Managing Seasonal Inventory

The biggest problem faced by fashion retailers right now is not CAC. Sure the fact CAC has doubled or tripled is a huge problem. The reality though is when you walk through your local mall or browse online, you will find lots of winter stock. 🧥Trenches, jackets, knits, jumpers… Yes there is an opportunity to sell this stock to the few Australians heading off off for a white Christmas ❄️, however, the reality is most of your customers are thinking about their summer wardrobes right now. For retailers this aging inventory results in less cash cycled through the business and available to invest in the next range. How can you avoid this happening? While getting demand planning right is the first step, it is not the only step. It is important during the colder months to make sure all winter clothing is receiving air time in advertising, merchandising, social media, creatives and owned media. We see so many brands only giving attention to 10-20% of the range. This results in a long tail of products that need to be sold at deep discounts or won’t sell at all. What needs to change? Marketing teams needs to work with product teams with real time data to make sure sell through is happening across the full range. If it isn’t happening they need to assess why and take immediate action. Ignoring this leads to margin erosion, dead inventory, lost profits and less cash in the bank to reinvest! Have you noticed all the winter clothes around as well?

FanShop is challenging for reasons you wouldn't expect. Here's an insider look at licensing with NFL, NCAA, NBA & NHL and the challenges. 1. Extreme Seasonality: Half of our annual FanShop sales happen between Thanksgiving & Christmas. They make for great gifts. To sell in Q4, we start manufacturing in June. Building the right amount of inventory feels impossible. Always too much or too little. The year’s performance depends on a 30-day window. It's a high pressure month. 2. Licensed Inventory isn’t Collateral: FanShop inventory can't be borrowed against. Banks aren’t approved by leagues to sell the product. In the event of liquidation, the inventory is worthless to banks. For that reason, licensed inventory isn’t as cash flow efficient. 3. Team Performance: Inventory planning is challenging because team performance is a significant driver. In the NFL last year, the Lions surprisingly went 15-2. The fan base was pumped and bought way more than we expected. LSU unexpectedly won the 2019 NCAA football Championship. We couldn’t capitalize on increased sales because we were out of stock for months. 4. Teams Change: Examples: • Re-location: Oakland Raiders → Las Vegas Raiders (2019) • Team Names Can be Cancelled: Washington Redskins, Cleveland Indians, Washington Bullets. • Logo Re-Branding: All team logos get updated over time. Each time teams change branding, old inventory must be liquidated and new SKUs added. It adds complexity and headache. 5. Cross Category Competition: In the drinkware category, we compete against… drinkware. In FanShop, we compete against all categories. Customers searching “Dallas Cowboys” on Amazon might end up buying a bottle or mouse pad or shirt. This makes ranking highly on generic team search terms difficult. It also creates a tension where our listings are competing against each other on “Dallas Cowboys” searches. It usually takes more ad spend and deals to get all of our listings seen by customers. 6. Approval, Royalties & Minimum Guarantees: To sell licensed product, the licensor must grant approval. Getting approval typically requires proving your brand can increase overall sales in a channel. Not just take from an existing licensee. Approval is for a specific category (drinkware) and a specific channel (Amazon US). To sell in an additional category, further approval must be granted. Typically 12-14% royalties are paid to the licensor. An annual minimum royalty guarantee is agreed on with each contract. If the minimum isn’t reached from sales, the licensee pays the difference out of pocket. Conclusion: So what’s the upside?😅 Licensed product has an incredible moat. The leagues are gatekeepers, they protect their brands from too much competition. FanShop is a different set of customers than those searching for water bottles. Fanshop customers search for their team first, then find your product. It’s great customer acquisition.

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.

How to Master Seasonal Inventory Management What is Seasonal Inventory? Seasonal inventory refers to the stock of goods or products that are specifically tailored to meet the demands of certain seasons or periods throughout the year. These goods are typically associated with seasonal trends, weather changes, holidays, or events that influence consumer behavior and purchasing patterns. Factors to Consider While handling seasonal inventory, there are several factors to take into account, including: 1. Demand Fluctuations Understand the seasonal variations in consumer demand for your products. 2. Lead Time Consider the lead time required to procure seasonal inventory to ensure timely availability. 3. Storage Space Assess the storage capacity needed for seasonal inventory, especially if it is bulky or perishable. 4. Marketing and Promotion Plan marketing campaigns and promotions to effectively promote seasonal products. 5. Trends and Forecasts Analyze historical sales data and market trends to anticipate demand and plan inventory levels accordingly. How to Deal with Seasonal Inventory? Various strategies can be contemplated when managing seasonal inventory, including: 1. Forecasting Utilize sales data, market research, and forecasting techniques to predict demand for seasonal products. 2. Flexible Supply Chain Maintain a flexible and agile supply chain to adjust production and procurement based on demand fluctuations. 3. Inventory Management Software Invest in inventory management software to track and manage seasonal inventory efficiently. 4. Collaboration with Suppliers Work closely with suppliers to ensure timely delivery of seasonal goods and negotiate favorable terms. 5. Discounting and Clearance Offer discounts or clearance sales for seasonal products to minimize inventory carrying costs and prevent overstocking. Benefits • Increased revenue • Enhanced customer satisfaction • Competitive advantage • Efficient inventory management • Brand loyalty Challenges • Demand variability • Inventory risks • Cash flow management • Storage costs • Competitive pressure Conclusion Seasonal inventory management is a critical aspect of retail and supply chain management, requiring careful planning, forecasting, and execution. While it presents opportunities for increased sales and customer satisfaction, it also poses challenges such as demand variability and inventory risks. By adopting proactive strategies and leveraging technology, businesses can effectively manage seasonal inventory to maximize profitability and customer engagement. #SeasonalInventory #RetailManagement #SupplyChain #InventoryManagement #SeasonalTrends #DemandForecasting #BusinessStrategy #CustomerEngagement #RetailTech #ProfitOptimization

Your ERP sees what sold. Not what could have sold. And simply "adding an AI agent" won't fix that. Here's why. The Fashion Reality Gap SAP was designed for manufacturing: Predictable demand. Stable SKUs. Long cycles. Fashion is the opposite: Demand changes weekly. Each store has a unique size curve. Seasonality hits at hyperlocal levels. Style-color-size creates infinite combinations. Rule-based systems can't adapt. They ratchet stock UP when demand grows, but never ratchet DOWN when it falls. Why GenAI Agents aren't the answer? You might think: "I'll just connect an AI agent to my ERP." Be careful. General-purpose AI agents are not built for ultra high-frequency execution. 1. Cost & Speed: Running LLM-based agents for 50,000+ daily SKU-store decisions is slow and cost-prohibitive. 2. Drift: General agents can "drift" from rules over time. You need strict mathematical precision for inventory, not creative interpretation. 3. The Blind Spot: Agents can only analyze data that exists. If your ERP never recorded "days at zero stock," the agent is just as blind as you are. The "Zara Frequency" Benchmark Zara stores turn inventory 12x per year. Competitors: 3-4x. Zara sells 85% at full price. Competitors: 60%. The difference isn't just "better predictions." It's shorter cycles. When you decide weekly instead of monthly or by season, forecast errors matter less. You correct course before the markdown pile grows. But it’s difficult to do it manually or with ERP-based systems. How Nextail solves this: Nextail isn't a chat-bot. It's a specialized decisioning system. It does what ERPs and GenAI agents can't: 1. Calculates the invisible: It estimates demand looking at the full sales context, lost sales from stockouts (the demand you didn't record). 2. Automates the scale: It executes millions of granular moves daily, balancing stock between DCs and stores based on probability, not just rules. 3. Enables "Zara Speed": It turns your seasonal or monthly planning cycle into a weekly, dynamic pulse. Don't just layer agents on top of a blind ERP. Give your inventory a brain that was built for fashion. #FashionRetail #InventoryManagement #RetailTech #Nextail #AI

One of the most expensive inventory mistakes in apparel is not a bad forecast. It is a PO that looks fine in the spreadsheet, but arrives after the demand window is already gone. This happens more often than teams admit: A bestseller has 28 days of inventory left. The PO tracker says more units are “on order.” So everyone feels safe. But the real ETA is 52 days away. That means the product is not protected. It is already on track to stock out. The problem is that many PO trackers answer the wrong question. They answer: “Did we place the order?” But the better question is: “Will this PO arrive before we lose sales?” Here is a practical AI workflow I would use with Codex or Claude. First, export 4 files: 1. Current inventory by SKU 2. Last 90 days of sales by SKU 3. Open PO file with vendor, order date, expected ship date, expected arrival date, ordered quantity, received quantity 4. Product master with style, color, size, category, season, core or seasonal flag Then ask AI to build a simple exception report. Prompt: """Create a Python script that reads these CSV files and creates a PO risk report for an apparel brand. Normalize SKU, style, color, and size fields. For every SKU, calculate: - Current inventory - Average daily sales over the last 30, 60, and 90 days - Days of cover - Estimated stockout date - Next inbound PO quantity - Expected arrival date - Gap days between stockout date and arrival date - PO risk status • Flag a SKU as high risk if the expected stockout date is before the PO arrival date. • Flag a SKU as medium risk if days of cover is less than lead time plus 14 days. • Flag a SKU as low risk if inbound arrives before projected stockout. • Flag missing ETAs, duplicate SKUs, quantity mismatches, and POs with no vendor confirmation. Add basic tests using sample rows to verify the calculations""" Most teams do not need a complicated AI transformation to start. They need a clean weekly exception report that says: “These products are about to stock out before the PO arrives” “These POs are missing ETAs” “These styles have demand, but no inbound inventory” “These seasonal products will arrive too late to matter” Instead of reviewing a giant PO spreadsheet line by line, the team can focus on the small number of decisions that actually protect revenue. Can we expedite? Can we split shipment? Can we transfer inventory? Can we increase the next PO? Can we cancel or reduce a PO that is arriving too late. AI becomes valuable when it turns messy planning data into a decision list.

Did any of the following happen to you during Black Friday this year? Inventory nightmares: Too much of what didn’t sell, too little of what did. New product delays: Arrivals after the sale—ouch. No demand plan: Sales forecasts were more "fingers crossed" than data-backed. Manufacturer mismanagement: Lead times? What lead times? Lack of strategy: No inventory plan or reorder process in place. The result? Overproduced seasonal SKUs that will sit for a year and missed opportunities from delayed products and stockouts. This was a real-life scenario I've faced in 2023. Here’s what we fixed: Built a demand plan grounded in data to align inventory with sales projections. Implemented an inventory strategy (including seasonal and reorder plans). Used a planning tool (we used Planster) to know exactly when to reorder. Tightened supplier management to enforce lead times and ensure on-time deliveries. Pre-built inventory ahead of the sale and adjusted reorders during and after, optimizing cash flow. The results? Forecast accuracy within 5-15% for top SKUs. Seasonal SKUs sold out. New products landed weeks early. Working capital stayed lean. So what are the top 5 mistakes brands make before big sales? No demand plan → "Guess-and-hope" forecasting. Poor inventory strategy → Overstock or stockouts. Supplier mismanagement → Missed lead times and late arrivals. No pre-built inventory → Scrambling during sales or leaving money on the table. Lack of post-sale strategy → Cash flow tied up in deadstock or delayed reorders. And here are 5 steps to crush your next big sale: Build a data-backed demand plan for sales projections. Create an inventory strategy for seasonal SKUs and reorders. Use tools (like Planster) to time reorders perfectly. Get tighter controls on supplier lead times and deliveries. Pre-build inventory and manage post-sale reorders to optimize working capital. Holiday sales are about more than flashy discounts—they're about execution. If you're missing the backend strategy, you're leaving growth (and profit) on the table. What’s been your biggest challenge when planning for big sales? Let’s talk. 👇

Thanksgiving week and Cyber Monday are behind us, and the holiday sprint has officially begun. The early signals are…mixed. Prices are up, discounts seem aggressive, and we won't really know the winners and losers until the dust settles, the returns are in, and margins have been calculated. But guess what, just as all that selling wraps up the next buying season kicks into gear.  It is the underappreciated part of the retail calendar. Building assortments, picking colors, adjusting size curves, and ultimately placing the POs. In fashion businesses, it is where the next year is won or lost, it is where best laid financial plans are missed before the selling starts. Merchants are placing orders right now for Fall ’26, and in this moment every inventory dollar truly counts. Demand feels unpredictable. Costs keep rising. The margin for error is shrinking. Every unit counts more than ever. And yet buyers and planners are still expected to make inventory decisions with partial data and a spreadsheet. This is where the real AI opportunity sits in retail today. Getting these decisions right means less excess, better sell-through, stronger margins, and fewer January headaches.