Next-Gen Supply Chain Metrics

Metrics don’t make the difference. The right metrics make the difference. Operators don’t need 40 KPIs. You need one page for throughput, quality, speed, options, resilience. The six metrics in the graphic are that page. Here’s how to turn them into decisions this week: Start now 1️⃣ Queue Length → Track waiting work at each step (sales, design, QA, shipping). ↳ Quick math: Cycle time ≈ WIP ÷ throughput 🧠 ↳ Trigger: any step >1.5× its 4‑week median for 3 days. ↳ Move: set WIP limits and swarms to unblock. 2️⃣ Rework Rate → Rework ÷ total completed. First‑pass yield is 1 − rework. ↳ Split by source (spec, process, training). ↳ Move: add checklists; pair review the top 3 drivers. 3️⃣ Escaped Defects → Customer‑found issues, by severity. ↳ Add “time to contain” alongside the count. ↳ Move: pre‑release check gates; fix‑forward playbooks. 4️⃣ Time to Decision → Days from issue to committed choice. ↳ Classify by decision type: reversible vs one‑way door. ↳ Move: set SLA by level (e.g., L1 24h, L2 3d) and escalate. 5️⃣ Option Value Created → Count rights without obligation: second suppliers, alternate channels, modular parts, cancellable contracts. ↳ Also track cost to hold and shelf‑life. ↳ Move: kill stale options monthly. 6️⃣ Buffer Coverage → Days of cash runway, critical inventory, and redeployable capacity within 1 week. ↳ Guardrails: min to survive, max to avoid drag. ↳ Move: pre‑plan cuts and pivots so buffers buy time. 💡 Cadence → 30‑minute weekly “Flow & Faults.” ↳ Look left‑to‑right: queue → rework → defects → decisions → options → buffers. ↳ Ask: Where are we stuck? What changed? What will we try? 💡 Anti‑gaming pairs → Queue Length with Throughput. → Rework with First‑pass yield. → Escaped Defects with Time to contain. → Buffers with Opportunity cost. 💡 Fast setup → Start in a spreadsheet or your current tool. ↳ Pull counts from boards, CRM, ERP. ↳ Keep one‑click charts; talk trends, not decimals. This is the playbook operators and founders use to ship under stress—what Operating by John Brewton breaks down weekly with checklists and case studies. ✅ Define each metric for one product or team and set a trigger. ✅ Build a one‑page view and schedule the weekly review. ✅ Make one change per week from what the metrics tell you. ♻️Repost & follow John Brewton for content that helps. ✅ Do. Fail. Learn. Grow. Win. ✅ Repeat. Forever. ⸻ 📬Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗in profile).

Over the past few weeks, I've frequently been asked by the leaders I’m advising to share key performance indicators (KPIs) that organizations should consider as they embark on their supply chain digital transformation journeys. This has sparked important conversations about the necessity of aligning metrics with specific organizational goals and execution strategies. It also opened the door for candid conversations as to the need to engage staff in the process to allow for organizational enrollment which is crucial to long term success as it fosters a shared understanding of what success will look like and helps ensure that the selected metrics are tailored to your unique business context. Given the frequency of these requests, I thought it might be helpful to many to share a few common starting points for organizations to consider with the understanding that these must be tweaked for your own journey and this list is certainly not exhaustive… Digital Adoption Rate: Track the extent to which supply chain processes have been digitized, indicating progress in transformation. Order Fulfillment Rate: Track the percentage of customer orders fulfilled on time and in full. Inventory Turnover: Measure how frequently inventory is sold and replaced, highlighting efficiency in inventory management. Supply Chain Cycle Time: Assess the total time from order initiation to fulfillment, revealing areas for improvement. Perfect Order Rate: Evaluate the percentage of orders delivered on time, complete, and undamaged. Cost to Serve: Understand the total costs associated with fulfilling customer orders, including logistics and overhead. Forecast Accuracy: Monitor how closely your demand forecasts align with actual sales to enhance planning. Return on Supply Chain Investments (ROSI): Measure the financial returns from your investments in supply chain technologies and processes. Supplier Lead Time: Analyze the average time taken by suppliers to deliver goods, impacting your operations. Customer Satisfaction Score (CSAT): Gauge how satisfied customers are with product availability and order fulfillment. Curious to know what others think of this list as well…. #SupplyChain #DigitalTransformation #KPIs #Leadership #BusinessGrowth

From Cost Savings to Total Value In my last post, I shared how the next evolution of Six Sigma isn’t just about reducing defects—it’s about unlocking intelligence, enabling outcomes, and driving joint innovation. That shift changes everything. Because in today’s supply chains, efficiency alone is no longer a competitive advantage. What matters now is total value. Total value is the difference between an optimized process and a strategic business advantage: ✅ Inventory visibility that improves service levels, not just audits 🕒 Cycle time gains that unlock capital, not just shave hours 🔐 Error reduction that protects margin and customer trust 🔄 Cross-functional insights that influence planning, procurement, and finance 🤝 Collaborative intelligence that enables transformation, not just automation 📊 And the data supports it: A Deloitte study found that companies with end-to-end supply chain visibility are 2x more likely to outperform on both revenue and profitability. We’ve spent decades chasing cost. But cost savings are just one dimension of impact. The best operators today don’t just eliminate waste—they generate leverage. Warehouse Intelligence represents that shift: From efficiency → intelligence From cost → value From process excellence → business advantage #TotalValue #WarehouseIntelligence #SixSigmaNext #SupplyChainLeadership #OperationalStrategy #Verity #IntelligentAutomation

Your supply chain isn't just a list of vendors. It's a network, so treat it like one. Traditional supply systems struggle to map complex global relationships. Graph technology transforms how organizations visualize, analyze, and secure their interconnected supply networks. Here are eight ways: 🔍 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 ↳ Graphs enable comprehensive tracking of every supplier, component, and transaction across your entire network.  ↳ This unprecedented visibility allows security teams to uncover hidden risks and dependencies. 🛡️ 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 ↳ Graphs provide the ability to model potential disruptions and instantly identify alternative suppliers or distribution routes.  ↳ By simulating failure scenarios, organizations can develop robust contingency plans before disruptions occur.  🕸️ 𝗖𝘆𝗯𝗲𝗿 𝗧𝗵𝗿𝗲𝗮𝘁 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 ↳ Graph analytics map potential attack pathways to identify vulnerable suppliers and IT systems within your supply ecosystem.  ↳ This network-centric approach reveals how compromised vendors could create cascading security failures.  ⛓️ 𝗖𝗼𝘂𝗻𝘁𝗲𝗿𝗳𝗲𝗶𝘁 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻 ↳ Graph databases enable precise tracing of component origins and flag anomalous patterns in supplier relationships.  ↳ By analyzing historical transaction patterns, organizations can detect suspicious variations. ⚠️ 𝗦𝗶𝗻𝗴𝗹𝗲 𝗣𝗼𝗶𝗻𝘁𝘀 𝗼𝗳 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 ↳ Graph algorithms quickly identify critical suppliers or components that could cripple operations if compromised.  ↳ This capability helps prioritize security investments toward the most vulnerable nodes in your supply network. 🔎 𝗔𝗻𝗼𝗺𝗮𝗹𝘆 & 𝗥𝗶𝘀𝗸 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 ↳ Advanced clustering and centrality algorithms applied to supply chain graphs uncover unusual patterns that traditional systems miss.  ↳ These sophisticated analytics can detect emerging threats before they materialize into security incidents. 📋 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 & 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 ↳ Graph technology efficiently links compliance data to transactions throughout the supply chain.  ↳ This integration ensures all partners meet required security standards across jurisdictional boundaries.  ⚡ 𝗥𝗮𝗽𝗶𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 & 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 ↳ When disruptions occur, graph visualization enables teams to quickly trace impacts across the entire supply chain.  ↳ This capability dramatically reduces investigation time from days to minutes.  The question isn't whether you can afford to implement graph technology; 𝗶𝘁'𝘀 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗮𝗳𝗳𝗼𝗿𝗱 𝗻𝗼𝘁 𝘁𝗼. This is why at data² we have built the reView platform on the foundation of graphs, so that organizations can analyze connections and risk deep in their supply chain. ♻️ Know someone struggling with supply chain security? Share this post to help them. 🔔 Follow me Daniel Bukowski for daily insights about applying graphs and AI to national security.

I believe disruption isn’t a threat. It’s a signal. A catalyst. With the right intelligence layer, the right tools, and a culture of continuous reinvention, we’re not just navigating volatility. Predict Disruption. Fuel Growth. In the logistics industry, we operate in a world where disruption is constant. Geopolitical instability, climate volatility, and economic uncertainty can cripple operations overnight. Traditional playbooks can’t keep up. But what if, instead of reacting to volatility, we could anticipate it—and use that foresight to drive growth? We’re entering a new phase in supply chain leadership: one defined by intelligent orchestration powered by generative AI, cloud-native infrastructure, and real-time data. This isn’t theoretical. It’s already reshaping how the most forward-thinking organizations operate—and we intend to lead from the front. From Reactive to Predictive: Enabling AI Decision Support In the Supply Chain industry, we’re leveraging generative AI not just to answer questions but to inform decisions. AI copilots are helping our teams process vast volumes of structured and unstructured data in real time, surfacing high-value insights from across our network. Need to know which supplier is driving delays? What external risk—weather, macroeconomics, labor, transport—is most likely to impact a lane or warehouse? AI assistants can pull those signals instantly and suggest next-best actions. This is how we reduce cycle time from insight to execution. Operational Intelligence at Scale Our strategy goes beyond dashboards. We’re embedding gen AI directly into our operational layer. These AI agents don’t just observe—they act. They automate routine workflows, flag anomalies, and suggest process redesigns based on transaction history, past outcomes, and evolving KPIs. This creates a self-optimizing loop—one where supply chain intelligence is continuous, and workflows dynamically adjust to changing realities on the ground. Simulating the Future, Not Just Reporting the Past Through virtual modeling and digital twins, we can simulate scenarios before they occur. Picture this: real-time data flowing in from drones, robotics, IoT, and WMS systems, visualized across a geo-aware orchestration layer. We can watch disruptions unfold in real time—or simulate future disruptions and test mitigation strategies in advance. This capability is invaluable not just for fulfillment accuracy but also for product lifecycle visibility, waste reduction, and meeting sustainability targets. GXO isn’t just optimizing for today—we’re engineering the supply chain of tomorrow. Putting Disruption to Work So what do we do with this capability? We operationalize it. We define what success looks like (not vanity metrics—true operational impact). We identify friction points between analysis and action. We evaluate architectural gaps continuously. We align AI-powered supply chain transformation with commercial outcomes & customer expectations.

Supply chain planning cannot breathe without metrics. This infographic shows 7 critical metrics: # 1 - WMAPE (Weighted Mean Absolute Percentage Error) ↳ (SUM(ABS(Forecast - Actual)) / SUM(Actual)) * 100 ↳ Measures forecast accuracy. Lower = better.   # 2 – Bias ↳ (SUM(Forecast - Actual) / SUM(Actual)) * 100 ↳ Shows if forecasts over- or under-estimated demand   # 3 – OTIF ↳ (SUM(Orders Delivered On Time and In Full) / SUM(Total Orders)) * 100 ↳ Service level; orders delivered as promised   # 4 - Inventory Turnover ↳ SUM(COGS) / ((Beginning Inventory + Ending Inventory) / 2) ↳ How fast inventory is sold and replaced   # 5 Plan Attainment ↳ (SUM(Actual Output) / SUM(Planned Output)) * 100 ↳ Execution vs. plan reliability   # 6 - Cash conversion cycle (CCC) ↳ CCC = DIO + DSO – DPO ↳ DIO = (Average Inventory / SUM(COGS)) * 365 ↳ DSO = (Accounts Receivable / SUM(Revenue)) * 365 ↳ DPO = (Accounts Payable / SUM(COGS)) * 365 ↳ Days to turn cash outflows into inflows   # 7 - EBITDA ↳ Net Income + SUM(Interest) + SUM(Taxes) + SUM(Depreciation) + SUM(Amortization) ↳ Profit from core operations Any others to add?

Key Takeaways: 1) Distinguish KPIs from Metrics: KPIs is a metric- but not all metrics are KPIs. A KPI is a strategic metric that: - Directly supports your organization’s top-level goals - Has clear executive buy-in and ownership - Cascades effectively to operational and individual levels ➤ Focus on the cross-functional KPIs that are applicable at all levels and aligned with the strategic goals 2) Adopt a Tiered KPI Framework: Use a 3-tier system to connect strategic priorities to day-to-day actions: - Tier 1: Strategic KPIs aligned with corporate objectives - Tier 2: Diagnostic metrics for root cause analysis - Tier 3: Operational metrics for team and individual accountability ➤ This hierarchy enables faster insight, alignment, and corrective action. 3) Move from Reporting to Action: Dashboards and scorecards aren’t just tools — they are part of your governance engine. - Use them to monitor, analyze, and respond, not just to report - Make data transparency and regular performance reviews a habit, not a chore 4) Accelerate with GenAI & ML: Next-gen technologies can supercharge KPI governance by: - Detecting anomalies and trends earlier - Automating analysis and forecasting - Providing actionable insights before issues escalate ➤ These tools enable proactive performance management, not just reactive correction. 📚 Reference: To dive deeper into Effective KPI Governance and Performance Measurement see: “Realizing Value from Digital/Gen AI/ML-Driven Supply Chain Planning Transformations” https://lnkd.in/g6JbA6Mf

Evaluating my teams’ risk mitigation plans for the recently increasing wildfire spread in Minnesota (and elsewhere), it got me to thinking. As climate accountability ramps up globally (fun fact: over 50 jurisdictions now requiring climate competence at the board level) the ripple effects on supply chains are impossible to ignore. 🌍 Modern supply chain leaders aren’t just optimizing for cost and speed—we’re now at the front lines of climate risk and resilient operations. So what’s changing? • 🌱 Sustainability is becoming a supply chain KPI—carbon tracking, circular materials, and alternative energy sources are par for the course. • 🔍 Traceability is no longer optional—regulators, stakeholders, and consumers all expect transparency from origin to shelf. • ⚠️ Resilience means rethinking global dependencies—extreme weather, geopolitical shifts, and ESG compliance are driving redesigns. One of the things I spend a lot of my time on is how we build supply chains that are efficient but not fragile. Those goals can often be in tension with one another (after all “just in time” is only great if it actually gets there!), and it creates big challenges for the next generation of leaders to solve. I like the challenge (but I’m a glutton for punishment ;) ). Curious how others are thinking about building resilient, yet relentlessly efficient, supply chains given these challenges.