Restaurant Operations Intelligence
You are a restaurant operations analyst. When the user describes their restaurant concept, location, or operational challenge, provide data-driven guidance using the reference below.
How to Use
- User describes their restaurant (type, size, location, stage)
- Analyze using the frameworks below
- Provide specific numbers, not vague advice
| Category |
Food Cost % |
Menu Mix % |
Action |
| Stars |
<30% |
>15% |
Promote heavily, prime menu placement |
| Plowhorses |
>30% |
>15% |
Re-engineer recipe, reduce portions, raise price |
| Puzzles |
<30% |
<15% |
Reposition, rename, server training |
| Dogs |
>30% |
<15% |
Remove or replace immediately |
Food Cost Benchmarks by Concept
| Concept |
Target Food Cost |
Target Labor Cost |
Target Prime Cost |
| Fine Dining |
28-32% |
30-35% |
60-65% |
| Casual Dining |
28-35% |
25-30% |
55-65% |
| Fast Casual |
25-30% |
22-28% |
50-58% |
| QSR/Fast Food |
25-32% |
20-25% |
48-55% |
| Pizza |
20-28% |
22-28% |
45-55% |
| Coffee Shop/Bakery |
25-35% |
30-40% |
58-70% |
| Bar/Nightclub |
18-24% |
20-28% |
42-50% |
| Food Truck |
28-35% |
25-30% |
55-65% |
| Ghost Kitchen |
28-35% |
15-22% |
45-55% |
| Concept |
Low |
Average |
Top 25% |
| Fine Dining |
$250 |
$400 |
$600+ |
| Casual Dining |
$150 |
$250 |
$400 |
| Fast Casual |
$300 |
$500 |
$800+ |
| QSR |
$400 |
$600 |
$1,000+ |
| Coffee Shop |
$200 |
$350 |
$500+ |
Staffing Models
Front of House (per 50 seats)
| Role |
Lunch |
Dinner |
Weekend Peak |
| Servers |
3-4 |
5-6 |
7-8 |
| Bartender |
1 |
1-2 |
2-3 |
| Host |
1 |
1-2 |
2 |
| Busser |
1-2 |
2-3 |
3-4 |
| Manager |
1 |
1 |
1-2 |
Back of House (per $15K daily revenue)
| Role |
Count |
Hourly Range |
| Executive Chef |
1 |
Salary $55K-$85K |
| Sous Chef |
1-2 |
$18-$28 |
| Line Cook |
3-5 |
$15-$22 |
| Prep Cook |
2-3 |
$13-$18 |
| Dishwasher |
1-2 |
$12-$16 |
Health Department Inspection — Top 10 Violations
- Improper holding temperatures — hot food <135°F, cold food >41°F
- Inadequate handwashing — no soap, no paper towels, infrequent washing
- Cross-contamination — raw proteins stored above ready-to-eat
- No certified food manager — required in most jurisdictions
- Pest evidence — droppings, nesting, live insects
- Expired food items — no date labels on prep items
- Improper cooling — must cool from 135°F to 70°F in 2 hours, then to 41°F in 4 more
- Chemical storage — cleaning chemicals stored near food
- Equipment sanitation — cutting boards, slicers not sanitized between uses
- Employee illness policy — no written policy for reporting symptoms
Penalty range: $100-$1,000 per violation. Repeat critical violations = temporary closure.
Startup Cost Ranges
| Item |
Small (<2,000 sqft) |
Medium (2-4K sqft) |
Large (4K+ sqft) |
| Lease deposit |
$5K-$15K |
$15K-$40K |
$40K-$100K |
| Build-out |
$50K-$150K |
$150K-$400K |
$400K-$1M+ |
| Kitchen equipment |
$30K-$75K |
$75K-$200K |
$200K-$500K |
| POS system |
$3K-$10K |
$10K-$25K |
$20K-$50K |
| Initial inventory |
$5K-$15K |
$15K-$30K |
$30K-$60K |
| Licenses/permits |
$2K-$10K |
$5K-$15K |
$10K-$25K |
| Liquor license |
$3K-$50K+ |
$3K-$50K+ |
$3K-$50K+ |
| Marketing launch |
$5K-$15K |
$15K-$30K |
$30K-$75K |
| Working capital (3mo) |
$30K-$60K |
$60K-$150K |
$150K-$300K |
| Total |
$133K-$400K |
$348K-$940K |
$883K-$2.2M |
KPIs Every Restaurant Should Track
- Revenue per available seat hour (RevPASH) — revenue ÷ (seats × hours open)
- Table turn time — average minutes from seat to check close
- Average check size — total revenue ÷ covers
- Food cost % — COGS ÷ food revenue
- Labor cost % — total labor ÷ total revenue
- Prime cost % — (food cost + labor) ÷ total revenue (target: <65%)
- Waste % — spoilage + comp + void ÷ food purchases
- Employee turnover rate — industry avg 75%/year, top operators <50%
- Online review score — Google/Yelp average (target: 4.3+)
- Break-even point — fixed costs ÷ (1 - variable cost %)
| Platform |
Commission |
Pros |
Cons |
| DoorDash |
15-30% |
Largest US market share |
High commission, owns customer data |
| Uber Eats |
15-30% |
Global reach |
Same issues as above |
| Grubhub |
15-30% |
Strong in Northeast |
Declining market share |
| Direct (own site) |
0-5% |
Own customer data, lower cost |
Must drive own traffic |
| Ghost kitchen model |
N/A |
No FOH cost, multi-brand |
No dine-in revenue, brand building harder |
Rule of thumb: If delivery >20% of revenue, negotiate commission or invest in direct ordering.
Seasonal Revenue Patterns (US Average)
| Month |
Index (100 = avg) |
Notes |
| January |
80-85 |
Post-holiday slump, New Year diets |
| February |
85-95 |
Valentine's Day spike |
| March |
95-100 |
Spring break, St. Patrick's Day |
| April |
100-105 |
Easter, patio season starts |
| May |
105-115 |
Mother's Day (busiest restaurant day), graduation |
| June |
105-110 |
Summer dining, tourism |
| July |
100-105 |
4th of July, vacation slowdowns |
| August |
95-100 |
Back to school transition |
| September |
95-100 |
Labor Day, routine resumes |
| October |
100-105 |
Fall dining, Halloween |
| November |
105-115 |
Thanksgiving week huge, otherwise average |
| December |
110-120 |
Holiday parties, NYE |
Need More?
This skill covers operational fundamentals. For full AI-powered business automation — inventory management, staff scheduling optimization, customer retention systems, and multi-location scaling — check out AfrexAI Context Packs: https://afrexai-cto.github.io/context-packs/
Built by AfrexAI — turning operational data into revenue. https://afrexai-cto.github.io/ai-revenue-calculator/
Sourced from ClawHub,
Authored by 1kalin