The evolution of assortment planning in fashion retail: From spreadsheets to AI
Tamara Verdugo Justo, Product Manager for the Nextail Assortment Planning solution, is a former Buyer for Zara, Buying Manager for Forever 21, and Brand Manager for Amazon’s The Drop.
Download “Unlocking the power of Continuous Merchandising: Harnessing in-season data for pre-season planning” by RSR Research.
In the world of fashion retail, things are changing fast. We’re faced by an uncertain economy, the call to be more responsible with the world’s resources is louder than ever, and yet the focus on customer-centricity is at an all time high. This means the spotlight in the retail industry is now firmly fixed on getting product assortments right.
According to the latest insights, retailers are gearing up to make substantial changes, with Gartner projecting a 30% reduction in inventory by the end of the upcoming year. Meanwhile, Business of Fashion and McKinsey reveal that 75% of retailers are aiming to trim costs by streamlining their product offerings, and 45% of fashion executives anticipate adapting their assortment mixes to align with market dynamics.
So here’s the big question: How can you ensure that your most significant investment – your inventory – forms assortments that will sell at full price and not result in markdowns?
Artificial intelligence (AI) holds the answer and is helping fashion retailers strike the delicate balance between cost-efficiency and customer-centric assortments. Here are 3 examples of how it’s doing so.
Curated forecasts for local assortments
When nearly half of budget-conscious customers are delaying discretionary purchases, crafting highly curated, local assortments is now fundamental to a successful strategy.
AI-driven demand forecasting enables retailers to gain a much clearer understanding of future product demand at the SKU-point-of-sale level. In other words, these advanced demand forecasts determine what shoppers in a specific channel or point-of-sale will want to buy, right down to the specific item. This is a level of specificity that you can’t achieve if you are limited by spreadsheet-based processes that only allow for stock allocation based solely on historical data and overly-broad store clusters.
Robust AI-driven demand forecasts can seamlessly integrate various data sets, including historical, retailer-specific, and AI-generated data, to extrapolate demand for each product. The result? Intelligent recommendations on which products to stock, in what quantities, sizes, and across which locations or channels within a brand’s network.
Automated testing & decision-making for the ideal product mix
Assortments don’t exist in isolation, and while yes, whittling down your product offering will help you save costs, this strategy will work against you if you aren’t cutting out the right SKUs to continue meeting customer demand.
The problem with traditional spreadsheet approaches in this case is that they are unable to manage the complexity necessary for continually curating assortments that evolve by location and over time. They lack the intelligence and automation required for orchestrated, dynamic, and forward-thinking merchandising. Worse yet, they sacrifice accuracy and granularity in favor of reducing computational complexity. Nevermind the fact that working with them takes forever.
Newer AI-driven solutions for assortment planning in fashion, however, can automate the calculation of billions of data points and consider retailer goals and constraints to recommend the optimal product mix across stores, channels, and selling periods to maximize both sales and profit – all within a matter of minutes. Pinpointing the SKUs to keep, retire, or introduce has never been easier.
Unlike cumbersome spreadsheets laden with thousands of fragile macros or rigid ERP systems that impose a step-by-step, linear approach, digitized and automated placeholders let you iterate across teams and dimensions without breaking.
Continuous assortment analysis & dynamic curation
Embracing AI is a game-changer for the rapid evolution of assortment planning in fashion. Proactive retailers stand to gain a remarkable 118% boost in cash flow by 2030, thanks to AI’s transformative potential across operations, inventory management, customer insights, and more. Fashion retailers who are slower to adopt, however, risk falling behind in an era of shifting preferences due to inefficiencies and declining cash flow.
In this context, even if you made your assortment decisions a year ago, AI makes it possible to continue making the most of your inventory all throughout the season to maximize full price sales. From running pre-allocation scenarios as stock arrives at your distribution centers, to dynamically replenishing stock as demand shifts and rebalancing stock at the end of a product’s lifecycle, AI makes it possible to align these decisions with current demand.
These decision cycles are getting even faster, closing the loop between planning and execution. Assortments will no longer be rigid; instead, they adapt to real-time data for dynamic decisions on discontinuing products, introducing new offerings, and more. As the line between the pre- and in-season blurs, it will be easier than ever to offer fresh, relevant, and enticing product mix season after season.
Find out why fashion retailers are rethinking the traditional planning cycle in the guide from RSR Research: “Unlocking the power of Continuous Merchandising: Harnessing in-season data for pre-season planning”.
