Granular, hyper-local demand forecasts for curated, local assortments

With 45% of cash-strapped customers delaying discretionary purchases, collections that don’t speak to them at all, will certainly be rejected. So the ability to create highly curated, local assortments will be a key element to a winning strategy.

AI makes it possible to take a bottom-up approach to forecasting to actually understand future demand for products at the SKU-point-of-sale level instead of relying solely on historical data and overly-broad store clusters.

Especially robust AI demand forecasts can bind different data sets such as historical, retailer, and engine-generated data to extrapolate the demand for each product and deliver smart recommendations for which products to stock, in what quantities, sizes, and in which location or channel across a brand’s network.

AI is helping fashion retailers find the sweet spot between cutting inventory costs and remaining relevant to customers through curated assortments. If you’re still using spreadsheet tools, it's time to embed AI into your business.

Automated assortment planning for faster, more sophisticated decisions and less Excel

Continuous in-season analysis and curation as reality changes

Assortments don’t live in a vacuum, and simply whittling them down won’t work if you still want to offer customers an incredible assortment of products. But if you’re still using spreadsheet tools, you won’t be able to manage the complexity required to curate assortments that change over time and by location.

By and large, many Tier 1 retailers continue to rely on basic manual spreadsheets for managing crucial assortment decisions. However, these tools lack the intelligence and automation necessary for orchestrated, dynamic, and forward-looking merchandising.

Worse yet, these tools and approaches force merchandisers to sacrifice accuracy and granularity in favor of reducing calculation complexity. Nevermind the fact that they continue to be manual.

AI, on the other hand, makes it possible to run thousands of automated simulations to recommend the most optimal product mix across stores, channels, and selling periods to maximize sales and profit. And in a matter of minutes. This simply can’t be done manually or on a spreadsheet.

The numbers are stunning. McKinsey estimates that fashion businesses that have already embedded AI into their businesses to increase operational efficiencies could achieve a 118% increase in cash flow by 2030, while those who are just starting this journey could generate a 13% increase. Laggers, on the other hand, will actually see losses.

Even if you had to make assortment decisions 12 months ago, AI makes it possible to run scenarios as stock begins to arrive at your distribution centers to reconsider these decisions to create the best assortments to match the current reality which has likely changed since the time you made your purchase.

Soon, these decision cycles will become so short, the loop will close. Assortments won’t be planned and defined before a season starts, but rather will be dynamic as real-time data will inform new decisions to discontinue products, introduce new products, etc. The pre-season and in-season will become just one continuous activity with the goal of ensuring a fresh, relevant, and appealing product mix season after season.