Retail Calibration
Health Check

Real-time, role-specific dashboards on mobile — available to all teams

Some dashboards in place; not all roles have real-time access

System-generated reports via exports; no live dashboards

Weekly or monthly reports manually compiled in spreadsheets

Real-time inventory by size, style, store, and channel — accessible on any device

Near real-time cross-channel visibility; accessible by store teams and planners

Store inventory visible but not warehouse or eCommerce; updated overnight

Inventory visibility delayed 24–72+ hours; no cross-channel view

Full attribute depth — style, size, color, class, channel — available to every role in real time

Style and location available; attribute and channel breakdowns require manual effort

Some product-level data available; no consistent view by location or channel

Inventory data is financial only — units and cost; no style, size, or attribute visibility

Data-driven culture embedded; shared metrics, weekly cadences, AI-assisted signals in use

Most departments data-informed; shared KPIs exist across functions

Some teams use reports; decision culture inconsistent across departments

Decisions made primarily on instinct; data rarely the starting point

Fully modern mobile POS — integrated with data warehouse, real-time visibility, associate tools

Mobile-enabled POS with real-time inventory and reporting integration

Functional but aging — missing mobile capability or real-time sync

Legacy / on-premise, 10+ years old, limited reporting and no mobile capability

Fully unified — all systems connected in real time to a central data warehouse

Real-time integration across POS, inventory, and eCommerce; ERP partially connected

Partial integration; some automated feeds but significant manual gaps remain

Systems operate independently — data reconciled manually in spreadsheets

Full confidence — governed, validated data architecture with automated quality checks

High confidence — data clean and consistently accurate across most systems

Inconsistent — some reports trusted, others require verification before acting

Low confidence — frequent errors, manual reconciliation required before major decisions

AI-assisted planning with predictive OTB, automated allocation, and size-curve optimization

Integrated planning tools; OTB visible in near real-time; allocation driven by data

Basic OTB tracking; allocation decisions made manually based on buyer experience

Planning done manually in spreadsheets; OTB is informal or non-existent

AI-assisted markdown optimization — scenario planning, price elasticity, predictive lift models

Sell-through and weeks-of-supply signals inform markdown timing; style-level visibility available

Informal markdown calendar; decisions made without consistent data backing

Markdowns driven by end-of-season urgency; no structured timing or depth logic

AI fully operational — governed data models enable continuous improvement and margin impact

AI-ready infrastructure in progress; piloting use cases in pricing or forecasting

Exploring AI tools but data foundation not ready; siloed or unreliable data

Not yet considering AI; no infrastructure to support it