# Backstock Seasonal Intelligence **Status**: Idea / Early Discovery **Date**: 2026-04-02 **Origin**: Customer feedback from Autumn (Once Upon a Child - OUAC) **Problem**: Current 1-bin-1-subcategory system doesn't fit stores that organize by gender/season/size instead of POS subcategories --- ## Background Many OUAC locations (and likely other kids resale stores) organize backstock by **Gender + Season + Size** rather than by POS product subcategory. A single tote contains mixed clothing types (pants, shirts, dresses, outfits) all in the same gender/season/size combination. Forcing subcategory-level separation would multiply their tote count by 5-10x, which is physically impractical. These stores work in seasonal cycles: - **~March**: Pull Spring/Summer bins to floor, move Fall/Winter to storage - **~September**: Pull Fall/Winter bins to floor, move Spring/Summer to storage - The "flip" is a major operational event involving hundreds of bins over days/weeks - Timing varies by region (Florida vs Minnesota) --- ## Phase 1: Category Groups (Near-Term Fix) Allow stores to create **Category Groups** that map to multiple POS subcategories at once. ### How It Works - Store creates a group: "All Girls Clothing" -> maps to Girls Pants, Girls Shirts, Girls Dresses, Girls Outfits, etc. - Bin gets tagged with the group instead of a single subcategory - Store then adds custom tags on top (Spring/Summer, 4T) for their own filtering - Result: bin shows "All Girls Clothing" + "Spring/Summer" + "4T" - Behind the scenes, system knows which DRS subcategories could be in that bin for reporting ### Data Model Changes Needed - New table: `bsCategoryGroups` (id, storeDb/typeNum, name, color, textColor) - New junction table: `bsCategoryGroupMembers` (groupId, subcategoryCode) - maps group to DRS subcategories - Modify `bsBins.mainCategory` to support group references (or new column `categoryGroupId`) - Reporting queries aggregate across all subcategories in the group ### UX - Category management UI gets a "Create Group" option - When creating a group, user picks from DRS subcategory list (checkbox multi-select) - Bin creation/edit shows groups alongside individual subcategories - Reports roll up data by group ### Benefits - Stores tag bins the way they think (broad groups + custom tags) - System still knows which DRS subcategories could be in the bin - Reporting still works via group-to-subcategory mapping - Relatively small code change, works within current architecture --- ## Phase 2: Season Tag on Bins Add a **season** attribute to every bin. ### Season Values - Spring/Summer - Fall/Winter - All-Season - Untagged ### Detection - Manual: user sets it when creating/editing a bin - Auto-inferred: from creation date + store's seasonal calendar config - From POS category: coats = FW, swimwear = SS (where deterministic) ### Store Seasonal Calendar - Store-level config: when does each season start/end? - Defaults by region, customizable per store - Example: SS starts March 1, FW starts September 1 --- ## Phase 3: Seasonal Dashboard New view that shows bins through a seasonal lens instead of a flat list. ### Overview Panel - Current season indicator - Per-season stats: - Bin count (on-site vs off-site) - Estimated sell-through at current rate - Suggested pull/store date based on history - Attention alerts: - Bins in 2nd+ season (consider markdown/liquidation) - Bins that never made it to floor last season - Storage capacity utilization --- ## Phase 4: Season-Aware Aging Current age metric is linear (90 days = stale). For seasonal stores this creates noise. ### Seasonal Age Scoring - **In-season, on floor**: Normal age tracking, monitor sell-through velocity - **Off-season, in storage**: Age clock pauses - this is expected behavior - **In-season, still in storage**: Warning - missing selling days - **Multi-season survivor** (2+ seasons without emptying): Alert - items getting dated, consider liquidation This single change eliminates most false "stale" alerts for seasonal stores. --- ## Phase 5: Seasonal Flip Planner The killer operational feature. When the seasonal flip is approaching: ### Pull List Generation - Prioritized by predicted sell-through rate (fastest sellers first) - Grouped by size range for systematic floor filling - Shows which off-site storage location each bin is in (minimize trips) - Progress tracking: "142 of 380 SS bins moved to floor (37%)" ### Straggler Detection - Flags bins from the OTHER season still on the floor - "30 FW bins still on-site - move to storage?" ### Historical Comparison - When did you start the flip last year? - How many bins per season vs last year? --- ## Phase 6: Buy Cycle Intelligence Connect DRS buy/sales data to seasonal bins for predictive insights. ### Examples - "You're buying more SS Girls 4T than last year, but you have fewer bins" (capacity planning) - "Last year's FW Boys 2T sold through in 38 days. At current buy rate, you'll have 52 days of inventory" (buying pacing) - "Top-performing SS category: Girls Dresses (62% sell-through). Lowest: Boys Shorts (31%)" (buying focus) - "Your Girls SS 4T bin has 3 months of inventory based on sell-through rate" (bin-level intelligence) ### Year-Over-Year Benchmarking - Seasonal flip timing trends - Bin count growth/shrinkage by season - Average sell-through rate by season, trending over years - Which categories are growing/declining --- ## Build Sequence (Recommended) | Phase | Effort | Impact | Dependencies | |-------|--------|--------|-------------| | 1. Category Groups | Medium | High - unblocks OUAC customers immediately | None | | 2. Season Tags | Small | Medium - foundation for everything below | None | | 3. Seasonal Dashboard | Medium | High - new view, uses existing data | Phase 2 | | 4. Season-Aware Aging | Small | High - eliminates #1 source of noise | Phase 2 | | 5. Flip Planner | Large | Very High - operational game-changer | Phases 2, 3 | | 6. Buy Intelligence | Large | Very High - predictive/strategic value | Phases 1, 2, DRS data pipeline | Phases 1 and 2 can be built independently and in parallel. --- ## Open Questions - How many OUAC stores operate this way vs subcategory-based? (Validate with more customers) - Do Plato's Closet / other brands have similar seasonal patterns? - Should category groups be per-store or shared across the platform? - How granular should the seasonal calendar be? (2 seasons? 4 seasons? Custom?) - What's the minimum viable seasonal intelligence that would re-engage Autumn?