Role of AI in Supply Chain Management

When Dr. Miguel Rodríguez García of MIT Center for Transportation & Logistics and myself wrote the "warehouse of the future" paper ( https://lnkd.in/gFFiCAQR ) just a year ago, we took into account AI impact on warehouses, but with the rapid emerging capabilities of AI and it latest wave - Agentic AI, there is so much more that we will see coming. Agentic AI will revolutionize the warehouse of the future by enabling fully autonomous, adaptive, and highly efficient operations. Intelligent systems will manage inventory, optimize storage layouts, and orchestrate fleets of autonomous robots to handle picking, packing, and shipping with minimal human intervention. These AI-driven warehouses will continuously analyze real-time data to predict demand, reduce bottlenecks, and adjust workflows dynamically, maximizing productivity and minimizing costs. Moreover, agentic AI can integrate seamlessly with supply chain networks, providing end-to-end visibility and enhancing resilience to disruptions. By automating complex decision-making and operations, agentic AI will create smarter, faster, and more sustainable warehouse ecosystems. We are finally starting to see the light at the end of supply chain efficiency's tunnel. What do you think? #supplychain #innovation #Agentic #AI #automation Photo credit: DALL-E (another AI tool)

Dear My Network, I'm wrapping this series on Segmentation with the following key Takeaways: • ML and Agentic AI are powerful enablers of E2E supply chain segmentation by enhancing agility, automation, and intelligence across supply chain processes. • These technologies can dynamically adapt segmentation strategies based on real-time data, customer behavior, and changing market conditions. • It can identify profitable clusters, predict disruptions, and automate scenario planning across multiple supply chain models. • Agentic AI brings autonomy to processes—executing tasks, learning, and optimizing supply chain responses without constant human intervention. The insights for 4-part series are drawn from my chapter in our new book: https://lnkd.in/gVNSdWsW Lets close with another Example: Global Consumer Electronics Manufacturer - Context: A multinational consumer electronics company sells both premium and value-tier products across multiple channels—direct-to-consumer (DTC), big-box retailers, and e-commerce platforms. Each segment had distinct demand patterns, service expectations, and profitability margins. - Challenge: They were using a one-size-fits-all supply chain model, leading to: • Stockouts of premium products during product launches • Overstocking of slower-moving value-tier items • High logistics costs due to expedited shipments - E2E Segmentation in Action: 1. Planning Phase They used ML algorithms to profile and cluster customers and products based on buying behaviors, seasonality, margin contribution, and service requirements. 2. Implementation Phase They designed virtual supply chains: • One for high-margin flagship unpredictable products with make-to-order and expedited fulfillment • Another for value-tier SKUs using a low-cost, forecast-driven model with bulk shipments • A third for e-commerce with decentralized inventory and last-mile delivery partners 3. Sustain Phase Agentic AI systems monitored these segments in real time, dynamically adjusting planning parameters and alerting teams when service levels or cost thresholds were breached. - Results: • 15% reduction in working capital tied to inventory • 10% improvement in on-time delivery for premium products • Faster decision-making and fewer fire drills • Greater alignment between sales, supply chain, and finance This example reflects the core principles outlined in my book chapter on segmentation, showing how advanced technology and structured transformation can drive real business value. Now, How are you planning to use AI to enable e2E segmentation in your supply chain? Please share your thoughts in the comments!

The Hard Numbers Behind AI in Supply Chain: When a global manufacturer's VP told me "We can see problems coming, but we still can't prevent them," it crystallized a truth I've observed across the industry. Visibility isn't enough. The real ROI comes from digital workers that can ACT on that visibility. Companies implementing our AI-powered digital workers are seeing: - 50-75% reduction in detention charges - Payback periods of just 4-8 months - 3-year ROI of 500-1,000% I've shared the detailed metrics and customer success stories in my latest article. The cost of waiting? Up to $550K in unrealized benefits every month. #SupplyChain #DigitalTransformation #AI #SupplyChainTechnology FourKites, Inc. #IntelligentControlTower #DigitalWorkers #TracyAI #SamAI

Sourcing and procurement teams need to plan ahead of the potential supply chain impact coming soon! The upcoming aggressive tariffs on Canada, Mexico, and China could disrupt global supply chains, increase costs, and force businesses to rethink sourcing strategies. Here's how AI can help navigate this uncertainty: 1️⃣ Predicting Financial Impact: AI-powered models can analyze tariffs' impact on costs, pricing, and profitability. Businesses can simulate different scenarios to prepare for the worst while minimizing surprises. 2️⃣ Optimizing Supplier Networks: AI tools can assess supplier data, recommend alternative suppliers, and identify regions with lower risks or costs. This ensures flexibility when tariffs disrupt trade routes. 3️⃣ Real-Time Market Insights: AI-driven systems continuously monitor global trade policies, tariffs, and economic indicators. Companies stay informed and can react quickly as policies change. 4️⃣ Automating Logistics Decisions: AI can streamline logistics planning by identifying the most cost-effective routes, transport options, and inventory adjustments to counter rising costs. 5️⃣ Improving Forecasting and Planning: AI enhances demand forecasting and supply planning, helping companies prepare inventory levels and reduce unexpected expenses caused by tariffs. Trade disruptions feel overwhelming. But with AI, businesses can stay resilient and proactive in an unpredictable market. Is your organization ready to leverage AI for supply chain decision making? Let’s talk. #SupplyChain #AI #GlobalTrade #Tariffs #BusinessResilience

AI is transforming supply chain risk management. What used to take weeks: - Identifying risks - Analyzing data - Making decisions Can now happen in real time. Here’s how AI is reshaping the game: - Predictive Analytics AI models analyze vast amounts of data to forecast potential disruptions before they happen. - Real-Time Monitoring Sensors and AI tools provide 24/7 visibility, flagging risks as they emerge. - Scenario Planning Simulations powered by AI allow companies to test “what if” scenarios and prepare for the unexpected. - Dynamic Risk Scoring AI continuously evaluates risks based on changing conditions, helping prioritize where to focus resources. - Automation Routine tasks like supplier audits or compliance checks can now run autonomously, freeing up teams for strategic decisions. But here’s the challenge: AI isn’t a magic bullet. It’s only as good as the data and processes behind it. The companies that succeed will: - Invest in high-quality, integrated data systems. - Build teams that understand both supply chain risks and AI tools. - Blend human expertise with AI-driven insights for better decisions. The future of supply chain risk management isn’t just smarter. It’s faster and more proactive. Are you ready for what’s next?

🆕 Between Q4 2024 and Q1 2025, industrial companies have pivoted dramatically from AI exploration to active implementation. This represents a crucial inflection point in industrial AI adoption: ⛓️ AI agents now autonomously interpret Cargo Systems Messaging Service notifications, transforming complex tariff updates into actionable insights for supply chain teams without human intervention. 🛠️ Field service operations have seen efficiency gains where AI agents interpret technical maintenance manuals in the context of real-time anomaly detection and deliver step-by-step guidance to technicians. 🎛️ Quality control systems are employing AI agents to analyze real-time production data, identifying potential defects before they occur and automatically adjusting manufacturing parameters to maintain optimal output quality. The Mosaic AI Agent Framework is a suite of tooling designed to consistently measure and evaluate AI Agents to be accurate, safe and governed.

Here's how we are able to solve a supply chain problem that was untouchable until earlier this year. We’ve been building around generative AI for a year and a half now. We started with low-hanging fruit use-cases like questions and answers with data, then using it as a copilot to help us code faster. Most recently, LLMs became a core-feature of our product. In our previous fintech venture, we faced significant challenges in scaling data extraction. Scraping, reverse engineering, and standard API connections weren't enough. We found ourselves ramping up our engineering team to manage and maintain integrations. Recent advancements in generative AI, combined with our own learnings, have opened up new possibilities. We can now handle dynamic schemas and extract data from complex sources like ERPs, email threads, and PDFs with surprising accuracy. Instead of using AI to generate content, we're leveraging it to unlock and organize existing data—a subtle but powerful shift. Supply chains are rich with data, yet much of it remains siloed and inaccessible, hindering efficiency and visibility. By applying these AI advancements, we're able to unlock this data, providing supply chain teams with the insights and automations they need. At Mentum, we're excited to be at the forefront of these developments. We're working alongside supply chain teams to help them turn unstructured data into actionable intelligence, and then automate processes that help them manage risks more appropriately.

Unlocking the Potential of AI and ML in #Logistics and #SupplyChain: The logistics and supply chain sector is ripe for transformation. As digital technologies evolve, artificial intelligence (#AI) and machine learning (#ML) have become central to enhancing efficiency, agility, and resilience in this complex industry. But the promise of AI and ML isn’t just theoretical. Through best practices in application and deployment, logistics and supply chain businesses can unlock tangible improvements in operations, customer experience, and cost management. 1. Begin with Strategic Use Case Identification The logistics industry is diverse, spanning warehouse management, transportation optimization, inventory control, demand forecasting, and reverse logistics. Rather than attempting to implement AI and ML across all facets simultaneously, leaders should strategically select use cases that align with business goals and deliver immediate value. Common high-impact areas include: Predictive #DemandPlanning: AI and ML can analyze historical sales data, economic indicators, weather patterns, and even social trends to predict demand. This is particularly powerful for avoiding stockouts or overstocks, especially for seasonal items. Inventory Optimization: ML models can evaluate data on product flow, shelf life, and demand cycles to determine optimal stock levels, helping reduce holding costs while ensuring availability. Route Optimization: For transportation and delivery, ML algorithms help identify the most efficient routes, factoring in real-time traffic, fuel costs, and delivery windows to minimize delivery time and costs. Best Practice: Begin with data-rich, high-impact areas where #ROI can be quickly demonstrated. Doing so builds confidence within the organization and generates momentum for further AI initiatives. 2. Leverage #Data Lakes and Real-Time Data Feeds In logistics, data flows in vast volumes and from multiple sources: shipment tracking, customer orders, warehouse inventory, telematics, weather data, and more. Creating a centralized data lake—a repository of structured and unstructured data—is essential for harnessing AI’s full potential. Real-time data integration allows ML models to adapt dynamically, providing insights and enabling rapid response to evolving conditions. 3. Enhance Customer Experience through AI-Driven Personalization Customers increasingly expect real-time updates and personalized interactions. AI-driven customer experience platforms can improve customer satisfaction by providing tailored recommendations, customized delivery options, and real-time order tracking. Case in Point: A major logistics provider might use AI to predict delays based on weather patterns or traffic data and proactively notify customers, offering alternative delivery options or adjusted ETAs. Best Practice: Implement AI solutions that add value to the customer’s journey, building trust and loyalty while streamlining interactions

A CFO once asked me: “How can I digitally transform my supply chain… without adding to my cost?” I asked him a question back: “How do you get fit without buying a gym membership?” You walk more. You take the stairs. You change your habits — not your wallet. That’s what Decision AI does for your supply chain. It’s not just dashboards and data. It’s your supply chain making smarter choices every hour — so you don’t have to. 🌀 Think of it like hiring an operations brain that never sleeps: It reroutes a shipment before the storm hits Flags a cooling issue before your pharma spoils Reassigns idle assets before they cost you more At Roambee, we’ve seen this flip the script: 📍One pharma company cut loss claims by 28% — without hiring more people. 📍A retail giant saved millions by recovering store displays before they went missing. 📍An industrial firm turned asset chaos into control — just by letting AI run the playbook. And here’s the kicker: The savings from better decisions covered the cost of transformation. No bloated IT project. No new headcount. Just smarter moves — made by machines, funded by what you stop wasting. If you’re in CPG, pharma, logistics, electronics, automotive, or retail, and your market is slowing… This is your moment. The best part? You don’t need a revolution. Just a smarter first step. Let AI earn its keep — and your supply chain will pay its own way. #DecisionAI #SupplyChainTransformation #CostNeutralAI #Roambee #DigitalLogistics #SmartSupplyChain #PharmaTech #RetailOps #CPGStrategy #FreightForwarding #AIinBusiness #OperationalExcellence

AI has rapidly evolved from a strategic consideration to an operational necessity in the distribution industry. Recent insights from the Distribution Strategy Group and McKinsey & Company reinforce what I’ve seen firsthand. AI agents aren’t just automating tasks. They’re transforming how we serve customers. These tools can process orders, check inventory, apply pricing rules, and update systems in real time, all without human intervention. That’s not just efficient. It’s game-changing. According to McKinsey, AI can reduce inventory by up to 30%, logistics costs by 20%, and procurement spend by 15%. These metrics translate directly to enhanced competitive positioning and customer experience advantages. The heart of the matter is this: AI doesn’t replace your team; it empowers them. By redirecting talent from processing transactions to building relationships and solving complex challenges, organizations create dual value streams that benefit both operational metrics and the customer experience. In more tangible terms, it frees up sales and service reps to focus on building relationships and solving complex problems, not chasing down order status updates. Distributors that move early will gain speed, agility, and customer loyalty. AI is already the new standard. And it’s redefining what excellence looks like. Read more: https://lnkd.in/ekAW4qve