Minky Couture selects Domo to boost operational visibility and speed up decisions
Minky Couture, the Utah-based luxury blanket retailer, is deploying the Domo AI and Data Products Platform to bring inventory, supply chain, marketing, finance, logistics, and store operations into a single operating view.
The goal is simple: fewer blind spots, faster cycle times, and clearer accountability as the company scales its retail footprint and philanthropic reach.
What changes for operations teams
Domo gives Minky Couture the ability to create governed data products-clean, documented, and owned datasets that feed consistent metrics and near real-time insights. That means less spreadsheet wrangling and more time acting on exceptions.
- Track operational performance with shared KPIs and drill-downs by store, channel, and SKU.
- Optimize inventory movement with visibility from PO to shelf, highlighting aging stock and transfer opportunities.
- Improve demand forecasting accuracy with AI-assisted models that learn from sales, promos, events, and seasonality.
- Streamline decisions through alerting, approvals, and workflows embedded in dashboards.
- Tighten store operations with cleaner tasking, labor signals tied to traffic and sales, and faster feedback loops.
- Expose supply chain friction points-lead-time variability, carrier delays, vendor fill-rate drops-before they hit customers.
Why governed data products matter
Governed data products replace ad hoc extracts with versioned, trusted, reusable data building blocks. Each product has a clear owner, SLA, quality checks, and a defined purpose.
For operations, this translates to consistent metrics (e.g., stockout rate, weeks of supply, OTIF) and cleaner handoffs across merchandising, logistics, and stores-plus audit trails and policy controls that keep you ready for AI.
If you're planning to apply AI responsibly, aligning with frameworks like the NIST AI Risk Management Framework helps define guardrails early.
Metrics that should move
- Stockout rate and fill rate
- Forecast accuracy (MAPE) and bias
- Inventory turns and weeks of supply by class/store
- Lead-time variability and OTIF
- Sell-through and markdown percent
- Labor productivity and task completion rate
- Return rate and defect rate
A pragmatic 90-day rollout plan
- Weeks 1-2: Inventory your data sources and decide system of record per metric. Map owners and SLAs.
- Weeks 3-4: Standardize KPI definitions and build your first metric tree (demand to fulfillment).
- Weeks 5-6: Publish initial governed data products for sales, inventory, purchase orders, and store traffic.
- Weeks 7-8: Ship dashboards for replenishment, allocation, and vendor performance. Turn on exception alerts.
- Weeks 9-10: Pilot AI-assisted demand forecasting on a few key categories; compare to baseline.
- Weeks 11-12: Close the loop with workflow approvals (transfers, buys, markdowns). Train teams and codify rituals.
What this unlocks for Minky Couture
Teams can move beyond static reports to near real-time, AI-powered insights that anticipate demand, reduce operational friction, and keep stores in stock with fewer touches. Governance keeps data usable and safe, which sets the foundation for AI that's both effective and responsible.
That efficiency supports the company's mission-driven focus, allowing more resources to flow to community programs at national scale.
From the CEO
"Our purpose has always been bigger than blankets," said Sandi Hendry, CEO and founder of Minky Couture. "We exist to bring comfort to our customers, to families in hospitals and to people going through difficult moments. Domo gives us the clarity and confidence we need to run our business efficiently so we can continue expanding our reach and give back in even more meaningful ways."
Bottom line for operations leaders
Unified data, governed workflows, and operational AI create the conditions for faster, better decisions. Start with the few metrics that run your business, turn them into governed data products, and wire alerts to the moments that matter.
Want a quick primer on where AI fits in day-to-day ops? Explore AI for Operations.
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