Assortment Planning for Fashion & Apparel, scoped to finance
Ward raises assortment findings scoped to finance, throughout every one of your fashion locations.
Assortment Planning on a fashion fleet, scoped to finance
Assortment Planning is a insight card type Ward runs continuously. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
In a Fashion & Apparel footprint the job is 15,000+ SKUs over locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
Your P&L surprises are born on the store floor. Ward pulls forward the signals that change a finance decision.
How it runs. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
What you get
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled fall denim against last season’s curve at the same selling week. Two causes, both still fixable this week.
| Signal | Finding |
|---|---|
sell_through | Week 6 sell-through 38% vs. 52% plan, concentrated in 3 of 9 door clusters |
size_curve | Waist 30–32 sold out in 41 doors while 36–38 sits at 71% on hand |
markdown.ladder | First markdown is 3 weeks later than LY, weeks-of-supply now 11.4 |
Recommend: transfer 30–32 out of the 12 overstocked doors, hold the ladder on core indigo, and take the first markdown on light wash now while it still clears at 20%.
sell_through_weekly…
Reporting
Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.
| Model | Horizon | MAPE |
|---|---|---|
holt_winters | 4wk | 4.1% |
arima_sarimax | 13wk | 8.9% |
gbm_demand | 1wk | 2.1% |
bayes_hier | new store | 11.4% |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
shopify_orders_daily | import | 2m ago |
shopify_returns_reasons | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
cegid_store_sales | import | 1h ago |
retail_size_curve_actuals | import | 1h ago |
retail_markdown_ladder | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-markdown | permit | Model::"markdown_ladder" |
vendor-blocked | forbid | Model::"labor_*" |
ecom-read-returns | permit | Model::"returns_reasons" |
Why Assortment matters for Fashion retail
Ward doesn't replace the buyer's eye, it sharpens the math behind the buy. Which store clusters need wider assortment with shallow depth? Which need narrow-deep buys with full size runs? Ward analyzes sell-through by cluster, customer segment, and style attribute to recommend architecture that matches how customers actually shop each location.
Why this combination
is its own problem.
A generic assortment model applied to a fashion fleet produces alerts nobody trusts. The thresholds are wrong, because fashion baselines are wrong for it. Ward learns the baseline from your own locations instead of importing one.
- 01 Cluster definitions don't account for online inventory pooling; a store next to a strong DC has different effective assortment math than a remote one.
- 02 Cannibalization between similar styles (two black bodycon dresses) is ignored, so the second option doesn't add what its standalone sell-through suggests.
Benchmarks. Fashion option counts vary 2-4x across store clusters in a healthy assortment plan. Cluster-aware planning typically lifts full-price sell-through 2-5 points and reduces end-of-season markdown depth by 100-300 bps.
What Ward has eyes on.
Coverage is store by store, category by category. Ward keeps a running read on sell-through rate, markdown %, return rate throughout 15,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the fleet is fine and knowing which seven locations are not.
The assortment model runs on a daily cycle, not on a reporting calendar. It spots the pattern, attributes the driver, and attaches a recommended action before the number reaches a review deck.
At the metric level. Ward tracks assortment width vs depth by cluster, style attribute performance, size curve accuracy, and inter-style cannibalization. It measures revenue-per-option to identify when adding more styles dilutes overall performance.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to whatever holds your transaction and inventory data. Ward starts building baselines the same day. First insight cards land inside the first 48 hours, before baselines are stable, so you can see the shape of the output early.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: operating rhythm
Cards arrive every morning and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.
Your P&L surprises are born on the store floor.
- ×Margin erosion only surfaces at month-end close
- ×Inventory carrying costs are a black box
- ×Working capital tied up in slow-moving stock nobody is watching
- ×Same-store sales comps lack decomposition into actionable drivers
- ×Capex decisions for store remodels lack unit-economics evidence
- ✓GMROI tracking by category with weekly insight cards
- ✓Inventory carrying cost alerts when capital efficiency drops
- ✓Working capital optimization recommendations based on turnover trends
- ✓SSS decomposition into traffic, conversion, and basket components
- ✓Store-level unit economics cards for capex prioritization
Inventory distortion, overstock and out-of-stock combined, costs retailers $1.77 trillion globally. Source: IHL Group
Fashion KPI impact
Frequently asked questions
Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. For Fashion retail specifically, Ward monitors 15,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Sell-through rate, Markdown %, Return rate, Style velocity, Size accuracy at the store-category level. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Your P&L surprises are born on the store floor. Ward solves this with automated insight cards: GMROI tracking by category with weekly insight cards. Inventory carrying cost alerts when capital efficiency drops. Working capital optimization recommendations based on turnover trends.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Finance decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks assortment width vs depth by cluster, style attribute performance, size curve accuracy, and inter-style cannibalization. It measures revenue-per-option to identify when adding more styles dilutes overall performance.
A denim buyer has 200 styles to allocate across 90 stores. Ward reveals that urban flagships convert best with wide assortment at shallow depth, while suburban stores need fewer core styles with full size runs. The current uniform allocation starves variety in urban stores and creates size gaps in suburban ones. A cluster-specific matrix reduces markdown risk while lifting full-price sell-through.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish stable baselines for your specific operation.
No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards, so your team acts on findings instead of hunting for them.
Related solutions
See what Fashion assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
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Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.