Ocado, the British company that started as an online supermarket, rebuilt the whole problem from the ground up. Here is how the system works in plain terms: A) Groceries sit in crates stacked in a dense 3D grid called "The Hive". B) Thousands of robots, each about the size of a washing machine, race across the top of the grid at nearly 9 miles per hour, within 5 millimeters of each other. C) An AI "air traffic control" system talks to every robot ten times a second to plan routes and prevent collisions. D) The bots pull the right crates and bring them to picking stations, where robotic arms with machine vision now handle the final pick. E) The same system runs on digital twins, so Ocado tests and improves a warehouse virtually before changing anything physical. The most interesting part is the business model. Ocado does not just run its own warehouses. It licenses the entire system to grocers worldwide, including Kroger in the US, Coles in Australia, and chains across Europe. It became a robotics company that happens to sell groceries. Three takeaways for operations leaders: 1) When the economics do not work, rebuild the process, not the price. Ocado did not cut corners. It redesigned fulfillment from scratch. 2) Specialization beats general-purpose automation. Ocado wins precisely because it solved grocery, not warehouses in general. 3) Your hardest internal problem can become your product. Ocado turned its own fulfillment headache into a global licensing business. What is the most expensive, manual bottleneck in your operation, and could solving it become something you sell to others? If you want, you can see the video on how Ocado works (Video credit to Business Insider):youtube.com/watch?v=4DKrcpa8… #SupplyChain #Operations #Ocado #Robotics #WarehouseAutomation #Grocery #AI #Industry40 #Fulfillment #DigitalTwin #RetailOperations #OperationsManagement

𝘚𝘵𝘢𝘳𝘣𝘶𝘤𝘬𝘴 𝘴𝘩𝘶𝘵 𝘪𝘵𝘴 $20 𝘮𝘪𝘭𝘭𝘪𝘰𝘯 𝘧𝘭𝘢𝘨𝘴𝘩𝘪𝘱 𝘳𝘰𝘢𝘴𝘵𝘦𝘳𝘺 𝘪𝘯 𝘚𝘦𝘢𝘵𝘵𝘭𝘦. 𝘊𝘭𝘰𝘴𝘦𝘥 𝘩𝘶𝘯𝘥𝘳𝘦𝘥𝘴 𝘰𝘧 𝘭𝘰𝘤𝘢𝘵𝘪𝘰𝘯𝘴. 𝘈𝘣𝘴𝘰𝘳𝘣𝘦𝘥 $1 𝘣𝘪𝘭𝘭𝘪𝘰𝘯 𝘪𝘯 𝘳𝘦𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘪𝘯𝘨 𝘤𝘰𝘴𝘵𝘴. 𝘛𝘩𝘦 𝘵𝘳𝘪𝘨𝘨𝘦𝘳 𝘸𝘢𝘴 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘧𝘢𝘳 𝘢𝘸𝘢𝘺 𝘧𝘳𝘰𝘮 𝘢𝘯𝘺 𝘣𝘰𝘢𝘳𝘥𝘳𝘰𝘰𝘮: 𝘳𝘢𝘪𝘯 𝘵𝘩𝘢𝘵 𝘥𝘪𝘥 𝘯𝘰𝘵 𝘧𝘢𝘭𝘭 𝘪𝘯 𝘉𝘳𝘢𝘻𝘪𝘭. This is the story of how a weather event on one continent reshaped operations decisions across the entire global coffee industry, and it carries a lesson for anyone who depends on a single commodity. The chain of events: a) Multi-year drought in Brazil, source of nearly 40% of the world's coffee, wiped out close to 15% of global Arabica supply in the 2024-25 cycle. b) Vietnam, the top producer of Robusta, faced its worst drought in 70 years, then floods. c) Arabica futures hit $4.41 a pound in 2025, the highest in decades. d) US import tariffs of up to 50% on Brazil, Mexico, and Vietnam landed on a product that is 99% imported with no domestic alternative. Then came the twist. By early 2026, Brazil swung to a record harvest of roughly 70 million bags, and prices crashed back toward $1.20 a pound. From famine to flood, in under a year. How companies responded reveals two very different operations playbooks: a) Nestle used its multi-tier portfolio as a shock absorber, quietly moving price-sensitive buyers from premium Nespresso pods down to budget Nescafé soluble, keeping them inside the family instead of losing them. b) JDE Peet's passed a 19.5% price increase straight to consumers and watched volumes fall, a textbook price-volume trap. c) Starbucks absorbed the shock through restructuring rather than pricing, protecting the brand at the cost of margin Three takeaways for operations leaders: 1) A portfolio is a hedge. Nestle could move customers across price tiers because it built the range before the crisis, not during it. 2) Passing through every cost increase is not a strategy. JDE Peet's shows the limit of pricing your way out of a supply shock. 3) Volatility, not just shortage, is the real enemy. The whipsaw from $4.41 to $1.20 hurts planning more than a steady high price ever would. If your most important input doubled, then halved within a year, would your business be built to ride that swing, or be broken by it? Image Credit: @BusinessInsider @Reddit Patch.com #SupplyChain #Operations #Coffee #Starbucks #Nestle #CommodityRisk #DemandPlanning #FMCG #SupplyChainResilience #Industry40 #OperationsManagement #FoodSupplyChain

Most retailers bet on what shoppers will want a year from now. Zara stopped guessing. It reads what is selling today and restocks twice a week. This is one of the most quietly powerful operations stories in retail, and in early 2026, it got sharper. For decades, the fashion industry ran on long forecasts. Designers guessed a year ahead, factories in Asia produced in huge batches, and whatever did not sell ended up on the markdown rack. Big bets, long lead times, high waste. Zara's parent company, Inditex, built the opposite system and has now wrapped it in what it calls "Quiet AI." No flashy chatbots. Just AI woven into the supply chain itself. Here is the operations problem it solves: A) Fashion demand is almost impossible to predict far ahead. Trends move in weeks, not seasons. B) Before its full RFID rollout, Zara's store inventory accuracy sat around 65%. Roughly one in three items was misplaced, miscounted, or invisible to the system. C) Forecasting harder does not fix a problem this volatile. Reacting faster does. How Inditex solved it: a) Item-level RFID ("soft tagging," built with Intel) now tracks 100% of garments in real time across 5,600 stores. b) AI demand forecasting drives around 85% of initial production allocation decisions. c) AI trend detection scans social media, runways, and street style to spot trends 3 to 4 weeks faster than traditional methods. d) Nearshoring in Spain, Portugal, Morocco, and Turkey keeps design-to-rack time near two weeks. e) €1.8 billion invested in tech and logistics across 2025 to 2026. The result is a "test and react" model: make small bets, read real demand, and restock what actually sells. Three takeaways for operations leaders: 1) When demand is volatile, speed of reaction beats accuracy of forecast. You cannot predict a trend, but you can respond to one. 2) Visibility is the foundation. You cannot react to what you cannot see. RFID fixed the 65% blind spot first, then AI made it smart. 3) Small, frequent bets beat large, infrequent ones. Lower inventory risk, less markdown, faster learning. If your demand shifted next week, would your supply chain react in weeks, or would it still be working off last year's forecast? Image credit: RadiusDigital, ShanghaiGarment, @ETBrandEquity #SupplyChain #Operations #Zara #Inditex #FastFashion #RFID #AI #DemandForecasting #RetailOperations #Industry40 #InventoryManagement #Nearshoring #OperationsManagement

American farms are short ~2.5 million workers every year. John Deere's answer: a tractor that drives itself, controlled from a phone. Now rolling out across 18 states. Goal with NVIDIA: fully autonomous corn and soy by 2030. A 189-year-old company just became an AI company. #JohnDeere #PrecisionAgriculture #AI #Automation #Industry40

Pop Mart sold over 100 million Labubu dolls last year. Its hardest supply chain problem is not making too few. It is making too many. This is one of the most counter-intuitive operations stories of 2026, and it breaks a rule most of us were taught. For decades, the supply chain goal was simple: meet demand. Make enough. Avoid stock-outs. Empty shelves were a failure. Pop Mart, the Chinese maker of the viral "blind box" Labubu collectibles, lives in the opposite world. Its entire business model runs on engineered scarcity. You do not know which figure is inside the box. Supply is deliberately limited. The hunt is the product. That model created staggering results: A) Labubu drove sales past 100 million units worldwide in 2025. B) Pop Mart scaled to 30 million plush units a month by late 2025, a tenfold jump in a year. C) Revenue up 204%, net profit up 396% in the first half of 2025. D) The stock rose more than 200%, valuing Pop Mart above Mattel and Sanrio. Then the paradox hit. When Pop Mart made more to meet demand, resale prices fell over 50%, and the exclusivity that drove the hype started to fade. When it made too little, counterfeits exploded. Chinese customs seized 1.8 million fake Labubus in 2025 alone, many produced on 3D printers, some with unsafe materials. So Pop Mart is now solving a problem most companies never face: how to make exactly the right amount. Its response uses real Industry 4.0 tools: a) Regional production redundancy: new partner factories in Mexico, Cambodia, and Indonesia to shorten restock cycles without flooding the market. b) Blockchain authentication to separate real from fake. c) Demand sensing to balance scarcity against frustration. d) Portfolio diversification beyond Labubu (Crybaby, Skullpanda, Nyota) to reduce single-character risk Three takeaways for operations leaders: 1) More supply is not always the goal. For some products, availability and value move in opposite directions. 2) Scarcity is a strategy until it invites counterfeits. The gap you leave in the market gets filled by someone, legally or not. 3) Matching supply to demand is harder than maximizing either one. The precise middle is the hardest place to operate. If your product suddenly went viral tomorrow, would you know how much to make, or would you simply make as much as you could? Image credit: @NBCNews , @NPR , @BusinessTimes , @guardiannews #SupplyChain #Operations #PopMart #Labubu #DemandPlanning #InventoryManagement #Manufacturing #Counterfeiting #Blockchain #Industry40 #RetailOperations #OperationsManagement

Less than 20 months after its first car rolled off the line, Hyundai's $7.6 billion Georgia factory built its first Kia this month. Most car plants cannot do that. This week, Hyundai Motor Group Metaplant America (HMGMA) in Bryan County, Georgia, launched the Kia Sportage Hybrid. It is the plant's third vehicle, its first Kia model, and its first hybrid. All from the same building. For decades, the rule in auto manufacturing was rigid. One plant. One model family. One propulsion type. Switching costs were so high that retooling for a new vehicle was a multi-year project. The Metaplant was designed to break that rule. What makes it different: A) 11 buildings, 7.5 million square feet, the largest dedicated EV and hybrid plant in North America. B) Capacity rising from 300,000 to 500,000 vehicles a year (Tesla's Texas Gigafactory tops out at 375,000). C) Robots from Boston Dynamics on the line, alongside AI systems and real-time data tracking for every vehicle and component. D) Solar panels over 1,900 parking spaces generating ~5% of plant electricity, with a 100% renewable energy target. E) Battery joint ventures with LG Energy Solution and SK On on adjacent sites. F) A new steel mill in Louisiana completes the ecosystem. G) $21 billion new US investment through 2028, on top of $20.5B already committed. The deeper story is not the scale. It is the flexibility. The same line now produces three vehicles across Hyundai, Kia, and Genesis brands, and across full-electric and hybrid powertrains. This is what Industry 4.0 actually looks like when it works: not robots replacing people, but factories that can change what they build in weeks instead of years. Three reflections for operations leaders: 1) Single-product plants are becoming a liability. When demand patterns shift faster than rebuild cycles, flexibility beats throughput. 2) Vertical integration with partners is the new model. Hyundai does not own LG Energy Solution or SK On. They sit next door, share data, and respond as one system. 3) Tariff hedging is now operations strategy. Building local capacity is no longer just a political signal. It is a margin-protection lever. If your facility had to produce something completely different next quarter, how long would it take, and what would break first? Image credit: @HMGnewsroom, @ajc. @HyundaiMotorCompany, @HMGMetaplant #SupplyChain #Operations #Manufacturing #Hyundai #Kia #SmartFactory #EVSupplyChain #Industry40 #FlexibleManufacturing #LGEnergySolution #SKOn #BostonDynamics #USManufacturing #OperationsManagement

TSMC just put $165 billion into the Arizona desert. Apple is buying over 100 million chips from them this year. The factory did not exist three years ago. This is the most ambitious supply chain reshoring effort in modern history, and it tells a very different story than the political headlines suggest. For 25 years, the rule was simple. Design in California. Built in Asia. TSMC, headquartered in Taiwan, makes about 90% of the world's most advanced semiconductors. Almost all of them on a single island. Then came the AI boom, the Taiwan Strait risk, and the tariff regime. Suddenly, "single point of failure" was not an academic phrase. It was a quarterly earnings risk. What Apple is actually doing is the part most people miss. The company is not just reshoring final assembly. It is rebuilding the entire chip stack inside the US, layer by layer: A) Glass for displays: Corning in Kentucky. B) Silicon wafers: GlobalWafers in Sherman, Texas. C) Front-end fabrication: TSMC's Arizona campus (six fabs, 2,000 acres). D) Advanced packaging: TSMC in Arizona and Houston (the new bottleneck for AI chips). E) Rare-earth magnet recycling: California Apple's COO Sabih Khan has framed this clearly: focus on the layers that make Apple "Apple," and bring those home. The supporting cast tells the bigger story: a) TSMC Q1 2026 profits up 35.1% year on year. b) Hyperscalers (Microsoft, Google, Amazon, Meta) are spending $700 billion on AI infrastructure in 2026. c) Apple has secured over 50% of TSMC's initial 2nm capacity. d) Reshoring brings new fragilities, though: a helium shortage in early 2026 hit semiconductor fabs hard Three takeaways for operations leaders: 1) True reshoring is layered, not single-step. Moving the final assembly is symbolic. Moving raw materials, packaging, and tooling is structural. 2) Concentration risk is a slow-moving cost. It looks efficient on the spreadsheet until the day it does not. 3) Reshoring creates new dependencies. Helium, water, labor, power, all become local risks instead of foreign ones. If you had to rebuild your supply chain from raw materials up, where would you start? image credit- @tomshardware, @InBusinessPHX #SupplyChain #Apple #TSMC #Reshoring #Semiconductors #Manufacturing #Operations #VerticalIntegration #USManufacturing #AI #Industry40 #OperationsManagement

Novo Nordisk spent $16.5 billion buying a contract manufacturer to fix its weight-loss drug supply problem. Then, in January 2026, it launched a pill that does not need any of that infrastructure. This is one of the most expensive supply chain pivots in pharma history. And it tells you everything about how fast operations strategy has to move in 2026. The story so far: a) Wegovy and Ozempic injections need to be kept cold at every step from factory to patient. That means specialized cold-chain storage, sterile fill-and-finish lines, and refrigerated logistics. b) Demand exploded. For two years, both Novo Nordisk and Eli Lilly were on the FDA shortage list. c) Novo Nordisk bought CDMO Catalent for $16.5 billion to lock in fill-and-finish capacity. Eli Lilly poured $9 billion into a new Indiana manufacturing campus. d) Then Novo launched the first oral GLP-1 pill for obesity (Rybelsus 25mg), with Lilly's orforglipron pill on the way. The pill changes the supply chain math entirely. No refrigeration. No sterile injectables. No specialized cold-chain logistics. A normal pharmaceutical packaging line can produce it. And it can be shipped to remote areas, low-income markets, and rural pharmacies that cold-chain injections could never reach. For Eli Lilly and Novo Nordisk, this opens up roughly 40% of the world's population (China, India, Brazil, Canada, Turkey), where patents are also nearing expiry. Three takeaways for operations leaders: 1) Yesterday's bottleneck can become tomorrow's stranded asset. Cold-chain capacity was the right investment for the injection era. The pill era values different things. 2) Vertical integration is insurance, not a strategy. Novo's Catalent deal still made sense as a hedge. But owning capacity does not protect you from product-level innovation that changes what capacity you need. 3) The best supply chain decisions answer the next product, not the current one. Operations and R&D need to be in the same room far more often than they usually are. If the next version of your product needed half the infrastructure you have built, would your supply chain be ready, or stranded? #SupplyChain #Operations #Pharma #EliLilly #NovoNordisk #ColdChain #GLP1 #Manufacturing #VerticalIntegration #OperationsManagement #Industry40 #HealthcareSupplyChain #PharmaSupplyChain

BMW has 3D-printed over 1.6 million parts since 2020. That number stopped being a science experiment a while ago. What caught my attention isn't the headline. It's the quieter line underneath: BMW plans to start series production using wire arc additive manufacturing from 2027. Series production. Not prototypes, not jigs, not one-off tooling, actual parts going into actual cars, at volume. The first is BMW's IDAM project (Industrialization and Digitization of Additive Manufacturing), which successfully implemented a fully automated, digitally networked 3D printing production line for automotive series processes Here's why that matters from an operations lens. For two decades, 3D printing's real job in automotive was speed: print a fixture overnight, test a concept by Friday. It lived next to the assembly line, not on it. Moving to series production means it now has to meet the part of manufacturing that's genuinely hard, repeatability. A printed bracket has to be identical the 50,000th time, certified, traceable, and costed against a stamping press that's been optimized for 60 years. That's the problem most people skip past. The hard question in additive isn't "can we print it?" It's "can we qualify it?" Process variation in metal printing, thermal history, porosity, and residual stress is far messier to control than in a die that simply repeats its own geometry. The bottleneck has quietly shifted from the printer to the inspection and qualification pipeline behind it. So the interesting race isn't who prints the most parts. It's who builds the quality-assurance and certification infrastructure to trust those parts at scale. BMW's Oberschleißheim campus is really a bet on that backend, not on the machines. Which makes me wonder: in five years, will additive's competitive edge be the printers themselves, or the data and qualification systems nobody puts in the brochure? #supplychain #operationsmanagement #industry40 #additivemanufacturing #3dprinting #manufacturing #automotive #digitalmanufacturing #BMW #operations

The owner of Oreo and Cadbury just said the quiet part out loud: it is bringing more manufacturing and packaging back in-house. For decades, the rule was simple. Outsource what is not your core. Pay specialists to make it, package it, and ship it. Cheaper, faster, leaner. This month, Mondelez International quietly broke that rule. Speaking to investors, COO and CFO Luca Zaramella said the company plans to bring more manufacturing and packaging work back inside its own four walls. The reason he gave was direct: it will save quite a bit of money. The deeper context is what makes this interesting: A) Mondelez has been hit hard by cocoa price shocks, a major input it cannot fully control. B) Iran war ripple effects continue to push up input and delivery costs across the industry. C) The company is investing in cell-cultured and fermented cocoa as alternatives, an Industry 4.0 sustainability bet. D) The same week, the company elevated Zaramella to a combined COO and CFO role, putting operations and finance under one decision-maker This is not a one-company story. Across consumer goods, automotive, and electronics, the outsourcing pendulum is quietly swinging back. When external partners become a source of cost shocks rather than savings, owning the work starts to look cheaper again. Three simple takeaways for operations leaders: 1) Outsourcing decisions made in calm times need to be re-tested in stormy ones. What was efficient at one price level can become fragile at another. 2) Vertical integration is a hedge, not a step backward. When supply, prices, or partners turn unstable, in-house capacity buys you control. 3) Operations and finance belong at the same table. Mondelez putting both under one leader is a signal of how strategic supply chain decisions have become. Which parts of your operation are you outsourcing today that you would think twice about if you started over now? Image credited to @MDLZ #Mondelez #SupplyChain #Operations #VerticalIntegration #Manufacturing #ConsumerGoods #CocoaSupplyChain #Insourcing #OperationsManagement #Industry40 #BusinessStrategy #FMCG

Boeing keeps $14 billion in spare parts sitting in warehouses around the world. Most of it doesn't move for years. But the moment a plane is grounded, the clock starts. Every hour on the ground costs tens of thousands of dollars. So airlines stock more. Buffer everything. Hope the right part lands in the right place. That approach is starting to break down. Some manufacturers are now printing critical spare parts on demand using additive manufacturing right at the point of need. No giant warehouse. No cross-continental shipment. Just a certified digital file and a machine. GE Aviation already has 3D-printed fuel nozzles flying on commercial aircraft. Airbus prints over 1,000 components per plane. The technology is no longer the bottleneck. Here's what I keep coming back to as someone who researches this: The real problem isn't whether additive manufacturing can produce the part. It can. The question is which parts should you print, and when? Not everything makes sense to manufacture on demand. Getting that decision right using failure probability, demand patterns, and lead time data is where the actual value is created. Get it wrong, and you've traded one inventory problem for a production planning problem. That's the decision no one talks about. That's the problem worth solving. Image credit to - The Wall Street Journal #supplychain #additivemanufacturing #spareparts #manufacturing #operationsmanagement #3Dprinting #industry40 #aerospace #operations

Thought of the day... Must adopt to become transformational leader

Maersk put robots into a UK warehouse and tripled its sorting speed. Here is the part most people miss. Warehouses face a constant squeeze: more orders, more pressure to ship fast, and not enough hands to do it all. Sorting, the job of getting the right item to the right place, is often where everything slows down. In the UK, Maersk partnered with a robotics company called Berkshire Grey to fix this. The result was not a small improvement. It was a big one. The numbers: A. Sorting speed tripled. B. Inventory pickup rates improved by 33%. C. Faster, more reliable order handling. D. Less of the repetitive, tiring work that wears people down But here is the part most people miss. The robots did not replace the warehouse team. They took over the fast, repetitive sorting so people could focus on the work that actually needs human judgment, like handling exceptions and solving problems. This is the quiet truth about good automation. The best deployments do not aim for empty warehouses. They aim for people doing better work, supported by machines doing the boring, exhausting parts. Three simple takeaways: 1. The slowest step in your process is usually where automation pays off most. 2. A tripling of speed is not just efficiency. It changes what the whole network can promise customers. 3. The smartest automation removes the worst tasks, not the people. Where in your operation is the one bottleneck that, if it moved three times faster, would change everything downstream? @APMollerBRK @Maersk @BerkshireGrey image credit: @FreightWaves @Maersk @APMollerBRK hashtag#SupplyChain hashtag#Maersk hashtag#Warehousing hashtag#Robotics hashtag#Automation hashtag#Logistics hashtag#Operations hashtag#Industry40 hashtag#OperationsManagement hashtag#FutureOfWork

What happens to a port when a sudden traffic jam or a worker strike hits? Maersk now tests it on a copy first. Ports are some of the most stressful places in the global economy. One delay, one strike, one storm, and the ripple spreads across thousands of shipments. The hard part is you cannot exactly "experiment" on a live port handling millions of containers. So Maersk built a workaround: digital twins of its ports. Think of it as a practice version of a real terminal. It runs on live data from the actual port, so it behaves like the real thing. Port teams can then ask difficult "what if" questions and see what breaks, without anything breaking in reality. What this makes possible: (a)Testing sudden disruptions, like traffic jams or labor strikes, in a safe digital copy. (b) Spotting delays before they spread across the network. (c) Smarter rerouting of ships to dodge congested ports. (d) Real-time visibility across terminals, ships, and warehouses Maersk's data chief put it simply: they pull inputs from terminals, sensors, and data across the network, and let the copy do the worrying. This is resilience by rehearsal. You prepare for the bad day before it arrives. Three simple takeaways: 1. You cannot stress test reality, but you can stress test a copy of it. 2. The goal is not to predict the future perfectly. It is to be ready for several versions of it. 3. Visibility is only useful if it helps you act before the problem spreads. If you could run a "practice version" of your operation and throw one disaster at it, what would you test first? A.P. Moller - Maersk Maersk Line, Limited hashtag#SupplyChain hashtag#Maersk hashtag#Logistics hashtag#DigitalTwin hashtag#PortOperations hashtag#SupplyChainResilience hashtag#Operations hashtag#Industry40 hashtag#OperationsManagement hashtag#Shipping

Siemens once faced a shortage of a highly specialized resin used in medical packaging. AI helped it find a new source fast. Here is a problem that quietly terrifies operations leaders: a single, hard-to-replace material runs short, and there is no obvious backup. The production line is days away from stopping, and finding a new supplier the old way takes weeks of calls, emails, and vetting. Siemens hit exactly this. It needed Surlyn, a specialized resin made by DuPont, used in packaging for medical diagnostic products. When supply tightened, the clock started ticking. Instead of the slow manual hunt, Siemens used an AI supplier-discovery tool called Scoutbee. It scans the world for alternative sources, reads what companies actually make and who they serve, and surfaces options a keyword search would miss. Why this matters for everyone running a supply chain: (a) The hardest part of a shortage is not knowing who else can supply you. (b) Finding, vetting, and onboarding a new supplier the traditional way is painfully slow. (c) AI tools can shrink that search from weeks to days. (d) Companies like Walmart, Tyson Foods, Koch Industries, Maersk, and Unilever are doing the same. The clever move is doing this before a crisis, not during one. Many of these firms now pre-qualify backup suppliers in advance, so the answer is ready before the question becomes urgent. Three simple takeaways: 1. Your biggest risk is often a single material with a single source. 2. Speed of finding a backup is now a real competitive edge. 3. The best time to find your plan B is before you need it. Which single supplier, if they vanished tomorrow, would hurt your operation the most? And do you have a backup ready? @Siemens @DuPont #SupplyChain #Siemens #AI #Procurement #SupplyChainResilience #RiskManagement #Operations #Industry40 #OperationsManagement #SupplierManagement

A 1-degree rise in temperature can swing ice cream sales dramatically. Unilever is using AI to stay ahead of it. Here is a problem that sounds simple but is brutally hard: how much ice cream should you make, and where should you send it? Make too much, and it melts in storage, and you lose money. Make too little, the shelves go empty on the one hot weekend that mattered. And the thing that drives all of it, the weather, is the one thing nobody controls. Unilever's ice cream division tackled this head-on. They built AI systems that read weather data and turn it into production and delivery plans. The results so far: A) Forecast accuracy improved by 10% in Sweden. B) Better decisions on how much to sell, where, and even which freezer cabinet to stock. C) Orders sent to the right places at the right time, with less waste In the words of the division's head: "Using AI, we now understand where to sell, how much we are going to sell, in which cabinet we are going to sell, and when and where to send our orders." That is, demand planning is getting genuinely smarter. Three simple takeaways: 1) The biggest variable in your business might be something you cannot control. The edge comes from predicting it, not fighting it. 2) Small accuracy gains matter. A 10% better forecast means real money saved and less waste. 3) AI works best when pointed at one clear, painful problem, not sprinkled everywhere. What is the one outside force that throws off your planning the most, and could you predict it better than you do today? @Unilever #SupplyChain #Unilever #AI #DemandForecasting #Operations #FMCG #FoodSupplyChain #Industry40 #OperationsManagement #Sustainability