Fatih C.’s Post

𝐖𝐡𝐲 𝐬𝐮𝐩𝐩𝐥𝐲 𝐜𝐡𝐚𝐢𝐧 𝐭𝐞𝐚𝐦𝐬 𝐧𝐞𝐞𝐝 '𝐒𝐲𝐬𝐭𝐞𝐦 𝐨𝐟 𝐀𝐜𝐭𝐢𝐨𝐧𝐬' 𝐢𝐧 𝟐𝟎𝟐𝟔 ? In my last post, I spoke about the “Document-to-Decision” gap, where teams spend time validating data instead of acting on it. But what’s changing now isn’t just closing that gap. It’s how decisions get made after the data is ready. As we move through 2026, leading manufacturers and retailers are shifting: From: static reporting To: continuous, always-on planning 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐲?  Traditional Systems are great "Systems of Record", they manage your history. But they aren't built for the "always-on" volatility of modern global trade. 𝐀 '𝐒𝐲𝐬𝐭𝐞𝐦 𝐨𝐟 𝐀𝐜𝐭𝐢𝐨𝐧' 𝐢𝐬 𝐧𝐞𝐰 𝐞𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐥𝐚𝐲𝐞𝐫... 𝐖𝐡𝐚𝐭’𝐬 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐚𝐜𝐫𝐨𝐬𝐬 𝐭𝐞𝐚𝐦𝐬...  𝟏. 𝐃𝐞𝐦𝐚𝐧𝐝 𝐒𝐞𝐧𝐬𝐢𝐧𝐠 𝐯𝐬. 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠: Moving beyond historical averages to sense real-time shifts in sales velocity and external signals. 𝟐. 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐑𝐞𝐩𝐥𝐞𝐧𝐢𝐬𝐡𝐦𝐞𝐧𝐭: No more "one-size-fits-all" inventory rules. AI tailors decisions to specific SKUs and locations to protect availability without bloating stock. 𝟑. 𝐓𝐡𝐞 "𝐈𝐧𝐛𝐨𝐮𝐧𝐝" 𝐄𝐚𝐫𝐥𝐲 𝐖𝐚𝐫𝐧𝐢𝐧𝐠: Using ASN and production signals to flag delays before they hit the warehouse, giving you days, not hours, to pivot. This will not replace your teams; it will give them the structure to focus on 𝐡𝐢𝐠𝐡-𝐯𝐚𝐥𝐮𝐞 𝐭𝐫𝐚𝐝𝐞-𝐨𝐟𝐟𝐬 instead of chasing PDFs. 𝐖𝐡𝐚𝐭’𝐬 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐭𝐨 𝐞𝐦𝐞𝐫𝐠𝐞 𝐚𝐜𝐫𝐨𝐬𝐬 𝐭𝐞𝐚𝐦𝐬: ERP → financial anchor (compliance & transactions) AI layer → supporting faster, continuous decisions Teams → focusing more on judgment and trade-offs Does this reflect what you’re seeing, or is your experience different? Let's discuss in the comments...

ADAMS AI | Forging the Next Frontier in 2026 2026 isn’t about incremental upgrades anymore—it’s a full reset. The companies winning today aren’t just digitised… they’re intelligent. At ADAMS AI, we’re stepping into this new frontier by transforming supply chains from reactive engines into predictive, self-optimising ecosystems. Here’s the reality most CEOs are waking up to: ERP gave us control. Supply Chain + AI gives us advantage. Supply Chain vs ERP — What’s the Real Difference? #Aspect ERP (Enterprise Resource Planning) Supply Chain (AI-Driven) #CoreFocus Internal operations (finance, HR, reporting) End-to-end flow (suppliers → production → delivery) #Scope Entire organisation Cross-company ecosystem Data Historical, structured Real-time, dynamic Speed Stable, transactional Fast, adaptive, predictive Role System of record System of action Value Efficiency & control Agility & competitive edge ERP integrates the business into one system of truth, while supply chain systems optimise the movement of goods and relationships beyond the organisation Smarter Supply Chain +1 Where Competitive Edge is Won Modern supply chains are no longer linear—they’re volatile, multi-layered, and demand real-time decisions Companies that leverage AI-powered supply chains unlock: - Real-time visibility across suppliers, inventory, and logistics - Predictive demand planning instead of reactive forecasting - Faster decision loops to respond to disruptions instantly - Cost optimisation through smarter inventory and sourcing - Resilience in uncertain global conditions Traditional ERP systems weren’t built for this level of speed and volatility #How ADAMS AI is Driving Impact ADAMS AI is helping organisations shift from “planning cycles” to continuous intelligence: Turning fragmented supply chain data into a single, real-time command view Enabling AI-driven forecasting to reduce stockouts and overstock Automating decision-making across procurement, logistics, and inventory Connecting supply chain actions directly to business outcomes The result? Faster fulfilment Lower costs Stronger customer trust And most importantly—a defensible competitive edge 2026 belongs to companies that don’t just manage operations… but orchestrate intelligence across their entire ecosystem. That’s the ADAMS AI play Wanna know more, contact us ! #supplychain #erp #speed #adamsai

🔷 From Data to Decisions: Lessons from 25 Years in Manufacturing Part 1: We Don’t Have a Data Problem. We Have a Context Problem After 25+ years in manufacturing and industrial systems, I’ve realized something important… 👉 The industry has come a long way — but one critical gap still remains. Over the last two decades, we’ve made significant progress: • MES systems enabling real-time production visibility • EAM systems bringing structure to maintenance • ERP systems integrating core business processes This transformation is real — and it has fundamentally improved how plants operate. And yet… Many organizations still struggle to answer basic questions like: • “Which asset issue will impact tomorrow’s delivery?” • “Why are we missing targets even when all dashboards are green?” • “Where exactly should I act right now?” Because the challenge today is no longer about availability of data. It’s about availability of context. In most setups: • Data exists — but in silos • Systems exist — but operate in isolation • Dashboards exist — but lack decision relevance Over the years, I’ve observed a consistent pattern: We invest heavily in: ✔️ Collecting data ✔️ Visualizing data ✔️ Reporting data But not enough forý⁶: ❌ Connecting data across systems ❌ Mapping it to asset hierarchies and business processes ❌ Translating it into actionable decisions And now, we are entering the next wave — AI. Every organization is exploring AI, GenAI, and advanced analytics. But without context… 👉 AI is only as good as the meaning behind the data. If data is disconnected, if asset relationships are unclear or broken, if business context is missing… Then AI doesn’t solve the problem — it amplifies the confusion. A plant manager doesn’t need more dashboards. A maintenance head doesn’t need more alerts. And they certainly don’t need AI-generated noise. They need clarity: 👉 What should I do next, and why? That shift — from data to context to decision — is where the real transformation lies. If there’s one takeaway from my journey, it’s this: We’ve built systems that show us what is happening. Now it’s time to build solutions that help us decide what to do next. In my next post, I’ll share how this “context gap” actually shows up on the ground — across multiple systems and value chains.

𝐓𝐡𝐞 𝐁𝐎𝐌 𝐢𝐬 𝐭𝐡𝐞 "𝐒𝐢𝐧𝐠𝐥𝐞 𝐒𝐨𝐮𝐫𝐜𝐞 𝐨𝐟 𝐓𝐫𝐮𝐭𝐡" ... 𝐈𝐬 𝐘𝐨𝐮𝐫𝐬 𝐁𝐫𝐨𝐤𝐞𝐧? 📊 In 𝗱𝗶𝗿𝗲𝗰𝘁 𝗽𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁, the 𝗕𝗶𝗹𝗹 𝗼𝗳 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝘀 (𝗕𝗢𝗠) is more than just a list of parts; it’s the 𝗵𝗲𝗮𝗿𝘁𝗯𝗲𝗮𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲. Yet, most organizations are still bleeding margins due to 𝗳𝗿𝗮𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗱𝗮𝘁𝗮 𝘀𝗶𝗹𝗼𝘀 between Engineering and Procurement. From 𝗠𝘂𝗹𝘁𝗶-𝗟𝗲𝘃𝗲𝗹 𝗕𝗢𝗠 𝗘𝘅𝗽𝗹𝗼𝘀𝗶𝗼𝗻 𝗘𝗿𝗿𝗼𝗿𝘀 to 𝗖𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗩𝗼𝗹𝗮𝘁𝗶𝗹𝗶𝘁𝘆, the complexity is scaling faster than traditional ERP systems can handle. This is where 𝗚𝗲𝗻𝗔𝗜 shifts from a "buzzword" to a 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘁𝗼𝗼𝗹. 𝗞𝗲𝘆 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗮𝘁𝘁𝗮𝗰𝗵𝗲𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: 🔹𝗥𝗶𝘀𝗸 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻: Using LLMs for 𝗦𝗶𝗻𝗴𝗹𝗲-𝗦𝗼𝘂𝗿𝗰𝗲 𝗥𝗶𝘀𝗸 𝗙𝗹𝗮𝗴𝗴𝗶𝗻𝗴 and 𝗔𝗩𝗟 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 transforms reactive sourcing into a 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗿𝗶𝘀𝗸-𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗲𝗻𝗴𝗶𝗻𝗲. 🔹𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Moving beyond manual rollups to 𝗦𝗵𝗼𝘂𝗹𝗱-𝗖𝗼𝘀𝘁 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 per line item allows for 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻𝘀 that were previously too labor-intensive. 🔹𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Automating 𝗕𝗢𝗠 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝘀𝗶𝗻𝗴 and 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗮𝘁𝗶𝗼𝗻 reduces the "hidden factory" of 𝗮𝗱𝗺𝗶𝗻𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝘃𝗲 𝗿𝗲𝘄𝗼𝗿𝗸. The future of Direct Procurement isn't just about "buying parts", it's about intelligent orchestration. 👇 How is your team leveraging 𝗔𝗜 to bridge the gap between engineering specs and supplier realities? Let’s analyze the shift together. 🔖 𝗦𝗮𝘃𝗲 this breakdown to use as your 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 for automating complex BOM workflows. ♻️ 𝗥𝗲𝗽𝗼𝘀𝘁 to help your network bridge the gap between 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗦𝗽𝗲𝗰𝘀 and 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗥𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 using AI. 🔔 𝐅𝐨𝐥𝐥𝐨𝐰 Asmaa Gad & Supply Chain AI Pro for more insights! #SupplyChainStrategy #ProcurementAnalytics #DirectSourcing #BOMOptimization #AIinManufacturing

MIT's 2025 warehousing study surveyed 2,000+ supply chain professionals across 21 countries. The finding that should concern every COO: 44.6% of companies are at "advanced" automation. Only 12.9% reach "full." That gap isn't a technology problem. It's a context problem. The top barriers? System integration (47.7%), data quality (46.2%), technical expertise (48.6%). In practice, this means your WMS knows one thing, your TMS knows another, your ERP knows a third — and nobody has the full picture at once. Last week, Anthropic made the 1M token context window available at standard pricing — no long-context premium. That's a 5x increase from the previous 200K. Why does 5x matter for supply chain? 200K tokens was already enough to analyze a single system well — your WMS data, or your carrier contracts, or your supplier map. What it wasn't enough for was holding all of them at the same time while reasoning across them. 1M doesn't make the AI smarter. It reduces the number of times you have to break a problem into fragments and hope the model remembers what it saw three queries ago. The MIT study found that SCM end-to-end orchestration scored the lowest "high impact" rating (52%) and the highest "no impact" (9%). Companies know orchestration matters. They just can't do it — because no single system holds enough context to reason across the full chain. For the 44.6% sitting at "advanced" with clean data spread across siloed systems — that's a meaningful reduction in friction. Not a revolution. A wider lens on the same problem. The honest caveat: context window size doesn't fix bad data. If your inventory records are wrong, 1M tokens of wrong data is still wrong. What's the biggest system integration gap in your supply chain today? #SupplyChain #AI #Warehousing #Logistics

Dashboards gave us visibility. But visibility doesn’t make decisions. In her latest piece for Supply & Demand Chain Executive, Erica Frank outlines why the next generation of supply chain technology is focused on automating decisions — not just reporting metrics. Modern networks have outgrown human-scale tradeoffs like adjusting one lane, accepting one load, or repositioning one asset. Each decision has downstream economic consequences across the entire system. The question is no longer: “Which driver should take what load?” It’s: “What decision improves profitability — and by how much?” Check out the article here: https://lnkd.in/gB5P2sYS #ai #artificialintelligence #decisionintelligence #decisionautomation #trucking #transportation #supplychain #logistics #freight #freighttech

Last week in a strategy discussion around AI in Supply Chain Operations and the gap between visibility and intelligence is still massive. Most companies today have: • ERP systems • Demand forecasts • Inventory dashboards • Logistics tracking But they don’t have real-time decision intelligence. The conversation focused on how AI can move supply chains from reactive to predictive: ✔️ Demand sensing using live sales + external signals ✔️ Dynamic inventory rebalancing across warehouses ✔️ AI-driven procurement recommendations ✔️ Automated exception handling (delays, stockouts, supplier risks) ✔️ Intelligent documentation & compliance processing What’s fascinating is this: The real cost in supply chain isn’t just delays. It’s uncertainty. When leadership lacks clarity, they overstock. When data is fragmented, they miss early disruption signals. When teams operate manually, response time kills margin. AI agents layered on top of existing ERP systems can: – Predict stockouts before they happen – Recommend PO adjustments automatically – Flag supplier risk patterns – Reduce working capital lock-in – Improve OTIF metrics This isn’t about adding another dashboard. It’s about enabling autonomous operational decisions. Let’s talk. We’re working with businesses ready to pilot AI systems that directly impact margin, working capital, and operational resilience. Comment or DM if your organization is actively modernizing operations. To learn more visit : www.herantes.com The next competitive edge won’t be scale. It’ll be intelligence layered into execution. #ai #SupplyChain #SupplyChainManagement #AIinSupplyChain #ArtificialIntelligence #DigitalTransformation #Logistics #Procurement #DemandForecasting #InventoryOptimization #Operations #SmartManufacturing #EnterpriseAI #Automation #WorkingCapital #COO

Supply chain leaders: the agentic future isn’t blocked by models. It’s blocked by messy processes. In a recent TechCrunch interview, OpenAI COO Brad Lightcap said we “have not yet really seen” AI penetrate enterprise business processes at scale because enterprises are complex: many teams, tons of context, and lots of systems/tools that must work together. He also emphasized measuring success by business outcomes rather than seat licenses. (Link to the article in the comments.) That framing maps perfectly to warehouse ops. Agentic orchestration won’t start with an agent clicking around your WMS. It starts with the foundation: 🚦 Clean, reliable operational signals (work, capacity, constraints) 🤝 A shared system of context across sites 🫸 Clear decision guardrails (what can/can’t be automated) ♻️ Closed-loop execution (predict → plan → adjust → verify outcome) This is where CognitOps fits: not as a chatbot bolted onto operations, but as the operational layer that turns labor and workload data into decisions your leads, sups, and managers can trust, so agents can eventually orchestrate safely. If you’re a supply chain leader: what’s the first workflow you’d trust an agent to recommend (not execute) in your network?

Rootstock's annual manufacturing survey is a goldmine of information about trends in the software for manufacturing world. We've been able to glean meaningful insights into how the landscape in enterprise software is changing, and particularly with how AI is playing a more and more significant role in tech initiatives. Just think - how many of the new tech projects within your organization involve some form of AI?

📊 Five Major Shifts Shaping Manufacturing Tech Priorities in 2026. Our comparison of the 2024 and 2026 State of Manufacturing Technology Survey results reveal significant changes in how manufacturers are prioritizing tech investments. The survey comparison shows: 🔹 Workforce pressure intensified as talent shortages rose 🔹 AI investment shifted sharply into supply chain planning (+19 points) 🔹 Predictive AI adoption accelerated 🔹 ERP expectations expanded to include workforce retention (18% → 30%) 🔹 Tariffs and trade uncertainty increased cost and planning complexity While AI adoption is now widespread, only 5% of manufacturers consider themselves “far ahead” of their peers — signaling the next phase of AI maturity will be about achieving benefits, outcomes, and ROI. As Rootstock VP of Product Ohad Idan notes, “New technology must align with core business processes and well-defined metrics for success to drive lasting impact.” 📖 Read the full press release to learn more about these five shifts and access the complete 2026 report: https://hubs.ly/Q044DLsJ0 #Manufacturing #AI #ERP #DigitalTransformation #SupplyChain #ManufacturingTechnology