Queries · q389 · Director of Fulfilment Operations · week 2, night 1

Trend of stock-count discrepancies within 2025: steady climb or step change?

“Did the 24% year-over-year rise in routine stock-count discrepancies climb steadily, or did it step up at a specific point in the year (and is that step explained by more warehouses coming online, or a genuine per-location worsening)?”

Manager · asked the questionTomasz NowakDirector of Fulfilment Operations. Ran warehouses for a large retailer before joining.

Analyst · planned the reportFatima ZahraDefinition-minded: checks what each field really records before relying on it.

Engineer · wrote the SQLOmarPragmatic: the shortest correct query, aggregates first and joins dimensions afterwards, FILTER clauses for conditional counts.

Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told

A business event lands on the Director of Fulfilment Operations's desk
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?
One of the 35 events we wrote for the role play; it shaped the manager's first note. How events arise · All the events
Tomasz Nowak writes to Fatima Zahra
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:
1. The 24% rise in monthly shrinkage worries me more than the headline total. Is that a steady climb all year, or did it step up around a specific point (new site opening, staffing change, holiday peak)? I want to know if we're looking at a slow-burn trend or something that started at a moment we can point to.
2. The loss-reason split being almost perfectly even across three categories makes me suspicious, not reassured. Before we size any provision off that split, I want to know whether that's a genuine pattern or an artifact of how staff pick a reason from a dropdown (e.g., defaulting or cycling through options). Can you sanity-check whether specific people, shifts, or sites are behind the "theft-suspected" tag specifically — that's the one that would actually worry legal and the auditors.
3. On the December 31st batch — I need to understand the "why," not just the "who." If this is confirmed as a real annual B2B stocktake, fine. But I want to understand why a positive correction of that size existed in the first place — were those units sitting miscounted all year, or is this a sign that we simply don't track B2B/bulk inventory day-to-day the way we do pick locations? That has different implications for the auditors than just "we run quarterly/annual stocktakes."
4. On the apparel/footwear concentration, I want specifics, not just category names. Is this spread evenly across our apparel merchants and sites, or does it cluster around particular sites, shifts, or size runs? If it's a handful of locations, that's a training/process fix I can act on this quarter; if it's spread everywhere, that's a bigger systemic question about how we handle high-variant SKUs generally.
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
Fatima Zahra plans the night
The analyst · week 2, night 1, request 1 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.
Trend of stock-count discrepancies within 2025: steady climb or step change?
Question
Did the 24% year-over-year rise in routine stock-count discrepancies climb steadily, or did it step up at a specific point in the year (and is that step explained by more warehouses coming online, or a genuine per-location worsening)?
One row per
half-month period, 2025 (24 periods: each calendar month split into days 1-15 and 16-end)
Columns
- period_label (e.g. '2025-01 (1-15)')
- events (count of stock_adjustments rows with created_at in the period)
- abs_units_variance (sum of abs(quantity_delta))
- net_units_variance (signed sum; negative = net shrinkage)
- distinct_locations_active (distinct stock_location_id with >=1 stock_adjustments row in the period)
- events_per_active_location (events / distinct_locations_active, rounded 2dp)
- new_warehouses_opened_this_period (count of warehouses.created_at falling within the period, across all tenants)

Omar writes the SQL

The engineer · 2 dry runs on the empty schema, then the submission

Dry run 1 · STATUS: REJECTED

WITH periods AS (
  SELECT DISTINCT
    date_trunc('month', calendar_date)::date AS month_start,
    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half
  FROM calendar_days
  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'
),
adj AS (
  SELECT
    date_trunc('month', sa.created_at)::date AS month_start,
    CASE WHEN EXTRACT(day FROM sa.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,
    sa.stock_location_id,
    sa.quantity_delta
  FROM stock_adjustments sa
  JOIN stock_locations sl ON sl.id = sa.stock_location_id
  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'
),
adj_agg AS (
  SELECT
    month_start, half,
    COUNT(*) AS events,
    SUM(ABS(quantity_delta)) AS abs_units_variance,
    SUM(quantity_delta) AS net_units_variance,
    COUNT(DISTINCT stock_location_id) AS distinct_locations_active
  FROM adj
  GROUP BY month_start, half
),
wh AS (
  SELECT
    date_trunc('month', w.created_at)::date AS month_start,
    CASE WHEN EXTRACT(day FROM w.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,
    COUNT(*) AS new_warehouses
  FROM warehouses w
  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'
  GROUP BY month_start, half
)
SELECT
  to_char(p.month_start, 'YYYY-MM') || ' (' ||
    CASE WHEN p.half = 1 THEN '1-15'
         ELSE '16-' || EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day'))::int
    END || ')' AS period_label,
  COALESCE(a.events, 0) AS events,
  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,
  COALESCE(a.net_units_variance, 0) AS net_units_variance,
  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,
  ROUND(
    CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL
         ELSE a.events::numeric / a.distinct_locations_active
    END, 2
  ) AS events_per_active_location,
  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_period
FROM periods p
LEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.half
LEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.half
ORDER BY p.month_start, p.half

Dry run 2 · 1 change from the dry run before · STATUS: COMPILES (dry run on the empty schema, no data read)

WITH periods AS (  SELECT DISTINCT    date_trunc('month', calendar_date)::date AS month_start,    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half  FROM calendar_days  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'),adj AS (  SELECT    date_trunc('month', sa.created_at)::date AS month_start,    CASE WHEN EXTRACT(day FROM sa.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,    sa.stock_location_id,    sa.quantity_delta  FROM stock_adjustments sa  JOIN stock_locations sl ON sl.id = sa.stock_location_id  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'),adj_agg AS (  SELECT    month_start, half,    COUNT(*) AS events,    SUM(ABS(quantity_delta)) AS abs_units_variance,    SUM(quantity_delta) AS net_units_variance,    COUNT(DISTINCT stock_location_id) AS distinct_locations_active  FROM adj  GROUP BY month_start, half),wh AS (  SELECT    date_trunc('month', w.created_at)::date AS month_start,    CASE WHEN EXTRACT(day FROM w.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,    COUNT(*) AS new_warehouses  FROM warehouses w  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'  GROUP BY month_start, half)SELECT  to_char(p.month_start, 'YYYY-MM') || ' (' ||  strftime(p.month_start, '%Y-%m') || ' (' ||    CASE WHEN p.half = 1 THEN '1-15'         ELSE '16-' || EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day'))::int    END || ')' AS period_label,  COALESCE(a.events, 0) AS events,  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,  COALESCE(a.net_units_variance, 0) AS net_units_variance,  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,  ROUND(    CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL         ELSE a.events::numeric / a.distinct_locations_active    END, 2  ) AS events_per_active_location,  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_periodFROM periods pLEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.halfLEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.halfORDER BY p.month_start, p.half

“The query compiles and returns the expected columns. Submitting now.”

Submitted · the SQL of the last dry run, unchanged · Submitted to tonight's batch. Your work on this request is done.

“Submitted. The report builds all 24 half-month periods from calendar_days (so months/halves with zero activity still appear), aggregates stock_adjustments (joined to stock_locations to validate location identity) for events/variance/active-location counts, and separately counts warehouses.created_at falling in each window across all tenants — exactly as specified, with no tenant filter since the question is platform-wide.”

The report comes back overnight

As Fatima Zahra saw it, from the small dataset the agents worked on

period_label    | events | abs_units_variance | net_units_variance | distinct_locations_active | events_per_active_location | new_warehouses_opened_this_period
----------------+--------+--------------------+--------------------+---------------------------+----------------------------+----------------------------------
2025-01 (1-15)  |    363 |                363 |               -101 |                        67 |                       5.42 |                                 0
2025-01 (16-31) |    401 |                401 |               -155 |                        77 |                       5.21 |                                 0
2025-02 (1-15)  |    425 |                425 |               -147 |                        82 |                       5.18 |                                 0
2025-02 (16-28) |    335 |                335 |               -123 |                        67 |                        5.0 |                                 0
2025-03 (1-15)  |    371 |                371 |               -159 |                        77 |                       4.82 |                                 1
2025-03 (16-31) |    451 |                451 |               -177 |                        76 |                       5.93 |                                 1
2025-04 (1-15)  |    385 |                385 |               -131 |                        79 |                       4.87 |                                 1
2025-04 (16-30) |    398 |                398 |               -128 |                        78 |                        5.1 |                                 1
2025-05 (1-15)  |    413 |                413 |               -177 |                        77 |                       5.36 |                                 2
2025-05 (16-31) |    441 |                441 |               -181 |                        84 |                       5.25 |                                 5
2025-06 (1-15)  |    398 |                398 |               -138 |                        73 |                       5.45 |                                 0
2025-06 (16-30) |    420 |                420 |               -128 |                        90 |                       4.67 |                                 1
2025-07 (1-15)  |    401 |                401 |               -133 |                        83 |                       4.83 |                                 0
2025-07 (16-31) |    448 |                448 |               -178 |                        89 |                       5.03 |                                 2
2025-08 (1-15)  |    405 |                405 |               -127 |                        80 |                       5.06 |                                 0
2025-08 (16-31) |    460 |                460 |               -184 |                        91 |                       5.05 |                                 3
2025-09 (1-15)  |    454 |                454 |               -166 |                        89 |                        5.1 |                                 2
2025-09 (16-30) |    421 |                421 |               -175 |                        91 |                       4.63 |                                 2
2025-10 (1-15)  |    480 |                480 |               -210 |                        89 |                       5.39 |                                 0
2025-10 (16-31) |    461 |                461 |               -183 |                        94 |                        4.9 |                                 0
2025-11 (1-15)  |    460 |                460 |               -202 |                        82 |                       5.61 |                                 0
2025-11 (16-30) |    457 |                457 |               -181 |                        94 |                       4.86 |                                 2
2025-12 (1-15)  |    464 |                464 |               -206 |                        89 |                       5.21 |                                 0
2025-12 (16-31) |    482 |                482 |               -190 |                        91 |                        5.3 |                                 0
(24 rows)

What came of it
The memo and the queries that followed; links found by title, so inferred
Fatima Zahra's memo to Tomasz Nowak at the end of the week:
- 1. The 24% rise is a scale effect, not a worsening trend. Half-month data shows events-per-active-location flat all year (4.6-5.9 range, no drift: ~5.2-5.4 in January, ~5.2-5.3 in December). Raw counts rose because 23 new warehouses came online over the year, tracking almost exactly with the growth in active locations (85→111). (Report: Trend of stock-count discrepancies within 2025.) No step-change moment to point to — it's rollout, not a deteriorating process.
Later queries that build on this report:
- q393Apparel/footwear discrepancies by warehouse: scale effect or genuine elevated rate?
It builds on:
- q193Monthly volume of stock_adjustments table entries, 2025
- q184Scale of stock count discrepancies by month

It becomes benchmark query q389

Changed only where the benchmark needs the same result on every system

spellingcast (7×)
placeholderyear month
numericsdocs/NUMERICS.md: rounding removed; results keep full precision; bare NUMERIC cast that only served the rounding removed; intended fraction: integer numerator cast to NUMERIC(18,6) before the division
made explicita selected t.col gets AS col, its standard name made explicit (2×)
determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results (2×)

The benchmark text, against the submitted SQL

WITH periods AS (  SELECT DISTINCT    date_trunc('month', calendar_date)::date AS month_start,    CAST(date_trunc('month', calendar_date) AS date) AS month_start,    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half  FROM calendar_days  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'),adj AS (  SELECT    date_trunc('month', sa.created_at)::date AS month_start,    CASE WHEN EXTRACT(day FROM sa.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,    sa.stock_location_id,    sa.quantity_delta    CAST(date_trunc('month', sa.created_at) AS date) AS month_start,    CASE WHEN CAST(EXTRACT(day FROM sa.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,    sa.stock_location_id AS stock_location_id,    sa.quantity_delta AS quantity_delta  FROM stock_adjustments sa  JOIN stock_locations sl ON sl.id = sa.stock_location_id  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'),adj_agg AS (  SELECT    month_start, half,    COUNT(*) AS events,    SUM(ABS(quantity_delta)) AS abs_units_variance,    SUM(quantity_delta) AS net_units_variance,    COUNT(DISTINCT stock_location_id) AS distinct_locations_active  FROM adj  GROUP BY month_start, half),wh AS (  SELECT    date_trunc('month', w.created_at)::date AS month_start,    CASE WHEN EXTRACT(day FROM w.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,    CAST(date_trunc('month', w.created_at) AS date) AS month_start,    CASE WHEN CAST(EXTRACT(day FROM w.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,    COUNT(*) AS new_warehouses  FROM warehouses w  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'  GROUP BY month_start, half)SELECT  strftime(p.month_start, '%Y-%m') || ' (' ||  {{year_month(p.month_start)}} || ' (' ||    CASE WHEN p.half = 1 THEN '1-15'         ELSE '16-' || EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day'))::int         ELSE '16-' || CAST(EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day')) AS int)    END || ')' AS period_label,  COALESCE(a.events, 0) AS events,  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,  COALESCE(a.net_units_variance, 0) AS net_units_variance,  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,  ROUND(    CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL         ELSE a.events::numeric / a.distinct_locations_active    END, 2  ) AS events_per_active_location,  CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL       ELSE CAST(a.events AS numeric(18,6)) / a.distinct_locations_active  END AS events_per_active_location,  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_periodFROM periods pLEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.halfLEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.halfORDER BY p.month_start, p.halfORDER BY p.month_start NULLS LAST, p.half NULLS LAST

As each system received it; highlighted, the parts spelled for that system

WITH periods AS (
  SELECT DISTINCT
    CAST(date_trunc('month', calendar_date) AS date) AS month_start,
    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half
  FROM calendar_days
  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'
),
adj AS (
  SELECT
    CAST(date_trunc('month', sa.created_at) AS date) AS month_start,
    CASE WHEN CAST(EXTRACT(day FROM sa.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,
    sa.stock_location_id AS stock_location_id,
    sa.quantity_delta AS quantity_delta
  FROM stock_adjustments sa
  JOIN stock_locations sl ON sl.id = sa.stock_location_id
  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'
),
adj_agg AS (
  SELECT
    month_start, half,
    COUNT(*) AS events,
    SUM(ABS(quantity_delta)) AS abs_units_variance,
    SUM(quantity_delta) AS net_units_variance,
    COUNT(DISTINCT stock_location_id) AS distinct_locations_active
  FROM adj
  GROUP BY month_start, half
),
wh AS (
  SELECT
    CAST(date_trunc('month', w.created_at) AS date) AS month_start,
    CASE WHEN CAST(EXTRACT(day FROM w.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,
    COUNT(*) AS new_warehouses
  FROM warehouses w
  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'
  GROUP BY month_start, half
)
SELECT
  strftime(p.month_start, '%Y-%m') || ' (' ||
    CASE WHEN p.half = 1 THEN '1-15'
         ELSE '16-' || CAST(EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day')) AS int)
    END || ')' AS period_label,
  COALESCE(a.events, 0) AS events,
  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,
  COALESCE(a.net_units_variance, 0) AS net_units_variance,
  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,
  CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL
       ELSE CAST(a.events AS numeric(18,6)) / a.distinct_locations_active
  END AS events_per_active_location,
  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_period
FROM periods p
LEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.half
LEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.half
ORDER BY p.month_start NULLS LAST, p.half NULLS LAST

WITH periods AS (
  SELECT DISTINCT
    CAST(date_trunc('month', calendar_date) AS date) AS month_start,
    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half
  FROM calendar_days
  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'
),
adj AS (
  SELECT
    CAST(date_trunc('month', sa.created_at) AS date) AS month_start,
    CASE WHEN CAST(EXTRACT(day FROM sa.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,
    sa.stock_location_id AS stock_location_id,
    sa.quantity_delta AS quantity_delta
  FROM stock_adjustments sa
  JOIN stock_locations sl ON sl.id = sa.stock_location_id
  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'
),
adj_agg AS (
  SELECT
    month_start, half,
    COUNT(*) AS events,
    SUM(ABS(quantity_delta)) AS abs_units_variance,
    SUM(quantity_delta) AS net_units_variance,
    COUNT(DISTINCT stock_location_id) AS distinct_locations_active
  FROM adj
  GROUP BY month_start, half
),
wh AS (
  SELECT
    CAST(date_trunc('month', w.created_at) AS date) AS month_start,
    CASE WHEN CAST(EXTRACT(day FROM w.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,
    COUNT(*) AS new_warehouses
  FROM warehouses w
  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'
  GROUP BY month_start, half
)
SELECT
  to_char(p.month_start, 'YYYY-MM') || ' (' ||
    CASE WHEN p.half = 1 THEN '1-15'
         ELSE '16-' || CAST(EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day')) AS int)
    END || ')' AS period_label,
  COALESCE(a.events, 0) AS events,
  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,
  COALESCE(a.net_units_variance, 0) AS net_units_variance,
  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,
  CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL
       ELSE CAST(a.events AS numeric(18,6)) / a.distinct_locations_active
  END AS events_per_active_location,
  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_period
FROM periods p
LEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.half
LEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.half
ORDER BY p.month_start NULLS LAST, p.half NULLS LAST

WITH periods AS (
  SELECT DISTINCT
    CAST(date_trunc('month', calendar_date) AS date) AS month_start,
    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half
  FROM calendar_days
  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'
),
adj AS (
  SELECT
    CAST(date_trunc('month', sa.created_at) AS date) AS month_start,
    CASE WHEN CAST(EXTRACT(day FROM sa.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,
    sa.stock_location_id AS stock_location_id,
    sa.quantity_delta AS quantity_delta
  FROM stock_adjustments sa
  JOIN stock_locations sl ON sl.id = sa.stock_location_id
  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'
),
adj_agg AS (
  SELECT
    month_start, half,
    COUNT(*) AS events,
    SUM(ABS(quantity_delta)) AS abs_units_variance,
    SUM(quantity_delta) AS net_units_variance,
    COUNT(DISTINCT stock_location_id) AS distinct_locations_active
  FROM adj
  GROUP BY month_start, half
),
wh AS (
  SELECT
    CAST(date_trunc('month', w.created_at) AS date) AS month_start,
    CASE WHEN CAST(EXTRACT(day FROM w.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,
    COUNT(*) AS new_warehouses
  FROM warehouses w
  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'
  GROUP BY month_start, half
)
SELECT
  to_char(CAST(p.month_start AS timestamp), 'YYYY-MM') || ' (' ||
    CASE WHEN p.half = 1 THEN '1-15'
         ELSE '16-' || CAST(EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day')) AS int)
    END || ')' AS period_label,
  COALESCE(a.events, 0) AS events,
  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,
  COALESCE(a.net_units_variance, 0) AS net_units_variance,
  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,
  CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL
       ELSE CAST(a.events AS numeric(18,6)) / a.distinct_locations_active
  END AS events_per_active_location,
  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_period
FROM periods p
LEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.half
LEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.half
ORDER BY p.month_start NULLS LAST, p.half NULLS LAST

WITH periods AS (
  SELECT DISTINCT
    CAST(date_trunc('month', calendar_date) AS date) AS month_start,
    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half
  FROM calendar_days
  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'
),
adj AS (
  SELECT
    CAST(date_trunc('month', sa.created_at) AS date) AS month_start,
    CASE WHEN CAST(EXTRACT(day FROM sa.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,
    sa.stock_location_id AS stock_location_id,
    sa.quantity_delta AS quantity_delta
  FROM stock_adjustments sa
  JOIN stock_locations sl ON sl.id = sa.stock_location_id
  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'
),
adj_agg AS (
  SELECT
    month_start, half,
    COUNT(*) AS events,
    SUM(ABS(quantity_delta)) AS abs_units_variance,
    SUM(quantity_delta) AS net_units_variance,
    COUNT(DISTINCT stock_location_id) AS distinct_locations_active
  FROM adj
  GROUP BY month_start, half
),
wh AS (
  SELECT
    CAST(date_trunc('month', w.created_at) AS date) AS month_start,
    CASE WHEN CAST(EXTRACT(day FROM w.created_at) AS int) <= 15 THEN 1 ELSE 2 END AS half,
    COUNT(*) AS new_warehouses
  FROM warehouses w
  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'
  GROUP BY month_start, half
)
SELECT
  formatDateTime(p.month_start, '%Y-%m') || ' (' ||
    CASE WHEN p.half = 1 THEN '1-15'
         ELSE '16-' || CAST(EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day')) AS int)
    END || ')' AS period_label,
  COALESCE(a.events, 0) AS events,
  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,
  COALESCE(a.net_units_variance, 0) AS net_units_variance,
  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,
  CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL
       ELSE CAST(a.events AS numeric(18,6)) / a.distinct_locations_active
  END AS events_per_active_location,
  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_period
FROM periods p
LEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.half
LEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.half
ORDER BY p.month_start NULLS LAST, p.half NULLS LAST

DataFusionrow 1: col 1: "2025-01 (1-15)" vs "YYYY-MM (1-15)"

Scale factor 1000, c8a.24xlarge, after the last load. q389 on the start page, with every configuration.

Everything, as recorded

The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted

Omar's whole session, 9 turns

The request, as Omar received it

Report request from Fatima Zahra (analyst for the Director of Fulfilment Operations):

Title: Trend of stock-count discrepancies within 2025: steady climb or step change?
Question: Did the 24% year-over-year rise in routine stock-count discrepancies climb steadily, or did it step up at a specific point in the year (and is that step explained by more warehouses coming online, or a genuine per-location worsening)?
Grain: half-month period, 2025 (24 periods: each calendar month split into days 1-15 and 16-end)
Columns:
- period_label (e.g. '2025-01 (1-15)')
- events (count of stock_adjustments rows with created_at in the period)
- abs_units_variance (sum of abs(quantity_delta))
- net_units_variance (signed sum; negative = net shrinkage)
- distinct_locations_active (distinct stock_location_id with >=1 stock_adjustments row in the period)
- events_per_active_location (events / distinct_locations_active, rounded 2dp)
- new_warehouses_opened_this_period (count of warehouses.created_at falling within the period, across all tenants)
Details: Source: stock_adjustments, created_at between 2025-01-01 and 2025-12-31 (half-open bound: >= 2025-01-01 and < 2026-01-01), split into 24 half-month periods using day_of_month <= 15 vs > 15 (use calendar_days.day_of_month to derive this, or equivalent date-part logic). Join stock_locations on stock_location_id only to confirm location identity for distinct_locations_active (no warehouse rollup needed here, kept for report 3 below). For new_warehouses_opened_this_period, count warehouses.created_at falling in the same half-month window, independent of the stock_adjustments join — this is a separate count over the same time axis, not a join. Order by period ascending (chronological), which with 24 rows is already a unique, deterministic order.

Check your query with the warehouse's dry_run tool, then hand it in with submit.

Omar calls dry_run

WITH periods AS (
  SELECT DISTINCT
    date_trunc('month', calendar_date)::date AS month_start,
    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half
  FROM calendar_days
  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'
),
adj AS (
  SELECT
    date_trunc('month', sa.created_at)::date AS month_start,
    CASE WHEN EXTRACT(day FROM sa.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,
    sa.stock_location_id,
    sa.quantity_delta
  FROM stock_adjustments sa
  JOIN stock_locations sl ON sl.id = sa.stock_location_id
  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'
),
adj_agg AS (
  SELECT
    month_start, half,
    COUNT(*) AS events,
    SUM(ABS(quantity_delta)) AS abs_units_variance,
    SUM(quantity_delta) AS net_units_variance,
    COUNT(DISTINCT stock_location_id) AS distinct_locations_active
  FROM adj
  GROUP BY month_start, half
),
wh AS (
  SELECT
    date_trunc('month', w.created_at)::date AS month_start,
    CASE WHEN EXTRACT(day FROM w.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,
    COUNT(*) AS new_warehouses
  FROM warehouses w
  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'
  GROUP BY month_start, half
)
SELECT
  to_char(p.month_start, 'YYYY-MM') || ' (' ||
    CASE WHEN p.half = 1 THEN '1-15'
         ELSE '16-' || EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day'))::int
    END || ')' AS period_label,
  COALESCE(a.events, 0) AS events,
  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,
  COALESCE(a.net_units_variance, 0) AS net_units_variance,
  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,
  ROUND(
    CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL
         ELSE a.events::numeric / a.distinct_locations_active
    END, 2
  ) AS events_per_active_location,
  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_period
FROM periods p
LEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.half
LEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.half
ORDER BY p.month_start, p.half

The warehouse answers

STATUS: REJECTED
  - does not compile: Catalog Error: Scalar Function with name to_char does not exist!

Omar calls dry_run

WITH periods AS (
  SELECT DISTINCT
    date_trunc('month', calendar_date)::date AS month_start,
    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half
  FROM calendar_days
  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'
),
adj AS (
  SELECT
    date_trunc('month', sa.created_at)::date AS month_start,
    CASE WHEN EXTRACT(day FROM sa.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,
    sa.stock_location_id,
    sa.quantity_delta
  FROM stock_adjustments sa
  JOIN stock_locations sl ON sl.id = sa.stock_location_id
  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'
),
adj_agg AS (
  SELECT
    month_start, half,
    COUNT(*) AS events,
    SUM(ABS(quantity_delta)) AS abs_units_variance,
    SUM(quantity_delta) AS net_units_variance,
    COUNT(DISTINCT stock_location_id) AS distinct_locations_active
  FROM adj
  GROUP BY month_start, half
),
wh AS (
  SELECT
    date_trunc('month', w.created_at)::date AS month_start,
    CASE WHEN EXTRACT(day FROM w.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,
    COUNT(*) AS new_warehouses
  FROM warehouses w
  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'
  GROUP BY month_start, half
)
SELECT
  strftime(p.month_start, '%Y-%m') || ' (' ||
    CASE WHEN p.half = 1 THEN '1-15'
         ELSE '16-' || EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day'))::int
    END || ')' AS period_label,
  COALESCE(a.events, 0) AS events,
  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,
  COALESCE(a.net_units_variance, 0) AS net_units_variance,
  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,
  ROUND(
    CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL
         ELSE a.events::numeric / a.distinct_locations_active
    END, 2
  ) AS events_per_active_location,
  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_period
FROM periods p
LEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.half
LEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.half
ORDER BY p.month_start, p.half

The warehouse answers

STATUS: COMPILES (dry run on the empty schema, no data read)
COLUMNS: period_label, events, abs_units_variance, net_units_variance, distinct_locations_active, events_per_active_location, new_warehouses_opened_this_period

OmarThe query compiles and returns the expected columns. Submitting now.

Omar calls submit

WITH periods AS (
  SELECT DISTINCT
    date_trunc('month', calendar_date)::date AS month_start,
    CASE WHEN day_of_month <= 15 THEN 1 ELSE 2 END AS half
  FROM calendar_days
  WHERE calendar_date >= DATE '2025-01-01' AND calendar_date < DATE '2026-01-01'
),
adj AS (
  SELECT
    date_trunc('month', sa.created_at)::date AS month_start,
    CASE WHEN EXTRACT(day FROM sa.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,
    sa.stock_location_id,
    sa.quantity_delta
  FROM stock_adjustments sa
  JOIN stock_locations sl ON sl.id = sa.stock_location_id
  WHERE sa.created_at >= DATE '2025-01-01' AND sa.created_at < DATE '2026-01-01'
),
adj_agg AS (
  SELECT
    month_start, half,
    COUNT(*) AS events,
    SUM(ABS(quantity_delta)) AS abs_units_variance,
    SUM(quantity_delta) AS net_units_variance,
    COUNT(DISTINCT stock_location_id) AS distinct_locations_active
  FROM adj
  GROUP BY month_start, half
),
wh AS (
  SELECT
    date_trunc('month', w.created_at)::date AS month_start,
    CASE WHEN EXTRACT(day FROM w.created_at)::int <= 15 THEN 1 ELSE 2 END AS half,
    COUNT(*) AS new_warehouses
  FROM warehouses w
  WHERE w.created_at >= DATE '2025-01-01' AND w.created_at < DATE '2026-01-01'
  GROUP BY month_start, half
)
SELECT
  strftime(p.month_start, '%Y-%m') || ' (' ||
    CASE WHEN p.half = 1 THEN '1-15'
         ELSE '16-' || EXTRACT(DAY FROM (p.month_start + INTERVAL '1 month' - INTERVAL '1 day'))::int
    END || ')' AS period_label,
  COALESCE(a.events, 0) AS events,
  COALESCE(a.abs_units_variance, 0) AS abs_units_variance,
  COALESCE(a.net_units_variance, 0) AS net_units_variance,
  COALESCE(a.distinct_locations_active, 0) AS distinct_locations_active,
  ROUND(
    CASE WHEN COALESCE(a.distinct_locations_active, 0) = 0 THEN NULL
         ELSE a.events::numeric / a.distinct_locations_active
    END, 2
  ) AS events_per_active_location,
  COALESCE(w.new_warehouses, 0) AS new_warehouses_opened_this_period
FROM periods p
LEFT JOIN adj_agg a ON a.month_start = p.month_start AND a.half = p.half
LEFT JOIN wh w ON w.month_start = p.month_start AND w.half = p.half
ORDER BY p.month_start, p.half

The warehouse answers

Submitted to tonight's batch. Your work on this request is done.

OmarSubmitted. The report builds all 24 half-month periods from `calendar_days` (so months/halves with zero activity still appear), aggregates `stock_adjustments` (joined to `stock_locations` to validate location identity) for events/variance/active-location counts, and separately counts `warehouses.created_at` falling in each window across all tenants — exactly as specified, with no tenant filter since the question is platform-wide.

Not shown here: 240 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

Omar's transcript (281 KB) · Fatima Zahra's transcript (935 KB) · Tomasz Nowak's transcript (261 KB) · the night's plan (6 KB) · the night's reports (26 KB) · the week's memo (3 KB)

Trend of stock-count discrepancies within 2025: steady climb or step change?

A business event lands on the Director of Fulfilment Operations's desk

Tomasz Nowak writes to Fatima Zahra

Fatima Zahra plans the night

Omar writes the SQL

The report comes back overnight

What came of it

It becomes benchmark query q389

Everything, as recorded