Automation Tools for Supply Chain Efficiency

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)

After 30+ years in supply chain tech and visiting hundreds of warehouses globally, it's rare that something stops me in my tracks. UK startup Dexory just did exactly that. Here's what blew my mind: 🏗️ 39-foot-tall autonomous inventory scanners - literally the tallest robots on Earth 📊 10,000+ pallets scanned per hour with 99.9% accuracy 🧠 AI-powered warehouse optimization that learns and adapts 🌡️ Multi-sensor technology (HD cameras, temperature, humidity) perfect for cold chain 📱 Real-time digital twins creating living, breathing warehouse simulations But here's the REAL game-changer... Unlike most robotics companies that bolt solutions onto existing operations, Dexory thinks deeply about process integration. They're not just building robots - they're reimagining how warehouses think. Their AI doesn't just scan inventory. It predicts optimal storage locations, suggests put-away strategies, and creates digital twins that enable real-time simulations. The bigger picture? This isn't about full warehouse autonomy yet. It's about creating self-aware facilities - the foundation needed before everything becomes truly autonomous. My prediction: When you control the data, you control the flow. Don't be surprised if Dexory expands into real-time warehouse control systems. What's your take? Are we ready for 39-foot robots managing our supply chains? #supplychain #truckl #innovation

Back in 2018, we had a big problem at Tesla. We needed to scale Model 3 production from 20k to 100k cars per quarter. But the existing supply chain systems simply couldn’t handle this growth. With only a month of cash left, we had to keep the cars moving. We were far too dependent on spreadsheets for planning. They couldn’t keep up with the business and it was having a serious negative impact. Neal Suidan and Michael Rossiter, both leading global demand planning, created something remarkable out of necessity: a unit-level planning system that could simulate and track individual cars through the entire supply chain and match them to demand. This reduced Tesla's inventory from 75 days to just 15, unlocking billions of dollars in working capital at a time when every dollar mattered. Fast forward 7 years and it occurred to us that thousands of companies can use this. They are now bringing that framework to customers with Atomic. Most planning software requires costly integrations and months of setup. Atomic uses AI to eliminate the dreaded spreadsheets, and gets clients onboarded in an hour. The results speak for themselves: - 20-50% reduction in inventory costs while improving in-stock rates - 40+ hours saved per week for planning teams - 3.5x increase in inventory turnover, freeing up millions in cash Today, they announced $3M in seed funding to bring this capability to companies still trapped in supply chain spreadsheet hell. Can’t wait to see what Atomic accomplishes next. https://lnkd.in/e4HrHgqB

This #WorkLab article showcases an inspiring example of Microsoft #Copilot in action. Dow partnered with Microsoft to transform its freight invoicing system, uncovering millions in potential savings.    With billions spent annually on shipping, small errors like surcharges and duplicate invoices added up quickly. By leveraging #AI agents powered by Copilot, Dow automated the review of 4,000 daily invoices, flagging anomalies and streamlining global operations. In just weeks, the pilot identified significant savings, and once fully deployed, Dow anticipates reducing freight costs by up to 3%.    By grounding AI in data, Dow is not only cutting costs but also building a foundation for automation across logistics and customer service—showcasing the transformative power of AI in action.

Because inventory causes exponential pain with multiple warehouses... This infographics shows how to manage inventory in this context: ➡️ Centralize Inventory Visibility ↳ Issue: not knowing inventory levels across locations can lead to overstock in one warehouse and stockouts in another ↳ Action: Implement an inventory management system/ ERP that shows real-time inventory positions for all warehouses in one snapshot ➡️ Classify Products and Prioritize ↳ Why: Not all SKUs deserve the same treatment; some are high-value, others are seasonal ↳ Action: Use ABC analysis to rank products by focusing on A-items for tighter control ➡️ Define Replenishment Rules by Warehouse ↳ Why: Different warehouses cater to different regions or demand patterns. One-size-fits-all reorder points (ROP) won’t cut it ↳ Action: Tailor ROP, safety stock, and min-max levels by location. Consider lead times from central distribution centers or suppliers for each site ➡️ Breakdown Forecast by Warehouse ↳ Why: Each warehouse faces unique market dynamics ↳ Action: Generate warehouse-level forecasts, combining local sales trends with broader S&OP inputs ➡️ Plan Transfers Strategically ↳ Why: Sometimes it’s of lower cost or faster to transfer stock than reordering from suppliers ↳ Action: Set up a transfer framework; regularly review surplus vs. deficit at each location. Automate triggers for transfer orders when it’s cost-effective. ➡️ Monitor KPIs Proactively ↳ Why: Multi-warehouse complexity can hide inefficiencies when not tracking the right metrics ↳ Action: Track fill rate, inventory turnover, stock aging, and transfer costs at each site. ➡️ Plan Direct Dispatches & Save Costs ↳ Why: Dispatch directly from the plant to save logistics costs ↳ Action: Prepare daily dispatch plans targeting direct replenishment from the plant and use these warehouses for milk runs for distributors Any others to add?

The increasing complexity and vulnerabilities of supply chains have been a growing concern for many businesses, with intricate products, variable components and sources, new materials and technologies. Customers now expect expanded product availability, more buying options, and faster delivery. These complexities strain supply chain performance, and customers have shared their need to increase operational resilience, enable real-time tracking, mitigate disruptions, and optimize networks to meet expectations. AWS Supply Chain addresses these pressing needs with a data-driven approach that enables advanced functionality and increases effective collaboration. Key capabilities include a unified data lake that aggregates disparate information, demand forecasting and inventory optimization with machine learning, supply planning to minimize costs and respond quickly to changes, multi-tier visibility for detecting risks and collaborating across the supply chain, and sustainability tracking for streamlined ESG data collection. By connecting data and powering strategic insights, AWS Supply Chain boosts efficiency, resilience, and sustainability. Supply chain leaders will have enhanced visibility, improved risk mitigation strategies, and optimized inventory. Supply chain resilience is no longer just an option, it's an imperative for every industry and organization. Let's discuss how AWS Supply Chain can help you handle complexity and supercharge your supply chain operations! #AWS #AWSSupplyChain #SupplyChainResilience #DataDrivenApproach #Collaboration #Efficiency #Sustainability #supplychain #esg #ml #ai #genai #amazonq

Transforming Supply Chain Management with Large Language Models (LLMs) In the dynamic world of supply chain management, staying ahead means embracing the latest in technology. Enter Large Language Models (LLMs), the game-changers that are set to revolutionize how we understand, predict, and optimize our supply chains. Why LLMs in Supply Chain? - Predictive Analytics: Imagine being able to forecast demand, supply disruptions, or logistic bottlenecks with unprecedented accuracy. LLMs can analyze vast datasets, identify patterns, and predict outcomes, helping businesses stay one step ahead. - Automated Decision-Making: From automating routine tasks to making complex supply chain decisions, LLMs can process information and suggest actions much faster than traditional methods, reducing human error and increasing efficiency. - Enhanced Customer Service: LLMs can power chatbots and virtual assistants to provide real-time, personalized customer support, order tracking, and FAQs, improving the customer experience and freeing up human resources for more strategic tasks. - Sustainability Insights: By analyzing data on supply chain operations, LLMs can identify areas where improvements can be made for sustainability, helping companies reduce their carbon footprint and meet ESG goals. - Risk Management: LLMs can monitor a multitude of sources to identify potential supply chain risks, from natural disasters to geopolitical tensions, providing businesses with the insights needed to mitigate these risks proactively. Real-World Applications: - A leading logistics company uses LLMs to optimize route planning, reducing delivery times and fuel consumption. - A global retailer leverages LLMs for demand forecasting, significantly reducing overstock and stockouts. - A manufacturing firm utilizes LLMs for supplier risk assessment, enhancing resilience in its supply chain. The Future Is Now: The integration of LLMs into supply chain management marks a pivotal shift towards more agile, efficient, and resilient supply chains. As these technologies continue to evolve, the possibilities are limitless. Get ready to embrace the future of supply chain management with LLMs #SupplyChainInnovation #LLMs #AI #TechnologyInSupplyChain #FutureOfLogistics

Fascinating real-world case study from Walmart on supply chain optimization. They built an approach where the decision to build a new DC and the choice to reroute a single truck are part of the same conversation. A few thoughts: 1 - Simulation will be more important than ever with all of the volatility going on. Their simulation platform runs what-if scenarios that helps leadership stress-test network changes before committing capital. 2 - The load planner function is impressive. It accounts for DOT rules, temperature zones, axle weights, and loading patterns down to the pallet. 3 - From a strategy perspective, the four-tier system: strategic network design, facility alignment/capacity planning, execution planning, and real-time/dynamic execution is really solid. Each layer pushes and pulls on the others so decisions stay relevant.

What if the key to achieving our global sustainability goals isn’t just more renewable energy or circular economy practices but the criticality of deploying AI, too? A new 2025 study published in Nature reveals that AI investment is a powerful accelerator for UN Sustainable Development Goals in the US. Here’s what every supply chain and sustainability leader needs to know: 1) AI drives measurable sustainability progress: Every 1% increase in AI investment correlates with a 0.26% improvement in SDG performance, proving technology can be a force multiplier for environmental and social impact. 2) Green electricity amplifies results: The study confirms that renewable energy and AI create a powerful synergy effect, with both factors independently boosting sustainability outcomes. 3) Economic growth paradox: Traditional GDP growth actually negatively impacts SDG scores, highlighting why we need smarter, not just bigger, economic models. 4) Innovation over expansion: The research validates that strategic technology investments outperform pure economic expansion for sustainable development. Supply Chain Implications: From my perspective leading supply chain transformation, this research validates what we’re seeing in practice: - Precision agriculture powered by AI is revolutionizing food system sustainability - Smart energy grids are optimizing renewable resource allocation - Predictive analytics in healthcare is improving access and outcomes - Supply chain optimization is reducing waste and emissions at scale The Critical Caveat: The study emphasizes that AI’s sustainability impact depends ENTIRELY on responsible deployment. What does that mean? -Robust data infrastructure -Ethical oversight frameworks -Equitable access to benefits -Strong governance structures Bottom Line for Leaders: This isn’t about choosing between profit and planet. It’s about leveraging intelligent technology to achieve both. Companies investing in AI for sustainability aren’t just future proofing their operations. They’re actively contributing to global development goals. How is your organization balancing AI innovation with sustainability objectives? What barriers are you encountering? I hope you find this research and perspective useful.

Sustainability has never been for the faint of heart. It will only get harder if we don’t apply the same data strategies and AI investments that other CxOs are adopting.   AI has jumped from talking point to your colleagues' budget: 72% of organizations have implemented AI in at least one business function (McKinsey 2024).    When done right, CSOs’ use of AI can create a flywheel effect that integrates sustainability data into CxOs’ tools, what-if scenarios, and business cases —driving sustainable decision making.   To get there, they need to deploy AI solutions that automate resource-intensive tasks, like: 🔷 Sourcing: Screening suppliers for attributes that contribute to your KPIs and capture those to build towards annual goals. 🔷 Product claims: Transferring those attributes from suppliers to product claims, extracting data from sustainability declarations and analyzing images— reducing errors and ensuring supply chain compliance. 🔷 Carbon footprints: Expediting data collection by mapping emission factors— SAP's Sustainability Footprint Management customers report up to 80% reduction in manual effort and time. 🔷 Reporting: Aggregating those KPIs into auditable, public reports in minutes— freeing their team to focus on strategy and execution.   The tools exist and the data is there. Sustainability leaders need the same level of access to AI that their colleagues have to meet their mandate. #SAPSustainability #AI #Sustainability #BusinessAI