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Roberto Cavalli cuts buying cycle by 85% and stockouts by 75%
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43%
SKU count reduction
85%
Buying cycle reduction
75%
Stockout rate reduction
Roberto Cavalli’s collection buying process was relying on intuition, manual files and disconnected channel decisions.
Every season, teams had to rebuild the picture from fragmented information, with limited visibility into past performance, carryovers, best sellers or SKU-location demand. autone gave the Merchandising team one place to work from, combining AI-driven forecasts, historical sales and boutique-level guidance across Retail and E-commerce.
The result was an 85% reduction in buying cycle time, a 43% leaner SKU count and stockouts reduced from 20% to 5%.
Fewer unnecessary SKUs. Faster decisions. Stronger collection buying.
About the brand.
Roberto Cavalli is an Italian luxury fashion house known for bold prints, distinctive design codes and a strong global presence across ready-to-wear, accessories and lifestyle collections.
With a retail network spanning boutiques and E-commerce, the brand manages complex seasonal collections across multiple channels and locations.
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Why autone?
Roberto Cavalli needed more than faster reporting. The brand needed a way to turn past collection performance into sharper future buys, while bringing Retail and E-commerce into the same buying logic.
autone’s Buying module gave the team structured access to sales history, category best sellers, carryover analysis and SKU-location forecasts in one workflow.
Instead of buying wide to cover uncertainty, the team could see which products, sizes and quantities had the strongest commercial potential by channel and location.
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Buying forecasts driven by dynamic taxonomy and scoring
The challenge.
Before autone, collection buying relied heavily on intuition. The team did not have a structured way to connect each new buy with what had happened in previous collections.
Best-seller analysis by category, carryover performance and SKU-location demand were either unavailable or difficult to pull together. So the safer answer was often to buy wider than needed. That led to collections of over 700 SKUs, with many products recording zero sales before markdown.
Excel files and email threads carried much of the process, with no central data repository and no system that could transfer learnings from one collection to the next. Retail and E-commerce also made buying decisions separately, which created frequent channel misalignment.
The solution.
autone consolidated Roberto Cavalli’s buying data into a single platform. The Merchandising team can now access sales history, forecast models, performance analytics and SKU-location data without manually gathering or reconciling information across files.
Each new collection can now be planned using previous collection performance, category best sellers, carryover analysis and location-level forecasts. This helped rationalise the assortment from over 700 SKUs to around 400, with a stronger focus on central sizes and proven references.
Retail and E-commerce now work from the same data and the same strategic framework, creating stronger channel coherence across buying decisions.
"The impact of autone on our teams has been radical. Replacing manual files with AI-driven forecasts allowed us to cut our buying cycle from 3 weeks to just 3 days. That’s an 85% reduction in buying cycle time."
The results.
Allocation also became more precise. Roberto Cavalli moved from an 80/20 model, where 80% of stock was sent to boutiques at the start of the season, to a 30/70 model, with 70% retained centrally in the warehouse.
Stores now receive the quantities and sizes they are projected to sell, while stock is distributed across planned drops rather than pushed all at once. That gives the business more flexibility and increases E-commerce fulfilment capacity throughout the season.
SKU count reduction
The collection was rationalised from over 700 SKUs to around 400.
Buying cycle reduction
The buying cycle moved from 3 weeks to 3 days.
Stockout rate reduction
Stockouts dropped from 20% to 5%.
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