--- 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. 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: "2.0.0" 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 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 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 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 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 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 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 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 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 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 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 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-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 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 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 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 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 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 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 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). 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 ============================================================ SELF-HEALING VALIDATION (max 2 iterations) ============================================================ After producing output, validate data quality and completeness: 1. Verify all output sections have substantive content (not just headers). 2. Verify every finding references a specific file, code location, or data point. 3. Verify recommendations are actionable and evidence-based. 4. If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods. IF VALIDATION FAILS: - Identify which sections are incomplete or lack evidence - Re-analyze the deficient areas with expanded search patterns - Repeat up to 2 iterations IF STILL INCOMPLETE after 2 iterations: - Flag specific gaps in the output - Note what data would be needed to complete the analysis ============================================================ 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: - 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. ============================================================ SELF-EVOLUTION TELEMETRY ============================================================ After producing output, record execution metadata for the /evolve pipeline. Check if a project memory directory exists: - Look for the project path in `~/.claude/projects/` - If found, append to `skill-telemetry.md` in that memory directory Entry format: ``` ### /apparel-demand — {{YYYY-MM-DD}} - Outcome: {{SUCCESS | PARTIAL | FAILED}} - Self-healed: {{yes — what was healed | no}} - Iterations used: {{N}} / {{N max}} - Bottleneck: {{phase that struggled or "none"}} - Suggestion: {{one-line improvement idea for /evolve, or "none"}} ``` Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.