“It is not linear anymore,” said Tapestry’s Global Head of Data and Analytics Technology Fabio Luzzi. That shift — from slow, sequential decision-making to real-time, data-driven action — is the result of a multi-year investment by the global fashion house to unify its systems and move data upstream across the entire value chain.
For a retail brand of Tapestry’s scale, the stakes are high. Forecasting accuracy, production timelines, assortment decisions, and the ability to spot and respond to fast-changing consumer trends all depend on having the right data at the right moment. Yet, as Luzzi noted, “retail companies tend to focus on the end of the value chain… when the product is ready to be sold,” a backward-looking orientation that makes mismatches between supply and demand almost inevitable.
Speaking with PYMNTS CEO Karen Webster for the SKU series, Luzzi explained how Tapestry’s unified architecture now gives merchandising, planning, design, and supply chain teams access to shared, real-time signals. Instead of waiting weeks for one step to finish before another begins, “people can react faster to changes” because the entire organization is working from a single source of truth. That investment is helping Tapestry shorten lead times, align more precisely with consumer demand, and ultimately drive better sales outcomes.
Why Data Has Become Retail’s Most Urgent Priority
If the SKU series has revealed anything so far, it’s the industry-wide urgency around real-time data. Retail brands need visibility into customer demand, inventory positions, operational efficiency, and marketing performance.Not after the fact, but as signals emerge.
To compete for share of wallet, brands must anticipate what shoppers will want next, not simply analyze last month’s receipts. That means delivering products with greater precision and communicating at the right moment with the right information.
Advertisement: Scroll to Continue
But between setting merchandising targets and getting products onto shelves, retailers must navigate long production cycles, external volatility, and customer behavior influenced by inflation and fluctuating confidence. With legacy structures and outdated operating rhythms, most retailers still lack the real-time responsiveness needed to adapt.
Shifting Data Upstream Shortens Lead Times
Luzzi was clear: shortening the value chain could transform retail outcomes. “Being able to reduce the value chain and the lead times… would have a huge impact.”
Tapestry’s unified data architecture creates real-time or near-real-time feedback loops across the organization. Merchandising, planning, design, and supply chain teams no longer operate in a rigid sequence. They work in parallel, reacting to consumer signals as they appear. With shared access to the same data, they can identify demand shifts sooner and adjust production, allocation, and inventory strategies accordingly.
Luzzi said that the result is a more agile, responsive network capable of catching trend changes early, avoiding costly stockouts or overstocks, and improving the top and bottom line.
Fashion Still Depends on Magic — Data Protects It
Despite the new data-driven backbone, fashion still requires creative judgment at the center. Luzzi emphasized that “there is a lot of magic that happens” in design and brand expression — and AI cannot replace that.
But it can protect it.
By automating the complexity of forecasting, SKU-level analysis, and demand prediction, AI gives designers, planners, and buyers more freedom to focus on the creative work only humans can do. Tapestry uses sophisticated machine learning to detect baseline demand signals for evergreen products, then blends those insights with expert judgment. With six- to nine-month lead times, this hybrid approach ensures that both data and human insight shape the final decisions.
Fixing Retail’s Biggest Technology Problem
Luzzi’s experience across industries gave him a clear perspective: managing data at retail scale is fundamentally “a technology problem.” Most retailers have not historically operated as tech companies and often outsource critical data functions resulting in siloed information that cannot support enterprise-wide decisions.
Tapestry took a different path. Among its innovations is the “product historian,” a massive archive of digital assets and purchasing data for hundreds of thousands of SKUs. It allows teams to identify analogs for new products and pull insights quickly, even when historical sales data doesn’t exist.
Next: AI Agents for Real-Time Answers
The next phase of Tapestry’s transformation involves conversational analytics. Historically, answering a business question required waiting days for a dashboard or an analyst’s SQL query. Now, Tapestry is building domain-specific AI agents that let employees ask questions in plain English and receive answers in minutes.
These agents span merchandising, supply chain, and inventory use cases — areas where speed and clarity directly influence sales, production, and planning outcomes.
Luzzi’s advice for other retailers was simple and grounded: start with the business problem, bring people into the process from day one, and let technology be the enabler — not the end goal.
“There is no one silver bullet,” he said. “But if I have to pick, I would say be business driven, let technology enable the outcome, and bring people into the process from day one.”
