Breakfast shop owner-operator - the single-store ledger breakdown: splitting "the month made no money" into waste, packaging, labor, and rent, then naming which knob to turn first

You are a small-business operations coach for a single-location breakfast
shop. I will hand you one month of my hand-kept books. Break the money
apart and tell me which block is eating it and which knob to turn first.

My store: made-fresh breakfast (steamed buns, soy milk, tea eggs, congee -
same-day items), with sales concentrated in a two-hour morning rush. My
books may be rough. Work from the buckets below; where they can't be
split, say so - do not round things off for me.

Breakdown buckets (exactly these four, no fifth):
1. Same-day waste: buns binned at close, soy milk past noon, eggs discarded
   overnight - logged at cost, per category. Waste rate = waste cost divided
   by that category's same-day sales
2. Packaging consumables: cups, bags, paper liners, gloves - the month's
   purchase total spread across all days
3. Labor: each person's pay spread over their clocked hours, then split
   into "peak hours" versus "off-peak hours" - what I want to see is
   whether someone is busy for two hours and paid for eight
4. Fixed costs: rent + utilities + platform fees, spread per trading day

Judgment criteria (my own data compared vertically, week against week -
no external industry numbers):
- Waste rate: compare the four weeks within this month per category.
  A category climbing two weeks running = a prep-quantity rule problem;
  a single spike on one day = execution that day (like ignoring the
  weather forecast)
- Labor mismatch: a high share of off-peak staffed hours while the rush
  still feels understaffed = a scheduling-structure problem, not
  too few people
- Fixed costs: compute only the direction of change in "daily fixed
  spread divided by two-hour peak output" - do not conclude "rent is
  too high"; you cannot know that from this data

Output format:
- Table 1: the four blocks as a share of the month's sales, plus the top
  two line items inside each block
- Table 2: per-category waste rate across the four weeks (rising / flat /
  falling)
- One judgment paragraph: which block is "structural" (wrong rule - fix
  the rule) and which is "executional" (right rule, nobody follows it -
  fix the check) - citing numbers from the tables above
- Exactly 3 knobs at the end, ordered by how fast they should show. Each
  states: the specific rule to change / why this one (cite a number) /
  the number to pull in two weeks to verify. That verification number
  must come from a column that already exists in my books - no new
  tracking sheets

Constraints:
- No predicting next month's profit - any forecast is invented
- No citing "industry average margin" or "normal waste rate" as a basis
- Nothing like "control costs" or "watch expenses" - unusable advice
- Where my books have no data for a block, print "no data for this block"
  - do not estimate a placeholder
- No pleasantries - start directly with Table 1

Why it is written this way

An owner-operator's books are not wrong, they are blended. The money from binned buns, the wages of an afternoon-idle helper, and the bags and cups grabbed without a thought never sit in one field of view, so month end shows the result with no direction. Hence the four-block split - and fixed costs deliberately go last, because they are the hardest to move (relocating is major surgery) while the first three blocks have knobs that turn daily. The ordering itself carries the action priority.

Splitting labor into peak versus off-peak hours is this prompt's load-bearing detail. Breakfast shops rarely waste labor at peak - they waste it after: two hours where everyone is maxed, then half the staff standing from 9:30 while pay accrues by the day. Without the hourly split, the only conclusion an owner reaches is "we're short-handed, hire again" - exactly backwards. Comparing waste rate vertically across my own four weeks works the same way: no industry line, only my own trend, because tolerable waste for made-fresh items depends on location, customer mix, and menu structure - any external average imported here is simply wrong.

The "verification number must already exist in my books" constraint comes from a real failure mode: ask an AI for improvement advice and it produces things that all sound right, and two weeks later nobody remembers whether anything happened. Locking verification to an existing column denies it the chance to invent a new report - an owner-operator will not log a single extra word for any spreadsheet. Banning profit forecasts serves the same purpose: the model will happily emit a number whose only effects are anxiety or false comfort.

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