Fred Lemoine

Only a few shine in demand planning. This document shows the 7 habits of a high-performing demand planner: # 1 - Starts with Clean Data ↳ Validates and cleans data before running any forecast # 2 - Balances Art & Science ↳ Uses statistical models as the base, but applies market intelligence to adjust for reality # 3 - Tracks Forecast Accuracy & Bias Religiously ↳ Monitors WMAPE, Bias, and FVA (forecast value added) every cycle. Knows where they win and where they miss. # 4 - Builds Strong Cross-Functional Relationships ↳ Engages sales, marketing, and supply early. Takes Forecasting as a team sport. # 5 - Plans for the Known Unknowns ↳ Has a playbook for promotions, product launches, and external shocks # 6 - Reviews & Learns Every Cycle ↳ Post-mortems are standard. Every miss is a lesson for the next forecast. # 7 - Communicates in Business Language ↳ Doesn't just talk numbers but explains the impact on revenue, service, and inventory Any others to add?

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98% of supply chain leaders now use AI. But most are stuck at the pilot stage. The adoption spike is real. But execution is where most companies stall. A 2024 Economist Impact survey shows: - 40% use AI to enhance customer experience - 35% for demand forecasting - 35% to optimize inventory - 35% to detect disruptions - Early adopters report 34% cost reduction and 32% better planning Yet only <30% of GenAI efforts have made it to full production (Deloitte). Here’s why: AI is powerful, but not plug-and-play. The real value isn’t in having AI. It’s in integrating it with how your team makes decisions. BCG says AI is evolving into a self-learning layer that can: - Parse both structured and unstructured data - Predict outcomes - Recommend actions - Automate responses from emails to GPS to supplier risk Still, it won’t replace your team. It’ll challenge how your team works. Because AI doesn’t just make you faster. It makes your old assumptions visible. That’s the real transformation. And only those who rethink their systems and their thinking will see the upside.

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Data from multiple manufacturing diffusion index surveys from September suggested a slight strengthening of new order momentum observed in August was ephemeral. Two charts below show seasonally adjusted new orders relative to the prior month. Thoughts: •The top chart shows ISM’s new orders data. New orders fell back into negative territory at 48.9 (values of 50 or more indicate expansion). Informant comments continue to be unkind about tariffs (https://lnkd.in/gqecsayZ). One respondent in transportation equipment stated, “The addition of the derivative steel and aluminum tariffs in the middle of the month — with no announcement — was devastating. Interest-rate lowering or the ‘One Big Beautiful Bill’ will not impact our business, as all capital projects are on hold until there is some level of certainty and customers start to place orders for new equipment again.” Another respondent in electrical equipment, appliances, and components stated, “Customer orders are depressed for heavy machinery because tariffs are so impactful to high-end capital equipment.” •The bottom chart shows the aggregated seasonally adjusted new orders across five Federal Reserve Banks (New York, Philadelphia, Richmond, Kansas City, and Dallas). After rising to slight expansion in August (+3.66), new orders fell negative again in September (-4.56). Responses to the Dallas Fed’s survey https://lnkd.in/gwGYg-zk included a fabricated metal producer stating, “Orders have been put on hold, and we are not receiving any new [purchase orders] since Aug. 15 on open RFQ's [requests for quotation].” A printing and related products firm stated, “Incoming orders have definitely slowed way down, and all we can attribute it to is the crazy tariff-induced environment we are living in. Instead of Washington, D.C. policies increasing business and making it better, they seem to be adding way too much uncertainty and making things worse.” Implication: 2025 should have been the year where manufacturing activity in the USA recovered. Instead, erratic tariff policies have caused all forward momentum that we observed in January to evaporate. For trucking companies, these latest data indicate we will not see the type of sustained increase in freight demand necessary to trigger the next bull market pricing cycle. As I’ve noted previously, Q2 2026 is the soonest we could see a significant shift in pricing dynamics in carriers’ favor (outside of normal seasonal fluctuations). #supplychain #trucking #freight #truckload #logistics #transportation     

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Back when I was working as a project manager at BAT, I found myself in a situation where a delay in one SKU component forced me to map out exactly what could be impacted, how many days of delay to launch, which tasks in the Gantt plan would slide, what knock-on effects ripple across production and market windows. I remember thinking: I don’t want to keep chasing impacts. If a system could tell me ahead of time which component in the BOM is likely to slip and by how much plus shows how that will push the go-live date, I could spend less time diagnosing and more time fixing. That’s where the leap from visibility to predictability changes the game. Imagine a connected architecture where your AI engine sits on top of your ERP (e.g. SAP), consumes real-time ETA and lead time data per individual SKU, flags which parts in the BOM are trending late, and directly ties that into your Gantt / project schedule logic, projecting how much your launch might slip. Instead of scrambling to assess what’s broken, you get alerts telling you what’s next. Then you can focus your energy on solutions. Recently I read an article titled “AI is reshaping the supply chain and IBP” which talks about how predictive intelligence and integrated planning are changing how we preempt disruptions. As I read, I couldn’t help but think of that BAT moment and how far ahead we’d be if more supply chains had systems that do the heavy lifting of impact analysis before the crisis. Article Link: https://lnkd.in/d8xvDRbE #SupplyChain #AI #PredictiveAnalytics #ERP #SAP #Operations #ProjectManagement #Innovation

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“Products in your warehouse don’t sell.” 📦 This retail truism applies just as strongly to fashion. The traditional push model—stocking stores to the brim, followed by a “hope strategy”—has retailers praying for good weather and high conversions before the dreaded end-of-season markdowns. As any seasoned exec might quip: “Hope is not a strategy, kiddo.” But the real game-changer often isn’t about boosting sales... It’s about eliminating waste, like Toyota’s manufacturing philosophy taught us. 🚀 One of our customers recently uncovered something remarkable: “Same sales, but with a double-digit reduction in in-store inventory!” This was the eye-opening result of implementing an AI solution designed to better align supply with demand. On top of saving some 🌿 trees (less product was moved from warehouse to store), they also built a more accurate data foundation for future forecasting. Plus, less crammed stores are easier to operate — and cheaper to run. The real payoff, though, comes when purchasing decisions for upcoming seasons are recalibrated. For a mid-size retailer, a 10% inventory reduction can mean millions in savings — capital that can boost shareholder value or fund growth initiatives. 💰 Pre-season planning often feels like a high-stakes chess match — blindfolded. You’re making bold moves based on forecasts that are, frankly, guesstimates. One trend shift and you’re back to square one. And yet, building a robust pre-season system is a massive undertaking: multi-million euro investments, 2+ years of work, and results that don’t show up until long after go-live. Can you really wait that long? Now, imagine an in-season strategy where agility meets opportunity. 🤝 Here, you’re powered by live data, ready to pivot fast. With AI and real-time analytics, you can sync supply with demand in ways that were previously out of reach. And you can make better decisions for your next Buy. The ROI is quick — and it compounds. For all the supply chain savants, merchandising virtuosos, and operational prodigies out there: Pre-season is important. But in-season is where quick wins happen — and where you lay the bricks for a better future. 🏆 In summary: In-season optimization creates value fast — and sets up a better pre-season. The other way around? Not so much. We’re seeing this shift across several fashion retailers. Curious what it could look like for you? Let’s talk. @Nextail_co

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𝗥𝗲𝘁𝗮𝗶𝗹𝗲𝗿𝘀 𝗖𝗮𝗻’𝘁 𝗔𝗳𝗳𝗼𝗿𝗱 𝗡𝗢𝗧 𝘁𝗼 𝗛𝗮𝘃𝗲 𝗮 𝗖𝗵𝗶𝗲𝗳 𝗣𝗼𝗹𝗶𝘁𝗶𝗰𝗮𝗹 𝗥𝗶𝘀𝗸 𝗢𝗳𝗳𝗶𝗰𝗲𝗿. Retailers are learning fast that politics and business no longer run in parallel, they’re colliding. And the fallout is instant. 🌍 Tariff shocks disrupting supply chains overnight. 👖 “Good Jeans” campaign sparking political backlash and boycotts. 🔫 Gun sale decisions reshaping brand reputations. These aren’t side issues anymore. They’re strategic landmines that shape growth, loyalty, and resilience. Enter the new 𝗖-𝘀𝘂𝗶𝘁𝗲 𝗿𝗼𝗹𝗲: 𝘁𝗵𝗲 𝗽𝗼𝗹𝗶𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝘀𝗲𝗮𝘀𝗼𝗻𝗲𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿. Call it a Chief Political Risk Officer - a leader who can: 🔹Decode policy, culture, and media simultaneously 🔹Anticipate tariff, trade, and regulatory risks 🔹Navigate governments, communities, and consumer sentiment 🔹Guide strategic pivots to keep brands resilient and relevant With the stakes this high, can retailers afford NOT to have this role - as common and critical as a Chief Marketing Officer? #Retail #CSuite #Tariffs #FutureOfRetail #RajulThinks #RetailStrategy #GCC #Reinvention RETHINK Retail #ConsumerIndustries

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Retailers shift from Google to AI agents – what this means for FMCG brands A silent shift is underway in digital commerce — and FMCG brands should take note. In August 2025, ChatGPT drove 20% of referral traffic to Walmart and Etsy Shop, with Target at ~15% and eBay at 10%. Just a month earlier, these numbers were significantly lower. While referral traffic is still under 5% of total visits, the growth velocity is clear. Consumers are replacing search with conversation. Instead of using Google, users now ask ChatGPT: - “Which toothpaste is best for sensitive teeth?” - “Top healthy snacks for kids?” - “Why is Swiss Cheese so good and where can I buy it?” - “Best laundry detergent for cold wash?” This behavioral shift matters. AI agents filter and surface product recommendations based on trust, brand recognition, and relevance — not just ad spend. For FMCG producers, the implications are clear: – Visibility is no longer guaranteed by shelf space or SEO. – If your brand isn’t part of AI agents’ product surfaces, you’re invisible. – Retailer data access policies now shape your discoverability. Retailers like Walmart (420 million SKUs) and Target are gaining exposure by remaining open to AI crawlers. Amazon, however, has blocked many bots — causing its ChatGPT-driven traffic to drop 18% in August. This evolving ecosystem affects how FMCG brands are discovered, recommended, and ultimately purchased. And unlike paid search, where placement is auctioned, AI-driven recommendation engines operate in more opaque, model-based hierarchies. Key facts: – 2.5 billion daily ChatGPT prompts – ~50 million daily shopping-related queries – 60% of US shoppers have used genAI for shopping (Omnisend, Aug 2025) As OpenAI and others move toward affiliate fees and embedded checkout, FMCG brands must act now — ensuring their products are correctly indexed, accurately represented, and promoted within retailer ecosystems that are embracing AI traffic. The next shelf is conversational. And it's already stocked. #retail #ecommerce #fmcg #omnichannel #ai #chatgpt #openai #shoppingagents #digitalcommerce #referraltraffic #amazon #walmart #etsy #target #ebay #rufus #retailtech #consumertrends #searchvschat #affiliate #onlineshopping #generativeai #shoppingbots #conversion #usa #northamerica #martech #digitalmarketing #adtech #aiincommerce #futureofshopping #platformeconomy #brandvisibility #fmcgmarketing

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