Queries · q394 · Director of Fulfilment Operations · week 2, night 2
“At Atlanta (Cragsley Outfitters), Reading (Marrowby & Vane) and Den Haag (Studio Vrij) — the three sites with by far the most stock-count discrepancies — is the problem spread across the catalog, or concentrated in a handful of specific products/size-color variants?”
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 2
Subject: Re: Inventory accuracy memo — good start, a few things need tightening before I take this to Tobias/auditors
Fatima,
This is a strong first cut — the two-mechanism split and the "reservations are fine" finding are exactly what I needed, and I can rule out overselling as a risk now. A few things surprised me and I want them firmed up before we put numbers in front of auditors:
If you can only run two this week, make it #1 and #2 — those directly affect what Tobias and I say about trend direction and provisioning, which is what the auditors will press hardest on.
Thanks — good work.
Tomasz
The analyst · week 2, night 2, request 3 of 3
Night 1 settled #1 and #2 confidently, so I'm moving to #3 and #4 tonight.
#1 resolved: events_per_active_location hovers in a tight 4.6-5.9 band all year with no upward drift (Jan ~5.2-5.4, Dec ~5.2-5.3) — essentially flat. The raw event count rise tracks almost exactly with new warehouses coming online (23 new warehouses opened across the year, cumulative matches the ~85→111 growth in active locations). So the "24% rise" is a scale effect from warehouse rollout, not a genuine per-location worsening, and there's no identifiable step-change moment — I'm confident in this and will write it up without more slots.
#2 resolved, and more decisively than expected: pct_theft_of_own_adjustments sits in a tight band (~15-23%) across essentially every one of the 35 staff identities and every warehouse, and — tellingly — each named staff member's total_adjustments_logged exactly equals one specific warehouse's total (Christopher Evans = 1,506 = Atlanta's total; Omar Taylor = 1,058 = Reading's total, etc.). That's a 1:1 staff-to-warehouse mapping, not real distributed rosters, and distinct_staff_logging_theft_here is never more than 1 per site. Combined with avg_hour_of_day clustering at ~11-12 (UTC) for nearly everyone regardless of which country their warehouse is in, this looks like a systematic/automated tagging pattern, not incident-by-incident human investigation. I'm flagging this as a data-provenance caveat for the memo rather than spending more slots on it.
Tonight: one report for #3 (does the B2B/bulk segment get routine counts at all, or only the Dec-31 catch-up — using the "pick-only" finding and the 8 B2B tenants identified from last week's correction report as the test group), and two reports for #4 (is the apparel concentration driven by genuine elevated rate per location or just by warehouse size, and which specific products/variants are actually driving it at the three standout sites: Atlanta/Cragsley, Reading/Marrowby & Vane, Den Haag/Studio Vrij — notably the same three merchants Support Ops flagged as generating 34% of all support tickets).
The engineer · 1 dry run on the empty schema, then the submission
WITH three_warehouses AS ( SELECT id, name FROM warehouses WHERE name IN ('Atlanta Fulfilment Centre','Reading Fulfilment Centre','Den Haag Fulfilment Centre') ), adjustments AS ( SELECT sa.variant_id, sa.reason, sa.created_at, w.name AS warehouse_name FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN three_warehouses w ON w.id = sl.warehouse_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), variant_info AS ( SELECT pv.id AS variant_id, pv.sku, pv.name AS variant_name, p.name AS product_name FROM product_variants pv JOIN products p ON p.id = pv.product_id ), aggregated AS ( SELECT a.warehouse_name, vi.product_name, vi.sku, vi.variant_name, COUNT(*) AS adjustment_count, ROUND(100.0 * SUM(CASE WHEN a.reason = 'Missing - suspected theft' THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_theft_reason, COUNT(DISTINCT DATE(a.created_at)) AS distinct_days_affected FROM adjustments a JOIN variant_info vi ON vi.variant_id = a.variant_id GROUP BY a.warehouse_name, vi.product_name, vi.sku, vi.variant_name ) SELECT warehouse_name, product_name, sku, variant_name, adjustment_count, pct_theft_reason, distinct_days_affected, COUNT(*) OVER () AS total_distinct_variants FROM aggregated ORDER BY adjustment_count DESC, warehouse_name ASC, sku ASC LIMIT 50
“Looks good. Submitting.”
“Submitted. Summary of what the query does:
- Scope: stock_adjustments in 2025 (created_at in [2025-01-01, 2026-01-01)), restricted to the three named warehouses via stock_locations → warehouses.
- Grain: one row per warehouse × product variant, joining product_variants → products for names/SKU.
- Measures: adjustment_count (row count), pct_theft_reason (share tagged 'Missing - suspected theft', 1dp), distinct_days_affected (distinct calendar dates with ≥1 adjustment).
- Ordering/cap: sorted by adjustment_count desc, then warehouse_name, then sku; capped at 50 rows via LIMIT 50.
- Extra: added total_distinct_variants, a window-function count of all qualifying variant rows (computed before the cap), so the printed report also shows how many variants total had discrepancies even though only the top 50 are listed — flagged this addition in the submit note.”
As Fatima Zahra saw it, from the small dataset the agents worked on
warehouse_name | product_name | sku | variant_name | adjustment_count | pct_theft_reason | distinct_days_affected | total_distinct_variants ---------------------------+---------------------------------------+--------------------+-----------------+------------------+------------------+------------------------+------------------------ Atlanta Fulfilment Centre | Chunky Lambswool Scarf | CO-10927-FORE | Forest Green | 5 | 20.0 | 5 | 2927 Den Haag Fulfilment Centre | Fine Knit Merino Scarf | SV-10293-BLAC | Black | 5 | 0.0 | 5 | 2927 Atlanta Fulfilment Centre | Pom-Pom Merino Wool Beanie | CO-10248-WHIT | White | 4 | 0.0 | 4 | 2927 Atlanta Fulfilment Centre | Checked Scarf | CO-10530-GREY | Grey Marl | 4 | 25.0 | 4 | 2927 Atlanta Fulfilment Centre | Fisherman Beanie | CO-10745-FORE | Forest Green | 4 | 0.0 | 3 | 2927 Den Haag Fulfilment Centre | Ribbed Merino Wool Beanie | SV-10004-CHAR | Charcoal | 4 | 0.0 | 4 | 2927 Den Haag Fulfilment Centre | Oversized Lambswool Scarf | SV-10291-WHIT | White | 4 | 50.0 | 4 | 2927 Reading Fulfilment Centre | Fisherman Recycled Wool Beanie | MV-10038-GREY | Grey Marl | 4 | 0.0 | 4 | 2927 Reading Fulfilment Centre | Pom-Pom Merino Wool Beanie | MV-10544-RUST | Rust | 4 | 50.0 | 4 | 2927 Atlanta Fulfilment Centre | Graphic Organic Cotton Kids T-Shirt | CO-10089-1213-LILA | 12-13Y / Lilac | 3 | 0.0 | 3 | 2927 Atlanta Fulfilment Centre | Ribbed Beanie | CO-10205-GREY | Grey Marl | 3 | 0.0 | 3 | 2927 Atlanta Fulfilment Centre | Striped Organic Cotton Kids T-Shirt | CO-10221-45Y-NAVY | 4-5Y / Navy | 3 | 33.3 | 3 | 2927 Atlanta Fulfilment Centre | Oversized Merino Scarf | CO-10388-SKYB | Sky Blue | 3 | 0.0 | 3 | 2927 Atlanta Fulfilment Centre | Cardigan Cashmere Blend Sweater | CO-10400-S-CAME | S / Camel | 3 | 0.0 | 3 | 2927 Atlanta Fulfilment Centre | Fisherman Recycled Wool Beanie | CO-10487-WHIT | White | 3 | 33.3 | 3 | 2927 Atlanta Fulfilment Centre | Ribbed Merino Wool Beanie | CO-10609-GREY | Grey Marl | 3 | 33.3 | 3 | 2927 Atlanta Fulfilment Centre | Bootcut Organic Denim Jeans | CO-10682-W36-BURG | W36 / Burgundy | 3 | 0.0 | 3 | 2927 Atlanta Fulfilment Centre | Pullover Loopback Cotton Hoodie | CO-10786-XS-CHAR | XS / Charcoal | 3 | 0.0 | 3 | 2927 Atlanta Fulfilment Centre | Oversized Scarf | CO-11023-OLIV | Olive | 3 | 33.3 | 3 | 2927 Atlanta Fulfilment Centre | Pullover Organic Cotton Fleece Hoodie | CO-11172-XL-ECRU | XL / Ecru | 3 | 0.0 | 3 | 2927 Den Haag Fulfilment Centre | Checked Scarf | SV-10062-ECRU | Ecru | 3 | 33.3 | 3 | 2927 Den Haag Fulfilment Centre | Checked Scarf | SV-10115-WHIT | White | 3 | 33.3 | 3 | 2927 Den Haag Fulfilment Centre | Fisherman Beanie | SV-10221-BURG | Burgundy | 3 | 0.0 | 3 | 2927 Den Haag Fulfilment Centre | Checked Merino Scarf | SV-10296-SAND | Sand | 3 | 0.0 | 3 | 2927 Reading Fulfilment Centre | Knitted Viscose Dress | MV-10060-XL-BLAC | XL / Black | 3 | 0.0 | 3 | 2927 Reading Fulfilment Centre | Fisherman Beanie | MV-10496-RUST | Rust | 3 | 33.3 | 3 | 2927 Reading Fulfilment Centre | Pom-Pom Beanie | MV-10533-BLAC | Black | 3 | 0.0 | 3 | 2927 Reading Fulfilment Centre | Fine Knit Lambswool Scarf | MV-10618-FORE | Forest Green | 3 | 0.0 | 3 | 2927 Reading Fulfilment Centre | Ribbed Recycled Wool Beanie | MV-10724-SAND | Sand | 3 | 33.3 | 3 | 2927 Atlanta Fulfilment Centre | Fine Knit Scarf | CO-10020-CAME | Camel | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Court Sneakers | CO-10026-38-SKYB | 38 / Sky Blue | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Oversized Lambswool Scarf | CO-10034-BLAC | Black | 2 | 50.0 | 2 | 2927 Atlanta Fulfilment Centre | Chunky Lambswool Scarf | CO-10062-WHIT | White | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Ribbed Recycled Wool Beanie | CO-10067-WHIT | White | 2 | 50.0 | 2 | 2927 Atlanta Fulfilment Centre | Corduroy Shirt | CO-10068-S-BLAC | S / Black | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Bootcut Jeans | CO-10075-W36-GREY | W36 / Grey Marl | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Overcoat Coat | CO-10079-XXL-BLAC | XXL / Black | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Crew Neck Cashmere Blend Sweater | CO-10080-XL-SKYB | XL / Sky Blue | 2 | 50.0 | 2 | 2927 Atlanta Fulfilment Centre | Pom-Pom Merino Wool Beanie | CO-10095-SKYB | Sky Blue | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Crew Neck Lambswool Sweater | CO-10109-M-ECRU | M / Ecru | 2 | 50.0 | 2 | 2927 Atlanta Fulfilment Centre | 3-Pack Organic Cotton Socks | CO-10140-L-RUST | L / Rust | 2 | 50.0 | 2 | 2927 Atlanta Fulfilment Centre | Garment Dyed Loopback Cotton Hoodie | CO-10143-L-OLIV | L / Olive | 2 | 50.0 | 2 | 2927 Atlanta Fulfilment Centre | Ribbed Merino Wool Beanie | CO-10160-BURG | Burgundy | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Striped Cotton Jersey T-Shirt | CO-10161-M-ECRU | M / Ecru | 2 | 50.0 | 2 | 2927 Atlanta Fulfilment Centre | Checked Merino Scarf | CO-10166-SAND | Sand | 2 | 50.0 | 2 | 2927 Atlanta Fulfilment Centre | V-Neck Merino Wool Sweater | CO-10173-L-ECRU | L / Ecru | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Pom-Pom Beanie | CO-10178-FORE | Forest Green | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Pom-Pom Beanie | CO-10178-GREY | Grey Marl | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Court Recycled Canvas Sneakers | CO-10186-37-NAVY | 37 / Navy | 2 | 0.0 | 2 | 2927 Atlanta Fulfilment Centre | Fine Knit Cashmere Scarf | CO-10190-CAME | Camel | 2 | 0.0 | 2 | 2927 (50 rows)
The memo and the queries that followed; links found by title, so inferred
It builds on:
Changed only where the benchmark needs the same result on every system
WITH three_warehouses AS ( SELECT id, name FROM warehouses WHERE name IN ('Atlanta Fulfilment Centre','Reading Fulfilment Centre','Den Haag Fulfilment Centre')),adjustments AS ( SELECT sa.variant_id, sa.reason, sa.created_at, w.name AS warehouse_name SELECT sa.variant_id AS variant_id, sa.reason AS reason, sa.created_at AS created_at, w.name AS warehouse_name FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN three_warehouses w ON w.id = sl.warehouse_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'),variant_info AS ( SELECT pv.id AS variant_id, pv.sku, pv.name AS variant_name, p.name AS product_name SELECT pv.id AS variant_id, pv.sku AS sku, pv.name AS variant_name, p.name AS product_name FROM product_variants pv JOIN products p ON p.id = pv.product_id),aggregated AS ( SELECT a.warehouse_name, vi.product_name, vi.sku, vi.variant_name, a.warehouse_name AS warehouse_name, vi.product_name AS product_name, vi.sku AS sku, vi.variant_name AS variant_name, COUNT(*) AS adjustment_count, ROUND(100.0 * SUM(CASE WHEN a.reason = 'Missing - suspected theft' THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_theft_reason, COUNT(DISTINCT DATE(a.created_at)) AS distinct_days_affected 100.0 * SUM(CASE WHEN a.reason = 'Missing - suspected theft' THEN 1 ELSE 0 END) / COUNT(*) AS pct_theft_reason, COUNT(DISTINCT CAST(a.created_at AS DATE)) AS distinct_days_affected FROM adjustments a JOIN variant_info vi ON vi.variant_id = a.variant_id GROUP BY a.warehouse_name, vi.product_name, vi.sku, vi.variant_name)SELECT warehouse_name, product_name, sku, variant_name, adjustment_count, pct_theft_reason, distinct_days_affected, COUNT(*) OVER () AS total_distinct_variantsFROM aggregatedORDER BY adjustment_count DESC, warehouse_name ASC, sku ASCLIMIT 50ORDER BY adjustment_count DESC NULLS FIRST, warehouse_name ASC NULLS LAST, sku ASC NULLS LAST, product_name ASC NULLS LAST, variant_name ASC NULLS LAST{{fetch_first(50)}}
WITH three_warehouses AS ( SELECT id, name FROM warehouses WHERE name IN ('Atlanta Fulfilment Centre','Reading Fulfilment Centre','Den Haag Fulfilment Centre') ), adjustments AS ( SELECT sa.variant_id AS variant_id, sa.reason AS reason, sa.created_at AS created_at, w.name AS warehouse_name FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN three_warehouses w ON w.id = sl.warehouse_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), variant_info AS ( SELECT pv.id AS variant_id, pv.sku AS sku, pv.name AS variant_name, p.name AS product_name FROM product_variants pv JOIN products p ON p.id = pv.product_id ), aggregated AS ( SELECT a.warehouse_name AS warehouse_name, vi.product_name AS product_name, vi.sku AS sku, vi.variant_name AS variant_name, COUNT(*) AS adjustment_count, 100.0 * SUM(CASE WHEN a.reason = 'Missing - suspected theft' THEN 1 ELSE 0 END) / COUNT(*) AS pct_theft_reason, COUNT(DISTINCT CAST(a.created_at AS DATE)) AS distinct_days_affected FROM adjustments a JOIN variant_info vi ON vi.variant_id = a.variant_id GROUP BY a.warehouse_name, vi.product_name, vi.sku, vi.variant_name ) SELECT warehouse_name, product_name, sku, variant_name, adjustment_count, pct_theft_reason, distinct_days_affected, COUNT(*) OVER () AS total_distinct_variants FROM aggregated ORDER BY adjustment_count DESC NULLS FIRST, warehouse_name ASC NULLS LAST, sku ASC NULLS LAST, product_name ASC NULLS LAST, variant_name ASC NULLS LAST FETCH FIRST 50 ROWS ONLY
WITH three_warehouses AS ( SELECT id, name FROM warehouses WHERE name IN ('Atlanta Fulfilment Centre','Reading Fulfilment Centre','Den Haag Fulfilment Centre') ), adjustments AS ( SELECT sa.variant_id AS variant_id, sa.reason AS reason, sa.created_at AS created_at, w.name AS warehouse_name FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN three_warehouses w ON w.id = sl.warehouse_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), variant_info AS ( SELECT pv.id AS variant_id, pv.sku AS sku, pv.name AS variant_name, p.name AS product_name FROM product_variants pv JOIN products p ON p.id = pv.product_id ), aggregated AS ( SELECT a.warehouse_name AS warehouse_name, vi.product_name AS product_name, vi.sku AS sku, vi.variant_name AS variant_name, COUNT(*) AS adjustment_count, 100.0 * SUM(CASE WHEN a.reason = 'Missing - suspected theft' THEN 1 ELSE 0 END) / COUNT(*) AS pct_theft_reason, COUNT(DISTINCT CAST(a.created_at AS DATE)) AS distinct_days_affected FROM adjustments a JOIN variant_info vi ON vi.variant_id = a.variant_id GROUP BY a.warehouse_name, vi.product_name, vi.sku, vi.variant_name ) SELECT warehouse_name, product_name, sku, variant_name, adjustment_count, pct_theft_reason, distinct_days_affected, COUNT(*) OVER () AS total_distinct_variants FROM aggregated ORDER BY adjustment_count DESC NULLS FIRST, warehouse_name ASC NULLS LAST, sku ASC NULLS LAST, product_name ASC NULLS LAST, variant_name ASC NULLS LAST LIMIT 50
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: Which specific products drive discrepancies at the three standout apparel sites Question: At Atlanta (Cragsley Outfitters), Reading (Marrowby & Vane) and Den Haag (Studio Vrij) — the three sites with by far the most stock-count discrepancies — is the problem spread across the catalog, or concentrated in a handful of specific products/size-color variants? Grain: product variant, restricted to stock_adjustments rows at these three specific warehouses in 2025: Atlanta Fulfilment Centre, Reading Fulfilment Centre, Den Haag Fulfilment Centre Columns: - warehouse_name - product_name - sku - variant_name (product_variants.name, which typically encodes size/color) - adjustment_count (count of stock_adjustments rows for this variant at this warehouse) - pct_theft_reason (% of this variant's adjustments at this warehouse tagged 'Missing - suspected theft', rounded 1dp) - distinct_days_affected (count of distinct calendar dates with >=1 adjustment for this variant at this warehouse) Details: Source: stock_adjustments, created_at between 2025-01-01 and 2025-12-31 (half-open bound), joined to stock_locations (filtered to the 3 named warehouses by name) and to product_variants (via variant_id) and products (for product_name). Group by warehouse, product, sku, variant_name. Order by adjustment_count descending, tie-break by warehouse_name, then sku. Cap at 50 rows and note the total distinct variant count even if it exceeds 50. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH three_warehouses AS ( SELECT id, name FROM warehouses WHERE name IN ('Atlanta Fulfilment Centre','Reading Fulfilment Centre','Den Haag Fulfilment Centre') ), adjustments AS ( SELECT sa.variant_id, sa.reason, sa.created_at, w.name AS warehouse_name FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN three_warehouses w ON w.id = sl.warehouse_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), variant_info AS ( SELECT pv.id AS variant_id, pv.sku, pv.name AS variant_name, p.name AS product_name FROM product_variants pv JOIN products p ON p.id = pv.product_id ), aggregated AS ( SELECT a.warehouse_name, vi.product_name, vi.sku, vi.variant_name, COUNT(*) AS adjustment_count, ROUND(100.0 * SUM(CASE WHEN a.reason = 'Missing - suspected theft' THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_theft_reason, COUNT(DISTINCT DATE(a.created_at)) AS distinct_days_affected FROM adjustments a JOIN variant_info vi ON vi.variant_id = a.variant_id GROUP BY a.warehouse_name, vi.product_name, vi.sku, vi.variant_name ) SELECT warehouse_name, product_name, sku, variant_name, adjustment_count, pct_theft_reason, distinct_days_affected, COUNT(*) OVER () AS total_distinct_variants FROM aggregated ORDER BY adjustment_count DESC, warehouse_name ASC, sku ASC LIMIT 50
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: warehouse_name, product_name, sku, variant_name, adjustment_count, pct_theft_reason, distinct_days_affected, total_distinct_variants
submitWITH three_warehouses AS ( SELECT id, name FROM warehouses WHERE name IN ('Atlanta Fulfilment Centre','Reading Fulfilment Centre','Den Haag Fulfilment Centre') ), adjustments AS ( SELECT sa.variant_id, sa.reason, sa.created_at, w.name AS warehouse_name FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN three_warehouses w ON w.id = sl.warehouse_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), variant_info AS ( SELECT pv.id AS variant_id, pv.sku, pv.name AS variant_name, p.name AS product_name FROM product_variants pv JOIN products p ON p.id = pv.product_id ), aggregated AS ( SELECT a.warehouse_name, vi.product_name, vi.sku, vi.variant_name, COUNT(*) AS adjustment_count, ROUND(100.0 * SUM(CASE WHEN a.reason = 'Missing - suspected theft' THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_theft_reason, COUNT(DISTINCT DATE(a.created_at)) AS distinct_days_affected FROM adjustments a JOIN variant_info vi ON vi.variant_id = a.variant_id GROUP BY a.warehouse_name, vi.product_name, vi.sku, vi.variant_name ) SELECT warehouse_name, product_name, sku, variant_name, adjustment_count, pct_theft_reason, distinct_days_affected, COUNT(*) OVER () AS total_distinct_variants FROM aggregated ORDER BY adjustment_count DESC, warehouse_name ASC, sku ASC LIMIT 50
Submitted to tonight's batch. Your work on this request is done.