Supply Chain Innovation Trends

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

📌 Procurement: the backbone of resilience in the automotive supply chain! 🚗💡 Traditional supply chain models, once optimized for efficiency above all else, are no longer enough in an era defined by global disruptions, regulatory shifts, electrification, and the rise of software-defined vehicles. 👏🏻 A clear example of this transformation comes from AUDI AG, with the use of Camunda’s process orchestration platform within its procure-to-pay function. As mentioned in Procurement Magazine, the strategy about setting a new standard for procurement excellence. By embedding orchestration, Audi has improved efficiency, transparency, and auditing, building a foundation it can scale across departments and even into other business units. ‼️ Across the automotive landscape, OEMs and suppliers are facing the same major challenges, all coming down to three key factors for future-proofing procurement: 📊 1. Digital-first sourcing ecosystems: AI, predictive analytics, and digital orchestration tools are transforming procurement into a proactive, intelligence-driven capability. Legacy just-in-time (JIT) systems are giving way to trade-insulated, data-backed strategies that balance cost with resilience. Supplier visibility combined with Predictive procurement means organizations can anticipate disruptions before they cascade. 🔄 2. Balancing cost with resilience: The term “cost of resilience” has entered boardroom conversations. Companies are learning that efficiency alone is fragile, and resilience has a price. Diversifying sourcing networks reduces dependency on single regions or suppliers. In the automotive industry, where raw materials like semiconductors and rare earths are vulnerable to geopolitical swings, resilience is not optional. ⭐️⭐️⭐️ This is where GAMUT shines ⭐️⭐️⭐️ 🌱 3. Sustainable procurement as a growth strategy: Sustainability is no longer a compliance box, it is a strategic driver for business. Procurement now incorporates: 🔸 Ethical sourcing and labor transparency. 🔸 Circular economy principles, ensuring materials are reused, recycled, or responsibly recovered. 🔸 Supplier management platforms that embed sustainability metrics into decision-making. Companies that digitize supplier management report up to a 25% increase in supply chain resilience, a tangible link between sustainability and operational performance. Procurement has evolved into a strategic enabler: ✔ Transparent: through orchestration and visibility ✔ Efficient: through automation and AI ✔ Resilient: through diversification and predictive sourcing ✔ Sustainable: through ethical and circular sourcing 🎯 In a world where software-defined vehicles, electrification, and fast-shifting regulations are redefining mobility, procurement is the foundation that determines which companies thrive in the next decade. #supplychain #automotivesupplychain #supplychaininnovation 👇🏻 See comments for sources GAMUT Timuçin Kip

Are your procurement practices stuck in a "ONE-SIZE-FITS-ALL" mindset? We’ve all seen it: A company with strong sustainability goals tries to enforce the same standards across every supplier, expecting one policy to work in vastly different environments. But when it comes to sustainable procurement, what if the key isn’t in replication but flexibility? Take Toyota Motor Corporation, for instance. Their long-standing relationships with suppliers show that collaboration and visibility drive better results than rigid rules ever could. In fact, they describe their interactions as “almost intrusive” but in the best way. This approach ensures both sides remain committed to shared goals, like reducing waste or enhancing resource efficiency, while allowing each partner to bring unique solutions to the table. Imagine this: Rather than prescribing exactly how each supplier should reduce packaging waste, set a shared target say, a 15% reduction. One supplier might use smaller boxes, another might swap materials entirely. Both achieve the goal, but each does it in a way that suits their specific setup. But here’s the trick: For this mindset shift to work, transparency is essential. It’s about creating a culture of openness, where every team and supplier feels empowered to innovate toward that common objective. Consider taking inspiration from the UN Sustainable Development Goals. Which aligns with your company’s values? Could you integrate these into your procurement practices to guide not just one supplier, but your entire supply chain toward a long-term vision? Switching from a prescriptive policy to a shared goal mindset doesn’t just drive sustainability it fosters trust, creativity, and results that everyone can own. So, Is it time to rethink how you define “BEST PRACTICES”?

📦 2025 Predictions: Data and AI in Supply Chain Management 🚀 Over the years, I have emphasized the importance of data visibility and management in supply chain management (SCM) to unify upstream and downstream processes. I have focused on enhancing data integration and utilization through Machine Learning (ML) and Artificial Intelligence (AI). Although my teams and I have achieved remarkable results, we have just begun to tap into the vast potential of data and AI in SCM and operations. ✨ As we enter 2025, several key advancements and trends in AI agents will redefine user experiences, significantly enhance autonomy in SCM, and unlock essential efficiencies and competitive advantages. 🔮 These predictions reflect the evolving landscape of AI and its integration into our industry: 1. Increased Autonomy: AI agents will evolve to operate autonomously in complex environments, enhancing decision-making capabilities. Real-time analysis of large datasets will empower AI agents to make informed choices throughout the supply chain, minimizing the reliance on human intervention. 2. Natural Language Processing (NLP): Advancements in NLP will empower AI agents to engage in more sophisticated, human-like conversations. This will foster seamless communication among stakeholders, facilitate information sharing, and enhance collaboration across the supply chain. 3. Personalization: AI agents will utilize advanced algorithms to deliver highly personalized recommendations and interactions, tailoring services to align with user preferences and behaviors. 4. Enhanced Collaboration: AI agents will streamline data exchange among stakeholders, from suppliers to retailers. Enhancing visibility into inventory levels, order statuses, and supply and demand forecasts will allow for informed decision-making and reduce delays. 5. Integration Across Platforms: Seamless integration of AI agents across various platforms will become standard practice, ensuring a cohesive user experience. The proliferation of AI agents will bolster supply chain resilience, enabling swift responses to disruptions. 6. Ethical AI Development: There will be a greater emphasis on ethical considerations in AI development as customers adopt guidelines to ensure fairness, transparency, and accountability in the actions of AI agents. 7. Training and Development Customers will utilize AI agents to support new training programs to provide personalized learning paths based on employees’ data proficiency levels. 🤖 The deployment of AI agents will significantly enhance the autonomy of supply chains, leading to improved efficiency, reduced costs, and increased responsiveness to market changes. #Accenture #SupplyChain #AI #2025 #2025Predictions #Innovation #AIagents #ML #genai #SCM #EthicalAI #data

This startup uses stickers to keep produce fresh 🍓 (extending shelf life up to 100%) Ryp Labs is tackling food waste with a simple, scalable solution that works across the entire supply chain. 🌱 The Challenge: ↳ 2.5 billion tons of food wasted globally every year as mould & premature ripening cause fresh produce to spoil before reaching consumers ↳ Existing shelf-life solutions often disrupt natural ripening or require costly equipment 💫 The Innovation: ↳ Biodegradable ‘Stix’ stickers infused with plant-based antimicrobial vapors ↳ Customizable for different crops & supply chain stages 🎯 How It Works: 1) Stix stickers or sachets are applied to fruit, packaging, or container lids 2) Release natural vapors that inhibit mold & slow spoilage, without halting ripening 3) Shelf-life of fruits & vegetables extended by 40–100% 4) Compostable & food-safe formulation leaves no residue 5) Easily compatible with existing supply chain processes 🌿 The Impact: ↳ Extends shelf life in transit - 60% reduction in mould for dragon fruit shipped from Ecuador to U.S. ↳ Over 170 potential customers in the pipeline, including major retailers and distributors From a simple sticker… ...to a global food waste solution. “Our mission is to end global food waste by using safe, sustainable, and plant-based technology.” – Moody Soliman, Co-founder & CEO 📥 Like this post? Follow me for more insights on NatureTech and Nature Finance

Forecasting solutions touting the use of AI/ML models are hard to avoid these days. But there is a hidden risk that most companies tend to ignore. The latest models are great but I worry these are being applied in a way that will only amplify “the bullwhip effect”. What do I mean? Bull whip effect is the distortion of the demand signal as it travels from the consumption end of the supply chain to the production end, while traversing multiple physical and informational nodes along the way. As a result, the demand signal at the production end could be orders of magnitude variable than actual consumption. This is nothing new and we have known about this effect for two decades plus. As we apply algorithms to forecasts, unless we account for the bullwhip effect, we are bound to amplify distortion despite best intentions. Now, this is not about outlier elimination which I believe algorithms do a pretty good job of eliminating. I am talking about misinterpreting noise as signal, over-interpreting variability and causing inventory gyrations that ultimately hurt customers. A classic example is using order/shipment data at Distribution Centers for forecasting or worse yet, factory shipments as a proxy for demand. Most S&OP plans only focus on order and shipment data without systematically factoring in channel inventory and demand. So what is the fix? In my opinion, if you are a consumer company (CPG, Hi-Tech, Retail, Pharma/Healthcare, and even manufacturing), build the capability to forecast a demand signal that is as close to the final consumption point. For example, a CPG brand could forecast retail/e-commerce sell-through demand, normalize it for channel inventory and then propagate that signal up into the supply chain. And the best part - those same AI/ML models will work much better for you. To be honest, B2B and industrial companies also benefit from a similar approach by getting closer to end customer demand. Better yet, this unlocks better demand intelligence which fuels better S&OP judgements, new product forecasting quality, lifecycle management, capacity planning and more. If you are looking for a 10x transformation, this is one of them. It’s bizarre to me when I see companies side-stepping this fundamental step and then complain about forecast accuracy, or data cleanliness or something else hurting their supply chain service levels and costs. Leaders who are pursuing unlocking growth from their supply chains while reducing cost-to-serve need to lead from the front in championing this capability. 

🚀 Exciting News: New Paper Published! 🚀 I am happy to share that our paper, "Revolutionize Cold Chain: An AI/ML Driven Approach to Overcome Capacity Shortages," has been published in the International Journal of Production Research. Feel free to read and download. Open Access https://lnkd.in/eGMUWhWm In this research, we explored how integrating AI and machine learning methodologies can significantly enhance cold chain management by improving efficiency, planning, and capacity utilization. By leveraging the Customer Segmentation Matrix, we were able to provide more accurate forecasts tailored to different customer segments, addressing the complexity of managing a diverse customer base. 🔍 Key Insights & Managerial Implications: 1) AI/ML Integration: Our study highlights the benefits of using AI/ML models for accurate forecasting and as part of a comprehensive approach to capacity planning, allowing companies to be more agile and responsive to market fluctuations and disruptions. 2) Customer Segmentation: By segmenting customers, companies can tailor their strategies to meet individual needs, reducing forecast inaccuracies and optimizing capacity allocation. 3) Dynamic Planning: The proposed framework goes beyond traditional forecasting, offering a dynamic and flexible planning process that can adapt to changing market conditions, ensuring businesses can maintain operations even during unexpected disruptions. This research was made possible with the invaluable support of Americold, who provided the data and collaboration necessary to translate these findings into real-world applications that can enhance industry practices. A huge thank you to my co-authors Jafar Namdar, Maria Jesus Saenz, Richard Elmquist, and Luis Rodrigo Dávila for the dedication and hard work on this project. Of course, as always, my sincere thanks to the MIT Center for Transportation & Logistics and MIT Digital Supply Chain Transformation Lab for providing me with a fruitful environment and endless inspiration. #AI #MachineLearning #ColdChain #SupplyChain #CapacityPlanning #Research #IJPR #Americold #MITCTL

Work is changing faster than your org chart—and that’s not a prediction; it’s what I’ve witnessed doing AI-based deployments for 15+ years across Fortune 100's. Did you know that by 2030, AI is expected to automate 45% of current work activities? That sounds terrifying—until you realize that nearly every role I’ve led has changed completely every 2–3 years anyway 🤯 . 🛍️ Let me take you inside a retailer you know. They adopted AI to optimize their supply chain: predictive restocking, dynamic pricing, and warehouse robotics. Yes, automation changed the roles - but it didn’t eliminate them! 💡 The planners became simulation analysts. 💡 The merchandisers became AI auditors. 💡 And those freed from manual grunt work? They started tackling the backlog of work that had been pilin gup. AI didn’t reduce the workforce — it redefined it, and with redefinition comes opportunity – if we choose to take it! (topic of my 3rd #TEDx talk, releasing in May) Here’s the funny, slightly tragic truth: One executive told me they were “fully embracing AI.” When I asked how, he proudly said: “We bought 200 ChatGPT licenses.” That’s like preparing for a tsunami with a kiddie pool. 🤯 The companies winning in this next era aren’t just using AI — they’re training their people to thrive with it. Operative phrase: “training their people” So here’s how to prepare your workforce for what’s next: 🚀 Assess the now. Map roles and skills most likely to be disrupted or augmented. 🚀 Invest in reskilling. Don’t wait for the job to vanish. Train ahead of the curve. 🚀 Foster a learning culture. Create space (and incentives!) to experiment, fail, and evolve. Use AI responsibly. Don’t just optimize. Humanize. Ethics are part of your product now. One last thought: We’re not competing with AI. We’re competing with people who know how to use AI better than us. What steps are you taking to prepare your team? Share below. #FutureOfWork #AI #Leadership #DigitalTransformation #WorkplaceInnovation #SkillDevelopment #EthicalAI #SolRashidi #TEDx

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)

Everyone’s talking about AI in supply chain. Forecasting. Optimization. Demand sensing. And yes, the tools are powerful. But here’s what I’ve noticed: the tech isn’t the barrier anymore. It’s adoption. We interviewed a supply chain leader who built a custom planning tool. The algorithm worked. The math was sound. The output was more accurate than what the team had been using. But the real challenge wasn’t the model. It was getting sales and operations to trust it. Convincing people to stop doing things “the way we’ve always done it.” Helping leaders let go of gut feel and start relying on data-driven insights. That’s the overlooked skill set. The best supply chain leaders aren’t just tech-savvy. They’re change managers. They know how to communicate, build buy-in, and make people feel confident about new ways of working. Because the future of supply chain isn’t just digital. It’s human alignment around the digital. If you’re building your next wave of supply chain talent, don’t just look for who understands the tools. Look for who can get others to use them. That’s the hire who will actually move your business forward.