Queries · q233 · Chief Operating Officer · week 2, night 1
“Is Albion Parcel's November-December breakdown concentrated in a specific warehouse/hub (or the merchants it serves), or did it hit all origins roughly equally?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
The manager, at the start of week 2
Subject: Carrier picture confirms it — now I want to know what we can actually fix
Lena,
This is exactly the clarity I was hoping for — "it's the carriers, not the warehouses" is a much more actionable story than "fulfillment is inconsistent." A few things jumped out, including from what Daniel's and Tomasz's teams posted this week, and I want to chase them down before we draft anything for merchants or carrier partners.
What surprised me: Albion Parcel going from solid to broken in November and staying broken through December is different from Landfall and Lowlands, which sound like long-term weak performers we've just tolerated. One is a relationship we may need to end; the other looks like it could be an acute problem (capacity, a route, a depot) that might actually be fixable if we understood the trigger. I don't want to lump all three into one "bad carriers" conversation when they may need completely different responses.
I also noticed Daniel's team's finding that "where's my order" tickets spike in lockstep with late deliveries each November/December — that's a direct line from the carrier problem to support cost and customer satisfaction, and I'd like to be able to say that explicitly in my own numbers rather than borrowing his.
Questions for this week:
If it's a tight week, prioritize #1 and #2 — I want the carrier narrative airtight before I take it anywhere. Returns/restock cycle time can wait one more week if needed.
Thanks, Priya
The analyst · week 2, night 1, request 2 of 3
Priya wants the carrier narrative airtight before it goes anywhere, and specifically wants to know whether Albion Parcel's November collapse is a localized, fixable problem (one hub/country/merchant) versus Landfall Freight and Lowlands Express being broadly, chronically weak (a relationship-ending problem). That's priority #1 and it needs its own evidence on both the destination side (country/region) and the origin side (warehouse/hub), split into the pre-collapse baseline (Jan-Oct) vs. the collapse window (Nov-Dec) for Albion specifically. I'm spending all 3 slots tonight on that, since getting this distinction right shapes two very different conversations with carrier partners. I'll tackle priority #2 (the reliability "ceiling" if weak carriers matched good ones) first thing tomorrow night, using a full-year, non-truncated carrier table I don't yet have cleanly. Priority #3 (clean PO backlog view) and #4 (picking discrepancies vs. delivery) will follow later this week as time allows, per Priya's own prioritization.
The engineer · 1 dry run on the empty schema, then the submission
WITH base AS ( SELECT f.warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, CASE WHEN s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2025-11-01' THEN 'baseline' WHEN s.shipped_at >= DATE '2025-11-01' AND s.shipped_at < DATE '2026-01-01' THEN 'collapse' END AS period, s.delivered_at, s.shipped_at FROM shipments s JOIN fulfillments f ON f.id = s.fulfillment_id JOIN warehouses w ON w.id = f.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE s.carrier = 'Albion Parcel' AND s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, period, COUNT(*) AS shipments_shipped, AVG(EXTRACT(EPOCH FROM (delivered_at - shipped_at)) / 86400.0) FILTER (WHERE delivered_at IS NOT NULL) AS avg_transit_days_raw, 100.0 * COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND delivered_at - shipped_at <= INTERVAL '3 days') / NULLIF(COUNT(*) FILTER (WHERE delivered_at IS NOT NULL), 0) AS pct_within_3_days_raw FROM base WHERE period IS NOT NULL GROUP BY warehouse_id, warehouse_name, tenant_name, period ), period_totals AS ( SELECT period, SUM(shipments_shipped) AS total_shipments FROM agg GROUP BY period ), qualified AS ( SELECT warehouse_id FROM agg GROUP BY warehouse_id HAVING MAX(shipments_shipped) >= 10 ) SELECT a.warehouse_name, a.tenant_name, a.period, a.shipments_shipped, ROUND(a.avg_transit_days_raw, 2) AS avg_transit_days, ROUND(a.pct_within_3_days_raw, 1) AS pct_within_3_days, ROUND(100.0 * a.shipments_shipped / pt.total_shipments, 1) AS share_of_period_shipments_pct FROM agg a JOIN period_totals pt ON pt.period = a.period JOIN qualified q ON q.warehouse_id = a.warehouse_id ORDER BY a.warehouse_name ASC, CASE a.period WHEN 'baseline' THEN 0 ELSE 1 END
“Query compiles and returns the expected columns. Submitting.”
“Submitted. The report gives one row per origin warehouse × period (baseline vs. collapse) for Albion Parcel shipments, limited to warehouses with ≥10 shipments in at least one period, with shipment counts, average transit days, on-time share, and each warehouse's share of total Albion volume in that period — which should make it clear whether the breakdown is concentrated in specific hubs/merchants or spread broadly.”
As Lena Hartmann saw it, from the small dataset the agents worked on
warehouse_name | tenant_name | period | shipments_shipped | avg_transit_days | pct_within_3_days | share_of_period_shipments_pct --------------------------------------+-------------------------------+----------+-------------------+------------------+-------------------+------------------------------ Birmingham Fulfilment Centre | Folio & Fern | baseline | 21 | 2.75 | 66.7 | 1.0 Birmingham Fulfilment Centre | Folio & Fern | collapse | 7 | 7.63 | 16.7 | 1.3 Bromley Fulfilment Centre | Kibblewick | baseline | 240 | 2.28 | 77.1 | 11.3 Bromley Fulfilment Centre | Petalcourt Beauty | baseline | 226 | 2.26 | 79.6 | 10.6 Bromley Fulfilment Centre | Kibblewick | collapse | 74 | 8.05 | 33.8 | 13.5 Bromley Fulfilment Centre | Petalcourt Beauty | collapse | 88 | 10.44 | 20.5 | 16.1 Cambridge Fulfilment Centre | Thistlecombe | baseline | 18 | 2.15 | 83.3 | 0.8 Cambridge Fulfilment Centre | Thistlecombe | collapse | 11 | 10.86 | 18.2 | 2.0 Dublin Fulfilment Centre | Sourdough Club | baseline | 59 | 2.26 | 76.3 | 2.8 Dublin Fulfilment Centre | Sourdough Club | collapse | 17 | 8.08 | 40.0 | 3.1 Exeter Fulfilment Centre | Kayak Corner | baseline | 31 | 1.96 | 87.1 | 1.5 Exeter Fulfilment Centre | Kayak Corner | collapse | 7 | 17.48 | 14.3 | 1.3 Galway Fulfilment Centre | Ballycorran Industrial Supply | baseline | 73 | 2.39 | 72.6 | 3.4 Galway Fulfilment Centre | Ballycorran Industrial Supply | collapse | 19 | 9.04 | 26.3 | 3.5 Leeds Fulfilment Centre | The Balm Room | baseline | 44 | 2.45 | 68.2 | 2.1 Leeds Fulfilment Centre | The Balm Room | collapse | 14 | 8.95 | 21.4 | 2.6 Liverpool Fulfilment Centre | Fennel & Flint | baseline | 112 | 2.18 | 79.5 | 5.3 Liverpool Fulfilment Centre | Fennel & Flint | collapse | 56 | 9.16 | 30.9 | 10.2 Manchester Fulfilment Centre | Willow & Wren | baseline | 30 | 2.0 | 83.3 | 1.4 Manchester Fulfilment Centre | Haven Linen | baseline | 10 | 1.95 | 90.0 | 0.5 Manchester Fulfilment Centre | Grind Collective | baseline | 399 | 2.22 | 81.0 | 18.7 Manchester Fulfilment Centre | Grind Collective | collapse | 116 | 8.31 | 26.1 | 21.2 Manchester Fulfilment Centre | Willow & Wren | collapse | 11 | 10.8 | 27.3 | 2.0 Manchester Fulfilment Centre | Haven Linen | collapse | 2 | 10.49 | 0.0 | 0.4 Newcastle upon Tyne Fulfilment Centre | Deskline Supplies | baseline | 25 | 2.47 | 72.0 | 1.2 Newcastle upon Tyne Fulfilment Centre | Deskline Supplies | collapse | 5 | 8.32 | 0.0 | 0.9 Oxford Fulfilment Centre | Tiny Atelier | baseline | 10 | 2.58 | 90.0 | 0.5 Oxford Fulfilment Centre | Tiny Atelier | collapse | 2 | 11.9 | 50.0 | 0.4 Preston Fulfilment Centre | Rosehip Lab | baseline | 10 | 2.56 | 70.0 | 0.5 Preston Fulfilment Centre | Rosehip Lab | collapse | 10 | 9.44 | 33.3 | 1.8 Reading Fulfilment Centre | Marrowby & Vane | baseline | 778 | 2.19 | 81.2 | 36.5 Reading Fulfilment Centre | Marrowby & Vane | collapse | 85 | 5.2 | 36.5 | 15.5 (32 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
WITH base AS ( SELECT f.warehouse_id, f.warehouse_id AS warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, CASE WHEN s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2025-11-01' THEN 'baseline' WHEN s.shipped_at >= DATE '2025-11-01' AND s.shipped_at < DATE '2026-01-01' THEN 'collapse' END AS period, s.delivered_at, s.shipped_at s.delivered_at AS delivered_at, s.shipped_at AS shipped_at FROM shipments s JOIN fulfillments f ON f.id = s.fulfillment_id JOIN warehouses w ON w.id = f.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE s.carrier = 'Albion Parcel' AND s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2026-01-01'),agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, period, COUNT(*) AS shipments_shipped, AVG(EXTRACT(EPOCH FROM (delivered_at - shipped_at)) / 86400.0) FILTER (WHERE delivered_at IS NOT NULL) AS avg_transit_days_raw, AVG({{seconds_between(shipped_at, delivered_at)}} / 86400.0) FILTER (WHERE delivered_at IS NOT NULL) AS avg_transit_days_raw, 100.0 * COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND delivered_at - shipped_at <= INTERVAL '3 days') / NULLIF(COUNT(*) FILTER (WHERE delivered_at IS NOT NULL), 0) AS pct_within_3_days_raw FROM base WHERE period IS NOT NULL GROUP BY warehouse_id, warehouse_name, tenant_name, period),period_totals AS ( SELECT period, SUM(shipments_shipped) AS total_shipments FROM agg GROUP BY period),qualified AS ( SELECT warehouse_id FROM agg GROUP BY warehouse_id HAVING MAX(shipments_shipped) >= 10)SELECT a.warehouse_name, a.tenant_name, a.period, a.shipments_shipped, ROUND(a.avg_transit_days_raw, 2) AS avg_transit_days, ROUND(a.pct_within_3_days_raw, 1) AS pct_within_3_days, ROUND(100.0 * a.shipments_shipped / pt.total_shipments, 1) AS share_of_period_shipments_pct a.warehouse_name AS warehouse_name, a.tenant_name AS tenant_name, a.period AS period, a.shipments_shipped AS shipments_shipped, a.avg_transit_days_raw AS avg_transit_days, a.pct_within_3_days_raw AS pct_within_3_days, 100.0 * a.shipments_shipped / pt.total_shipments AS share_of_period_shipments_pctFROM agg aJOIN period_totals pt ON pt.period = a.periodJOIN qualified q ON q.warehouse_id = a.warehouse_idORDER BY a.warehouse_name ASC, CASE a.period WHEN 'baseline' THEN 0 ELSE 1 ENDORDER BY a.warehouse_name ASC NULLS LAST, CASE a.period WHEN 'baseline' THEN 0 ELSE 1 END NULLS LAST, a.warehouse_id NULLS LAST
WITH base AS ( SELECT f.warehouse_id AS warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, CASE WHEN s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2025-11-01' THEN 'baseline' WHEN s.shipped_at >= DATE '2025-11-01' AND s.shipped_at < DATE '2026-01-01' THEN 'collapse' END AS period, s.delivered_at AS delivered_at, s.shipped_at AS shipped_at FROM shipments s JOIN fulfillments f ON f.id = s.fulfillment_id JOIN warehouses w ON w.id = f.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE s.carrier = 'Albion Parcel' AND s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, period, COUNT(*) AS shipments_shipped, AVG(CAST(EXTRACT(EPOCH FROM (delivered_at - shipped_at)) AS NUMERIC(18,6)) / 86400.0) FILTER (WHERE delivered_at IS NOT NULL) AS avg_transit_days_raw, 100.0 * COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND delivered_at - shipped_at <= INTERVAL '3 days') / NULLIF(COUNT(*) FILTER (WHERE delivered_at IS NOT NULL), 0) AS pct_within_3_days_raw FROM base WHERE period IS NOT NULL GROUP BY warehouse_id, warehouse_name, tenant_name, period ), period_totals AS ( SELECT period, SUM(shipments_shipped) AS total_shipments FROM agg GROUP BY period ), qualified AS ( SELECT warehouse_id FROM agg GROUP BY warehouse_id HAVING MAX(shipments_shipped) >= 10 ) SELECT a.warehouse_name AS warehouse_name, a.tenant_name AS tenant_name, a.period AS period, a.shipments_shipped AS shipments_shipped, a.avg_transit_days_raw AS avg_transit_days, a.pct_within_3_days_raw AS pct_within_3_days, 100.0 * a.shipments_shipped / pt.total_shipments AS share_of_period_shipments_pct FROM agg a JOIN period_totals pt ON pt.period = a.period JOIN qualified q ON q.warehouse_id = a.warehouse_id ORDER BY a.warehouse_name ASC NULLS LAST, CASE a.period WHEN 'baseline' THEN 0 ELSE 1 END NULLS LAST, a.warehouse_id NULLS LAST
WITH base AS ( SELECT f.warehouse_id AS warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, CASE WHEN s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2025-11-01' THEN 'baseline' WHEN s.shipped_at >= DATE '2025-11-01' AND s.shipped_at < DATE '2026-01-01' THEN 'collapse' END AS period, s.delivered_at AS delivered_at, s.shipped_at AS shipped_at FROM shipments s JOIN fulfillments f ON f.id = s.fulfillment_id JOIN warehouses w ON w.id = f.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE s.carrier = 'Albion Parcel' AND s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, period, COUNT(*) AS shipments_shipped, AVG(CAST(divideDecimal(toDecimal128(dateDiff('microsecond', shipped_at, delivered_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 86400.0) FILTER (WHERE delivered_at IS NOT NULL) AS avg_transit_days_raw, 100.0 * COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND delivered_at - shipped_at <= INTERVAL '3 days') / NULLIF(COUNT(*) FILTER (WHERE delivered_at IS NOT NULL), 0) AS pct_within_3_days_raw FROM base WHERE period IS NOT NULL GROUP BY warehouse_id, warehouse_name, tenant_name, period ), period_totals AS ( SELECT period, SUM(shipments_shipped) AS total_shipments FROM agg GROUP BY period ), qualified AS ( SELECT warehouse_id FROM agg GROUP BY warehouse_id HAVING MAX(shipments_shipped) >= 10 ) SELECT a.warehouse_name AS warehouse_name, a.tenant_name AS tenant_name, a.period AS period, a.shipments_shipped AS shipments_shipped, a.avg_transit_days_raw AS avg_transit_days, a.pct_within_3_days_raw AS pct_within_3_days, 100.0 * a.shipments_shipped / pt.total_shipments AS share_of_period_shipments_pct FROM agg a JOIN period_totals pt ON pt.period = a.period JOIN qualified q ON q.warehouse_id = a.warehouse_id ORDER BY a.warehouse_name ASC NULLS LAST, CASE a.period WHEN 'baseline' THEN 0 ELSE 1 END NULLS LAST, a.warehouse_id 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 Lena Hartmann (analyst for the Chief Operating Officer):
Title: Albion Parcel performance by origin warehouse, baseline vs collapse
Question: Is Albion Parcel's November-December breakdown concentrated in a specific warehouse/hub (or the merchants it serves), or did it hit all origins roughly equally?
Grain: one row per origin warehouse and period, for Albion Parcel shipments only
Columns:
- warehouse_name
- tenant_name
- period ('baseline' = shipped_at 2025-01-01 to 2025-10-31; 'collapse' = shipped_at 2025-11-01 to 2025-12-31)
- shipments_shipped
- avg_transit_days
- pct_within_3_days
- share_of_period_shipments_pct (this warehouse's shipments / all Albion shipments in that period, x100)
Details: Tables: shipments (carrier='Albion Parcel', shipped_at, delivered_at) joined via fulfillments (fulfillment_id, warehouse_id) -> warehouses (name, tenant_id) -> tenants (name). Same transit_days definition as the destination-country report. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Include only warehouses with at least 10 shipments in at least one of the two periods. Order by warehouse_name ascending, then period (baseline before collapse).
Check your query with the warehouse's dry_run tool, then hand it in with submit.dry_runWITH base AS ( SELECT f.warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, CASE WHEN s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2025-11-01' THEN 'baseline' WHEN s.shipped_at >= DATE '2025-11-01' AND s.shipped_at < DATE '2026-01-01' THEN 'collapse' END AS period, s.delivered_at, s.shipped_at FROM shipments s JOIN fulfillments f ON f.id = s.fulfillment_id JOIN warehouses w ON w.id = f.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE s.carrier = 'Albion Parcel' AND s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, period, COUNT(*) AS shipments_shipped, AVG(EXTRACT(EPOCH FROM (delivered_at - shipped_at)) / 86400.0) FILTER (WHERE delivered_at IS NOT NULL) AS avg_transit_days_raw, 100.0 * COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND delivered_at - shipped_at <= INTERVAL '3 days') / NULLIF(COUNT(*) FILTER (WHERE delivered_at IS NOT NULL), 0) AS pct_within_3_days_raw FROM base WHERE period IS NOT NULL GROUP BY warehouse_id, warehouse_name, tenant_name, period ), period_totals AS ( SELECT period, SUM(shipments_shipped) AS total_shipments FROM agg GROUP BY period ), qualified AS ( SELECT warehouse_id FROM agg GROUP BY warehouse_id HAVING MAX(shipments_shipped) >= 10 ) SELECT a.warehouse_name, a.tenant_name, a.period, a.shipments_shipped, ROUND(a.avg_transit_days_raw, 2) AS avg_transit_days, ROUND(a.pct_within_3_days_raw, 1) AS pct_within_3_days, ROUND(100.0 * a.shipments_shipped / pt.total_shipments, 1) AS share_of_period_shipments_pct FROM agg a JOIN period_totals pt ON pt.period = a.period JOIN qualified q ON q.warehouse_id = a.warehouse_id ORDER BY a.warehouse_name ASC, CASE a.period WHEN 'baseline' THEN 0 ELSE 1 END
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: warehouse_name, tenant_name, period, shipments_shipped, avg_transit_days, pct_within_3_days, share_of_period_shipments_pct
submitWITH base AS ( SELECT f.warehouse_id, w.name AS warehouse_name, t.name AS tenant_name, CASE WHEN s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2025-11-01' THEN 'baseline' WHEN s.shipped_at >= DATE '2025-11-01' AND s.shipped_at < DATE '2026-01-01' THEN 'collapse' END AS period, s.delivered_at, s.shipped_at FROM shipments s JOIN fulfillments f ON f.id = s.fulfillment_id JOIN warehouses w ON w.id = f.warehouse_id JOIN tenants t ON t.id = w.tenant_id WHERE s.carrier = 'Albion Parcel' AND s.shipped_at >= DATE '2025-01-01' AND s.shipped_at < DATE '2026-01-01' ), agg AS ( SELECT warehouse_id, warehouse_name, tenant_name, period, COUNT(*) AS shipments_shipped, AVG(EXTRACT(EPOCH FROM (delivered_at - shipped_at)) / 86400.0) FILTER (WHERE delivered_at IS NOT NULL) AS avg_transit_days_raw, 100.0 * COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND delivered_at - shipped_at <= INTERVAL '3 days') / NULLIF(COUNT(*) FILTER (WHERE delivered_at IS NOT NULL), 0) AS pct_within_3_days_raw FROM base WHERE period IS NOT NULL GROUP BY warehouse_id, warehouse_name, tenant_name, period ), period_totals AS ( SELECT period, SUM(shipments_shipped) AS total_shipments FROM agg GROUP BY period ), qualified AS ( SELECT warehouse_id FROM agg GROUP BY warehouse_id HAVING MAX(shipments_shipped) >= 10 ) SELECT a.warehouse_name, a.tenant_name, a.period, a.shipments_shipped, ROUND(a.avg_transit_days_raw, 2) AS avg_transit_days, ROUND(a.pct_within_3_days_raw, 1) AS pct_within_3_days, ROUND(100.0 * a.shipments_shipped / pt.total_shipments, 1) AS share_of_period_shipments_pct FROM agg a JOIN period_totals pt ON pt.period = a.period JOIN qualified q ON q.warehouse_id = a.warehouse_id ORDER BY a.warehouse_name ASC, CASE a.period WHEN 'baseline' THEN 0 ELSE 1 END
Submitted to tonight's batch. Your work on this request is done.