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/
