Retail Management And Merchandising

The cost to retailers and brands of failing to align inventory and marketing teams is exponential. While outdated C-suites remain fixated on traditional metrics such as lowering Customer Acquisition Cost (CAC) or driving higher Return on Ad Spend (ROAS), the most effective, forward-thinking teams are focusing on how to leverage inventory insights alongside marketing strategies to enhance overall profitability. To achieve this, teams need to take a more integrated approach by: 1. Understanding which products have depth to market Inventory depth refers to the quantity and availability of a product across sales channels. Knowing which products have strong stock levels enables marketing teams to prioritise campaigns that avoid stockouts and capitalise on sustained demand. For example, a product with healthy inventory can be promoted continuously, creating consistent revenue streams without risking customer dissatisfaction due to unavailability. 2. Identifying products suitable as headline sale offers Headline offers are the star attractions in promotional campaigns — products that draw customers in. These typically have a strong appeal or brand recognition, combined with sufficient inventory to meet increased demand. By aligning marketing efforts with inventory data, brands can ensure that headline products are always available in quantities that support campaign goals, maximising footfall or online traffic without disappointing buyers. 3. Determining which products require deeper discounts to accelerate cash conversion cycles Some products may have slower turnover or be approaching end-of-season, requiring more aggressive pricing to convert inventory into cash swiftly. Marketing and inventory teams must collaborate to identify these items early and design targeted promotions with deeper discounts to reduce holding costs, free up warehouse space, and improve liquidity. This approach not only drives cash flow but also reduces the risk of markdown erosion across the entire product range. By fostering close collaboration between inventory management and marketing functions, retailers and brands can create more intelligent, data-driven promotional strategies. This alignment ensures that marketing spend is optimally directed to products that can deliver maximum impact — whether that means maintaining steady sales on well-stocked items, driving customer acquisition through attractive headline deals, or clearing excess inventory via tactical discounting. Ultimately, this integrated approach transforms profitability from a simple function of volume or acquisition metrics into a sustainable balance of supply and demand, cash flow, and customer satisfaction.

90% of ‘sustainability problems’ in fashion have nothing to do with fabric. Everyone talks about: → Organic cotton. → Recycled polyester. → Carbon-neutral shipping. But after helping brands source $15m + worth of product, The biggest sustainability killer isn’t the material. It’s how brands plan and produce. Here’s what actually drives waste (and destroys margins) behind the scenes: 1) Overproduction from poor planning You commit to 1,000 units when you’ll only sell 600. The rest ends up in clearance or landfill. 2) Last-minute design changes You swap fabrics or colours after sampling - meaning remakes, offcuts, and waste. 3) Rushing sampling to hit marketing dates You skip proper testing and fit approvals. The result? QC fails, rework, and unsellable stock. 4) Inconsistent sizing or poor grading Return rates soar. That’s more waste, more packaging, more freight. 5) No process for using leftovers End-of-roll fabrics, trims, and sample yardage just… sit. Every metre you waste was paid for with your profit. The truth? Most brands don’t have a sustainability issue. They have an operational discipline issue. Sustainability starts in your production calendar - not your marketing copy.

Are you producing more than your customers actually need? If so, you're burning cash without even knowing it. Overproduction is a silent profit killer. Making more than needed costs you time, space, and money. Here’s how to cut waste and boost efficiency: 1/ Match Supply with Demand  ↳ Track sales data daily.  ↳ Adjust production to real needs.  ↳ Stop guessing—use data. 2/ Work in Smaller Batches  ↳ Make less, more often.  ↳ Reduce leftover stock.  ↳ Stay flexible for demand shifts. 3/ Sell Before You Produce  ↳ Confirm orders early.  ↳ Avoid making extras.  ↳ Use pre-orders to gauge demand. 4/ Keep Storage Clean  ↳ Empty shelves weekly.  ↳ Donate or repurpose excess stock.  ↳ Recycle scraps to reduce waste. 5/ Train Your Team  ↳ Teach lean principles.  ↳ Reward efficiency.  ↳ Align goals with lean thinking. 6/ Use Smart Tech  ↳ Automate inventory tracking.  ↳ Predict trends with AI.  ↳ Keep systems up to date. 7/ Monitor & Fix Waste Fast  ↳ Track overproduction costs.  ↳ Review reports weekly.  ↳ Act quickly to prevent loss. 8/ Celebrate Smart Planning  ↳ Recognize teams for efficiency.  ↳ Share success stories.  ↳ Set new improvement goals. Produce smarter, not more. Share ♻️ to help your network and follow Sergio D’Amico for more insights on continuous improvement and organizational excellence. 📌 P.S. Which of these do you need to improve?

Every brand says they’re “data-driven.” Most still forecast like it’s 1999. The traditional fashion cycle was built for confidence, not accuracy. Big commitments. Big calendars. Big waste. But the smartest modern brands have flipped it: proof first, production second. Reformation tests 100–300 units per style before scaling. Cuyana measures sell-through before committing to reorders. Even Zara, long known for speed, now treats the first shipment as a live market test. That’s how they protect margin and avoid overproduction — they build from evidence. The pattern is clear: → Forecast less. → Test smaller. → Scale faster. This is the same philosophy powering fast-moving Chinese DTC brands like Cider or Peacebird. They don’t “launch lines.” They launch hypotheses. If the data proves it, they scale overnight. If not, they delete it. That’s not chaos. It’s controlled iteration. And it’s why forecasting as we know it is dying. You don’t need to predict demand when you can read it in real time. You just need the system to act on it.

Fashion founders make one decision every season that can make or break the entire year How much inventory to produce. It sounds simple on the surface. But it is one of the most complex calls you make. Produce too much, and you are stuck with dead stock, heavy discounting, and locked capital. Produce too little, and you miss demand, lose customers, and leave revenue on the table. There is no perfect answer. And that’s what makes this decision so critical. Over the years, building KAZO and expanding across markets, I have realised that fashion is not just about what you design. It is about how accurately you read demand. A McKinsey report estimates that fashion brands lose billions every year due to overproduction and unsold inventory. In some cases, up to 30% of inventory never sells at full price. That is not a design problem. That is a decision problem. Early on, like most brands, we believed growth meant producing more. More styles. More pieces. More options. But scale taught us something different. Restraint is as important as ambition. Understanding what not to produce is as important as knowing what will sell. Today, our approach is far more deliberate. We look at past data, yes. But we also look at signals. What customers are engaging with. What is being repeated. What is sustaining interest beyond the first few weeks. Because fashion is not just about trends. It is about behaviour. And behaviour is what ultimately determines whether inventory moves or sits. If there is one thing I would tell any fashion founder, it is this: Your designs bring customers in. Your inventory decisions determine whether you build a business.

𝐉𝐮𝐥𝐲 𝟐𝟎𝟐𝟔 marks the official end of fashion's "𝐯𝐨𝐥𝐮𝐧𝐭𝐚𝐫𝐲 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞" era. With California’s SB 707 and the EU’s historic ban on destroying unsold textiles hitting large brands on July 19, the industry has crossed into strict financial enforcement. Many apparel leaders are falling into a dangerous financial trap: 𝐅𝐨𝐜𝐮𝐬𝐢𝐧𝐠 𝐞𝐧𝐭𝐢𝐫𝐞𝐥𝐲 𝐨𝐧 𝐠𝐫𝐞𝐞𝐧 𝐝𝐞𝐬𝐢𝐠𝐧 𝐰𝐡𝐢𝐥𝐞 𝐢𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐬𝐡𝐞𝐞𝐫 𝐰𝐞𝐢𝐠𝐡𝐭 𝐨𝐟 𝐨𝐯𝐞𝐫𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧. Under incoming Extended Producer Responsibility (EPR) laws, compliance fees are calculated using an unyielding equation: [𝐓𝐨𝐭𝐚𝐥 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐓𝐨𝐧𝐧𝐚𝐠𝐞] 𝐱 [𝐁𝐚𝐬𝐞 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥 𝐑𝐚𝐭𝐞] 𝐱 [𝐄𝐜𝐨-𝐌𝐨𝐝𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐢𝐞𝐫] Because volume acts as an aggressive accelerator, the math reveals a counterintuitive truth: 🚨 𝐀 "𝐆𝐨𝐨𝐝 𝐃𝐞𝐬𝐢𝐠𝐧 + 𝐇𝐢𝐠𝐡 𝐕𝐨𝐥𝐮𝐦𝐞" 𝐦𝐨𝐝𝐞𝐥 𝐜𝐚𝐧 𝐞𝐚𝐬𝐢𝐥𝐲 𝐝𝐫𝐚𝐢𝐧 𝐦𝐨𝐫𝐞 𝐜𝐚𝐬𝐡 𝐭𝐡𝐚𝐧 𝐚 "𝐁𝐚𝐝 𝐃𝐞𝐬𝐢𝐠𝐧 + 𝐋𝐨𝐰 𝐕𝐨𝐥𝐮𝐦𝐞" 𝐦𝐨𝐝𝐞𝐥. 𝐈𝐭 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐦𝐚𝐭𝐭𝐞𝐫 𝐢𝐟 𝐚 𝐠𝐚𝐫𝐦𝐞𝐧𝐭 𝐢𝐬 𝐦𝐚𝐝𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐜𝐢𝐫𝐜𝐮𝐥𝐚𝐫, 𝐭𝐞𝐱𝐭𝐢𝐥𝐞-𝐭𝐨-𝐭𝐞𝐱𝐭𝐢𝐥𝐞 𝐟𝐢𝐛𝐞𝐫 𝐨𝐧 𝐞𝐚𝐫𝐭𝐡; 𝐢𝐟 𝐚 𝐛𝐫𝐚𝐧𝐝 𝐨𝐯𝐞𝐫𝐩𝐫𝐨𝐝𝐮𝐜𝐞𝐬 𝟏𝟎𝟎 𝐭𝐨𝐧𝐬 𝐨𝐟 𝐢𝐭, 𝐭𝐡𝐞 𝐄𝐏𝐑 𝐰𝐞𝐢𝐠𝐡𝐭-𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐢𝐞𝐫 𝐰𝐢𝐥𝐥 𝐬𝐭𝐢𝐥𝐥 𝐫𝐞𝐬𝐮𝐥𝐭 𝐢𝐧 𝐚 𝐦𝐚𝐬𝐬𝐢𝐯𝐞 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐩𝐞𝐧𝐚𝐥𝐭𝐲. Conversely, a brand with a bad fabric design but a tight, lean inventory footprint keeps its total cash exposure strictly capped. 𝐘𝐨𝐮 𝐜𝐚𝐧𝐧𝐨𝐭 𝐝𝐞𝐬𝐢𝐠𝐧 𝐲𝐨𝐮𝐫 𝐰𝐚𝐲 𝐨𝐮𝐭 𝐨𝐟 𝐚𝐧 𝐨𝐯𝐞𝐫𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. EPR compliance is a supply chain volume challenge, not just a materials challenge. To navigate this without sacrificing gross margins, brands must transition to active inventory containment: 👕 𝐓𝐢𝐠𝐡𝐭𝐞𝐧 𝐭𝐡𝐞 𝐕𝐨𝐥𝐮𝐦𝐞 𝐂𝐚𝐩 𝐅𝐢𝐫𝐬𝐭: Compress your supply chain agility. Shift to small-batch manufacturing to lower total tonnage placed on the market. 👕 𝐄𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐞 𝐃𝐞𝐚𝐝𝐬𝐭𝐨𝐜𝐤 𝐃𝐫𝐚𝐠: By minimizing overproduction, you naturally insulate your business from the logistical nightmare of handling unsold stock. 👕 𝐋𝐚𝐲𝐞𝐫 𝐨𝐧 𝐆𝐫𝐞𝐞𝐧 𝐃𝐞𝐬𝐢𝐠𝐧 𝐒𝐞𝐜𝐨𝐧𝐝: Once inventory volumes are lean and responsive, transitioning to circular materials unlocks the lowest-tier eco-discounts. 𝐒𝐮𝐩𝐩𝐥𝐲 𝐜𝐡𝐚𝐢𝐧 𝐚𝐠𝐢𝐥𝐢𝐭𝐲 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐬𝐭𝐫𝐨𝐧𝐠𝐞𝐬𝐭 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐬𝐡𝐢𝐞𝐥𝐝 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐄𝐏𝐑 𝐭𝐚𝐱𝐞𝐬. 𝐈𝐬 𝐲𝐨𝐮𝐫 𝐛𝐫𝐚𝐧𝐝 𝐟𝐨𝐜𝐮𝐬𝐢𝐧𝐠 𝐭𝐨𝐨 𝐦𝐮𝐜𝐡 𝐨𝐧 𝐟𝐚𝐛𝐫𝐢𝐜 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐧𝐝 𝐧𝐨𝐭 𝐞𝐧𝐨𝐮𝐠𝐡 𝐨𝐧 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐭𝐨𝐧𝐧𝐚𝐠𝐞? It's time to rethink overproduction. If you're a brand trying to figure out how to navigate this profitably, let's talk. #RegenerativeRetail #SupplyChain #ApparelRetail #Sustainability #EPR #Overproduction 📌 Note: Regulatory updates and brief breakdowns added in the comments below!

Every company says sales and marketing are aligned...right up until pipeline misses target. Then it’s war stories and finger-pointing: - “The leads are garbage.” - “The reps aren’t following up.” - “We’re doing everything, and they’re still not closing.” That’s not alignment. That’s cohabitation. IMO alignment isn’t agreement. It’s accountability. Sitting in the same meetings? Not alignment. Agreeing on the MQL definition? Still not alignment. Real alignment looks like shared risk and shared wins. And it has to be engineered, not assumed. Here’s a few things that I've seen which helps teams stay truly aligned: 1. Start with shared OKRs, not shared dashboards. Don’t split goals: “Marketing = MQLs,” “Sales = revenue.” Build joint objectives that reflect the full journey: - Pipeline coverage - Conversion rates - Sales velocity by segment Make marketing own a revenue number. Not just lead gen. Shared targets eliminate blame. They replace “your fault” with “our forecast.” 2. Install feedback loops that don’t wait until QBRs. - Set weekly syncs between Sales and Demand Gen. - Review lead quality by persona, stage, and velocity...not just volume. - Use tools like Gong to pipe buyer objections directly into campaign messaging. Most importantly: set SLAs. If Sales flags lead quality, Marketing responds within 72 hours. No black holes. If Marketing is building campaigns in a vacuum, they’re not aligned. They’re guessing. 3. Align on the definition of “good” - together. - What does a qualified buyer actually look like? - What red flags are reps seeing early in deals? - What’s missing from form fills, content, or email follow-up that’s slowing down conversion? You’re not just aligning on lead scoring. You’re aligning on deal quality...the part no one talks about. Your personas are a hypothesis. Your sales calls are the experiment. Your feedback loop is the data pipeline. Alignment isn’t static. It’s a system. When it works, the machine hums: - Marketing runs campaigns grounded in truth, not theory. - Sales walks into better informed conversations. - Both teams point fingers at the next opportunity, not each other. tl;dr = Sales and marketing shouldn’t “get along.” They should be fused. You’re not building alignment to win Q1. You’re building it so that when things break (and they will), both teams solve the problem, not just survive the meeting.

🌿 The most sustainable garment isn't made of organic cotton, it’s the one that actually sells at full price For years, "Sustainability" has lived in the marketing department. It was about choosing the right hangtags or finding a recycled polyester blend. But if we’re honest in the boardroom, we know where the real waste is: It’s the 30% of production that ends up in a "Sale" bin or a liquidator’s truck because we placed a massive bet six months ago that didn't pay off. In 2026, overproduction isn't just an ESG problem. It’s a Capital Productivity crisis. The shift we’re seeing right now: The most profitable mid-market brands are realizing that the CFO and the Sustainability Officer actually want the same thing: Precision. Instead of the traditional "Big Bet" buy, we’re seeing a move toward what I call the Stage-Gate model: Commit to the 60%: Secure your foundational volume early. Hedge with the 40%: Hold back capacity for "greige" fabric or factory slots. Yes, you might pay a few cents more per unit for that agility. But when you compare that "speed premium" to the 40% margin hit of a clearance rack (not to mention the carbon footprint of shipping unsold goods twice), the math changes completely. The "Green" reality: At Milkyway X AI we’ve found that the best way to hit ESG goals is simply to stop guessing. When you use AI to "sense" demand rather than "forecast" it, you naturally stop overproducing. The result? You end the season with a cleaner warehouse, a healthier net margin, and almost as a byproduct a significantly smaller environmental footprint. It turns out that being "Green" is actually just the ultimate form of operational excellence.

A new MIT/Harvard study just explained how AI agents can collapse coordination costs, and solve fashion's low fullprice sell-through / overproduction crisis. The real barrier to demand-driven buying isn't technology. It's the tedious process of responsive ordering. Moving from 4-season buying to 52-week responsive ordering means 8x more purchase orders. That's 8x more supplier coordination, SKU decisions, delivery scheduling - tasks so tedious they've kept the entire industry locked into overproduction cycles. Shahidi et al., 2025's paper on AI agents in markets crystallizes why this changes now: "AI agents enable undertakings that would not have been attempted at all. By lowering the cost of exploration and execution, they expand the feasible set of options and reduce the threshold of 'worth doing.'" Placing micro-orders at high frequency is the exact use case agents are built for: — Low-stakes decisions (unlike strategic sourcing) — Low dimensionality (supplier, SKU, quantity, timing) — High volume, repetitive (perfect for compute vs. human time) — Historical data-rich (existing POs train agent preferences) Here's what this means practically: Instead of your merchandising team spending weeks consolidating 4 seasonal buys, an agent places optimized orders weekly based on real sell-through data. The agent handles: — Monitoring inventory levels across channels — Generating order recommendations per supplier — Negotiating standard terms — Scheduling deliveries — Tracking PO status The human role shifts to oversight and exception-handling, not administrative execution. We're looking for 2 forward-thinking fashion brands to pilot this approach. If your team is frustrated by the lag between customer demand and inventory response, let's experiment together. 📄 Full paper: "The Coasean Singularity? Demand, Supply, and Market Design with AI Agents" — https://lnkd.in/eSyF94K2 (Next post: why the supplier side is the real unlock—and harder problem to solve)

🚨 Your metrics aren't just misaligned. They're sabotaging your business. Imagine this (or just check last quarter’s QBR): A $2B retailer nearly derails its marketing strategy over one seemingly simple metric: 💰 Customer Lifetime Value (CLV). ⬆️ Marketing is celebrating—CLV is up 40%. 🔻 Merchandising is pushing back—CLV is down 15%. Why? Marketing measured CLV over 24 months from first purchase, focused solely on revenue—because their job is to justify acquisition spend. Merchandising used a 36-month window, subtracted returns, and included margin—because they care about long-term product profitability. Both are “right.” But neither is aligned. The CMO, stuck in the middle, splits the budget. Six months later: ❌ Profits drop ❌ Strategies are underfunded ❌ Everyone points fingers 💥 This isn’t a corner-case. It’s enterprise default mode. So, what fixes this? 🚫 Not another dashboard. 🚫 Not another data reconciliation meeting. 🚫 Not another analyst acting as a human translator. ⚡ The answer is operational data governance. Done right. Here’s what that looks like: ✅ Single source of truth One authoritative definition for each metric, consistent across systems and reports. ✅ Clear ownership model Each metric has a steward—someone responsible for its definition, accuracy, and updates. ✅ Standardized calculation logic Every metric spells out the timeframe, inclusions, exclusions, and business rules. ✅ Cross-functional validation Marketing, merchandising, finance—all have to sign off before a metric goes live. ✅ Change management process You don’t wake up to new numbers. Changes are reviewed, approved, and communicated. What doesn’t solve the problem? 🚫 Weekly “reconciliation” meetings that argue over the output instead of aligning on the input 🚫 Dashboards with team-specific versions of the truth 🚫 Hiring more analysts to patch systemic misalignment with spreadsheets Your metrics are business infrastructure. Treat them like it. Because when every team defines success differently, no one really wins. ❓ Seen this play out in your org? What’s your favorite “one metric, five definitions” horror story? Drop it below.👇