Data Analytics for Disruption Response

𝓦𝓱𝓮𝓷 𝓹𝓪𝓷𝓲𝓬-𝓫𝓾𝔂𝓲𝓷𝓰 𝓼𝔀𝓮𝓹𝓽 𝓪𝓬𝓻𝓸𝓼𝓼 𝓽𝓱𝓮 𝓰𝓵𝓸𝓫𝓮 𝓲𝓷 𝓮𝓪𝓻𝓵𝔂 2020, 𝓻𝓮𝓽��𝓲𝓵𝓮𝓻𝓼 𝔀𝓮𝓻𝓮 𝓫𝓵𝓲𝓷𝓭𝓼𝓲𝓭𝓮𝓭 𝓫𝔂 𝓮𝓶𝓹𝓽𝔂 𝓼𝓱𝓮𝓵𝓿𝓮𝓼 𝓪𝓷𝓭 𝓫𝓻𝓸𝓴𝓮𝓷 𝓼𝓾𝓹𝓹𝓵𝔂 𝓬𝓱𝓪𝓲𝓷𝓼. 𝓦𝓪𝓵𝓶𝓪𝓻𝓽? 𝓣𝓱𝓮𝔂 𝓱𝓪𝓭 𝓪 𝓷𝓸𝓽-𝓼𝓸-𝓼𝓮𝓬𝓻𝓮𝓽 𝓮𝓭𝓰𝓮: 𝓭𝓪𝓽𝓪 𝓪𝓷𝓪𝓵𝔂𝓽𝓲𝓬𝓼. Walmart’s Data-Led Response to Pandemic Panic 🔍 Real-Time Inventory Intelligence By leveraging predictive models, Walmart tracked SKU-level movement across thousands of stores—restocking in real time, right where it mattered most. 🔍 Agile Supplier Collaboration Data helped forecast supply-side disruptions, enabling Walmart to reroute shipments, adjust SKUs, and keep shelves stocked. 🔍 Empowered Local Decision-Making Instead of waiting for top-down instructions, store managers used localized data to act fast—serving real needs in real time. The result? While others ran out, Walmart stepped up—ensuring availability, reducing chaos, and reinforcing customer loyalty. 📌 Takeaway: In a crisis, data isn't just a strategy tool—it’s an execution engine. 💬 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒓𝒆𝒂𝒍-𝒕𝒊𝒎𝒆 𝒅𝒂𝒔𝒉𝒃𝒐𝒂𝒓𝒅𝒔 𝒐𝒓 𝒅𝒂𝒕𝒂-𝒍𝒆𝒅 𝒐𝒑𝒔 𝒊𝒏 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔? 𝑯𝒐𝒘 𝒉𝒂𝒗𝒆 𝒕𝒉𝒆𝒚 𝒉𝒆𝒍𝒑𝒆𝒅 𝒚𝒐𝒖 𝒏𝒂𝒗𝒊𝒈𝒂𝒕𝒆 𝒖𝒏𝒄𝒆𝒓𝒕𝒂𝒊𝒏𝒕𝒚? #WalmartCaseStudy #CrisisResponse #SupplyChainAnalytics #DataDrivenDecisionMaking

Data and tariffs – what can data leaders do? You might assume there's little data leaders can do in the short term to help deal with the impacts of changing tariff policies in the US. That is absolutely 𝐧𝐨𝐭 the case. There are plenty of things they can do - or arguably - should have already done.  We learned many of these lessons in the global pandemic – as there were drastic impacts to the demand and supply sides of our businesses that could have been significantly mitigated with better data (and data management). The impacts to supply chains then, are not unlike what we're seeing unfold now. So, if you are in a position of influence in a data and analytics function, I recommend you quickly work to more deeply understand: 1.  Your customer relationships & behaviors 2.  Any dependencies or risks in your supply chains 3.  All product and material / ingredient related data What do all these things have in common? They are require a focus on 𝐦𝐚𝐬𝐭𝐞𝐫 𝐝𝐚𝐭𝐚 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 – as it's literally the data running the most critical aspects of your business. Unfortunately, most companies I’ve worked with struggle with creating ‘360’ views of these critical data domains, thanks to data silos that are running amuck. In a time of global disruption, this lack of visibility on your customers, products, and supply chain is creating massive risks for your business. If you’re one of these companies, then figuring out how to solve your MDM problem – quickly – must be a top priority.  Some things to consider: ✅ 70% of a customer/supplier master is better than none.  Perfection is the enemy. ✅ Forget that data cleanup, and forget doing a physical system consolidation. You don't have time, and besides, they aren't a hard requirement to create actionable insights. ✅ Use third party data to help accelerate your efforts. ✅ Forget that elaborate data governance framework or operating model.  You don’t have time for either. Develop a maniacal focus on using an analytical MDM to provide more accurate and complete product, customer, supplier, and material related data. ✅ Don't bother with that expensive maturity assessment from a top tier consultant. It won't help you much, and spoiler alert - I can probably already guess you're a maturity of 2 - 2.5 (out of 5) on most data management capabilities. ✅ Data catalogs are great, but you don't need one to solve your problem. Chances are, you already know where the most relevant master data is within your ecosystem. ✅ Yes, you optimally need business engagement on MDM - but for many, the risks here are existential. Bold CDOs should be ready to move quickly and confidently - and seek forgiveness later. Analytical styles of MDM can be deployed in weeks, not years - if you do things the right way. During Covid, too many companies were caught flat-footed with a lack of master data insights. Will that be you this time around? #cdo #masterdata #mdm

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.

𝗢𝗦𝗜𝗡𝗧 𝗶𝗻 𝗗𝗶𝘀𝗮𝘀𝘁𝗲𝗿 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 With Hurricane Melissa expected to impact Jamaica later today, it is a good time to look at how OSINT supports disaster response and recovery in real operational terms. In a crisis, information rarely comes through a single channel or in a structured format. OSINT allows responders and analysts to make sense of what is already available, such as public data, satellite imagery, local reporting, and social media updates, and turn it into something usable. There are a few clear ways OSINT can make a difference during a disaster: 𝟭. 𝗦𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀: Public posts, local media, and visual content can be geolocated to show where flooding, storm surge, or landslides are occurring. This gives emergency operations centers an early understanding of the scale and direction of impact. 𝟮. 𝗗𝗮𝗺𝗮𝗴𝗲 𝗠𝗮𝗽𝗽𝗶𝗻𝗴: Satellite and drone imagery combined with crowd-sourced photos and reports can be used to mark damaged areas, blocked routes, or isolated communities. It provides a fast visual reference before full assessments are available. 𝟯. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: Open data from utilities and transport networks can be layered with weather and flood models to identify areas likely to lose power or connectivity. 𝟰. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴: Verified public posts about shelter availability, missing persons, or road conditions can be consolidated to support coordination between agencies and NGOs. 𝟱. 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: Post-event OSINT helps compare pre- and post-impact imagery, monitor recovery progress, and identify ongoing disruptions to logistics or access. As Hurricane Melissa moves across Jamaica, OSINT workflows can support:  • Continuous monitoring of social and local reporting for flood indicators and infrastructure damage  • Mapping visual evidence from public sources to identify priority areas for response  • Cross-referencing data against infrastructure maps to confirm accessibility and service status  • Sharing verified situational data with partners and emergency coordination teams OSINT is most effective when it turns public information into a shared operational picture that supports decisions on the ground. As Melissa makes landfall, that ability to collect, validate, and visualize information from open sources will be essential to understanding conditions and directing response resources where they are needed most. For those monitoring Hurricane Melissa or supporting response efforts, a number of open tools can assist with real-time mapping, verification, and coordination. Copernicus EMS already has a mapping and reporting hub set up here: https://lnkd.in/gtZmjJ4Z Information saves time, and time saves lives. #OSINT #DisasterResponse #CrisisMapping #EmergencyManagement #HumanitarianIntelligence #SituationalAwareness #HurricaneMelissa

Back when I was working as a project manager at BAT, I found myself in a situation where a delay in one SKU component forced me to map out exactly what could be impacted, how many days of delay to launch, which tasks in the Gantt plan would slide, what knock-on effects ripple across production and market windows. I remember thinking: I don’t want to keep chasing impacts. If a system could tell me ahead of time which component in the BOM is likely to slip and by how much plus shows how that will push the go-live date, I could spend less time diagnosing and more time fixing. That’s where the leap from visibility to predictability changes the game. Imagine a connected architecture where your AI engine sits on top of your ERP (e.g. SAP), consumes real-time ETA and lead time data per individual SKU, flags which parts in the BOM are trending late, and directly ties that into your Gantt / project schedule logic, projecting how much your launch might slip. Instead of scrambling to assess what’s broken, you get alerts telling you what’s next. Then you can focus your energy on solutions. Recently I read an article titled “AI is reshaping the supply chain and IBP” which talks about how predictive intelligence and integrated planning are changing how we preempt disruptions. As I read, I couldn’t help but think of that BAT moment and how far ahead we’d be if more supply chains had systems that do the heavy lifting of impact analysis before the crisis. Article Link: https://lnkd.in/d8xvDRbE #SupplyChain #AI #PredictiveAnalytics #ERP #SAP #Operations #ProjectManagement #Innovation

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.