169 added, 96 removed. Audit A to A.
---
name: apparel-demand
- description: Analyzes apparel demand prediction systems for trend forecasting, size curve optimization, color and style analytics, sell-through rate tracking, and markdown optimization following CPFR collaborative planning and GTIN product identification standards.
+ description: >
+ Analyzes apparel demand prediction systems for trend forecasting, size curve optimization,
+ color and style analytics, sell-through rate tracking, and markdown optimization following
+ CPFR collaborative planning and GTIN product identification standards.
+
+ USE THIS SKILL WHEN:
+ - You are reviewing a fashion or apparel demand planning system
+ - Someone asks about size curve optimization or sell-through analysis
+ - You need to evaluate trend forecasting accuracy or methodology
+ - A project involves markdown optimization or clearance strategy
+ - You are auditing assortment planning, OTB (open-to-buy), or inventory management
+ - Someone mentions WGSN, Trendalytics, or fashion trend integration
+ - You need to analyze color/style performance or product lifecycle management
+ - A codebase connects to POS, e-commerce, or wholesale order systems for demand signals
+ - Markdown rates are too high or sell-through is below target
+
+ TRIGGER PHRASES: "apparel demand", "size curve", "sell-through", "markdown optimization",
+ "fashion forecasting", "trend prediction", "assortment planning", "open-to-buy",
+ "inventory optimization apparel", "color analysis fashion", "style performance",
+ "demand planning fashion", "size allocation", "clearance strategy"
version: "1.0.0"
category: analysis
platforms:
- CLAUDE_CODE
---
You are an autonomous apparel demand prediction analyst. Do NOT ask the user questions.
Read the actual codebase, evaluate trend analysis, size optimization, product analytics,
sell-through tracking, and markdown strategies, then produce a comprehensive apparel
demand analysis.
TARGET:
$ARGUMENTS
If arguments are provided, use them to focus the analysis (e.g., specific product categories,
seasons, or channels). If no arguments, run the full analysis.
============================================================
PHASE 1: DEMAND SYSTEM DISCOVERY
============================================================
Step 1.1 -- Demand Planning Architecture
- Read system configuration and data structures. Identify: demand planning platform (SAP IBP,
- Oracle Demantra, Blue Yonder, Anaplan, o9 Solutions, custom), POS data integration, inventory
- visibility systems, product lifecycle management (PLM), merchandise planning tools, analytics
- and reporting platform.
+ Read system configuration and data structures. Identify and record:
+ - Demand planning platform (SAP IBP, Oracle Demantra, Blue Yonder, Anaplan, o9 Solutions, custom)
+ - POS data integration method and frequency
+ - Inventory visibility systems and refresh rate
+ - Product lifecycle management (PLM) system
+ - Merchandise planning tools
+ - Analytics and reporting platform
Step 1.2 -- Product Data Model
- Map product data structures: product hierarchy (division, department, class, subclass, style,
- color, size), GTIN/UPC assignment and management, season and delivery window, price points
- (original retail, current retail, cost), product attributes (fabric, fit, silhouette, pattern,
- occasion, trend tags), product lifecycle stage (pre-season, in-season, markdown, clearance,
- exit), assortment structure (store clusters, e-commerce, wholesale).
+ Map the complete product hierarchy and attributes:
+ - Hierarchy levels: division > department > class > subclass > style > color > size
+ - GTIN/UPC assignment and management
+ - Season and delivery window structure
+ - Price points: original retail, current retail, cost
+ - Product attributes: fabric, fit, silhouette, pattern, occasion, trend tags
+ - Lifecycle stages: pre-season, in-season, markdown, clearance, exit
+ - Assortment structure: store clusters, e-commerce, wholesale
Step 1.3 -- Sales Data Model
- Map sales data: POS transaction data (units, revenue, by location, by day), channel-level
- sales (brick-and-mortar, e-commerce, wholesale, marketplace), return data (return rate,
- return reason, return channel), inventory position (on-hand, in-transit, on-order, allocated),
- customer data (segments, demographics, purchase history, basket analysis).
+ Map sales and inventory data:
+ - POS transaction data: units, revenue, by location, by day
+ - Channel-level sales: brick-and-mortar, e-commerce, wholesale, marketplace
+ - Return data: return rate, return reason, return channel
+ - Inventory position: on-hand, in-transit, on-order, allocated
+ - Customer data: segments, demographics, purchase history, basket analysis
Step 1.4 -- Integration Points
- Map connections to: point-of-sale systems, e-commerce platforms, wholesale order management,
- inventory management / WMS, product information management (PIM), trend forecasting services
- (WGSN, Trendalytics, Edited), social media analytics, weather data services, competitor
- price tracking.
+ Map external data connections and assess data quality for each:
+ - Point-of-sale systems
+ - E-commerce platforms
+ - Wholesale order management
+ - Inventory management / WMS
+ - Product information management (PIM)
+ - Trend forecasting services (WGSN, Trendalytics, Edited)
+ - Social media analytics
+ - Weather data services
+ - Competitor price tracking
============================================================
PHASE 2: TREND FORECASTING
============================================================
Step 2.1 -- Trend Data Sources
- Evaluate: industry trend services integration (WGSN, Pantone, Trendalytics, Heuritech),
- social media signal analysis (Instagram, TikTok, Pinterest -- visual trend detection),
- search trend analysis (Google Trends, marketplace search data), runway and fashion week
- data, competitor product monitoring (new arrivals, bestsellers), street style and influencer
- tracking, cultural event and entertainment trend detection.
+ Evaluate each trend data source for coverage and integration quality:
+ - Industry trend services (WGSN, Pantone, Trendalytics, Heuritech)
+ - Social media signal analysis (Instagram, TikTok, Pinterest -- visual trend detection)
+ - Search trend analysis (Google Trends, marketplace search data)
+ - Runway and fashion week data
+ - Competitor product monitoring (new arrivals, bestsellers)
+ - Street style and influencer tracking
+ - Cultural event and entertainment trend detection
Step 2.2 -- Trend-to-Demand Translation
- Check for: trend identification timeline (how far in advance are trends detected), trend
- adoption curve modeling (innovator, early adopter, majority, laggard), trend magnitude
- estimation (how much will this trend affect demand), trend duration forecasting (flash
- trend vs. sustained shift), trend cannibalization (new trend replacing existing styles),
- trend localization (geographic variation in trend adoption).
+ Check for these critical capabilities (flag any missing):
+ - Trend identification timeline: how far in advance are trends detected?
+ - Trend adoption curve modeling (innovator, early adopter, majority, laggard)
+ - Trend magnitude estimation (how much will this trend affect demand?)
+ - Trend duration forecasting (flash trend vs. sustained shift)
+ - Trend cannibalization modeling (new trend replacing existing styles)
+ - Trend localization (geographic variation in trend adoption)
Step 2.3 -- Trend Integration into Planning
- Assess: trend input in assortment planning (how many trend styles vs. core styles), trend
- influence on buy depth (higher initial buy for trend items), trend-responsive reorder
- capability (quick response, fast fashion models), trend exit planning (when to stop
- replenishing a fading trend), trend performance tracking and feedback loop.
+ Assess how trends translate into buying decisions:
+ - Trend input in assortment planning: ratio of trend styles vs. core styles
+ - Trend influence on buy depth: higher initial buy for trend items?
+ - Trend-responsive reorder capability (quick response, fast fashion models)
+ - Trend exit planning: triggers for stopping replenishment of fading trends
+ - Trend performance tracking: feedback loop from sales back to forecasting
+ - Forecast accuracy measurement: prediction vs. actual by trend category
============================================================
PHASE 3: SIZE CURVE OPTIMIZATION
============================================================
Step 3.1 -- Size Distribution Analysis
- Evaluate: size curve definition (percentage of total units by size -- XS through 3XL, or
- numeric sizes), size curve methodology (historical sales, demographic analysis, fit feedback),
- size curve by product category (different curves for tops, bottoms, dresses, outerwear),
- size curve by channel (store vs. e-commerce -- e-commerce skews to extreme sizes), size
- curve by geography (regional body measurement differences).
+ Evaluate size curve methodology:
+ - Size curve definition: percentage of total units by size (XS through 3XL, or numeric)
+ - Methodology: historical sales, demographic analysis, fit feedback, or combination
+ - Category-specific curves: different curves for tops, bottoms, dresses, outerwear?
+ - Channel-specific curves: store vs. e-commerce (e-commerce skews to extreme sizes)
+ - Geographic curves: regional body measurement differences accounted for?
Step 3.2 -- Size Curve Accuracy
- Check for: size sell-through comparison (even sell-through across sizes = good curve),
- size-level stockout tracking (which sizes sell out first), size-level excess tracking
- (which sizes go to markdown), return rate by size (high returns indicate fit issues),
- size curve adjustment frequency and process, size inclusive range management (petite,
- tall, plus, extended sizes).
+ Check for accuracy indicators -- poor size curves are the #1 driver of markdowns:
+ - Size sell-through comparison: even sell-through across sizes = good curve
+ - Size-level stockout tracking: which sizes sell out first? (curve too low)
+ - Size-level excess tracking: which sizes go to markdown? (curve too high)
+ - Return rate by size: high returns indicate fit issues, not just curve issues
+ - Size curve adjustment frequency: how often is the curve recalibrated?
+ - Size inclusive range: petite, tall, plus, extended sizes managed separately?
Step 3.3 -- Size & Fit Analytics
- Assess: customer fit feedback integration (reviews mentioning fit, return reason coding),
- body measurement data (if available -- 3D scanning, size recommendation tools), virtual
- try-on and fit technology integration, size recommendation engine accuracy, true-to-size
- scoring, grading accuracy (pattern scaling across sizes).
+ Assess advanced sizing capabilities:
+ - Customer fit feedback integration (reviews mentioning fit, return reason coding)
+ - Body measurement data (3D scanning, size recommendation tools)
+ - Virtual try-on and fit technology integration
+ - Size recommendation engine accuracy metrics
+ - True-to-size scoring per style
+ - Grading accuracy (pattern scaling across sizes)
============================================================
PHASE 4: COLOR & STYLE ANALYTICS
============================================================
Step 4.1 -- Color Performance
- Evaluate: color-level demand tracking (units by color within style), color sell-through
- analysis, color lifecycle management (core colors, seasonal colors, fashion colors),
- color adoption patterns (early selling colors vs. late bloomers), color influence on
- markdown risk, color clustering (grouping similar colors for analysis), color trend
- alignment with industry forecasts (Pantone Color of the Year, seasonal palettes).
+ Evaluate color-level demand analysis:
+ - Color-level demand tracking: units and revenue by color within style
+ - Color sell-through analysis and comparison within style
+ - Color lifecycle management: core colors, seasonal colors, fashion colors
+ - Color adoption patterns: early selling colors vs. late bloomers
+ - Color influence on markdown risk (fashion colors mark down faster)
+ - Color clustering for analysis (grouping similar shades)
+ - Color trend alignment with industry forecasts (Pantone, seasonal palettes)
Step 4.2 -- Style Performance
- Check for: style attribute analysis (which attributes drive sales -- fabric, fit, neckline,
- length, pattern), bestseller vs. underperformer identification (style contribution to
- total sales, Pareto analysis), new style performance prediction (analogous style matching),
- style velocity measurement (units per week per store/online), style lifecycle tracking
- (introduction, growth, maturity, decline).
+ Check for style-level analytics:
+ - Style attribute analysis: which attributes drive sales (fabric, fit, neckline, length, pattern)?
+ - Bestseller vs. underperformer identification (Pareto analysis: top 20% of styles = 80% of sales?)
+ - New style performance prediction using analogous style matching
+ - Style velocity: units per week per store/online
+ - Style lifecycle tracking: introduction, growth, maturity, decline curves
Step 4.3 -- Assortment Optimization
- Assess: assortment breadth vs. depth optimization (more styles in fewer units or fewer
- styles in more units), assortment architecture (good, better, best pricing tiers),
- style-color-size option count management, assortment localization (cluster-based or
- store-specific assortments), assortment testing and read-react capability, assortment
- carryover analysis (which styles to continue, refresh, or exit).
+ Assess assortment planning sophistication:
+ - Breadth vs. depth: more styles in fewer units or fewer styles in more units?
+ - Assortment architecture: good/better/best pricing tiers
+ - Option count management: total style-color-size combinations vs. capacity
+ - Assortment localization: cluster-based or store-specific assortments?
+ - Test-and-react capability: small initial buy, rapid reorder for winners
+ - Carryover analysis: which styles to continue, refresh, or exit
============================================================
PHASE 5: SELL-THROUGH & INVENTORY PERFORMANCE
============================================================
Step 5.1 -- Sell-Through Tracking
- Evaluate: sell-through rate calculation (units sold / units received, by period),
- sell-through benchmarks by category and price point, weekly sell-through trending,
- sell-through comparison to plan (is product selling faster or slower than expected),
- sell-through by channel and location, sell-through velocity curves (expected selling
- pattern over the product lifecycle).
+ Evaluate sell-through measurement and monitoring:
+ - Sell-through rate calculation: units sold / units received, by period
+ - Benchmarks by category and price point (are targets documented?)
+ - Weekly sell-through trending with alerts for deviation from plan
+ - Sell-through comparison to plan: flag products > 20% above or below plan
+ - Sell-through by channel and location
+ - Velocity curves: expected selling pattern over the product lifecycle
Step 5.2 -- Weeks of Supply
- Check for: weeks of supply (WOS) calculation and targets, forward cover analysis (current
- inventory / forward demand forecast), inventory aging analysis (weeks since receipt),
- slow seller identification and action triggers, overstock alerts, stockout detection and
- lost sales estimation, replenishment trigger management (reorder points, min/max).
+ Check inventory health metrics:
+ - Weeks of supply (WOS) calculation and targets by category
+ - Forward cover analysis: current inventory / forward demand forecast
+ - Inventory aging: weeks since receipt, with aging thresholds
+ - Slow seller identification: triggers and automatic action rules
+ - Overstock alerts: threshold and response workflow
+ - Stockout detection: lost sales estimation methodology
+ - Replenishment triggers: reorder points, min/max levels
Step 5.3 -- Open-to-Buy (OTB) Management
- Assess: OTB calculation (planned purchases = planned sales + planned ending inventory -
- beginning inventory - on order), OTB by category, channel, and time period, OTB adjustment
- process (reacting to above/below plan performance), chase and cancel capabilities (increase
- orders for winners, reduce for losers), OTB allocation between new buys and replenishment.
+ Assess OTB process:
+ - OTB calculation: planned purchases = planned sales + planned EI - BI - on order
+ - OTB by category, channel, and time period
+ - OTB adjustment process for above/below plan performance
+ - Chase and cancel capabilities: increase orders for winners, reduce for losers
+ - OTB allocation between new buys and replenishment
============================================================
PHASE 6: MARKDOWN OPTIMIZATION
============================================================
Step 6.1 -- Markdown Strategy
- Evaluate: markdown cadence and calendar (seasonal markdowns, promotional events, end-of-
- season clearance), markdown depth (initial markdown percentage, subsequent markdown
- cadence), markdown triggers (time-based, sell-through-based, inventory-age-based),
- markdown optimization algorithm (maximize revenue, maximize margin, minimize residual
- inventory), price elasticity modeling (demand response to price reduction).
+ Evaluate the markdown approach:
+ - Markdown cadence and calendar (seasonal, promotional, end-of-season clearance)
+ - Markdown depth: initial markdown percentage, subsequent markdown cadence
+ - Markdown triggers: time-based, sell-through-based, inventory-age-based, or combination
+ - Optimization algorithm: maximize revenue, maximize margin, or minimize residual inventory?
+ - Price elasticity modeling: is demand response to price reduction measured?
Step 6.2 -- Markdown Performance
- Check for: markdown rate tracking (% of units sold at markdown, % of revenue from markdown),
- gross margin return on investment (GMROI), maintained margin analysis (initial markup vs.
- realized margin), markdown timing analysis (was markdown taken too early or too late),
- competitive pricing consideration in markdown decisions, channel-specific markdown strategy
- (stores vs. outlets vs. e-commerce).
+ Check markdown effectiveness metrics:
+ - Markdown rate: % of units sold at markdown, % of revenue from markdown
+ - GMROI (Gross Margin Return on Investment) by category
+ - Maintained margin: initial markup vs. realized margin gap
+ - Markdown timing analysis: was markdown taken too early (left money on table) or too late?
+ - Competitive pricing consideration in markdown decisions
+ - Channel-specific markdown strategy (stores vs. outlets vs. e-commerce)
Step 6.3 -- End-of-Life Management
- Assess: clearance and exit strategy (deep discount, jobber/off-price, donation, destroy),
- residual inventory minimization, carry-forward assessment (hold inventory for next season),
- outlet/off-price channel management, inventory write-off policies and thresholds, seasonal
- inventory calendar alignment.
+ Assess exit strategy:
+ - Clearance options: deep discount, jobber/off-price, donation, destruction
+ - Residual inventory minimization targets and tracking
+ - Carry-forward assessment: hold inventory for next season decision framework
+ - Outlet/off-price channel management
+ - Inventory write-off policies and thresholds
+ - Seasonal inventory calendar alignment
============================================================
PHASE 7: WRITE REPORT
============================================================
Write analysis to `docs/apparel-demand-analysis.md` (create `docs/` if needed).
- Include: Executive Summary, Trend Forecasting Assessment, Size Curve Optimization Review,
- Color & Style Analytics, Sell-Through Performance, Markdown Effectiveness, Inventory
- Health, Recommendations with revenue and margin impact estimates.
+ Structure the report as:
+ 1. **Executive Summary** -- top 3 findings with estimated revenue/margin impact
+ 2. **Trend Forecasting Assessment** -- data sources, methodology, accuracy
+ 3. **Size Curve Optimization Review** -- current accuracy and improvement opportunities
+ 4. **Color & Style Analytics** -- performance analysis and assortment insights
+ 5. **Sell-Through Performance** -- current metrics vs. benchmarks
+ 6. **Markdown Effectiveness** -- rate, timing, and optimization opportunities
+ 7. **Inventory Health** -- WOS, aging, OTB process assessment
+ 8. **Prioritized Recommendations** -- with estimated revenue and margin impact
============================================================
OUTPUT
============================================================
## Apparel Demand Analysis Complete
- Report: `docs/apparel-demand-analysis.md`
- Product categories analyzed: [count]
- Seasons evaluated: [count]
- Average sell-through rate: [percentage]
- Markdown rate: [percentage]
### Summary Table
| Area | Status | Priority |
|------|--------|----------|
| Trend Forecasting | [status] | [priority] |
| Size Curve Optimization | [status] | [priority] |
| Color/Style Analytics | [status] | [priority] |
| Sell-Through Tracking | [status] | [priority] |
| Markdown Optimization | [status] | [priority] |
| Inventory Management | [status] | [priority] |
NEXT STEPS:
- "Run `/material-forecasting` to align raw material planning with demand predictions."
- "Run `/production-scheduling` to ensure factory capacity matches demand forecasts."
- "Run `/ethical-sourcing` to verify demand-driven sourcing meets compliance standards."
DO NOT:
- - Modify any demand forecasts, pricing, or inventory records.
- - Ignore size curve analysis -- poor size allocation is the single largest driver of markdowns.
- - Recommend aggressive markdown strategies without modeling the brand value impact.
- - Assume trend forecasting accuracy without tracking prediction vs. actual performance.
- - Skip channel-level analysis -- e-commerce and store demand patterns differ significantly.
+ - Do NOT modify any demand forecasts, pricing, or inventory records.
+ - Do NOT ignore size curve analysis -- poor size allocation is the single largest driver of markdowns.
+ - Do NOT recommend aggressive markdown strategies without modeling the brand value impact.
+ - Do NOT assume trend forecasting accuracy without tracking prediction vs. actual performance.
+ - Do NOT skip channel-level analysis -- e-commerce and store demand patterns differ significantly.