Vendor Managed Inventory Systems

What if you could track every item in your inventory—without lifting a finger? AI-powered machine vision is transforming inventory management. No more manual errors, no more stock surprises. Just real-time visibility, smart forecasting, and seamless logistics integration. 🔍 Here’s how machine vision is redefining warehouse and supply chain efficiency: Smarter Decision-Making – AI provides accurate data that supports better planning and forecasting. Instant Visibility – Continuous monitoring detects stock shortages immediately. Operational Efficiency – Automated checks reduce repetitive tasks and boost productivity. Accurate Stock Data – AI eliminates manual mismatches and keeps records precise. Seamless Integration – Machine vision tools connect with ERP and logistics systems to streamline operations. From personal experience working with businesses that deal with complex inventory systems, I’ve seen how even small AI implementations can deliver measurable improvements in accuracy and time savings. Don't miss upcoming insights on Digital Transformation 🔔 Activate the bell to stay up to date! And if you want to delve deeper, take a look at the DeltalogiX blog > https://bit.ly/4hDs9HU #MachineVision #InventoryManagement #AIinBusiness

Artificial intelligence (AI) offers limitless possibilities, it seems. Not so long ago, we appeared to be moving rapidly towards a supply chain where smart machines would completely take care of planning. In practice, it seems we are still a long way from that. Especially in these uncertain and unpredictable times, human intervention is indispensable. For now, that smart machine appears to be nothing more than a handy assistant. Realco is a cooperative of supermarket owners with 180 stores and three warehouses in northern Italy. For determining stock levels and placing purchase orders, the company could only use its Enterprise Resource Planning system (ERP) and Warehouse Management System (WMS) for many years. “Planning was largely a manual process,” says Elena Bassoli, Logistics Manager at Realco. “To establish purchase orders, the planners used historical sales data, supplemented by information on upcoming promotions. But because information on, for example, delivery schedules and promotions was not always in the ERP system, they also had to extract information from spreadsheets, loose notes and memo sheets on their screens.” During the pandemic, this modus operandi no longer proved adequate. Realco saw some items running out of stock unexpectedly quickly, while at the same time distribution centres (DCs) were bulging as other stock levels were rising rapidly. Bassoli: “It was clear that we needed a more sophisticated and thought-out solution that would allow us to generate a more accurate forecast. We decided to immediately look for a state-of-the-art solution with AI. We found that at RELEX Solutions.” Realco deploys the Relex solution to replenish stock in its distribution centres. Using machine learning (ML), the tool generates a forecast, which uses external data on, for example, the weather in addition to internal data. This forecast is then automatically translated into a purchasing proposal. “We forward as many as 91% of the purchasing proposals directly to our suppliers without a single adjustment. As a result, stock availability has improved by 4 to 5%, while at the same time inventory has decreased by 10% in volume,” Bassoli states. Read the complete article in the latest edition of Supply Chain Movement quarterly magazine: https://lnkd.in/e9WEZ_8z #ai #machinelearning #supplychainplanning #genai #forecasting Mette Krogh Elliot Cartwright Marin Shipe Amélie NICOLAS Bas van Lith Jasper Van Rijn Cazijn Langeler Maarten Vaessen Marcel te Lindert Nicole Messink

Manually processing 1000s of invoices? This is not how you scale a business. A very famous coffee house that operates 80+ outlets across India, serving 56 of the largest tech parks in Bangalore, we started working with them. They had thousands of vendor invoices each month, their finance operations were a challenge. Here’s how their process looked like before - ↳ Each outlet owner collected invoices weekly and submitted them to the head office. ↳ The finance team manually processed these invoices, making thousands of entries into Tally. ↳ This manual workflow took 7–8 team members several days, delaying inventory visibility by weeks. The result? Inefficiencies & errors They started working with us and utilizing our tools. The new process - ↳ Outlet owners started uploading invoices instantly via WhatsApp. ↳ Our invoice parser extracted relevant details with 95%+ accuracy, eliminating manual entry. ↳ Real time visibility into purchases across all cost centers became possible. ↳ Tally integration ensured automatic voucher entries, speeding up the book closure process. The outcome? Month end closings reduced to just 2 days. 30% fewer invoice processing errors. Real-time inventory visibility. There's no reward better than a happy client :) #Automation

I Love Pastries. One afternoon at work, I was feeling hungry and grabbed a sweet croissant bun from the office vending machine. Only two were left. The next morning, I noticed something curious. The pastries had already been restocked. No one had come around to check the machine. There was no dedicated staff assigned to monitor it. So how did the vendor know what was taken? They had implemented a real-time tracking system inside the vending machine. Each tray was equipped with sensors that detected the removal of an item and automatically updates inventory levels to the vendor’s central hub. This allowed the vendor to monitor consumption remotely and refill items precisely when needed, without manual intervention. What they applied on a small scale is actually a widely used method in large-scale inventory management. It’s called Vendor Managed Inventory (VMI). In a VMI setup, the vendor is responsible for tracking, managing, and replenishing stock levels. This minimizes stockouts, reduces excess inventory, and ensures seamless operations. In this article, you will learn: ➡️ What VMI is ➡️ Why organizations implement it ➡️ The operational and financial benefits it delivers Explore how this strategy can optimize your inventory management and reduce associated costs. Follow Deepak Thiru CPSS™ and Join this newsletter for More Procurement Insights. #VendorManagedInventory #Inventory #Procurement #SupplyChain

In September 2025, FourKites, Inc. introduced its Inventory Twin, marking the next stage in its transformation from a real-time visibility provider to a control tower provider. Building on its shipment and yard visibility foundation, it now combines live transaction data, a graph-based network model, and IoT inputs from its Chorus partnership to give organizations real-time insight and control over their inventory. By unifying inventory, transportation, and warehouse data, customers can identify and resolve issues such as stock imbalances, capacity limits, or order disruptions directly within the platform. Nucleus Research found that organizations adopting the Inventory Twin can expect a five to 15 percent reduction in inventory carrying costs and a three to eight percent improvement in service-level performance through better visibility, faster response times, and more accurate fulfillment. As FourKites continues to expand its suite of AI agents to automate tracking, compliance, and appointment management, these developments make the company’s Intelligent Control Tower increasingly attractive for enterprises seeking measurable ROI, stronger customer performance, and tighter operational control across their supply chains. Link in comments.

A supplier sent a delay notice at 6pm. Your team read it at 8:30am. Planning was notified at 9:15. The floor found out at the 10am production meeting. That unread email just cost your factory $50,000. It is a structural gap in how your supply chain handles reality. I see this happen constantly in mid-market manufacturing. The executive team thinks procurement is automated because they spent millions on an ERP. But the reality is entirely different. Clean EDI feeds only handle the perfect orders. The messy 80% of reality—weather delays, quality failures, missing components, supplier panic—happens in unstructured email threads. Last month, a supplier emailed an analyst at 4:30 PM on a Friday. "We are short 500 units of SKU-ABC. Will ship next week." The analyst missed the email. The ERP was never updated. Monday morning, the production line stopped. A $2 million order was delayed over a missing $2 component. Waiting for humans to manually read emails, comprehend the delay, and update the ERP causes massive reaction latency. You are paying smart people to act as manual data routers. Here is the exact playbook top supply chain organizations use to automate exception management: 1. Map the Supplier Reality Build a foundational ontology connecting specific parts, standard lead times, and associated supplier email domains. This maps your exact procurement network so the system understands which supplier dictates the timeline for which critical sub-assembly before an issue ever occurs. 2. Deploy an Exception Agent Implement an AI layer that automatically reads incoming supplier communications in real time. Configure it to extract revised delivery dates, quantity changes, and shortage reasons without human intervention. The system must understand unstructured text just like an analyst would. 3. Triage and Prioritize Automatically Have the system cross-reference the extracted supplier delays against your live production schedule. The AI must automatically flag critical shortages that will stop the line, while silently handling routine delays that have no immediate impact on production. 4. Automate the Resolution Loop Stop relying on manual email replies. Let the AI auto-draft professional vendor responses, suggest internal inventory re-routing from another facility, and stage the final ERP lead-time update for one-click human approval. If your supply chain resilience relies on a procurement analyst keeping up with 400 emails a day, you do not have a strategy. You have a ticking time bomb. Stop firefighting your inbox. Start automating execution. Comment "Exceptions" and I will send you the Blueprint.

2025 at Verusen AI: Turning Inventory at Scale into Measurable Opportunity In 2025, asset-intensive enterprises across multiple industries took a decisive step forward, bringing unprecedented levels of MRO inventory visibility into Verusen to drive real, measurable outcomes with our market-leading, proven AI Models. Across new and expanding customers, over $12B in on-hand inventory was onboarded into the platform this year alone. This represents approximately $2.5B in projected working-capital reduction opportunity through right-sizing, standardization, and network optimization. Here’s how that impact breaks down by industry: Food & Beverage (Industry Leaders) • $536M in on-hand inventory onboarded • ~$107M in projected savings Market-leading food and beverage manufacturers continued modernizing their MRO strategies—prioritizing uptime, safety, and cost discipline across high-throughput operations. These organizations are leveraging AI to reduce excess, eliminate duplication, and improve plant-level governance while maintaining service levels. Oil & Gas (Upstream & Offshore) • $2.2B in on-hand inventory onboarded • ~$408M in projected savings Operators managing remote assets, offshore rigs, and capital-intensive infrastructure adopted Verusen to balance resiliency with working-capital efficiency across some of the most complex supply chains in the world. Mining • $409M in on-hand inventory onboarded • ~$82M in projected savings Top-tier mining companies brought distributed, site-level inventories into a single decision layer—improving critical spares availability while reducing surplus across networks. Chemicals & Petrochemicals • $354M in on-hand inventory onboarded • ~$71M in projected savings Highly technical, specification-driven environments used Verusen to address duplicate materials, specification drift, and excess inventory tied up across plants. Industrial Manufacturing • $8.6B in on-hand inventory onboarded • ~$1.7B in projected savings Some of the world’s most sophisticated manufacturers, spanning building materials, specialty chemicals, life sciences, consumer goods, metals, and packaging, used Verusen to modernize how MRO inventory is governed at scale. Logistics Companies • $720M in on-hand inventory onboarded • ~$144M in projected savings From logistics providers in Canada, to the largest rail operator in the U.S., to the largest rail and logistics company in Africa, organizations are using Verusen to improve material visibility and capital efficiency across highly distributed networks. Across every industry, the takeaway was consistent: Better data alone is not enough. AI must drive action, operate within real ERP and EAM environments, and support the people responsible for planning, buying, and operations. Proud of what our customers accomplished in 2025—and even more excited about what’s ahead. #AI #SupplyChain #MRO #InventoryOptimization #IndustrialAI #AssetIntensive #YearInReview #Worki...

"Moving from predictive dashboards to autonomous execution using Multi-Agent Systems" Stop Building Dashboards. Start Building Supply Chain Agents. Most retailers are using AI to predict stockouts (Demand Forecasting) — but that’s where they stop. They still rely on a human to read the dashboard and place the PO. That is not automation; that is just better reporting. Here’s the trap: Prediction without execution is just latency. To solve the "last mile" of supply chain logic, you need Agentic AI, not just predictive ML. • The Old Way: A Databricks model predicts a 20% spike in demand for winter coats. A planner sees the alert 2 days later. • The Agentic Way: >Watcher Agent: Monitors real-time POS data in Snowflake + local weather APIs. Detects the spike. >Reasoning Agent (ReAct Pattern): Checks the ERP (SAP/Oracle) for current inventory and vendor lead times. It "reasons" that standard shipping will be too late. >Execution Agent: Autonomously triggers a PO via API to the supplier for expedited shipping, updates the "Estimated Delivery" on the e-commerce frontend, and alerts the warehouse team. We are seeing this built today using LangGraph or Microsoft AutoGen to orchestrate these agents, with Vector Databases (Pinecone/Weaviate) acting as the "Long Term Memory" for supplier reliability scores. If your AI can’t sign a Purchase Order, it’s just a glorified spreadsheet. #SupplyChainAI #AgenticAI #RetailTech #Databricks #LangChain #LogisticsAutomation #GenerativeAI Learn more about our Success:   https://lnkd.in/e7N3Xgew Learn more about our expertise: https://lnkd.in/db_Mzi96    #GenerativeAI #SARSoftwareInc #AmazonSageMaker #Innovation #RAG #LLMs #DigitalTransformation

𝗦𝗲𝗹𝗹𝗶𝗻𝗴 𝗮𝗰𝗿𝗼𝘀𝘀 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗰𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗯𝘂𝘁 𝘁𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗶𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗮𝘀 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗽𝗼𝗼𝗹… 𝘆𝗼𝘂’𝗿𝗲 𝗹𝗲𝗮𝘃𝗶𝗻𝗴 $$$ 𝗼𝗻 𝘁𝗵𝗲 𝘁𝗮𝗯𝗹𝗲. The “first-come, first-served” inventory approach works well for most pure-play Shopify brands. Things get complicated once you expand into B2B, marketplaces, or retail. Let’s say your brand just launched B2B. You’ve got 3 reps on the road, pitching your assortment to boutiques and working to get your apparel into stores. But every time they close a deal, they encounter the same problem: “There is no inventory left to fulfill orders—it has already been consumed by online.” At GoodDay Software, we’ve created a retail operating system that enables virtual inventory management. This system allows your operations to adequately serve every demand channel. Here’s what’s working for these brands: 𝟭. 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗶𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗮𝗰𝗿𝗼𝘀𝘀 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝗽𝗼𝗼𝗹𝘀 To scale across ecommerce, marketplaces, retail, and wholesale, you need virtual inventory pools, which pre—allocate stock so that one channel doesn’t drain the others. 𝟮. 𝗧𝗿𝗮𝗰𝗸 𝗶𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝘀𝗵𝗶𝗳𝘁𝘀 𝗮𝘁 𝗹𝗲𝗮𝘀𝘁 𝘄𝗲𝗲𝗸𝗹𝘆, 𝗻𝗼𝘁 𝗺𝗼𝗻𝘁𝗵𝗹𝘆 Most brands plan demand monthly, but proper inventory management requires more frequent check-ins. You should study sell-through rates, incoming POs, and available stock across channels at least once a week. And, make operational optimizations to improve your inventory positions. 𝟯. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗿𝗲𝗯𝗮𝗹𝗮𝗻𝗰𝗶𝗻𝗴 𝗳𝗼𝗿 𝘀𝗺𝗮𝗿𝘁 𝗮𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 As the number of demand channels continues to grow, the workload related to managing an omnichannel business expands as well. Or, does it? Future-ready brands will use AI-native inventory systems to continuously monitor stock across physical and virtual pools and dynamically help move inventory just-in-time to where demand is strongest. Omnichannel success starts with better inventory discipline. What’s your biggest challenge in managing stock across multiple channels? #inventory #DTC #retail

The most common bottlenecks in procurement right now are manual approvals, disconnected systems, and unclear routing paths. These bottlenecks are symptoms of outdated systems. The future of procurement is fast, intelligent, and fully integrated. In a modernized system, the time it takes to move from low inventory to a completed purchase order drops significantly. Inventory levels are tracked in real-time. When a threshold is hit, an AI agent can launch a requisition automatically, validate that it's within budget, route it for approval, and send it to purchasing. When approvals and integrations are dialed in, the system can even trigger the order with a vendor through an e-commerce platform. That’s not a hypothetical. That’s where the tech is heading, and the infrastructure to support it already exists. Modern procurement will give teams space to focus on strategic purchases, not routine ones. #Procurement #DigitalTransformation #AI #InventoryManagement #ERP #Automation #OperationalEfficiency #PublicSector #LocalGov #Technology #Innovation