Queries · q027 · Chief Operating Officer · week 1, night 1

Peak season weekly fulfillment performance (Oct-Dec 2025)

“Did delivery speed and reliability hold up during the most recent peak season, and did any backlog build up or clear by year-end?”

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

1. Priya Raman writes to Lena Hartmann

   The manager, at the start of week 1

   Subject: Getting my bearings on fulfillment — questions for this week

   Hi Lena,

   Thanks for taking these on. I'm new here and coming from the logistics side, so my first priority is understanding whether the delivery promise we make to shoppers actually holds up — that's the lens for most of what's below. Given the slot budget, here's what matters most, roughly in priority order:

   1. How long does it actually take us to get an order into a customer's hands, and how much does that vary? I want to understand the real end-to-end time from order placed to delivered, and whether that's gotten better, worse, or stayed flat over the past year or so. I'm especially interested in how consistent we are — are there merchants or categories where delivery is reliably fast, and others where it's a coin toss?
   2. How did we do during the last peak/busy season? I'd like to know whether fulfillment speed and reliability held up when volume spiked, or whether we saw things slip — delays, backlogs, stockouts — and how quickly we recovered afterward. This matters because peak is the real stress test of the whole operation.
   3. Where are the weak links — specific carriers, warehouses, or regions? I want to know if there are particular carriers or fulfillment sites that are consistently underperforming on delivery time or dependability, so we know where to focus attention or have a harder conversation with a partner.
   4. How well do we manage stock so we're not promising things we can't deliver? I'm curious about how often we run into stockouts or have to delay orders for lack of inventory, and whether our replenishment from suppliers (purchase orders) is keeping pace — are we chronically waiting too long on restocks?
   5. (If there's room) What does the returns process look like from a logistics standpoint? Specifically, how long it takes for returned goods to get back into our system and become sellable again — that's a cost and customer-experience lever I want to keep an eye on.

   What I'll use this for: I'm forming my first impressions of operational health in this area and want to walk into conversations with merchants and carrier partners armed with facts, not assumptions. I'd rather have a smaller number of really clear answers than a lot of surface-level ones — so if you need to cut scope to fit your slots this week, prioritize #1 and #2, and we can pick up the rest next week.

   Thanks — looking forward to Friday's memo.

   Priya

   Show the whole note

2. Lena Hartmann plans the night

   The analyst · week 1, night 1, request 3 of 3

   Priya's top two priorities are (1) end-to-end delivery speed and its consistency/trend, and (2) how the last peak season held up. I'm spending all 3 slots tonight on those, leaving carriers/warehouses, inventory and returns for later nights once I've seen where the baseline numbers point. I'm defining "delivered" as order status = fulfilled with all linked shipments having a non-null delivered_at, and delivery time as placed_at -> last shipment delivered_at. Report 1 gives the 24-month trend + variability (median/p90/SLA-hit-rate) so I can see direction of travel and spread in one page. Report 2 cuts the same metrics by merchant for 2025 to see who is reliably fast vs. a coin toss. Report 3 looks at the most recent peak window (Oct-Dec 2025) weekly to see if speed degraded under volume and whether a backlog built up and cleared by year-end. I'll sanity-check all three against each other (e.g. does the peak-season weekly average line up with the Nov-Dec rows in the monthly trend) before going further.

   Read the whole plan
   Peak season weekly fulfillment performance (Oct-Dec 2025)

   Question

   Did delivery speed and reliability hold up during the most recent peak season, and did any backlog build up or clear by year-end?

   One row per

   ISO week (week_start), based on order placement week

   Columns

   • week_start
   • orders_placed (all orders placed that week, any status)
   • orders_delivered (of those, count fully delivered by 2025-12-31)
   • avg_delivery_days (for the fully-delivered subset)
   • pct_within_5_days (share of fully-delivered orders in that week where delivery_days <= 5)
   • orders_still_undelivered (placed that week, status not 'fulfilled' or missing a delivered_at as of 2025-12-31 cutoff)

3. Ines writes the SQL

   The engineer · 1 dry run on the empty schema, then the submission

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

   with base as (
     select
       o.id as order_id,
       o.placed_at,
       o.status,
       cd.week_start as week_start
     from orders o
     join calendar_days cd on cd.calendar_date = o.placed_at::date
     where o.placed_at >= timestamp '2025-10-01'
       and o.placed_at < timestamp '2026-01-01'
   ),
   fulfillment_agg as (
     select
       f.order_id,
       count(s.id) as shipment_count,
       count(s.delivered_at) as delivered_shipment_count,
       max(s.delivered_at) as max_delivered_at
     from fulfillments f
     join shipments s on s.fulfillment_id = f.id
     group by f.order_id
   ),
   combined as (
     select
       b.order_id,
       b.placed_at,
       b.status,
       b.week_start,
       case
         when fa.shipment_count is not null and fa.shipment_count > 0
              and fa.shipment_count = fa.delivered_shipment_count
         then true else false
       end as is_fully_delivered,
       case
         when fa.shipment_count is not null and fa.shipment_count > 0
              and fa.shipment_count = fa.delivered_shipment_count
         then extract(epoch from (fa.max_delivered_at - b.placed_at)) / 86400.0
         else null
       end as delivery_days
     from base b
     left join fulfillment_agg fa on fa.order_id = b.order_id
   )
   select
     week_start,
     count(*) as orders_placed,
     count(*) filter (where is_fully_delivered) as orders_delivered,
     round(avg(delivery_days) filter (where is_fully_delivered), 2) as avg_delivery_days,
     round(
       100.0 * count(*) filter (where is_fully_delivered and delivery_days <= 5)
       / nullif(count(*) filter (where is_fully_delivered), 0),
       1
     ) as pct_within_5_days,
     count(*) filter (where status <> 'fulfilled' or not is_fully_delivered) as orders_still_undelivered
   from combined
   group by week_start
   order by week_start asc

   “Looks good — compiles cleanly with the expected 6 columns. Submitting.”

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

   “Submitted. Summary of what the report does: - Grain: one row per ISO week (week_start), based on order placement week, for orders placed 2025-10-01 through 2025-12-31 (~13 rows expected). - orders_placed: all orders placed that week, any status. - orders_delivered / avg_delivery_days / pct_within_5_days: computed over the subset where every shipment linked to the order (via fulfillments) has a delivered_at, with delivery_days = (max shipment delivered_at − placed_at) in days. - orders_still_undelivered: orders placed that week where status ≠ fulfilled, or status is fulfilled but delivery couldn't be fully resolved by the 2025-12-31 cutoff — this will mechanically trend upward in the last couple of weeks due to transit-time right-censoring, as flagged in the note to the analyst. Two things worth knowing: the report pools all tenants together (no merchant filter, since the question is about overall ops, not specific merchants), and orders_still_undelivered is intentionally literal per the spec, so it can flag orders that are already physically delivered but not yet status-flagged fulfilled.”

4. The report comes back overnight

   As Lena Hartmann saw it, from the small dataset the agents worked on

   week_start | orders_placed | orders_delivered | avg_delivery_days | pct_within_5_days | orders_still_undelivered
   -----------+---------------+------------------+-------------------+-------------------+-------------------------
   2025-09-29 |           279 |              271 |               4.5 |              72.7 |                       19
   2025-10-06 |           397 |              383 |              3.94 |              79.4 |                       25
   2025-10-13 |           423 |              414 |              3.92 |              80.7 |                       25
   2025-10-20 |           448 |              440 |              4.31 |              73.9 |                       21
   2025-10-27 |           441 |              427 |              3.91 |              82.4 |                       27
   2025-11-03 |           480 |              466 |              3.92 |              80.5 |                       41
   2025-11-10 |           470 |              452 |              4.73 |              64.8 |                       35
   2025-11-17 |           576 |              559 |              4.29 |              73.3 |                       41
   2025-11-24 |          1010 |              966 |              5.26 |              73.5 |                      104
   2025-12-01 |           969 |              928 |              5.73 |              56.1 |                       87
   2025-12-08 |           635 |              611 |              3.96 |              80.7 |                       34
   2025-12-15 |           660 |              628 |              3.43 |              87.9 |                       38
   2025-12-22 |           577 |              510 |               3.1 |              92.5 |                       67
   2025-12-29 |           271 |               38 |              1.87 |             100.0 |                      233
   (14 rows)

5. What came of it

   The memo and the queries that followed; links found by title, so inferred

   Later queries that build on this report:

   • q028Carrier transit performance by quarter, 2025

6. It becomes benchmark query q027

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

   • spellingcast
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • numericsdocs/NUMERICS.md: EXTRACT(EPOCH FROM (b - a)) cast to NUMERIC(18,6)
   • made explicita selected t.col gets AS col, its standard name made explicit (7×)
   • placeholderseconds between
   • determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results
   The benchmark text, against the submitted SQL

   with base as (  select    o.id as order_id,    o.placed_at,    o.status,    o.placed_at AS placed_at,    o.status AS status,    cd.week_start as week_start  from orders o  join calendar_days cd on cd.calendar_date = o.placed_at::date  join calendar_days cd on cd.calendar_date = CAST(o.placed_at AS date)  where o.placed_at >= timestamp '2025-10-01'    and o.placed_at < timestamp '2026-01-01'),fulfillment_agg as (  select    f.order_id,    f.order_id AS order_id,    count(s.id) as shipment_count,    count(s.delivered_at) as delivered_shipment_count,    max(s.delivered_at) as max_delivered_at  from fulfillments f  join shipments s on s.fulfillment_id = f.id  group by f.order_id),combined as (  select    b.order_id,    b.placed_at,    b.status,    b.week_start,    b.order_id AS order_id,    b.placed_at AS placed_at,    b.status AS status,    b.week_start AS week_start,    case      when fa.shipment_count is not null and fa.shipment_count > 0           and fa.shipment_count = fa.delivered_shipment_count      then true else false    end as is_fully_delivered,    case      when fa.shipment_count is not null and fa.shipment_count > 0           and fa.shipment_count = fa.delivered_shipment_count      then extract(epoch from (fa.max_delivered_at - b.placed_at)) / 86400.0      then {{seconds_between(b.placed_at, fa.max_delivered_at)}} / 86400.0      else null    end as delivery_days  from base b  left join fulfillment_agg fa on fa.order_id = b.order_id)select  week_start,  count(*) as orders_placed,  count(*) filter (where is_fully_delivered) as orders_delivered,  round(avg(delivery_days) filter (where is_fully_delivered), 2) as avg_delivery_days,  round(    100.0 * count(*) filter (where is_fully_delivered and delivery_days <= 5)    / nullif(count(*) filter (where is_fully_delivered), 0),    1  ) as pct_within_5_days,  avg(delivery_days) filter (where is_fully_delivered) as avg_delivery_days,  100.0 * count(*) filter (where is_fully_delivered and delivery_days <= 5)  / nullif(count(*) filter (where is_fully_delivered), 0) as pct_within_5_days,  count(*) filter (where status <> 'fulfilled' or not is_fully_delivered) as orders_still_undeliveredfrom combinedgroup by week_startorder by week_start ascorder by week_start asc NULLS LAST

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

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · DataFusion · Firebolt · clickhouse_brewClickHouse · chDB

   with base as (
     select
       o.id as order_id,
       o.placed_at AS placed_at,
       o.status AS status,
       cd.week_start as week_start
     from orders o
     join calendar_days cd on cd.calendar_date = CAST(o.placed_at AS date)
     where o.placed_at >= timestamp '2025-10-01'
       and o.placed_at < timestamp '2026-01-01'
   ),
   fulfillment_agg as (
     select
       f.order_id AS order_id,
       count(s.id) as shipment_count,
       count(s.delivered_at) as delivered_shipment_count,
       max(s.delivered_at) as max_delivered_at
     from fulfillments f
     join shipments s on s.fulfillment_id = f.id
     group by f.order_id
   ),
   combined as (
     select
       b.order_id AS order_id,
       b.placed_at AS placed_at,
       b.status AS status,
       b.week_start AS week_start,
       case
         when fa.shipment_count is not null and fa.shipment_count > 0
              and fa.shipment_count = fa.delivered_shipment_count
         then true else false
       end as is_fully_delivered,
       case
         when fa.shipment_count is not null and fa.shipment_count > 0
              and fa.shipment_count = fa.delivered_shipment_count
         then CAST(EXTRACT(EPOCH FROM (fa.max_delivered_at - b.placed_at)) AS NUMERIC(18,6)) / 86400.0
         else null
       end as delivery_days
     from base b
     left join fulfillment_agg fa on fa.order_id = b.order_id
   )
   select
     week_start,
     count(*) as orders_placed,
     count(*) filter (where is_fully_delivered) as orders_delivered,
     avg(delivery_days) filter (where is_fully_delivered) as avg_delivery_days,
     100.0 * count(*) filter (where is_fully_delivered and delivery_days <= 5)
     / nullif(count(*) filter (where is_fully_delivered), 0) as pct_within_5_days,
     count(*) filter (where status <> 'fulfilled' or not is_fully_delivered) as orders_still_undelivered
   from combined
   group by week_start
   order by week_start asc NULLS LAST

   Scale factor 1000, c8a.24xlarge, after the last load. q027 on the start page, with every configuration.

7. 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

   Ines's whole session, 7 turns

   • The request, as Ines received it

     Report request from Lena Hartmann (analyst for the Chief Operating Officer):
     
     Title: Peak season weekly fulfillment performance (Oct-Dec 2025)
     Question: Did delivery speed and reliability hold up during the most recent peak season, and did any backlog build up or clear by year-end?
     Grain: one row per ISO week (week_start), based on order placement week
     Columns:
     - week_start
     - orders_placed (all orders placed that week, any status)
     - orders_delivered (of those, count fully delivered by 2025-12-31)
     - avg_delivery_days (for the fully-delivered subset)
     - pct_within_5_days (share of fully-delivered orders in that week where delivery_days <= 5)
     - orders_still_undelivered (placed that week, status not 'fulfilled' or missing a delivered_at as of 2025-12-31 cutoff)
     Details: Grain: one row per week_start (Monday, from calendar_days.week_start) for orders.placed_at between 2025-10-01 and 2025-12-31 inclusive. orders_placed = count of all orders placed in that week regardless of status. delivery_days defined exactly as in the monthly trend report (placed_at to max linked shipment.delivered_at via fulfillments, only counted when all shipments for that order are delivered). orders_still_undelivered = orders placed in that week whose status is not 'fulfilled', or status is 'fulfilled' but no delivered_at could be resolved by the 2025-12-31 data cutoff -- this flags backlog/slippage, understanding the most recent weeks will naturally show more undelivered due to right-censoring (transit time), which is expected and should just be noted in the report, not treated as a defect. Order by week_start ascending. Expect about 13 rows.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Ines calls dry_run

     with base as (
       select
         o.id as order_id,
         o.placed_at,
         o.status,
         cd.week_start as week_start
       from orders o
       join calendar_days cd on cd.calendar_date = o.placed_at::date
       where o.placed_at >= timestamp '2025-10-01'
         and o.placed_at < timestamp '2026-01-01'
     ),
     fulfillment_agg as (
       select
         f.order_id,
         count(s.id) as shipment_count,
         count(s.delivered_at) as delivered_shipment_count,
         max(s.delivered_at) as max_delivered_at
       from fulfillments f
       join shipments s on s.fulfillment_id = f.id
       group by f.order_id
     ),
     combined as (
       select
         b.order_id,
         b.placed_at,
         b.status,
         b.week_start,
         case
           when fa.shipment_count is not null and fa.shipment_count > 0
                and fa.shipment_count = fa.delivered_shipment_count
           then true else false
         end as is_fully_delivered,
         case
           when fa.shipment_count is not null and fa.shipment_count > 0
                and fa.shipment_count = fa.delivered_shipment_count
           then extract(epoch from (fa.max_delivered_at - b.placed_at)) / 86400.0
           else null
         end as delivery_days
       from base b
       left join fulfillment_agg fa on fa.order_id = b.order_id
     )
     select
       week_start,
       count(*) as orders_placed,
       count(*) filter (where is_fully_delivered) as orders_delivered,
       round(avg(delivery_days) filter (where is_fully_delivered), 2) as avg_delivery_days,
       round(
         100.0 * count(*) filter (where is_fully_delivered and delivery_days <= 5)
         / nullif(count(*) filter (where is_fully_delivered), 0),
         1
       ) as pct_within_5_days,
       count(*) filter (where status <> 'fulfilled' or not is_fully_delivered) as orders_still_undelivered
     from combined
     group by week_start
     order by week_start asc

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: week_start, orders_placed, orders_delivered, avg_delivery_days, pct_within_5_days, orders_still_undelivered

   • InesLooks good — compiles cleanly with the expected 6 columns. Submitting.
   • Ines calls submit

     with base as (
       select
         o.id as order_id,
         o.placed_at,
         o.status,
         cd.week_start as week_start
       from orders o
       join calendar_days cd on cd.calendar_date = o.placed_at::date
       where o.placed_at >= timestamp '2025-10-01'
         and o.placed_at < timestamp '2026-01-01'
     ),
     fulfillment_agg as (
       select
         f.order_id,
         count(s.id) as shipment_count,
         count(s.delivered_at) as delivered_shipment_count,
         max(s.delivered_at) as max_delivered_at
       from fulfillments f
       join shipments s on s.fulfillment_id = f.id
       group by f.order_id
     ),
     combined as (
       select
         b.order_id,
         b.placed_at,
         b.status,
         b.week_start,
         case
           when fa.shipment_count is not null and fa.shipment_count > 0
                and fa.shipment_count = fa.delivered_shipment_count
           then true else false
         end as is_fully_delivered,
         case
           when fa.shipment_count is not null and fa.shipment_count > 0
                and fa.shipment_count = fa.delivered_shipment_count
           then extract(epoch from (fa.max_delivered_at - b.placed_at)) / 86400.0
           else null
         end as delivery_days
       from base b
       left join fulfillment_agg fa on fa.order_id = b.order_id
     )
     select
       week_start,
       count(*) as orders_placed,
       count(*) filter (where is_fully_delivered) as orders_delivered,
       round(avg(delivery_days) filter (where is_fully_delivered), 2) as avg_delivery_days,
       round(
         100.0 * count(*) filter (where is_fully_delivered and delivery_days <= 5)
         / nullif(count(*) filter (where is_fully_delivered), 0),
         1
       ) as pct_within_5_days,
       count(*) filter (where status <> 'fulfilled' or not is_fully_delivered) as orders_still_undelivered
     from combined
     group by week_start
     order by week_start asc

   • The warehouse answers

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

   • InesSubmitted. Summary of what the report does: - **Grain:** one row per ISO week (`week_start`), based on order placement week, for orders placed 2025-10-01 through 2025-12-31 (~13 rows expected). - **orders_placed:** all orders placed that week, any status. - **orders_delivered / avg_delivery_days / pct_within_5_days:** computed over the subset where every shipment linked to the order (via fulfillments) has a `delivered_at`, with `delivery_days` = (max shipment `delivered_at` − `placed_at`) in days. - **orders_still_undelivered:** orders placed that week where status ≠ `fulfilled`, or status is `fulfilled` but delivery couldn't be fully resolved by the 2025-12-31 cutoff — this will mechanically trend upward in the last couple of weeks due to transit-time right-censoring, as flagged in the note to the analyst. Two things worth knowing: the report pools all tenants together (no merchant filter, since the question is about overall ops, not specific merchants), and `orders_still_undelivered` is intentionally literal per the spec, so it can flag orders that are already physically delivered but not yet status-flagged `fulfilled`.

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

   Ines's transcript (276 KB) · Lena Hartmann's transcript (921 KB) · Priya Raman's transcript (266 KB) · the night's plan (5 KB) · the night's reports (14 KB) · the week's memo (3 KB)