=== Report 1: Is the December 31 correction one single batch event, or genuinely independent per merchant? (written by Bea) === Engineer's note: Grain is the distinct exact occurred_at timestamp among 2025 adjustment movements; warehouse/tenant joins are used only for the distinct counts, not to filter rows. The report will show however many distinct timestamp values exist (expected to be well under 50); if it turns out to exceed 50, let me know and I'll add a cap or pre-aggregate further. REPORT: Is the December 31 correction one single batch event, or genuinely independent per merchant? QUESTION: Did the 407 stock_movements kind='adjustment' events in 2025 all fire at the exact same timestamp (suggesting one system/data event), or do different merchants' corrections have distinct timestamps (consistent with independent, organic annual stocktakes)? occurred_at | event_count | distinct_tenants | distinct_warehouses | total_abs_quantity ---------------------------+-------------+------------------+---------------------+------------------- 2025-12-31 22:59:59.999999 | 406 | 59 | 59 | 4066 2025-05-19 09:00:00 | 1 | 1 | 1 | 1 (2 rows) === Report 2: Do the zero-activity 'Warehouse' sites hold real inventory? (written by Felix) === Engineer's note: The request names 10 warehouse/tenant pairs but refers to "9 zero-activity sites" — I included all 10 listed pairs since that's the explicit list given; please flag if one should be dropped. REPORT: Do the zero-activity 'Warehouse' sites hold real inventory? QUESTION: The apparel merchants' secondary sites named '...Warehouse' (as opposed to '...Fulfilment Centre') showed zero stock-count activity all year — are these empty/inactive shells, or do they hold meaningful live inventory that is simply never being counted (a blind spot)? warehouse_name | tenant_name | total_stock_on_hand | distinct_variants_stocked | pct_of_tenant_total_stock_units --------------------+----------------------+---------------------+---------------------------+-------------------------------- Aarhus Warehouse | Oakhurst Tailoring | 0 | 0 | 0.0 Boston Warehouse | Cragsley Outfitters | 0 | 0 | 0.0 Brussels Warehouse | Studio Vrij | 0 | 0 | 0.0 Calgary Warehouse | Cragsley Outfitters | 0 | 0 | 0.0 Glasgow Warehouse | Thistlecombe | 0 | 0 | 0.0 Groningen Warehouse | Studio Vrij | 0 | 0 | 0.0 Limerick Warehouse | Marrowby & Vane | 0 | 0 | 0.0 Preston Warehouse | Marrowby & Vane | 0 | 0 | 0.0 Toulouse Warehouse | Rue Lafayette Studio | 0 | 0 | 0.0 Uppsala Warehouse | Velvet Ash | 0 | 0 | 0.0 (10 rows) === Report 3: Is the ~1.08 adjustments-per-variant ratio apparel-specific or company-wide? (written by Felix) === Engineer's note: Grouped by tenants.industry (the free-text field on the tenants table) since that's the only industry/vertical attribute available; adjustment counts are all stock_adjustments rows (table has no distinction between "routine" and other reasons) in 2025, reached via stock_location_id -> stock_locations -> warehouses -> tenants. REPORT: Is the ~1.08 adjustments-per-variant ratio apparel-specific or company-wide? QUESTION: Is the near-constant rate of about 1.08 routine stock-adjustments per distinct affected variant per year a quirk of the apparel/footwear vertical, or does it hold across all merchant verticals — i.e., is the whole routine discrepancy stream essentially a flat background rate of roughly one single-unit miss per SKU per year, regardless of product type? industry | total_routine_adjustments_2025 | distinct_variants_with_adjustment | adjustments_per_variant | distinct_tenants_in_industry --------------------------+--------------------------------+-----------------------------------+-------------------------+----------------------------- Sports & Outdoor | 1343 | 1181 | 1.137 | 13 Jewelry & Accessories | 206 | 185 | 1.114 | 6 Industrial Supplies (B2B) | 165 | 150 | 1.1 | 5 Office Supplies (B2B) | 99 | 90 | 1.1 | 4 Apparel & Footwear | 6503 | 6000 | 1.084 | 25 Pet Supplies | 225 | 209 | 1.077 | 10 Home & Living | 589 | 552 | 1.067 | 16 Consumer Electronics | 293 | 275 | 1.065 | 9 Food & Coffee | 244 | 231 | 1.056 | 11 Beauty | 410 | 389 | 1.054 | 15 Books & Stationery | 117 | 111 | 1.054 | 6 (11 rows)