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✈️ Airport Baggage Handling Has Quietly Gotten Smarter — Thanks to AI. What do you think? Remember the days of delayed or lost luggage being the norm. That’s changing — fast. With AI, IoT, and automation transforming ground operations, the baggage handling system at modern airports is becoming a case study in quiet efficiency. Here’s how technology is making a difference: ✅ RFID & real-time tracking – No more guessing where your bag is. ✅ AI-powered sorting & routing – Faster, more accurate handling. ✅ Predictive analytics – Less congestion, fewer delays. ✅ Robotics & automation – Smarter, safer workflows. ✅ Passenger apps – Transparency right in your pocket. 🔍 Fun fact: Since 2007, global mishandled baggage rates have dropped by over 70%. Airports like Changi, Heathrow, and Schiphol are leading the way — and passengers are noticing. Sometimes the best tech transformations are the ones we don’t even realize are happening. #AI #AirportTech #Logistics #SmartTravel #DigitalTransformation #BaggageHandling #Innovation #IoT #Automation video by @theasybag

Yesterday’s sales can’t see tomorrow’s storm, But AI can 😎 Most manufacturers still build demand forecasts based on one thing: 𝐡𝐢𝐬𝐭𝐨𝐫𝐢𝐜𝐚𝐥 𝐬𝐚𝐥𝐞𝐬. Which is fine… until the market shifts. Or weather changes. Or a social post goes viral. (Which is basically always.) That’s why AI is changing the forecasting game. Not by making predictions perfect—just a lot less wrong. And a little less wrong can mean a lot more profitable. According to the Institute of Business Forecasting, the average tech company saves $𝟗𝟕𝟎𝐊 per year by reducing under-forecasting by just 1%, and another $𝟏.𝟓𝐌 by trimming over-forecasting. For consumer product companies, those same 1% improvements are worth $𝟑.𝟓𝐌 (under-forecasting) and $𝟏.𝟒𝟑𝐌 (over-forecasting). (Source: https://lnkd.in/e_NJNevk) And were are only talking 1 improvement%!!! Let that sink in... All that money just from getting a little better at predicting what customers will actually buy. And yes, AI can help you get there: • By ingesting external signals (weather, social, events, IoT, etc.) • By recognizing nonlinear patterns that Excel never will • And by constantly learning—unlike your spreadsheet But it’s not just about tech. It’s about process: • Use Forecast Value-Added (FVA) to track which steps help (or hurt) • Get sales, marketing, and ops aligned in S&OP—not working in silos • Focus on data quality—AI is only as smart as your ERP is clean • Plan continuously—forecasting is not a set-it-and-forget-it task Bottom line: If you’re still relying on history to predict the future, you’re underestimating the cost of being wrong. Your competitors aren’t. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

This isn’t just tomato sorting — it’s AI farming at full speed. Most people think machines like this just detect color. But today’s tomato sorters use advanced computer vision, powered by AI models that analyze every fruit in real time. High-speed cameras capture thousands of frames per second. In just milliseconds, deep learning systems like CNNs and YOLO evaluate color, shape, size, and even micro-defects invisible to the human eye. ✅ Detects ripeness and texture ✅ Identifies cracks, bruises, or mold ✅ Removes debris, dirt, and rocks automatically The result? Cleaner produce, less waste, and more efficient food processing — all thanks to AI making split-second decisions. To me, this shows how AI isn’t replacing human intuition — it’s scaling it, one tomato at a time. How long before every piece of food you eat passes through an AI system like this? #AI #Innovation #Technology #Automation #Agriculture #Engineering #ComputerVision #MachineLearning #FoodTech #FutureOfWork

RECYCLING GAME-CHANGER? CHINA SWITCHES ON FIRST FULLY AUTOMATED TEXTILE WASTE SORTING LINE: China has switched on its first fully automated textile-waste sorting line with Databeyond Technology. Using machine vision and hyperspectral imaging, it sorts post-consumer garments by fibre and blend, achieving over 90% purity for polyester, cotton and nylon and flagging elastane blends. The operator says a 15-tonne eight-hour shift that once needed more than 30 workers now runs with four, slashing labour and operating costs. The line is in operation at Zhangjiagang Shanhesheng Environmental Technology Co. Soon after commissioning, Shanhesheng says it received a 200-tonne order for high-purity post-consumer textiles from a global apparel company. A second phase will extend automated sorting to shredded garments and factory offcuts to feed both chemical and biological recyclers. Automated, blend-aware sorting tackles the sector’s key bottleneck between rising collections and the specification-grade inputs recyclers need. It also aligns with China’s push on textile circularity, which aims to expand recycling capacity, recycle roughly a quarter of textile waste, and produce millions of tonnes of recycled fibre. Apparel Insider Insider story in comments.

Inflation isn't just about rising prices; it's a catalyst for changing consumer behaviors. As purchasing power shifts, businesses must adapt swiftly to meet evolving demands. Hindustan Unilever Limited (HUL), a leader in the FMCG sector, showcases how embracing AI can turn these challenges into opportunities. 📌 The Challenge #HUL observed significant fluctuations in demand across its diverse product portfolio during inflationary periods. Premium products experienced slower sales, leading to overstock situations, while budget-friendly items frequently faced stockouts. Traditional forecasting methods, relying heavily on historical sales data, struggled to keep pace with these rapid changes in consumer preferences. 📊 The Solution: AI-Driven Demand Forecasting To address this, HUL integrated AI-powered analytics into its demand forecasting processes. This advanced system enabled the company to: Analyze Real-Time Consumer Behavior: By examining current purchasing patterns and consumer sentiment, HUL could detect emerging trends and shifts in preferences. Incorporate External Economic Indicators: The AI model factored in various economic indicators, such as inflation rates and consumer confidence indices, to predict their impact on product demand. Optimize Inventory Management: With precise demand forecasts, HUL adjusted its inventory levels accordingly, ensuring optimal stock across all product categories. 🔹 Key Insight: The AI-driven approach revealed that demand for budget-friendly products was increasing at a rate three times higher than traditional models had predicted, while premium product sales were declining in specific regions. 📈 The Impact 20% Reduction in Unsold Premium Stock: By aligning inventory with actual demand, HUL minimized excess stock of premium items. 35% Improvement in Stock Availability for Budget-Friendly Products: Ensuring that high-demand, cost-effective products were readily available led to increased customer satisfaction. Enhanced Revenue and Profit Margins: Optimized inventory management reduced holding costs and prevented lost sales, positively impacting the bottom line. 💡 The Lesson In times of economic uncertainty, relying solely on historical data can be a pitfall. HUL's proactive adoption of AI-driven demand forecasting exemplifies how leveraging advanced analytics allows businesses to stay agile and responsive to market dynamics, ensuring they meet consumer needs effectively How is your organization utilizing data analytics to navigate market fluctuations? #datadrivendecisionmaking #businessstrategies #dataanalytics #demandforecasting

Ever wonder why some e-commerce brands always seem to have the right products in stock, while others struggle with overstock or empty shelves? It all comes down to demand forecasting—and in 2025, it’s getting an AI-powered upgrade. ● From guesswork to precision Traditional forecasting relies on historical sales data. AI-driven tools now go beyond that, integrating real-time factors like weather, local events, and even social media trends. The result? Forecasts with 90%+ accuracy instead of the usual 50%. ● GenAI: the next step Generative AI takes it further by analyzing unstructured data (customer reviews, trends, emerging demand signals) and answering questions in plain language. No more complex spreadsheets—just instant insights for better inventory planning. ● AI tools leading the way: ✔ Simporter – AI-powered forecasting that integrates multiple data sources to predict sales trends. ✔ Forts – uses AI for demand and supply planning, ensuring optimized inventory. ✔ ThirdEye Data – AI-driven forecasting that factors in seasonality and customer behavior. ✔ Swap – AI-based logistics platform that enhances inventory management. ✔ Nosto – AI-driven personalization that recommends the right products at the right time. ● Why this matters for #ecommerce? ✔️ Avoid stockouts that frustrate customers ✔️ Reduce excess inventory and free up cash ✔️ Adapt quickly to market shifts How are you managing demand forecasting in your store? #shopify

Your machines and people are draining your margins. The hidden cost eating away your manufacturing profits You have the raw material. You have the machines. You even have the demand. But your production is still delayed. Because your workforce isn’t aligned to your operations. - Skilled technicians are scheduled when no high-skill tasks are running. - Maintenance teams are overworked during peak load. - Project deadlines are missed due to poor shift planning. - Plant downtime increases because human resources are reactive, not predictive. It’s a planning issue. One mid sized FMCG manufacturing unit in Gujarat was losing ₹1.2 Cr/month due to idle labor hours, rework, and unplanned overtime. They ran a 3 month pilot with predictive staffing models: 1) Workforce demand synced with production load 2) Skill mapped scheduling for critical batches 3) 24x7 visibility into shift gaps and role clashes 4) Plant uptime increased by 18% In manufacturing, efficiency comes from planning smarter. If you're running plants without syncing workforce planning to production cycles, you're building inefficiency into your business model. Sooner or later, your margins will show it. #Manufacturing #WorkforceEfficiency #PredictivePlanning

🛣️ Berg Insight just released the latest edition of our market research covering the Transport Management Systems (TMS) market. Solution vendors range from small specialised TMS developers active in local markets to the major enterprise software providers with worldwide presence.   ✔️ Some of the most notable players on the North American TMS market are Trimble Transportation and McLeod Software. Trimble is a major industrial technology company which offers a suite of TMS solutions following multiple acquisitions, while McLeod has focused specifically on serving the trucking industry for 40 years. Providers of broader supply chain and logistics offerings such as Blue Yonder, Manhattan Associates, e2open (acquired by WiseTech in 2025), Descartes Systems Group and Kinaxis are also competing in the TMS space. Mastery, Turvo, TMSfirst, FreightWise (including Kuebix) and Shipwell are additional examples of players with a primary TMS focus. The global logistics company C.H. Robinson (the former TMC division) is yet another example. Uber Freight also has a TMS business (stemming from the acquisition of Transplace).   ✔️ The major US-based cloud infrastructure and software provider Oracle is active in this space with its Oracle Transportation Management offering deployed across all geographic markets. The Germany-based enterprise application software giant SAP similarly offers SAP Transportation Management globally.   ✔️ The European TMS market is further served by players such as Transporeon (owned by Trimble), Infios, 4flow, proLogistik GmbH, AEB, ecovium, Solvares Group, Soloplan GmbH and LIS based in Germany; the French groups Generix, SINARI and AKANEA; Microlise, Aptean 3T, Mandata and HaulTech in the UK; Alpega headquartered in Austria; BlueRock TMS, Navitrans and Boltrics based in Benelux; nShift, Pagero, AddSecure and Opter in the Nordics; INELO headquartered in Poland; the Italian companies TESISQUARE and SIMA ; Alerce Group in Spain; as well as AndSoft based in Andorra.   🔍 Berg Insight has also recently released a new research paper covering real-time transportation visibility (RTTV) platforms. Players active in this space range from niche visibility providers and more general TMS providers, through broader supply chain and logistics software vendors, to providers of fully integrated business management IT solutions such as ERPs. In addition to project44, FourKites, Inc. and Shippeo which have had an explicit focus on visibility platforms specifically, other major players in this space include Transporeon and Descartes which are also key TMS providers. Visibility can hardly be defined as a product category of its own, and even the specific RTTV platforms are over time adding features increasingly positioning them as TMS players – thus resulting in also an element of competition between TMS and RTTV providers.   #TMS #RTTV #RTTVP #fleetmanagement #transportmanagement #transportationmanagement #supplychain #logistics #visibility

"Our salespeople are responsible for generating our forecasts, and they own the final numbers. They are crushing it." Said no one to me ever. Often, when I discuss with companies with low demand planning maturity, their process is driven by salespeople. This usually results in, ▪️ A lot of politics ▪️ Biased forecasts (either too high as they confuse demand forecasts and supply plans or under forecasts as salespeople want to beat targets) ▪️ Inaccurate forecasts, as humans aren't the best at generating baseline forecasts. ▪️ 100% manual and time-intensive process and poor utilization of salespeople. Here's how I would design a scalable demand planning process. 1️⃣ Use an ML-generated forecast as a baseline. This forecast should already leverage most of your business drivers (promotions, shortages, prices, per orders, sell-outs—if available) and generate forecasts for new products. 2️⃣ Allow demand planners to enrich forecasts if they have specific insights/information that the model isn't aware of ("I just called our client(s), they told me XXX.") Salespeople can propose insights to demand planners. 3️⃣ Track Forecast Value Added to ensure that the team is adding value. Coach people to success. If you have difficulties with step 2️⃣, focus on four essentials: new products, phased-out products, new clients, and lost clients. You will already add a lot of value. --- If you enjoy demand planning content, I forecast that you will love my mailing list. https://lnkd.in/gSWngz9u

A few months back, I interviewed a senior demand planner from a global skincare brand. I asked a simple question: "How do you improve your forecast when the system gives you a number that feels... off?" She replied, "We talk to the right people before we talk to the system." That line stayed with me. In Demand Planning, we often focus heavily on historical data, statistical models, and software outputs. But what truly differentiates an average forecast from a high-confidence, actionable one - is the process of Demand Enrichment. And no, it’s not just a buzzword. It’s a discipline - a method of adding intelligence beyond what the system predicts. In fact, according to a McKinsey study, companies that effectively integrate enriched demand signals (like promotions, competitor moves, distribution expansion, influencer campaigns, and even climate effects) can improve forecast accuracy by up to 25%. When I worked for a consumer brand in North India, we noticed our system forecast underestimated demand by 18% during Q4. Why? Because it didn’t factor in the impact of a regional festival that doubled store footfall across 3 key states. Our statistical model was flawless. But our insights were incomplete. That’s when we built a cross-functional "Demand Intelligence Loop" - gathering inputs from marketing, sales, trade partners, and retailers - and feeding it back into planning. The result? Forecast accuracy jumped. Inventory positioning improved. And stockouts during peak weeks were cut in half. If you're a planner reading this: Don't just accept the forecast. Enrich it. Challenge it. Elevate it. That’s how Demand Planning transforms from reactive to strategic.