Flagship

Will you be at #Shoptalk in Vegas? We are hosting an event with some of our favorite partners – stay tuned for details! Flagship, Shoptalk #FreeYourInventoryFromExcel

"How do we order inventory and not be stuck with too much inventory? This is a problem every physical brand has." – Shaan Puri, My First Million Podcast It’s an AI problem. real-time sales data is constantly coming in, and brands should be able to forecast demand with machine accuracy. Yet, many still rely on manual forecasting, a slow, imperfect process. What if AI could make demand planning faster, smarter, and more precise? At Flagship, we help brands move beyond guesswork with AI-powered inventory optimization, so you stock what sells and avoid what doesn’t. How is your brand handling inventory forecasting today? Let’s talk. #FashionTech

Many machine learning solutions for retail inventory planning have been “black boxes” that leave planners in the dark about how decisions are made. When an AI’s decision-making process isn’t transparent and errors occur, it can cause irreparable damage to user trust.Inventory planners end up with tools that lack the transparency, usability, and control they need – often feeling alienated by technology that should be helping them. Flagship was founded on the conviction that simply delivering another opaque algorithm wasn’t the answer, so we deliberately chose a different path focused on clarity and user empowerment. Our vision is to empower inventory planners with AI tools that enhance their decision-making rather than replace it. This means designing our platform as a transparent partner – one that illuminates the “why” behind every recommendation and even lets planners ask “what if?” by adjusting inputs to see how outcomes change. By giving planners visibility and control, the technology becomes a collaborator that works with their expertise, not a black box dictating decisions. When planners can understand and influence the system’s suggestions, it builds trust and turns decision-making into a true partnership between human insight and machine intelligence. It’s time to rethink retail tech: instead of sidelining human expertise, we need solutions that put brands’ experts in the driver’s seat with AI as a co-pilot – guiding, explaining, and empowering better decisions.

Amazon’s Inventory Playbook: What You Can Learn From the Best Amazon doesn’t just react to demand—it anticipates it with precision. Their forecasting isn’t based on gut instinct—it’s powered by a sophisticated data science engine that ensures the right products are available exactly when and where they’re needed. What sets their approach apart: ?? They Predict What You’ll Buy Before You Do Amazon’s AI models analyze search trends, browsing behavior, and purchase patterns to forecast demand with extreme accuracy—allowing them to pre-position inventory before an order is even placed. ?? They Adjust in Real Time Traditional forecasting methods rely on static, historical data. Amazon’s system continuously updates based on real-time signals like local trends, seasonal shifts, and even weather patterns. ?? They Forecast at the SKU Level Instead of broad category-level predictions, Amazon forecasts at the SKU level, ensuring that the exact color, size, and style of a product is available where it’s needed most. Bringing Amazon-Level Forecasting to Retail Brands At Flagship, we were inspired by Amazon’s internal data science excellence. We bring that same level of forecasting precision to retail brands—meaningfully cutting out errors, optimizing inventory investments, and preventing costly stock-outs and overstocks. You don’t need Amazon’s infrastructure to forecast like Amazon. Smarter demand planning = higher margins, faster turns, and lower risk. #RetailTech #InventoryOptimization #DemandForecasting #AI #Flagship Explore Amazon’s approach in more detail here. https://lnkd.in/eywRZG94

Can Generative AI Replace Your Inventory Planning Tool & Team? Not So Fast. At first glance, it sounds promising—why not just ask an AI about inventory and let it handle the rest? But in reality, generative AI has serious limitations when it comes to inventory planning. ?? Here’s the problem: Inventory planning involves millions of calculations—for example, a single planning sheet for 3,500 SKUs across 24 months could easily require 1,000,000+ values. Generative AI? It’s notoriously bad at math. On top of that, constantly asking questions to your dataset would be a nightmare—time-consuming, inconsistent, and prone to errors. Why Generative AI Falls Short for Inventory Planning: ?? Limited Data Understanding AI models are trained on broad internet data, not your business’s unique inventory history. One brand’s red shirt might be a bestseller, while another’s flops—generative AI doesn’t know the difference. ?? Not Built for Forecasting Inventory planning requires spotting trends, seasonality, and demand shifts. AI struggles with time-series predictions, often overshooting one month and undershooting the next. ?? Ignores Business Rules & Constraints Lead times, order minimums, budget constraints—generative AI doesn’t account for them. It might suggest cutting inventory on bestsellers or ordering in unrealistic quantities. ? Poor at Math & Data Handling AI miscalculates stock levels, forecast accuracy, and reorder quantities. You’ll spend more time fixing errors than saving time. ?? Opaque & Unpredictable Reasoning AI can’t explain why it makes certain recommendations, making it risky for financial and operational planning. ?? Hallucinations & Costly Mistakes AI often generates confident but incorrect answers. In inventory planning, bad data = millions of dollars in mistakes. The Bottom Line Generative AI isn’t a substitute for real forecasting tools or experienced planners. It can be a helpful assistant—helping surface insights and automate reporting—but when it comes to real decision-making, accuracy, and logic still matter. Flagship #retail #inventory #planning #generative #ai #machinelearning #forecasting

?? Warehouse Costs Are Rising in 2025—What’s Next? With industrial rents up 6.6% YoY (CommercialEdge), every extra pallet in storage is eating into margins. Slow-moving SKUs aren’t just taking up space—they’re tying up cash flow and increasing holding costs. How brands are staying ahead: ? More accurate forecasting to prevent over-ordering ? Faster inventory turns to keep warehouses lean ? Lower safety stock so inventory moves before it piles up (and you’re covered with accuracy) Why it matters: ?? The average holding cost for excess inventory is 25-30% of its value per year (Gartner) ?? Overstocks cost retailers $300B annually (IHL Group) At Flagship, we meaningfully reduce forecast errors, helping brands stock exactly what they need—reducing warehouse bloat and unlocking millions in working capital. If rising storage costs are squeezing your margins, it’s time for a smarter inventory strategy. #Retail #InventoryPlanning #SupplyChain #FashionTech #Flagship

Year-Over-Year Forecasting Error Rate vs Flagship: YoY assumes history will repeat itself, ignoring shifts in trends, promotions, and external factors like market disruptions or changes in consumer behavior. Our data shows an average error (MAPE) of 110-120%. It leads to overstock & stock outs – brands either buy too much or miss demand altogether What we’ve achieved at Flagship: ? 3–4x more accurate than YoY across brands and inventory levels ? 80% of the time, our models outperform YoY forecasts At scale, even small improvements in accuracy could translate into millions in cost savings. Are you still relying on YoY? Let’s talk. #AI #RetailInnovation #InventoryPlanning #Forecasting

Forecasting at the style and color level is where many brands struggle. Inventory decisions happen at a much more detailed level. With thousands of SKUs to manage, no human can plan it manually with the same amount of precision as machine learning. We analyzed how often our forecasts outperform year-over-year (YoY) at these granular levels, and the results were clear: ? At the style level, our forecasts are more accurate 90% of the time ? At the color level, we outperform 70% of the time Smarter forecasting means less guesswork, fewer stock outs, and a better bottom line. #AIinRetail #InventoryPlanning

Ready to join a team that's transforming the retail landscape? Flagship is hiring an Implementation Manager to lead the onboarding and implementation of our AI-powered inventory planning platform. This is an exciting opportunity to work with leading brands and be at the forefront of retail innovation. https://lnkd.in/eRJQDRwj #retail #inventory #demandplanning #ai #machinelearning #supplychain #customersuccess