Cloud kitchen daily P&L — single-screen template for Swiggy/Zomato-only brands

About this piece. A cloud kitchen running 1–5 brands on Swiggy and Zomato has a different P&L shape from a dine-in restaurant. There's no walk-in revenue, no covers metric, no till to count. Instead there's commission %, Hyperpure invoices, ad-spend bidding, packaging cost per order, and rider hand-off variance. Most cloud kitchens look at monthly aggregator dashboards and discover too late that brand B has been losing ₹40/order for 3 weeks. This piece gives you the daily P&L template that catches that on day 2.

Why monthly aggregator dashboards fail cloud kitchens

Three structural reasons:

1. The dashboard shows you GMV and net payout. It doesn't show contribution margin per order net of food cost, packaging, and ad spend.
2. Aggregator settlement is T+7 to T+14. By the time the money lands, the operating decisions are 2 weeks old.
3. Multi-brand cross-subsidy is invisible. Brand A profitable, brand B bleeding — total payout looks fine, but the resource allocation is wrong. You'd kill brand B if you could see it daily.

The fix isn't a better aggregator dashboard. It's a daily P&L sheet at the brand level — built from your kitchen data, not the aggregator's.

The daily P&L — one screen, per brand

Brand: [BRAND NAME]                          Date: ___ / ___ / ___

REVENUE
  Gross orders (count)                          ___
  Gross order value (Swiggy)                  ₹ ___
  Gross order value (Zomato)                  ₹ ___
  Total GOV                                   ₹ ___ (A)
  
DEDUCTIONS (from GOV)
  Aggregator commission (avg 22%)             ₹ ___
  Ad spend (Swiggy/Zomato dashboard)          ₹ ___
  Discounts funded by brand                   ₹ ___
  Net realisation                             ₹ ___ (B)
  
DIRECT COSTS
  Food cost (recipe × orders)                 ₹ ___
  Packaging cost (per-order × orders)         ₹ ___
  Direct kitchen labour (allocated)           ₹ ___
  Total direct cost                           ₹ ___ (C)
  
CONTRIBUTION MARGIN
  CM = B − C                                  ₹ ___
  CM per order = CM / orders                  ₹ ___
  CM %  = CM / A × 100                            ___ %
  
GREEN / AMBER / RED  (CM% target = 18%+)

That's the template. One brand per page. Per day.

How to fill each row (the data sources)

The food cost row is the one most kitchens get wrong. You don't need invoice-level precision daily; you need a standardised recipe cost per SKU that you've calculated once and updated quarterly. Then daily food cost = sum of (orders per SKU × standard cost per SKU).

The contribution margin formula

CM₹  = (GOV − Commission − Ads − Discounts) − (Food + Packaging + Direct labour)
CM%  = CM₹ / GOV × 100
CM/order = CM₹ / Order count

Three thresholds:

A brand at 18% CM% on GMV is healthy. A brand at 9% is a brand that needs surgery — usually either ad-spend bid too high, or menu pricing too low, or food cost crept up because Hyperpure prices moved.

The five most-common cloud kitchen leakage points

1. Ad spend ROI not measured per brand

Common failure: ₹400/day ad spend on brand B that drives ₹2,800 GMV → looks like 7× ROI. But subtract commission (22%), food cost (32%), packaging (4%), labour (8%) — net contribution = ₹952. After ad spend = ₹552. ROI on ad spend in CM₹ terms = 1.4×, not 7×. Look at CM₹ per ad rupee, not GMV per ad rupee.

2. Packaging cost crept up but menu price didn't

Foil container price went from ₹4 to ₹6. Multiply by 80 orders/day × 30 days = ₹4,800/month silent margin loss. Quarterly packaging-cost re-baseline is the only defence. Update template with new per-order packaging cost the day the new invoice comes.

3. Hyperpure / vendor price hikes not flagged

Hyperpure quietly raises chicken price by ₹14/kg. Recipe cost on chicken biryani goes up ₹3.20/order. 60 orders/day = ₹192/day = ₹5,800/month. Without monthly recipe-cost re-calculation you discover it at quarter-end. Set a calendar reminder: 1st of every month, recipe costs re-validated against last week's invoices.

4. Discount-funded promotions losing money

"Flat ₹100 off above ₹400" on a brand whose CM₹ per order is ₹95 = every promo order loses ₹5. Sounds tiny, but 40 promo orders/day = ₹200/day = ₹6,000/month. Rule: every promo's economics get modelled before launch using the contribution margin sheet, not after.

5. Multi-brand kitchen labour mis-allocation

If brand A produces 70% of orders but takes 40% of kitchen labour time (because brand B's menu has more prep), allocating labour by order-count under-costs brand B. Quarterly time-and-motion: head chef logs time per brand for 7 days. Allocate labour by time, not order count.

Composite Bangalore HSR cloud-kitchen operator runs 4 brands. Quarter-1 2026: monthly P&L showed total profitable. Started daily per-brand sheet in Feb. By Feb-week-3 brand C was visibly bleeding (CM% −4%). Audit: ad spend bid too aggressive on a low-margin biryani SKU. Bid cut, menu repriced. Brand C went to +12% CM% by April. Without the daily sheet, the next monthly review would've been April 5 — 6 weeks of avoidable loss.

The weekly owner pulse — Monday morning

Daily P&L is operational. Weekly pulse is strategic. Every Monday:

1. Per-brand CM% trend (7-day) — any brand trending down 3 weeks straight = surgery candidate
2. Ad spend ROI in CM₹ (per brand) — kill any campaign below 1.5× CM-ROI
3. Top 5 SKUs by CM₹ contribution — these get menu-position priority
4. Bottom 5 SKUs by CM% — drop or reprice candidates
5. Aggregator commission % paid (Swiggy vs Zomato) — negotiate tier upgrades

Five numbers. 15 minutes. Surgical decisions.

What about Swiggy/Zomato base settlement variance?

Cloud kitchens routinely see ₹500–₹2,000/week in unexplained settlement variance — a Swiggy net payout that doesn't match what the operator's daily sheet expected. Three things help:

1. Reconcile weekly, not monthly. Saturday morning: the past week's Swiggy net payout vs your daily sheets' expected. Unexplained gaps go into a "to chase" list.
2. Raise tickets immediately. Swiggy/Zomato dispute windows are 30/45 days. After that, recovery probability drops sharply.
3. Track recovery rate. Of the gaps you raise tickets on, what % do you actually recover? Below 60% = your raising process is broken (probably missing reference numbers).

Where this fits — the cloud kitchen full stack

Restaurant Daily automates the per-brand daily P&L — pulls aggregator data, applies your recipe costs, flags red brands. You can also run it in Excel; the loop is the value, the tool is one way to run it.

What to do this week

Pick your two highest-volume brands. Build the per-brand daily sheet for both — even in pen-and-paper form for week one. Fill it for 7 days. By next Sunday you'll know which brand has CM% you didn't expect — almost always at least one surprise. That surprise is the entire ROI of starting the loop.

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