Queries · q187 · Director of Fulfilment Operations · week 1, night 2
“Is the finding that every recorded cycle-count discrepancy is exactly 1 unit (not a range of sizes) real, or does it only show up because other movement kinds behave differently — i.e., is quantity magnitude on stock_movements generally variable, or is ±1 specific to cycle_count?”
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 2, request 1 of 3
Reading night 1 critically, two things jump out before I trust the headline numbers, plus one new question worth opening.
(1) Suspicious invariant: in report 1, abs_units_variance equals cycle_count_events exactly in every single month (e.g. Jan 764=764), and avg_abs_units_per_event is 1.0 in all 12 months. Report 3 shows the same identity for every one of 105 categories (Bikes 102=102, Tops 1086=1086, etc). That means every recorded cycle-count discrepancy is off by exactly one unit, with zero variation — never 2, never 5. That's either a real and important finding (our "size of discrepancy" story is really a "frequency of single-unit misses" story, which changes the materiality narrative for Tobias) or a sign that stock_movements.quantity isn't what I think it is for this kind. I want to verify this against other movement kinds before I put it in the memo as fact.
(2) Report 2's top 50 rows (ranked by value) are all location_kind='pick' — not one bulk/overflow/other row appears. That's a strong concentration signal for Tomasz's "particular kinds of storage locations" question, but I need a company-wide total by location_kind (not filtered to top-50-by-warehouse) to confirm it isn't just pick locations dominating the value ranking while other kinds still carry meaningful event counts further down the list.
(3) Tomasz's Q4 (reservation reliability) hasn't been touched yet. With two slots going to validate/sharpen findings 1 and 2 above, I have a free slot to open Q4 tonight rather than wait, since nights 3-4 will still be needed for deeper drill-down and the trend view (Q5).
Tonight's three requests: (A) movement-magnitude distribution by kind, to sanity-check the ±1 pattern and see if 'adjustment'-kind movements (a different, less formal correction path) carry larger swings; (B) a clean location-kind-only rollup (not crossed with warehouse) so kinds with little/no cycle-count activity are visible as explicit zero/low rows; (C) a first read on stock reservation reliability — how often holds are released normally vs. left stuck past their expiry, which overstates sellable availability.
The engineer · 1 dry run on the empty schema, then the submission
SELECT kind, count(*) AS event_count, min(abs(quantity)) AS min_abs_qty, max(abs(quantity)) AS max_abs_qty, round(avg(abs(quantity)), 2) AS avg_abs_qty, percentile_cont(0.5) WITHIN GROUP (ORDER BY abs(quantity)) AS median_abs_qty, round(100.0 * count(*) FILTER (WHERE abs(quantity) = 1) / count(*), 1) AS pct_abs_qty_eq_1, sum(abs(quantity)) AS total_abs_qty FROM stock_movements WHERE occurred_at >= timestamp '2025-01-01' AND occurred_at < timestamp '2026-01-01' GROUP BY kind ORDER BY kind
“Query compiles correctly.”
“Submitted. The report groups stock_movements by kind for 2025, computing count, min/max/avg/median of abs(quantity), the percentage of rows with abs(quantity)=1, and the total absolute quantity — directly answering whether the ±1 pattern is unique to cycle_count or common across movement kinds.”
As Fatima Zahra saw it, from the small dataset the agents worked on
kind | event_count | min_abs_qty | max_abs_qty | avg_abs_qty | median_abs_qty | pct_abs_qty_eq_1 | total_abs_qty ------------+-------------+-------------+-------------+-------------+----------------+------------------+-------------- adjustment | 407 | 1 | 192 | 9.99 | 2.0 | 38.6 | 4067 cycle_count | 10193 | 1 | 1 | 1.0 | 1.0 | 100.0 | 10193 receipt | 32687 | 1 | 397 | 4.36 | 3.0 | 0.6 | 142469 return | 3899 | 1 | 46 | 1.34 | 1.0 | 90.2 | 5220 shipment | 40177 | 1 | 236 | 2.14 | 1.0 | 81.3 | 86074 (5 rows)
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
SELECT kind, count(*) AS event_count, min(abs(quantity)) AS min_abs_qty, max(abs(quantity)) AS max_abs_qty, round(avg(abs(quantity)), 2) AS avg_abs_qty, percentile_cont(0.5) WITHIN GROUP (ORDER BY abs(quantity)) AS median_abs_qty, round(100.0 * count(*) FILTER (WHERE abs(quantity) = 1) / count(*), 1) AS pct_abs_qty_eq_1, avg(cast(abs(quantity) as numeric(18,6))) AS avg_abs_qty, {{percentile_cont(0.5, abs(quantity))}} AS median_abs_qty, 100.0 * count(*) FILTER (WHERE abs(quantity) = 1) / count(*) AS pct_abs_qty_eq_1, sum(abs(quantity)) AS total_abs_qtyFROM stock_movementsWHERE occurred_at >= timestamp '2025-01-01' AND occurred_at < timestamp '2026-01-01'GROUP BY kindORDER BY kindORDER BY kind NULLS LAST
SELECT kind, count(*) AS event_count, min(abs(quantity)) AS min_abs_qty, max(abs(quantity)) AS max_abs_qty, avg(cast(abs(quantity) as numeric(18,6))) AS avg_abs_qty, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY abs(quantity)) AS median_abs_qty, 100.0 * count(*) FILTER (WHERE abs(quantity) = 1) / count(*) AS pct_abs_qty_eq_1, sum(abs(quantity)) AS total_abs_qty FROM stock_movements WHERE occurred_at >= timestamp '2025-01-01' AND occurred_at < timestamp '2026-01-01' GROUP BY kind ORDER BY kind NULLS LAST
SELECT kind, count(*) AS event_count, min(abs(quantity)) AS min_abs_qty, max(abs(quantity)) AS max_abs_qty, avg(cast(abs(quantity) as numeric(18,6))) AS avg_abs_qty, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(abs(quantity) AS DOUBLE PRECISION)) AS median_abs_qty, 100.0 * count(*) FILTER (WHERE abs(quantity) = 1) / count(*) AS pct_abs_qty_eq_1, sum(abs(quantity)) AS total_abs_qty FROM stock_movements WHERE occurred_at >= timestamp '2025-01-01' AND occurred_at < timestamp '2026-01-01' GROUP BY kind ORDER BY kind NULLS LAST
SELECT kind, count(*) AS event_count, min(abs(quantity)) AS min_abs_qty, max(abs(quantity)) AS max_abs_qty, avg(cast(abs(quantity) as numeric(18,6))) AS avg_abs_qty, quantileExactInclusive(0.5)(toFloat64(abs(quantity))) AS median_abs_qty, 100.0 * count(*) FILTER (WHERE abs(quantity) = 1) / count(*) AS pct_abs_qty_eq_1, sum(abs(quantity)) AS total_abs_qty FROM stock_movements WHERE occurred_at >= timestamp '2025-01-01' AND occurred_at < timestamp '2026-01-01' GROUP BY kind ORDER BY kind 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 movement magnitude by kind, 2025 Question: Is the finding that every recorded cycle-count discrepancy is exactly 1 unit (not a range of sizes) real, or does it only show up because other movement kinds behave differently — i.e., is quantity magnitude on stock_movements generally variable, or is ±1 specific to cycle_count? Grain: stock_movements.kind (one row per kind: receipt, shipment, adjustment, transfer, return, cycle_count) Columns: - kind - event_count (rows in 2025) - min_abs_qty - max_abs_qty - avg_abs_qty (rounded 2dp) - median_abs_qty - pct_abs_qty_eq_1 (% of rows where abs(quantity)=1, rounded 1dp) - total_abs_qty (sum of abs(quantity) for the kind) Details: Source: stock_movements, occurred_at between 2025-01-01 and 2025-12-31 inclusive, across all tenants. Group by kind. abs_qty = abs(quantity). Compute min, max, average, median and the total sum of abs(quantity), plus the percentage of rows per kind where abs(quantity) equals exactly 1. No joins needed beyond stock_movements itself. Order by kind alphabetically for determinism. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT kind, count(*) AS event_count, min(abs(quantity)) AS min_abs_qty, max(abs(quantity)) AS max_abs_qty, round(avg(abs(quantity)), 2) AS avg_abs_qty, percentile_cont(0.5) WITHIN GROUP (ORDER BY abs(quantity)) AS median_abs_qty, round(100.0 * count(*) FILTER (WHERE abs(quantity) = 1) / count(*), 1) AS pct_abs_qty_eq_1, sum(abs(quantity)) AS total_abs_qty FROM stock_movements WHERE occurred_at >= timestamp '2025-01-01' AND occurred_at < timestamp '2026-01-01' GROUP BY kind ORDER BY kind
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: kind, event_count, min_abs_qty, max_abs_qty, avg_abs_qty, median_abs_qty, pct_abs_qty_eq_1, total_abs_qty
submitSELECT kind, count(*) AS event_count, min(abs(quantity)) AS min_abs_qty, max(abs(quantity)) AS max_abs_qty, round(avg(abs(quantity)), 2) AS avg_abs_qty, percentile_cont(0.5) WITHIN GROUP (ORDER BY abs(quantity)) AS median_abs_qty, round(100.0 * count(*) FILTER (WHERE abs(quantity) = 1) / count(*), 1) AS pct_abs_qty_eq_1, sum(abs(quantity)) AS total_abs_qty FROM stock_movements WHERE occurred_at >= timestamp '2025-01-01' AND occurred_at < timestamp '2026-01-01' GROUP BY kind ORDER BY kind
Submitted to tonight's batch. Your work on this request is done.