Bubble tea staffing mismatch audit - schedule vs timesheet vs cup-volume curve, mismatches found in both directions, and a swap list that survives real constraints

You are a labor-scheduling auditor for a bubble tea shop.
Audit last week's alignment of planned schedule vs actual
attendance vs order volume, and output a mismatch list
plus an executable swap list.

Inputs I will provide:
- Last week's schedule: per person per shift - role,
  start time, end time
- Last week's actual clock-ins: actual in/out times per
  person per day
- Last week's POS cup counts per half-hour slot during
  opening hours
- Our capacity figure: drinks one person can steadily
  make per hour at peak (shaking, sealing, handing off
  included)
- Customer-side waiting evidence if any: time
  distribution of late delivery tickets, moments when
  the queue was photographed or logged
- Hard constraints: each person's available windows
  (class schedules), each person's weekly hour cap,
  minimum staff on site

Audit steps and fixed classification:
1. For every half-hour slot compute planned person-hours
   and actual person-hours (from clock-ins), plus cups
   per actually-present person
2. Class A "understaffed peak": slots where throughput
   persistently exceeds capacity x people present AND
   waiting evidence exists in that window - both
   conditions required. Throughput exceeded but no
   waiting evidence gets marked A? and no verdict
   (maybe the team was just fast that day)
3. Class B "overstaffed valley": N or more consecutive
   slots with M+ people actually present while cups per
   slot run below half of one person's capacity
4. Class C "off-schedule drift": every clock-in gap
   beyond 30 minutes (left early / late / ad-hoc
   overtime). This is not a statistics problem - it is
   the seed from which next week's mismatch regrows.

Output:
- Table 1, mismatch list: slot / class / facts (cups,
  person-hours, evidence, quoting specific numbers) /
  people involved
- Table 2, swap list: as concrete as "move Alex's hours
  from Tue 14:00-17:00 to 11:00-14:00," each swap
  checked against that person's availability and weekly
  cap; swaps that fail go in a separate list stating
  exactly which constraint blocks them
- Table 3, drift list: who, how many minutes off, how
  many times this week - frequent drifters need buffer
  or a conversation before next week's schedule

Constraints:
- Swap suggestions move only existing part-timers'
  windows and incremental hours. Do not recommend
  "hire someone" or "cut someone" - that is a different
  decision and not an audit output.
- A? items are neither confirmed understaffing nor
  discardable noise - list them separately for my
  judgment.
- Do not cite industry labor-productivity benchmarks;
  use only the per-person capacity figure I gave.
- If a day's data is missing, skip that day and note it
  in the table header - do not average over the hole.

Why it is written this way

Aligning three sources - schedule, timesheet, cup curve - is what separates this from "adjust the schedule by looking at the report." Most shops reconcile only the plan against volume, but in a part-timer shop the plan is fiction: someone shows at 17:40, someone leaves at 21:00, and the mismatch persists. Clock-in data splits "the schedule was wrong" from "the schedule wasn't followed" - the first needs a new table, the second needs a conversation, and conflating them means forever rewriting tables while the real problem is untouched.

Class A requiring both throughput exceeded AND waiting evidence guards against diagnosing efficiency as understaffing. High cups-per-person has two explanations: the team is drowning, or the team is on fire in a good way - only customer-side waiting (late tickets, queue records) distinguishes them. The A? tier exists because audits do hit cases they cannot resolve; handing those to the supervisor un resolved is more honest than forcing a verdict.

Every swap checked against availability and weekly caps reflects how swap advice actually dies: "just move a few hours" sounds trivial until it hits a class schedule that cannot move. Advice without constraint-checking collapses on the first line, and the rest of the list gets thrown out with it.

Class C drift stands apart from the statistics on purpose - it is not a scheduling problem at all. A part-timer drifting several times a week needs buffer built into their shifts or a direct conversation. The audit points at the person but reports only frequency, no editorializing: what to do about it is the supervisor's call.

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