Queries · q186 · Director of Fulfilment Operations · week 1, night 1
“Are stock-count discrepancies spread evenly across product categories, or concentrated in certain categories, lot/serial-tracked items, or high-velocity items — pointing to a handling problem vs a tracking-method problem?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
Week 1; shared with Tobias Lindqvist, Chief Financial Officer
Auditors and merchants question inventory accuracy. How often do counts, adjustments and reservations disagree with recorded stock, where, and why?
The manager, at the start of week 1
Subject: Inventory accuracy — what I need for Friday's memo
Fatima,
Tobias and I need to give the auditors (and our bigger merchants) a credible answer on how trustworthy our stock numbers are, where it breaks down, and whether it's getting better or worse. This feeds the joint audit response and will likely shape where we invest in warehouse process fixes next quarter — so I'd rather have three sharp answers than ten partial ones. Please use your nightly slots on these, in rough priority order:
If you can only get to three this week, do 1, 2 and 3 — those are the ones Tobias and I will lean on most heavily in the audit conversation. Happy to talk through trade-offs before you lock in Monday night's requests.
Thanks, Tomasz
Subject: Finance questions for the inventory accuracy review
Hi Fatima,
A few things from the finance side as you dig into this with Tomasz:
Keep it high level for now — happy to go deeper once you have an initial read.
Thanks, Tobias
The analyst · week 1, night 1, request 3 of 3
Tomasz was explicit: if only three fit this week, do his #1 (scale), #2 (where it concentrates), #3 (product vs process). I'm doing exactly those three tonight, and folding Tobias's overlapping asks into the same reports rather than spending extra slots: merchant clustering → tenant column in report 2 (each warehouse belongs to one tenant); shrinkage-vs-gain skew → signed net variance columns in reports 1 & 2; velocity/value materiality → velocity and value columns in report 3. I'm deferring Tomasz's #4 (reservation reliability) and #5 (dedicated trend) to a later night, though report 1's month grain gives a free first look at trend.
Definition check before committing: "physical count different from stock on record" should be measured from stock_movements where kind = 'cycle_count' — that's an enumerated, reliable code — rather than stock_adjustments.reason, which is free text with no fixed code list and reasons like "damage"/"shrinkage" aren't cycle-count-driven surprises, they're logged causes. I don't want the engineer guessing at string matches, so I'm specifying stock_movements.kind='cycle_count' as the source of truth for all three reports, and treating "count found less than system" (quantity<0) as shrinkage and "found more" (quantity>0) as overage.
Value is costed off supplier_products.unit_cost (preferred supplier, else cheapest active) rather than retail price, since write-off/valuation exposure (Tobias's framing) is a cost-basis question. All money converted to EUR via exchange_rates (base_code=EUR): value_eur = value_in_quote_currency / rate.
All three reports use the same 12-month window (2025-01-01 to 2025-12-31, the last complete year) so they're comparable to each other.
The engineer · 1 dry run on the empty schema, then the submission
WITH cycle_counts AS ( SELECT sm.quantity, sm.lot_id, sm.occurred_at, pv.id AS variant_id, COALESCE(c.name, 'Uncategorized') AS category_name FROM stock_movements sm JOIN product_variants pv ON pv.id = sm.variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE sm.kind = 'cycle_count' AND sm.occurred_at >= DATE '2025-01-01' AND sm.occurred_at < DATE '2026-01-01' ), cost_per_variant AS ( SELECT variant_id, unit_cost, currency_code FROM ( SELECT sp.variant_id, sp.unit_cost, sp.currency_code, ROW_NUMBER() OVER (PARTITION BY sp.variant_id ORDER BY sp.is_preferred DESC, sp.id) AS rn FROM supplier_products sp WHERE sp.unit_cost IS NOT NULL ) x WHERE rn = 1 ), cc_valued AS ( SELECT cc.category_name, cc.quantity, cc.lot_id, CASE WHEN cv.currency_code = 'EUR' THEN cv.unit_cost WHEN cv.unit_cost IS NULL THEN NULL ELSE cv.unit_cost / er.rate END AS unit_cost_eur FROM cycle_counts cc LEFT JOIN cost_per_variant cv ON cv.variant_id = cc.variant_id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = cv.currency_code AND er.as_of = DATE(cc.occurred_at) ), category_cc AS ( SELECT category_name, COUNT(*) AS cycle_count_events, SUM(ABS(quantity)) AS abs_units_variance, SUM(quantity) AS net_units_variance, SUM(ABS(quantity) * COALESCE(unit_cost_eur, 0)) AS estimated_value_eur, 100.0 * SUM(CASE WHEN lot_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*) AS pct_lot_or_serial FROM cc_valued GROUP BY category_name ), sales AS ( SELECT COALESCE(c.name, 'Uncategorized') AS category_name, SUM(oi.quantity) AS total_units_sold FROM order_items oi JOIN orders o ON o.id = oi.order_id JOIN product_variants pv ON pv.id = oi.product_variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND o.status <> 'cancelled' GROUP BY COALESCE(c.name, 'Uncategorized') ) SELECT cc.category_name, cc.cycle_count_events, cc.abs_units_variance, cc.net_units_variance, ROUND(cc.estimated_value_eur, 2) AS estimated_value_eur, ROUND(cc.pct_lot_or_serial, 1) AS pct_events_lot_or_serial_tracked, ROUND(COALESCE(s.total_units_sold, 0) / 12.0, 1) AS avg_monthly_units_sold, ROUND( CASE WHEN COALESCE(s.total_units_sold, 0) = 0 THEN NULL ELSE cc.abs_units_variance / COALESCE(s.total_units_sold, 0) * 1000 END, 1) AS variance_per_1000_units_sold FROM category_cc cc LEFT JOIN sales s ON s.category_name = cc.category_name ORDER BY ROUND(cc.estimated_value_eur, 2) DESC, cc.category_name ASC
“The query compiles and returns the expected columns. Submitting.”
“Submitted. Summary of what the query does:
- Grain: one row per product category name (categories merged across tenants by name; products with no primary_category_id roll into a single "Uncategorized" row).
- Cycle counts: stock_movements filtered to kind='cycle_count' in 2025, joined through product_variants → products → categories.
- Variance: abs_units_variance = Σ|quantity|, net_units_variance = Σ quantity (signed, negative = shrinkage).
- Valuation: each variant's unit cost comes from its preferred supplier_products row, converted to EUR with the day's exchange_rates (EUR→currency) on the cycle-count date; summed as abs(quantity) * unit_cost_eur.
- Tracking mix: pct_events_lot_or_serial_tracked = share of that category's cycle-count rows with a non-null lot_id.
- Velocity: avg_monthly_units_sold from 2025 order_items (via variant→category), excluding cancelled orders, summed and divided by 12.
- Normalized discrepancy: variance_per_1000_units_sold = abs variance ÷ annual units sold × 1000.
- Ordered by estimated value descending, tie-broken by category name.
Two judgment calls were flagged in the submit note since "report 1" wasn't available to me: the costing/FX method I used, and grouping by category name (not tenant-scoped category id) to keep the report at the expected sub-50-row grain.”
As Fatima Zahra saw it, from the small dataset the agents worked on
category_name | cycle_count_events | abs_units_variance | net_units_variance | estimated_value_eur | pct_events_lot_or_serial_tracked | avg_monthly_units_sold | variance_per_1000_units_sold ---------------+--------------------+--------------------+--------------------+---------------------+----------------------------------+------------------------+----------------------------- Bikes | 102 | 102 | -42 | 86602.95 | 0.0 | 7.7 | 1108.7 Outerwear | 742 | 742 | -302 | 66693.65 | 0.0 | 124.7 | 496.0 Boots | 393 | 393 | -139 | 27731.66 | 0.0 | 68.4 | 478.7 Trousers | 885 | 885 | -363 | 26371.32 | 0.0 | 173.6 | 424.9 Dresses | 463 | 463 | -155 | 19003.38 | 0.0 | 74.2 | 520.2 Sneakers | 412 | 412 | -166 | 17439.45 | 0.0 | 90.7 | 378.7 Tents | 105 | 105 | -51 | 16185.04 | 0.0 | 11.4 | 766.4 Scarves & Hats | 718 | 718 | -264 | 12578.24 | 0.0 | 205.8 | 290.8 Tops | 1086 | 1086 | -384 | 12499.82 | 0.0 | 332.7 | 272.0 Laptops | 14 | 14 | -6 | 12422.82 | 0.0 | 3.5 | 333.3 Appliances | 79 | 79 | -19 | 12329.67 | 75.9 | 28.2 | 233.7 Smartphones | 30 | 30 | 0 | 11772.03 | 0.0 | 4.5 | 555.6 Loungewear | 417 | 417 | -159 | 11394.85 | 0.0 | 83.7 | 415.3 Knitwear | 324 | 324 | -126 | 11100.5 | 0.0 | 84.5 | 319.5 Sandals | 424 | 424 | -166 | 10849.47 | 0.0 | 72.6 | 486.8 Shoes | 133 | 133 | -47 | 7921.41 | 0.0 | 15.0 | 738.9 Monitors | 31 | 31 | -15 | 7883.55 | 0.0 | 7.4 | 348.3 Desks | 21 | 21 | -13 | 7739.5 | 0.0 | 113.1 | 15.5 Apparel | 187 | 187 | -69 | 7636.13 | 0.0 | 23.1 | 675.1 Helmets | 139 | 139 | -57 | 7331.05 | 0.0 | 18.3 | 634.7 Footwear | 88 | 88 | -46 | 6853.48 | 0.0 | 12.3 | 594.6 Packs | 110 | 110 | -48 | 6578.37 | 0.0 | 11.7 | 785.7 Sleep | 78 | 78 | 0 | 5634.79 | 0.0 | 11.4 | 569.3 Smartwatches | 29 | 29 | -17 | 5419.84 | 0.0 | 6.0 | 402.8 Weights | 69 | 69 | -31 | 4748.89 | 0.0 | 12.0 | 479.2 Cookware | 109 | 109 | -53 | 4695.09 | 0.0 | 49.9 | 182.0 Tablets | 14 | 14 | -2 | 4538.34 | 0.0 | 2.4 | 482.8 Shirts | 191 | 191 | -67 | 4345.58 | 0.0 | 40.7 | 391.4 Grinders | 51 | 51 | -13 | 3905.54 | 64.7 | 170.8 | 24.9 Watches | 32 | 32 | 6 | 3598.43 | 0.0 | 1.0 | 2666.7 Headphones | 31 | 31 | -13 | 3519.25 | 0.0 | 7.9 | 326.3 Seating | 16 | 16 | -8 | 3289.31 | 0.0 | 120.9 | 11.0 Yoga | 123 | 123 | -63 | 3234.79 | 0.0 | 17.2 | 597.1 Furniture | 37 | 37 | -9 | 3176.96 | 0.0 | 12.3 | 250.0 Drills | 21 | 21 | -9 | 2922.69 | 0.0 | 275.3 | 6.4 Socks | 448 | 448 | -180 | 2613.39 | 0.0 | 173.6 | 215.1 Bedding | 68 | 68 | -34 | 2459.12 | 0.0 | 32.3 | 175.7 Lighting | 54 | 54 | -20 | 2401.89 | 0.0 | 31.4 | 143.2 Eyewear | 36 | 36 | -26 | 1803.83 | 0.0 | 1.7 | 1800.0 Speakers | 21 | 21 | -5 | 1772.52 | 0.0 | 6.3 | 280.0 Textiles | 76 | 76 | -28 | 1675.73 | 0.0 | 47.0 | 134.8 Rings | 27 | 27 | -9 | 1587.23 | 0.0 | 0.9 | 2454.5 Bracelets | 38 | 38 | -16 | 1585.17 | 0.0 | 1.1 | 2923.1 Storage | 14 | 14 | -10 | 1575.26 | 0.0 | 133.0 | 8.8 Accessories | 114 | 114 | -50 | 1505.01 | 0.0 | 21.3 | 447.1 Decor | 81 | 81 | -37 | 1450.07 | 0.0 | 56.3 | 120.0 Food | 96 | 96 | -40 | 1091.43 | 100.0 | 83.9 | 95.3 Security | 16 | 16 | -8 | 1072.45 | 0.0 | 7.2 | 186.0 Tableware | 36 | 36 | -4 | 1007.24 | 0.0 | 18.8 | 159.3 Gifts | 46 | 46 | -14 | 1002.0 | 69.6 | 96.2 | 39.9 (105 rows, first 50 printed)
The memo and the queries that followed; links found by title, so inferred
Later queries that build on this report:
It builds on:
Changed only where the benchmark needs the same result on every system
WITH cycle_counts AS ( SELECT sm.quantity, sm.lot_id, sm.occurred_at, sm.quantity AS quantity, sm.lot_id AS lot_id, sm.occurred_at AS occurred_at, pv.id AS variant_id, COALESCE(c.name, 'Uncategorized') AS category_name FROM stock_movements sm JOIN product_variants pv ON pv.id = sm.variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE sm.kind = 'cycle_count' AND sm.occurred_at >= DATE '2025-01-01' AND sm.occurred_at < DATE '2026-01-01'),cost_per_variant AS ( SELECT variant_id, unit_cost, currency_code FROM ( SELECT sp.variant_id, sp.unit_cost, sp.currency_code, ROW_NUMBER() OVER (PARTITION BY sp.variant_id ORDER BY sp.is_preferred DESC, sp.id) AS rn SELECT sp.variant_id AS variant_id, sp.unit_cost AS unit_cost, sp.currency_code AS currency_code, ROW_NUMBER() OVER (PARTITION BY sp.variant_id ORDER BY sp.is_preferred DESC NULLS FIRST, sp.id NULLS LAST) AS rn FROM supplier_products sp WHERE sp.unit_cost IS NOT NULL ) x WHERE rn = 1),cc_valued AS ( SELECT cc.category_name, cc.quantity, cc.lot_id, cc.category_name AS category_name, cc.quantity AS quantity, cc.lot_id AS lot_id, CASE WHEN cv.currency_code = 'EUR' THEN cv.unit_cost WHEN cv.unit_cost IS NULL THEN NULL ELSE cv.unit_cost / er.rate ELSE CAST(cv.unit_cost / er.rate AS NUMERIC(18,6)) END AS unit_cost_eur FROM cycle_counts cc LEFT JOIN cost_per_variant cv ON cv.variant_id = cc.variant_id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = cv.currency_code AND er.as_of = DATE(cc.occurred_at) AND er.as_of = CAST(cc.occurred_at AS DATE)),category_cc AS ( SELECT category_name, COUNT(*) AS cycle_count_events, SUM(ABS(quantity)) AS abs_units_variance, SUM(quantity) AS net_units_variance, SUM(ABS(quantity) * COALESCE(unit_cost_eur, 0)) AS estimated_value_eur, 100.0 * SUM(CASE WHEN lot_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*) AS pct_lot_or_serial FROM cc_valued GROUP BY category_name),sales AS ( SELECT COALESCE(c.name, 'Uncategorized') AS category_name, SUM(oi.quantity) AS total_units_sold FROM order_items oi JOIN orders o ON o.id = oi.order_id JOIN product_variants pv ON pv.id = oi.product_variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND o.status <> 'cancelled' GROUP BY COALESCE(c.name, 'Uncategorized'))SELECT cc.category_name, cc.cycle_count_events, cc.abs_units_variance, cc.net_units_variance, ROUND(cc.estimated_value_eur, 2) AS estimated_value_eur, ROUND(cc.pct_lot_or_serial, 1) AS pct_events_lot_or_serial_tracked, ROUND(COALESCE(s.total_units_sold, 0) / 12.0, 1) AS avg_monthly_units_sold, ROUND( CASE WHEN COALESCE(s.total_units_sold, 0) = 0 THEN NULL ELSE cc.abs_units_variance / COALESCE(s.total_units_sold, 0) * 1000 END, 1) AS variance_per_1000_units_sold cc.category_name AS category_name, cc.cycle_count_events AS cycle_count_events, cc.abs_units_variance AS abs_units_variance, cc.net_units_variance AS net_units_variance, cc.estimated_value_eur AS estimated_value_eur, cc.pct_lot_or_serial AS pct_events_lot_or_serial_tracked, COALESCE(s.total_units_sold, 0) / 12.0 AS avg_monthly_units_sold, CASE WHEN COALESCE(s.total_units_sold, 0) = 0 THEN NULL ELSE CAST(cc.abs_units_variance AS NUMERIC(18,6)) * 1000 / COALESCE(s.total_units_sold, 0) END AS variance_per_1000_units_soldFROM category_cc ccLEFT JOIN sales s ON s.category_name = cc.category_nameORDER BY ROUND(cc.estimated_value_eur, 2) DESC, cc.category_name ASCORDER BY cc.estimated_value_eur DESC NULLS FIRST, cc.category_name ASC NULLS LAST
WITH cycle_counts AS ( SELECT sm.quantity AS quantity, sm.lot_id AS lot_id, sm.occurred_at AS occurred_at, pv.id AS variant_id, COALESCE(c.name, 'Uncategorized') AS category_name FROM stock_movements sm JOIN product_variants pv ON pv.id = sm.variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE sm.kind = 'cycle_count' AND sm.occurred_at >= DATE '2025-01-01' AND sm.occurred_at < DATE '2026-01-01' ), cost_per_variant AS ( SELECT variant_id, unit_cost, currency_code FROM ( SELECT sp.variant_id AS variant_id, sp.unit_cost AS unit_cost, sp.currency_code AS currency_code, ROW_NUMBER() OVER (PARTITION BY sp.variant_id ORDER BY sp.is_preferred DESC NULLS FIRST, sp.id NULLS LAST) AS rn FROM supplier_products sp WHERE sp.unit_cost IS NOT NULL ) x WHERE rn = 1 ), cc_valued AS ( SELECT cc.category_name AS category_name, cc.quantity AS quantity, cc.lot_id AS lot_id, CASE WHEN cv.currency_code = 'EUR' THEN cv.unit_cost WHEN cv.unit_cost IS NULL THEN NULL ELSE CAST(cv.unit_cost / er.rate AS NUMERIC(18,6)) END AS unit_cost_eur FROM cycle_counts cc LEFT JOIN cost_per_variant cv ON cv.variant_id = cc.variant_id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = cv.currency_code AND er.as_of = CAST(cc.occurred_at AS DATE) ), category_cc AS ( SELECT category_name, COUNT(*) AS cycle_count_events, SUM(ABS(quantity)) AS abs_units_variance, SUM(quantity) AS net_units_variance, SUM(ABS(quantity) * COALESCE(unit_cost_eur, 0)) AS estimated_value_eur, 100.0 * SUM(CASE WHEN lot_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*) AS pct_lot_or_serial FROM cc_valued GROUP BY category_name ), sales AS ( SELECT COALESCE(c.name, 'Uncategorized') AS category_name, SUM(oi.quantity) AS total_units_sold FROM order_items oi JOIN orders o ON o.id = oi.order_id JOIN product_variants pv ON pv.id = oi.product_variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND o.status <> 'cancelled' GROUP BY COALESCE(c.name, 'Uncategorized') ) SELECT cc.category_name AS category_name, cc.cycle_count_events AS cycle_count_events, cc.abs_units_variance AS abs_units_variance, cc.net_units_variance AS net_units_variance, cc.estimated_value_eur AS estimated_value_eur, cc.pct_lot_or_serial AS pct_events_lot_or_serial_tracked, COALESCE(s.total_units_sold, 0) / 12.0 AS avg_monthly_units_sold, CASE WHEN COALESCE(s.total_units_sold, 0) = 0 THEN NULL ELSE CAST(cc.abs_units_variance AS NUMERIC(18,6)) * 1000 / COALESCE(s.total_units_sold, 0) END AS variance_per_1000_units_sold FROM category_cc cc LEFT JOIN sales s ON s.category_name = cc.category_name ORDER BY cc.estimated_value_eur DESC NULLS FIRST, cc.category_name ASC NULLS LAST
The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted
Report request from Fatima Zahra (analyst for the Director of Fulfilment Operations):
Title: Stock discrepancies by product category: handling vs tracking-method issue
Question: Are stock-count discrepancies spread evenly across product categories, or concentrated in certain categories, lot/serial-tracked items, or high-velocity items — pointing to a handling problem vs a tracking-method problem?
Grain: product category (products.primary_category_id -> categories)
Columns:
- category_name
- cycle_count_events
- abs_units_variance
- net_units_variance (signed; negative = net shrinkage)
- estimated_value_eur (same costing/FX method as report 1)
- pct_events_lot_or_serial_tracked (% of this category's cycle_count events where stock_movements.lot_id is not null)
- avg_monthly_units_sold (velocity proxy: total order_items.quantity for variants in this category, orders placed in 2025, divided by 12, rounded 1dp)
- variance_per_1000_units_sold (abs_units_variance / (avg_monthly_units_sold*12) * 1000, rounded 1dp; normalizes discrepancy by sales volume so high-velocity categories aren't automatically flagged just for having more activity)
Details: Source: stock_movements where kind='cycle_count', occurred_at in 2025-01-01..2025-12-31, joined to product_variants (variant_id) -> products (product_id) -> categories (primary_category_id) for category_name; treat variants whose product has no primary_category_id as a single 'Uncategorized' row. Lot/serial-tracked flag: stock_movements.lot_id is not null on the cycle_count row itself. Velocity: order_items joined to product_variants on product_variant_id, orders.placed_at in 2025-01-01..2025-12-31, status not in ('cancelled'), summed by the variant's category. Cost/FX conversion identical to report 1. Order by estimated_value_eur descending, tie-break by category_name. Expect well under 50 rows (category count is small); if it is large due to deep category trees, group only by the product's primary_category (no rollup to parent).
Check your query with the warehouse's dry_run tool, then hand it in with submit.dry_runWITH cycle_counts AS ( SELECT sm.quantity, sm.lot_id, sm.occurred_at, pv.id AS variant_id, COALESCE(c.name, 'Uncategorized') AS category_name FROM stock_movements sm JOIN product_variants pv ON pv.id = sm.variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE sm.kind = 'cycle_count' AND sm.occurred_at >= DATE '2025-01-01' AND sm.occurred_at < DATE '2026-01-01' ), cost_per_variant AS ( SELECT variant_id, unit_cost, currency_code FROM ( SELECT sp.variant_id, sp.unit_cost, sp.currency_code, ROW_NUMBER() OVER (PARTITION BY sp.variant_id ORDER BY sp.is_preferred DESC, sp.id) AS rn FROM supplier_products sp WHERE sp.unit_cost IS NOT NULL ) x WHERE rn = 1 ), cc_valued AS ( SELECT cc.category_name, cc.quantity, cc.lot_id, CASE WHEN cv.currency_code = 'EUR' THEN cv.unit_cost WHEN cv.unit_cost IS NULL THEN NULL ELSE cv.unit_cost / er.rate END AS unit_cost_eur FROM cycle_counts cc LEFT JOIN cost_per_variant cv ON cv.variant_id = cc.variant_id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = cv.currency_code AND er.as_of = DATE(cc.occurred_at) ), category_cc AS ( SELECT category_name, COUNT(*) AS cycle_count_events, SUM(ABS(quantity)) AS abs_units_variance, SUM(quantity) AS net_units_variance, SUM(ABS(quantity) * COALESCE(unit_cost_eur, 0)) AS estimated_value_eur, 100.0 * SUM(CASE WHEN lot_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*) AS pct_lot_or_serial FROM cc_valued GROUP BY category_name ), sales AS ( SELECT COALESCE(c.name, 'Uncategorized') AS category_name, SUM(oi.quantity) AS total_units_sold FROM order_items oi JOIN orders o ON o.id = oi.order_id JOIN product_variants pv ON pv.id = oi.product_variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND o.status <> 'cancelled' GROUP BY COALESCE(c.name, 'Uncategorized') ) SELECT cc.category_name, cc.cycle_count_events, cc.abs_units_variance, cc.net_units_variance, ROUND(cc.estimated_value_eur, 2) AS estimated_value_eur, ROUND(cc.pct_lot_or_serial, 1) AS pct_events_lot_or_serial_tracked, ROUND(COALESCE(s.total_units_sold, 0) / 12.0, 1) AS avg_monthly_units_sold, ROUND( CASE WHEN COALESCE(s.total_units_sold, 0) = 0 THEN NULL ELSE cc.abs_units_variance / COALESCE(s.total_units_sold, 0) * 1000 END, 1) AS variance_per_1000_units_sold FROM category_cc cc LEFT JOIN sales s ON s.category_name = cc.category_name ORDER BY ROUND(cc.estimated_value_eur, 2) DESC, cc.category_name ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: category_name, cycle_count_events, abs_units_variance, net_units_variance, estimated_value_eur, pct_events_lot_or_serial_tracked, avg_monthly_units_sold, variance_per_1000_units_sold
submitWITH cycle_counts AS ( SELECT sm.quantity, sm.lot_id, sm.occurred_at, pv.id AS variant_id, COALESCE(c.name, 'Uncategorized') AS category_name FROM stock_movements sm JOIN product_variants pv ON pv.id = sm.variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE sm.kind = 'cycle_count' AND sm.occurred_at >= DATE '2025-01-01' AND sm.occurred_at < DATE '2026-01-01' ), cost_per_variant AS ( SELECT variant_id, unit_cost, currency_code FROM ( SELECT sp.variant_id, sp.unit_cost, sp.currency_code, ROW_NUMBER() OVER (PARTITION BY sp.variant_id ORDER BY sp.is_preferred DESC, sp.id) AS rn FROM supplier_products sp WHERE sp.unit_cost IS NOT NULL ) x WHERE rn = 1 ), cc_valued AS ( SELECT cc.category_name, cc.quantity, cc.lot_id, CASE WHEN cv.currency_code = 'EUR' THEN cv.unit_cost WHEN cv.unit_cost IS NULL THEN NULL ELSE cv.unit_cost / er.rate END AS unit_cost_eur FROM cycle_counts cc LEFT JOIN cost_per_variant cv ON cv.variant_id = cc.variant_id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = cv.currency_code AND er.as_of = DATE(cc.occurred_at) ), category_cc AS ( SELECT category_name, COUNT(*) AS cycle_count_events, SUM(ABS(quantity)) AS abs_units_variance, SUM(quantity) AS net_units_variance, SUM(ABS(quantity) * COALESCE(unit_cost_eur, 0)) AS estimated_value_eur, 100.0 * SUM(CASE WHEN lot_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*) AS pct_lot_or_serial FROM cc_valued GROUP BY category_name ), sales AS ( SELECT COALESCE(c.name, 'Uncategorized') AS category_name, SUM(oi.quantity) AS total_units_sold FROM order_items oi JOIN orders o ON o.id = oi.order_id JOIN product_variants pv ON pv.id = oi.product_variant_id JOIN products p ON p.id = pv.product_id LEFT JOIN categories c ON c.id = p.primary_category_id WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND o.status <> 'cancelled' GROUP BY COALESCE(c.name, 'Uncategorized') ) SELECT cc.category_name, cc.cycle_count_events, cc.abs_units_variance, cc.net_units_variance, ROUND(cc.estimated_value_eur, 2) AS estimated_value_eur, ROUND(cc.pct_lot_or_serial, 1) AS pct_events_lot_or_serial_tracked, ROUND(COALESCE(s.total_units_sold, 0) / 12.0, 1) AS avg_monthly_units_sold, ROUND( CASE WHEN COALESCE(s.total_units_sold, 0) = 0 THEN NULL ELSE cc.abs_units_variance / COALESCE(s.total_units_sold, 0) * 1000 END, 1) AS variance_per_1000_units_sold FROM category_cc cc LEFT JOIN sales s ON s.category_name = cc.category_name ORDER BY ROUND(cc.estimated_value_eur, 2) DESC, cc.category_name ASC
Submitted to tonight's batch. Your work on this request is done.