Documentation
Calculator Methodology
Everything that goes into the Kitchen Utilisation Calculator: what each input means, the industry benchmarks we assume, and how the numbers are computed step-by-step.
Input definitions
Kitchen size
sq ftDefault: 1,000Total floor area of your kitchen and prep space. Used as a sanity-check reference; the capacity model is driven by staff and hours, not square footage.
Current orders
orders / dayDefault: 20The average number of delivery orders you currently fulfil per day across all aggregator platforms (Swiggy, Zomato, etc.).
Total staff
peopleDefault: 5Full-time kitchen staff available during operating hours — includes chefs, helpers, and packers who can work on delivery orders.
Prep time
minutes / orderDefault: 15Average end-to-end time to prepare, cook, and pack one order from ticket to hand-off. This is your kitchen's single-order cycle time.
Operating hours
hours / dayDefault: 12Total hours your kitchen is open and staffed for delivery each day. Typically 10–14 hours for QSR and cloud kitchens in India.
Average order value (AOV)
₹Default: 300Average basket size of a delivery order after discounts and before platform commissions. Typical for mid-market Indian QSR.
Margin per order
%Default: 31Net profit margin on each order after food cost, packaging, and direct labour — but before rent, utilities, and fixed overheads. Range: 28–34%. Category average is 31%.
Assumed constants & benchmarks
These values are baked into the model and sourced from NRAI reports, Swiggy/Zomato operations data, and Foodopolis internal benchmarks. They are not editable in the calculator because they represent stable industry norms.
| Constant | Value | Source / Rationale |
|---|---|---|
| Food production cost | 35% of AOV | NRAI average for mid-market Indian QSR. Covers raw materials and packaging. |
| Practical capacity buffer | 75% of theoretical | Accounts for cleaning, idle time, shift changeover, and menu complexity (Swiggy ops norm). |
| Peak hours per day | 4 hours | ~62% of delivery orders land in a 4-hour dinner peak window (7–11 PM) across Indian metros. |
| Peak-hour friction factor | 0.85 | Real-world congestion: not all staff cook simultaneously; hand-off and packaging create bottlenecks. |
| Peak-to-day spread | 1.6× peak capacity | Spreads peak-hour output across the remaining 8 hours at ~60% of peak pace. |
| Working days | 30 / month, 365 / year | Standard calendar; no holiday adjustment. |
| Brand gap unit | 25 orders / brand | Each Foodopolis brand is designed to absorb roughly 25 incremental orders/day at steady state. |
| Brand recommendation cap | 1 – 6 brands | Practical kitchen complexity limit. More than 6 brands introduce operational drag. |
Step-by-step computation
Using the on-screen defaults (5 staff · 12 hrs · 15 min prep · 20 orders/day · ₹300 AOV · 31% margin):
Man-minutes per day
Total labour time available in one day.
Theoretical capacity
Absolute maximum if every minute is spent cooking with zero waste.
Day-wide practical capacity
Applies the 75% industry buffer for real-world friction.
Peak-hour sanity check
What the kitchen can physically push during the 4-hour dinner rush, accounting for congestion.
The peak hour is the bottleneck. 1.6 spreads peak output across the rest of the day.
Unused capacity
Under-utilisation
Profit per incremental order
Additional monthly revenue (net profit)
This is the headline number — net additional profit, not gross revenue.
Gross revenue uplift
Food production cost deduction
Other operating costs
Platform commissions, packaging, utilities, and misc. overheads.
Per-day and per-year uplift
Brand recommendation
Each Foodopolis brand is sized to absorb ~25 incremental orders/day.
In plain English
With 5 people working 12 hours, your kitchen could theoretically make 240 orders. But real life — cleaning, shift changes, peak-hour congestion — drops that to about 108 practical orders/day. You are currently doing 20, so you have 88 unused orders of headroom. At a ₹300 AOV and 31% margin, filling that gap means roughly ₹2.45 lakhs of additional net profit per month.
Disclaimer: These are estimates based on industry benchmarks. Actual results vary by city, cuisine mix, aggregator performance, seasonality, and kitchen-specific operational efficiency.