Queries · q391 · Director of Fulfilment Operations · week 2, night 1
“Does the 'Missing - suspected theft' reason concentrate in a handful of specific warehouses/sites, or is it spread evenly across all sites that log stock adjustments?”
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 1, request 3 of 3
Tomasz was explicit: if I can only get to two this week, do #1 (trend shape) and #2 (theft-tag concentration), since those are what he and Tobias will be pressed on. I'm spending all three slots tonight on those two, since each deserves more than a single pass.
For #1: my existing monthly view (764→946 events/month) shows a fairly flat Jan-Sep and then a clear step up in Oct (865→941, +8.8%) that holds through Nov-Dec. Monthly resolution could be smoothing over or mis-locating the actual inflection point, and it conflates two possible causes: a genuine worsening rate, versus simply more warehouses coming online and doing cycle counts (warehouses_affected already grew 85→111 across the year in my night-1 report). So tonight's trend report uses half-month buckets (fine enough to spot a step, coarse enough to stay well under the row cap) and adds a per-active-location rate alongside raw counts, plus a count of newly-opened warehouses per period, so I can tell scale growth apart from a true rate increase and point to when it started.
For #2: Tomasz's suspicion is that "theft-suspected" being almost exactly the same size as "damage" and "expired" (1,781/1,755/1,717 events) smells like a dropdown default or habit rather than real incident variety. The sharpest test is whether the tag concentrates in a handful of specific staff or sites rather than being spread proportionally across everyone who logs adjustments. I'm running two cuts — by the staff member who logged the adjustment (adjusted_by_user_id), with a bonus column on what time of day their theft-tagged entries get logged (to catch an end-of-shift batch-logging pattern), and by warehouse/site — since Tomasz asked about both people and sites, and these are two genuinely different grains, not a bundle of the same question.
#3 (why the Dec-31 B2B batch exists) and #4 (apparel concentration by site/shift) are real work but lower priority per Tomasz's note — I'll pick those up on a later night this week.
The engineer · 1 dry run on the empty schema, then the submission
WITH base AS ( SELECT w.id AS warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, sa.reason, sa.adjusted_by_user_id FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN warehouses w ON w.id = sl.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, COUNT(*) AS total_adjustments_at_site, COUNT(*) FILTER (WHERE reason = 'Missing - suspected theft') AS theft_tagged_count, COUNT(DISTINCT adjusted_by_user_id) FILTER (WHERE reason = 'Missing - suspected theft') AS distinct_staff_logging_theft_here FROM base GROUP BY warehouse_id, warehouse_name, tenant_name ) SELECT warehouse_name, tenant_name, total_adjustments_at_site, theft_tagged_count, ROUND(theft_tagged_count * 100.0 / NULLIF(total_adjustments_at_site, 0), 1) AS pct_theft_of_site_adjustments, distinct_staff_logging_theft_here, COUNT(*) OVER () AS total_warehouses_with_adjustments_2025 FROM agg ORDER BY theft_tagged_count DESC, pct_theft_of_site_adjustments DESC, warehouse_name ASC LIMIT 50
“The query compiles cleanly. Submitting now.”
“Submitted. The report returns one row per warehouse with at least one 2025 stock adjustment, ranked by theft-tagged count (then share, then name), capped at 50 rows, with a window-computed total_warehouses_with_adjustments_2025 column so Fatima can see whether more warehouses exist beyond the printed 50.”
As Fatima Zahra saw it, from the small dataset the agents worked on
warehouse_name | tenant_name | total_adjustments_at_site | theft_tagged_count | pct_theft_of_site_adjustments | distinct_staff_logging_theft_here | total_warehouses_with_adjustments_2025 ---------------------------------------+-------------------------------+---------------------------+--------------------+-------------------------------+-----------------------------------+--------------------------------------- Atlanta Fulfilment Centre | Cragsley Outfitters | 1506 | 262 | 17.4 | 1 | 120 Reading Fulfilment Centre | Marrowby & Vane | 1058 | 186 | 17.6 | 1 | 120 Den Haag Fulfilment Centre | Studio Vrij | 622 | 115 | 18.5 | 1 | 120 Cambridge Fulfilment Centre | Thistlecombe | 253 | 57 | 22.5 | 1 | 120 Uppsala Fulfilment Centre | Velvet Ash | 304 | 53 | 17.4 | 1 | 120 Paris Fulfilment Centre | Rue Lafayette Studio | 286 | 52 | 18.2 | 1 | 120 München Fulfilment Centre | Ostwind Mode | 244 | 42 | 17.2 | 1 | 120 Exeter Fulfilment Centre | Kayak Corner | 173 | 40 | 23.1 | 1 | 120 København Fulfilment Centre | Oakhurst Tailoring | 216 | 39 | 18.1 | 1 | 120 Dortmund Fulfilment Centre | Gratlinie Outdoor | 219 | 37 | 16.9 | 1 | 120 Arnhem Fulfilment Centre | Noordzee Surfwear | 199 | 35 | 17.6 | 0 | 120 Boulogne-Billancourt Fulfilment Centre | Atelier Nouveau | 209 | 35 | 16.7 | 1 | 120 San Francisco Fulfilment Centre | Cinder Peak Sports Co. | 262 | 33 | 12.6 | 1 | 120 Delft Fulfilment Centre | Lumen Collective | 153 | 30 | 19.6 | 0 | 120 Nice Fulfilment Centre | Maison Clair | 157 | 30 | 19.1 | 0 | 120 Trondheim Fulfilment Centre | Fjordline Wear | 209 | 30 | 14.4 | 0 | 120 Oslo Fulfilment Centre | Brick & Bobbin | 151 | 27 | 17.9 | 0 | 120 Seattle Fulfilment Centre | Meadow Row | 138 | 24 | 17.4 | 0 | 120 Manchester Fulfilment Centre | Little Larch Kids | 113 | 23 | 20.4 | 0 | 120 Odense Fulfilment Centre | Fell & Fjell | 135 | 23 | 17.0 | 1 | 120 Hamburg Fulfilment Centre | Ampfeld Elektronik | 145 | 23 | 15.9 | 1 | 120 Berlin Fulfilment Centre | Strandgut | 95 | 22 | 23.2 | 0 | 120 København Fulfilment Centre | Paddock & Pine | 102 | 21 | 20.6 | 0 | 120 Groningen Fulfilment Centre | Velo Noord | 134 | 20 | 14.9 | 1 | 120 Wiesbaden Fulfilment Centre | Kiezkind | 87 | 19 | 21.8 | 0 | 120 Buffalo Fulfilment Centre | Greyfriars Supply | 123 | 19 | 15.4 | 0 | 120 Utrecht Fulfilment Centre | Saltmarsh Knitwear | 125 | 18 | 14.4 | 0 | 120 Stockholm Fulfilment Centre | Halden Boots | 85 | 16 | 18.8 | 0 | 120 Hamburg Fulfilment Centre | Seilschaft | 86 | 16 | 18.6 | 0 | 120 Berlin Fulfilment Centre | Wiesgrund Wohnen | 71 | 15 | 21.1 | 1 | 120 Amsterdam Fulfilment Centre | Linden & Loom | 76 | 13 | 17.1 | 1 | 120 Rotterdam Fulfilment Centre | Ridgeback Gear | 87 | 13 | 14.9 | 0 | 120 Trondheim Fulfilment Centre | Courtside Collective | 58 | 12 | 20.7 | 0 | 120 Dublin Fulfilment Centre | Basecamp Nord | 59 | 12 | 20.3 | 0 | 120 Philadelphia Fulfilment Centre | Silverthread | 59 | 12 | 20.3 | 0 | 120 Köln Fulfilment Centre | Brennquell Kaffee | 76 | 12 | 15.8 | 1 | 120 Leiden Fulfilment Centre | Northstar Watches | 46 | 11 | 23.9 | 0 | 120 Galway Fulfilment Centre | Ballycorran Industrial Supply | 57 | 10 | 17.5 | 1 | 120 Amsterdam Fulfilment Centre | Trailmark | 65 | 10 | 15.4 | 0 | 120 Birmingham Fulfilment Centre | Folio & Fern | 42 | 9 | 21.4 | 0 | 120 Rotterdam Fulfilment Centre | Pedal & Pine | 43 | 9 | 20.9 | 0 | 120 Nürnberg Fulfilment Centre | Wohnglück | 54 | 9 | 16.7 | 1 | 120 Liège Fulfilment Centre | Spice Route Box | 39 | 8 | 20.5 | 1 | 120 Odense Fulfilment Centre | Nordic Bloom Cosmetics | 45 | 8 | 17.8 | 1 | 120 Bromley Fulfilment Centre | Kibblewick | 48 | 8 | 16.7 | 1 | 120 Cork Fulfilment Centre | Kestrel Denim | 27 | 7 | 25.9 | 0 | 120 Houston Fulfilment Centre | Feather & Fin | 38 | 7 | 18.4 | 0 | 120 Augsburg Fulfilment Centre | Hautnah | 42 | 7 | 16.7 | 0 | 120 Paris Fulfilment Centre | Bijoux Lune | 45 | 7 | 15.6 | 0 | 120 Göteborg Fulfilment Centre | Coastline Interiors | 46 | 7 | 15.2 | 0 | 120 (50 rows)
The memo and the queries that followed; links found by title, so inferred
Later queries that build on this report:
Changed only where the benchmark needs the same result on every system
WITH base AS ( SELECT w.id AS warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, sa.reason, sa.adjusted_by_user_id sa.reason AS reason, sa.adjusted_by_user_id AS adjusted_by_user_id FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN warehouses w ON w.id = sl.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'),agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, COUNT(*) AS total_adjustments_at_site, COUNT(*) FILTER (WHERE reason = 'Missing - suspected theft') AS theft_tagged_count, COUNT(DISTINCT adjusted_by_user_id) FILTER (WHERE reason = 'Missing - suspected theft') AS distinct_staff_logging_theft_here FROM base GROUP BY warehouse_id, warehouse_name, tenant_name)SELECT warehouse_name, tenant_name, total_adjustments_at_site, theft_tagged_count, ROUND(theft_tagged_count * 100.0 / NULLIF(total_adjustments_at_site, 0), 1) AS pct_theft_of_site_adjustments, theft_tagged_count * 100.0 / NULLIF(total_adjustments_at_site, 0) AS pct_theft_of_site_adjustments, distinct_staff_logging_theft_here, COUNT(*) OVER () AS total_warehouses_with_adjustments_2025FROM aggORDER BY theft_tagged_count DESC, pct_theft_of_site_adjustments DESC, warehouse_name ASCLIMIT 50ORDER BY theft_tagged_count DESC NULLS FIRST, pct_theft_of_site_adjustments DESC NULLS FIRST, warehouse_name ASC NULLS LAST, warehouse_id NULLS LAST{{fetch_first(50)}}
WITH base AS ( SELECT w.id AS warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, sa.reason AS reason, sa.adjusted_by_user_id AS adjusted_by_user_id FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN warehouses w ON w.id = sl.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, COUNT(*) AS total_adjustments_at_site, COUNT(*) FILTER (WHERE reason = 'Missing - suspected theft') AS theft_tagged_count, COUNT(DISTINCT adjusted_by_user_id) FILTER (WHERE reason = 'Missing - suspected theft') AS distinct_staff_logging_theft_here FROM base GROUP BY warehouse_id, warehouse_name, tenant_name ) SELECT warehouse_name, tenant_name, total_adjustments_at_site, theft_tagged_count, theft_tagged_count * 100.0 / NULLIF(total_adjustments_at_site, 0) AS pct_theft_of_site_adjustments, distinct_staff_logging_theft_here, COUNT(*) OVER () AS total_warehouses_with_adjustments_2025 FROM agg ORDER BY theft_tagged_count DESC NULLS FIRST, pct_theft_of_site_adjustments DESC NULLS FIRST, warehouse_name ASC NULLS LAST, warehouse_id NULLS LAST FETCH FIRST 50 ROWS ONLY
WITH base AS ( SELECT w.id AS warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, sa.reason AS reason, sa.adjusted_by_user_id AS adjusted_by_user_id FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN warehouses w ON w.id = sl.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, COUNT(*) AS total_adjustments_at_site, COUNT(*) FILTER (WHERE reason = 'Missing - suspected theft') AS theft_tagged_count, COUNT(DISTINCT adjusted_by_user_id) FILTER (WHERE reason = 'Missing - suspected theft') AS distinct_staff_logging_theft_here FROM base GROUP BY warehouse_id, warehouse_name, tenant_name ) SELECT warehouse_name, tenant_name, total_adjustments_at_site, theft_tagged_count, theft_tagged_count * 100.0 / NULLIF(total_adjustments_at_site, 0) AS pct_theft_of_site_adjustments, distinct_staff_logging_theft_here, COUNT(*) OVER () AS total_warehouses_with_adjustments_2025 FROM agg ORDER BY theft_tagged_count DESC NULLS FIRST, pct_theft_of_site_adjustments DESC NULLS FIRST, warehouse_name ASC NULLS LAST, warehouse_id 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: Where 'Missing - suspected theft' tags concentrate: by warehouse Question: Does the 'Missing - suspected theft' reason concentrate in a handful of specific warehouses/sites, or is it spread evenly across all sites that log stock adjustments? Grain: warehouse (one row per warehouse with at least one stock_adjustments row in 2025) Columns: - warehouse_name - tenant_name - total_adjustments_at_site (count of stock_adjustments rows at this warehouse) - theft_tagged_count (reason = 'Missing - suspected theft') - pct_theft_of_site_adjustments (theft_tagged_count / total_adjustments_at_site * 100, rounded 1dp) - distinct_staff_logging_theft_here (count of distinct adjusted_by_user_id among this warehouse's theft-tagged rows) Details: Source: stock_adjustments, created_at between 2025-01-01 and 2025-12-31 (half-open bound). Join stock_locations on stock_location_id to get warehouse_id, then warehouses (name) and tenants (name) via warehouses.tenant_id. Group by warehouse. Order by theft_tagged_count descending, tie-break by pct_theft_of_site_adjustments descending, then warehouse_name ascending. Cap at 50 rows and note the total distinct warehouse count even if it exceeds 50. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH base AS ( SELECT w.id AS warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, sa.reason, sa.adjusted_by_user_id FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN warehouses w ON w.id = sl.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, COUNT(*) AS total_adjustments_at_site, COUNT(*) FILTER (WHERE reason = 'Missing - suspected theft') AS theft_tagged_count, COUNT(DISTINCT adjusted_by_user_id) FILTER (WHERE reason = 'Missing - suspected theft') AS distinct_staff_logging_theft_here FROM base GROUP BY warehouse_id, warehouse_name, tenant_name ) SELECT warehouse_name, tenant_name, total_adjustments_at_site, theft_tagged_count, ROUND(theft_tagged_count * 100.0 / NULLIF(total_adjustments_at_site, 0), 1) AS pct_theft_of_site_adjustments, distinct_staff_logging_theft_here, COUNT(*) OVER () AS total_warehouses_with_adjustments_2025 FROM agg ORDER BY theft_tagged_count DESC, pct_theft_of_site_adjustments DESC, warehouse_name ASC LIMIT 50
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: warehouse_name, tenant_name, total_adjustments_at_site, theft_tagged_count, pct_theft_of_site_adjustments, distinct_staff_logging_theft_here, total_warehouses_with_adjustments_2025
submitWITH base AS ( SELECT w.id AS warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, sa.reason, sa.adjusted_by_user_id FROM stock_adjustments sa JOIN stock_locations sl ON sl.id = sa.stock_location_id JOIN warehouses w ON w.id = sl.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, COUNT(*) AS total_adjustments_at_site, COUNT(*) FILTER (WHERE reason = 'Missing - suspected theft') AS theft_tagged_count, COUNT(DISTINCT adjusted_by_user_id) FILTER (WHERE reason = 'Missing - suspected theft') AS distinct_staff_logging_theft_here FROM base GROUP BY warehouse_id, warehouse_name, tenant_name ) SELECT warehouse_name, tenant_name, total_adjustments_at_site, theft_tagged_count, ROUND(theft_tagged_count * 100.0 / NULLIF(total_adjustments_at_site, 0), 1) AS pct_theft_of_site_adjustments, distinct_staff_logging_theft_here, COUNT(*) OVER () AS total_warehouses_with_adjustments_2025 FROM agg ORDER BY theft_tagged_count DESC, pct_theft_of_site_adjustments DESC, warehouse_name ASC LIMIT 50
Submitted to tonight's batch. Your work on this request is done.