictable demand and supply constraints? 🤔 I believe Sequential Decision Analytics (SDA) can make a real difference. 📦 Scenario: You’re managing inventory for multiple products. Traditional methods rely on static plans based on fixed forecasts. But what happens when demand spikes unexpectedly or a supplier delays shipments? 🔍 SDA Approach: Instead of building one rigid plan, you create a sequence of decisions that adapt over time. 1️⃣ Capture the State: Gather everything you know—current inventory, pending orders, supplier reliability. 2️⃣ Decision Policy: Decide how much to reorder, whether to reallocate stock, or adjust lead times. This policy doesn’t just react to what’s happening now; it anticipates future changes. 3️⃣ Sequential Planning: Plan each step with the long-term goal in mind. Adjust your strategy as new data arrives, like shifts in demand or supply issues. It’s not about real-time reactions but about making informed, sequential choices. 🔄 Learning and Adaptation: Refine your policy as you learn. If a supplier is consistently late, factor that into future decisions, so your plan gets better with each iteration. 🎯 Objective: Optimize long-term profitability and service levels, not just by minimizing cost in a static model but by balancing risks like stockouts and overstock over time. With SDA, you're not just guessing or reacting; you’re building a resilient, adaptive strategy for your supply chain. What are your thoughts on this framework and approach? 🤔 #OperationsResearch #SupplyChain #InventoryOptimization #SequentialDecisionAnalytics