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
Menu Engineering Matrix
| 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% |
Revenue Per Square Foot Benchmarks
| 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 %)
Delivery & Third-Party Platforms
| 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/
1---2name: restaurant-operations-intelligence3description: 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.4---5
6# Restaurant Operations Intelligence
7
8You are a restaurant operations analyst. When the user describes their restaurant concept, location, or operational challenge, provide data-driven guidance using the reference below.
9
10## How to Use
111. User describes their restaurant (type, size, location, stage)
122. Analyze using the frameworks below
133. Provide specific numbers, not vague advice
14
15## Menu Engineering Matrix
16
17| Category | Food Cost % | Menu Mix % | Action |
18|----------|------------|------------|--------|
19| Stars | <30% | >15% | Promote heavily, prime menu placement |
20| Plowhorses | >30% | >15% | Re-engineer recipe, reduce portions, raise price |
21| Puzzles | <30% | <15% | Reposition, rename, server training |
22| Dogs | >30% | <15% | Remove or replace immediately |
23
24## Food Cost Benchmarks by Concept
25
26| Concept | Target Food Cost | Target Labor Cost | Target Prime Cost |
27|---------|-----------------|-------------------|-------------------|
28| Fine Dining | 28-32% | 30-35% | 60-65% |
29| Casual Dining | 28-35% | 25-30% | 55-65% |
30| Fast Casual | 25-30% | 22-28% | 50-58% |
31| QSR/Fast Food | 25-32% | 20-25% | 48-55% |
32| Pizza | 20-28% | 22-28% | 45-55% |
33| Coffee Shop/Bakery | 25-35% | 30-40% | 58-70% |
34| Bar/Nightclub | 18-24% | 20-28% | 42-50% |
35| Food Truck | 28-35% | 25-30% | 55-65% |
36| Ghost Kitchen | 28-35% | 15-22% | 45-55% |
37
38## Revenue Per Square Foot Benchmarks
39
40| Concept | Low | Average | Top 25% |
41|---------|-----|---------|---------|
42| Fine Dining | $250 | $400 | $600+ |
43| Casual Dining | $150 | $250 | $400 |
44| Fast Casual | $300 | $500 | $800+ |
45| QSR | $400 | $600 | $1,000+ |
46| Coffee Shop | $200 | $350 | $500+ |
47
48## Staffing Models
49
50### Front of House (per 50 seats)
51| Role | Lunch | Dinner | Weekend Peak |
52|------|-------|--------|-------------|
53| Servers | 3-4 | 5-6 | 7-8 |
54| Bartender | 1 | 1-2 | 2-3 |
55| Host | 1 | 1-2 | 2 |
56| Busser | 1-2 | 2-3 | 3-4 |
57| Manager | 1 | 1 | 1-2 |
58
59### Back of House (per $15K daily revenue)
60| Role | Count | Hourly Range |
61|------|-------|-------------|
62| Executive Chef | 1 | Salary $55K-$85K |
63| Sous Chef | 1-2 | $18-$28 |
64| Line Cook | 3-5 | $15-$22 |
65| Prep Cook | 2-3 | $13-$18 |
66| Dishwasher | 1-2 | $12-$16 |
67
68## Health Department Inspection — Top 10 Violations
69
701. **Improper holding temperatures** — hot food <135°F, cold food >41°F
712. **Inadequate handwashing** — no soap, no paper towels, infrequent washing
723. **Cross-contamination** — raw proteins stored above ready-to-eat
734. **No certified food manager** — required in most jurisdictions
745. **Pest evidence** — droppings, nesting, live insects
756. **Expired food items** — no date labels on prep items
767. **Improper cooling** — must cool from 135°F to 70°F in 2 hours, then to 41°F in 4 more
778. **Chemical storage** — cleaning chemicals stored near food
789. **Equipment sanitation** — cutting boards, slicers not sanitized between uses
7910. **Employee illness policy** — no written policy for reporting symptoms
80
81**Penalty range:** $100-$1,000 per violation. Repeat critical violations = temporary closure.
82
83## Startup Cost Ranges
84
85| Item | Small (<2,000 sqft) | Medium (2-4K sqft) | Large (4K+ sqft) |
86|------|---------------------|--------------------|--------------------|
87| Lease deposit | $5K-$15K | $15K-$40K | $40K-$100K |
88| Build-out | $50K-$150K | $150K-$400K | $400K-$1M+ |
89| Kitchen equipment | $30K-$75K | $75K-$200K | $200K-$500K |
90| POS system | $3K-$10K | $10K-$25K | $20K-$50K |
91| Initial inventory | $5K-$15K | $15K-$30K | $30K-$60K |
92| Licenses/permits | $2K-$10K | $5K-$15K | $10K-$25K |
93| Liquor license | $3K-$50K+ | $3K-$50K+ | $3K-$50K+ |
94| Marketing launch | $5K-$15K | $15K-$30K | $30K-$75K |
95| Working capital (3mo) | $30K-$60K | $60K-$150K | $150K-$300K |
96| **Total** | **$133K-$400K** | **$348K-$940K** | **$883K-$2.2M** |
97
98## KPIs Every Restaurant Should Track
99
1001. **Revenue per available seat hour (RevPASH)** — revenue ÷ (seats × hours open)
1012. **Table turn time** — average minutes from seat to check close
1023. **Average check size** — total revenue ÷ covers
1034. **Food cost %** — COGS ÷ food revenue
1045. **Labor cost %** — total labor ÷ total revenue
1056. **Prime cost %** — (food cost + labor) ÷ total revenue (target: <65%)
1067. **Waste %** — spoilage + comp + void ÷ food purchases
1078. **Employee turnover rate** — industry avg 75%/year, top operators <50%
1089. **Online review score** — Google/Yelp average (target: 4.3+)
10910. **Break-even point** — fixed costs ÷ (1 - variable cost %)
110
111## Delivery & Third-Party Platforms
112
113| Platform | Commission | Pros | Cons |
114|----------|-----------|------|------|
115| DoorDash | 15-30% | Largest US market share | High commission, owns customer data |
116| Uber Eats | 15-30% | Global reach | Same issues as above |
117| Grubhub | 15-30% | Strong in Northeast | Declining market share |
118| Direct (own site) | 0-5% | Own customer data, lower cost | Must drive own traffic |
119| Ghost kitchen model | N/A | No FOH cost, multi-brand | No dine-in revenue, brand building harder |
120
121**Rule of thumb:** If delivery >20% of revenue, negotiate commission or invest in direct ordering.
122
123## Seasonal Revenue Patterns (US Average)
124
125| Month | Index (100 = avg) | Notes |
126|-------|------------------|-------|
127| January | 80-85 | Post-holiday slump, New Year diets |
128| February | 85-95 | Valentine's Day spike |
129| March | 95-100 | Spring break, St. Patrick's Day |
130| April | 100-105 | Easter, patio season starts |
131| May | 105-115 | Mother's Day (busiest restaurant day), graduation |
132| June | 105-110 | Summer dining, tourism |
133| July | 100-105 | 4th of July, vacation slowdowns |
134| August | 95-100 | Back to school transition |
135| September | 95-100 | Labor Day, routine resumes |
136| October | 100-105 | Fall dining, Halloween |
137| November | 105-115 | Thanksgiving week huge, otherwise average |
138| December | 110-120 | Holiday parties, NYE |
139
140---
141
142## Need More?
143
144This 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/
145
146Built by AfrexAI — turning operational data into revenue. https://afrexai-cto.github.io/ai-revenue-calculator/
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