How to Forecast Sales Across Multiple Restaurant Locations

Running one restaurant, you can feel when a busy weekend is coming. You know your regulars, you see the reservation book filling up, and you adjust on the fly. With multiple locations, that gut instinct doesn’t scale—and guessing wrong at three or four spots adds up fast.

Forecasting for multi-location restaurants means pulling sales data from each location into one place, identifying patterns unique to each site, and building weekly projections that actually connect to your schedules. This guide walks through the methods, formulas, and step-by-step process to get it right.

What is restaurant sales forecasting?

Restaurant sales forecasting means using historical data, current trends, and external factors to predict future revenue. For multi-location operators, the key is centralizing data from all your restaurants into one place, then analyzing patterns by week, daypart, and location to project what’s coming.

When you’re running one restaurant, you can often feel when a busy weekend is approaching. You know your regulars, you see the reservation book filling up, and you adjust on the fly. With two, three, or five locations, though, that gut instinct doesn’t scale.

Each restaurant has its own rhythm, its own busy nights, and its own slow seasons. A forecast that works for your downtown spot won’t fit your suburban location.

Why forecasting matters for multi-location restaurants

A small forecasting error at one location is manageable. That same error multiplied across four locations adds up to thousands of dollars in wasted labor or missed sales every week.

Overstaffing one location while understaffing another means you’re paying people to stand around at one spot while your team drowns at the other. Neither situation is good for your bottom line or your staff.

Reduce labor costs across all locations

Labor typically runs 25-35% of revenue for most restaurants, though the exact percentage varies by concept and service style. When your forecast is off, you’re either paying for hours you don’t need or scrambling to cover shifts you didn’t plan for.

Accurate forecasting lets you schedule the right number of people for each shift at each location. Fewer overtime surprises, less money spent on last-minute coverage.

Also watch: What should your labor cost percentage be?

Prevent over-ordering and food waste

Your forecast doesn’t just affect labor. It drives your prep lists and inventory orders too.

Predict a slow Tuesday that turns busy, and you’re 86’ing menu items by 7 PM. Predict a rush that never comes, and you’re tossing product at the end of the week. Each location has different sales patterns, which means each location has different inventory needs.

Staff the right people at the right time

It’s not just about having enough bodies. It’s about having the right mix of experience on each shift.

Your Friday night dinner rush at your flagship location might call for four seasoned servers and two trainees. The same night at your newer location might only call for three servers total. Forecasting helps you match skill levels to expected demand.

Make better expansion and budgeting decisions

When you’re thinking about opening location number four or setting next year’s restaurant budget, you’re making educated guesses about future revenue. The better your forecasting system, the more accurate those guesses become.

Historical data from your existing locations, especially ones in similar markets, gives you a baseline for projecting what a new spot might do.

Download: Restaurant Budgeting Template

Three restaurant sales forecasting methods

Not every forecasting approach works for every situation. Here’s a quick comparison:

Method	Best for	Data required
Time-series	Established locations with consistent patterns	1-3 years of historical sales
Causal	Locations affected by external factors	Sales data plus external variables
Qualitative	New locations or major market changes	Team input and market knowledge

Time-series forecasting

Time-series forecasting is the most common method. You look at what happened last year during the same week, adjust for any growth trends, and use that as your baseline.

This approach works well when your locations have predictable patterns: steady weekday lunch crowds, reliable weekend dinner rushes, and consistent seasonal swings.

Causal forecasting

Some locations don’t follow predictable patterns because external factors drive their traffic. A restaurant near a stadium sees massive swings based on game schedules. A downtown lunch spot depends on whether nearby offices are in or working remote.

Causal forecasting factors in variables like weather, local events, and promotions. It takes more work to set up, but it’s more accurate for locations with irregular traffic patterns.

Qualitative forecasting

When you don’t have historical data, like when you’re opening a new location, you rely on manager input, market research, and comparisons to similar restaurants.

This method is less precise, but it’s better than guessing blind. Start conservative and adjust as real data comes in.

What data you need to forecast for multiple locations

Multi-location forecasting falls apart when you’re working with incomplete or inconsistent data. Each location needs its own numbers, not just company-wide averages.

Historical sales by location and daypart

You want at least a year of sales data broken down by location, day of week, and daypart. Daypart refers to the time segments of your service: breakfast, lunch, dinner, late night. Two to three years of data is better because it helps you spot seasonal patterns.

Your downtown location might do 60% of its sales at lunch. Your suburban spot might flip that ratio toward dinner. Treating them the same will throw off your forecasts.

Local events, seasonality, and weather patterns

Each location operates in its own micro-market. A college town location sees traffic drop during summer break. A tourist-area restaurant spikes during holiday weekends.

Build a calendar of local events, school schedules, and seasonal factors for each location. These adjustments make the difference between a decent forecast and an accurate one.

Labor hours and cover counts

Covers, the number of guests served, connect directly to labor needs. Track covers per labor hour at each location to understand productivity patterns.

If Location A serves 15 covers per labor hour on Friday nights and Location B serves 12, you’ll staff them differently even if their sales numbers are similar.

How to build a restaurant sales forecast step by step

Here’s the practical process. You can start this week with whatever data you have.

1. Centralize sales data from all locations

Get all your POS data into one place. This might mean exporting reports into a shared spreadsheet, or using software that pulls data automatically from each location.

The goal is a single view of sales across all your restaurants. Chasing down numbers from three different systems every week isn’t sustainable.

2. Identify patterns unique to each location

Look at each location’s data separately before comparing them. When does each restaurant peak? Which days are consistently slow?

Your assumptions about one location often don’t apply to another. Let the data show you each restaurant’s actual patterns.

3. Factor in local variables and events

Layer in the external factors that affect each location:

• Local events: Concerts, sports games, festivals, conventions
• School schedules: College breaks, K-12 holidays, graduation weekends
• Weather patterns: Seasonal tourism, extreme weather impacts
• Access changes: Construction or road closures affecting your location

4. Create weekly forecasts by daypart

Monthly forecasts are too broad for restaurant scheduling. By the time you realize your monthly projection was off, three weeks have already passed.

Build weekly forecasts broken down by daypart. This gives you the detail you need to schedule accurately and order inventory appropriately.

5. Review forecast vs. actual results weekly

Every Monday morning, compare what you predicted to what actually happened. Where were you off? Why?

This feedback loop is how your forecasts improve over time. If you’re consistently over-projecting Tuesday dinners at one location, adjust your model.

How to forecast for a new restaurant location

Opening a new location means forecasting without historical data. Here’s how to approach it:

• Use comparable locations: Pull data from your existing restaurant most similar in size, concept, and market type
• Research the local market: Look at foot traffic patterns, competitor density, and local demographics
• Start conservative: Begin with lower projections and scale up as you gather real data
• Lean on qualitative input: Your GM’s read on the neighborhood matters when data doesn’t exist yet

Plan to revisit your forecasts frequently during the first few months. Real data will replace your assumptions quickly.

Restaurant forecasting formulas you can use

A few calculations help you turn raw data into actionable forecasts.

Sales per labor hour

Total Sales ÷ Total Labor Hours = Sales per Labor Hour

This tells you how productive each labor hour is. Track it by location and daypart to understand where you’re efficient and where you’re overstaffed.

Average covers per day

Total Covers ÷ Number of Days = Average Covers

Segment this by daypart for better accuracy. Knowing you average 85 covers at lunch and 120 at dinner is more useful than knowing you average 205 covers per day.

Week-over-week sales growth

((This Week’s Sales – Same Week Last Year) ÷ Same Week Last Year) × 100

Compare to the same week last year, not just last week. This accounts for seasonality and gives you a cleaner growth trend.

How to connect sales forecasts to labor scheduling

A forecast that sits in a spreadsheet doesn’t help anyone. The payoff comes when you connect it to how you build schedules.

1. Match forecasted sales to staffing needs

Use your forecast to determine how many servers, cooks, and hosts each shift needs. If you’re projecting $8,000 in sales for Friday dinner and your target is $45 in sales per labor hour, you know you’re budgeting around 175 labor hours for that shift.

2. Set labor cost targets for each location

Each location gets its own labor target based on its sales forecast. Your high-volume downtown location might run 28% labor. Your newer suburban spot might run 32% while it builds traffic.

Don’t apply a blanket percentage across all locations. It doesn’t account for their different realities.

Download: Restaurant labor cost calculator template

3. Build schedules based on forecasted demand

Schedule to the forecast, not just to availability. Staff your busy shifts heavy and cut the slow ones.

Building schedules manually while tracking labor costs means spreadsheets, a calculator, and about 30 minutes per location. Scheduling software like 7shifts calculates labor costs in real-time as you build, showing you exactly where you stand against your forecast.

Common restaurant forecasting mistakes to avoid

A few errors trip up even experienced operators.

Using gut instinct instead of historical data

“It felt busy” isn’t a forecast. Your perception of one location doesn’t tell you what’s happening at the others. Data beats intuition, especially when you can’t personally observe every shift at every restaurant.

Treating all locations the same

Copy-paste forecasting doesn’t work. Your downtown location and your suburban location have different patterns, different peak times, and different customer behaviors. Each one needs its own forecast.

Forecasting monthly instead of weekly

Monthly forecasts are too broad for restaurant operations. By the time you realize your projection was off, the month is over. Weekly forecasts give you time to adjust.

Not linking forecasts to your schedule

A forecast that doesn’t connect to scheduling is just an interesting number. The whole point is to use your projections to make better staffing decisions.

How to build a consistent forecasting routine across locations

Forecasting isn’t a one-time project. It’s an ongoing system.

Set a weekly forecasting cadence

Pick a day, Monday morning works for most operators, to review last week’s actuals and update next week’s forecasts. Make it a recurring calendar item.

Consistency matters more than perfection. A decent forecast you update every week beats a perfect forecast you build once and forget.

Assign forecast ownership at each location

Someone at each location owns the numbers. Usually, that’s the GM. They know the local factors, they see the trends firsthand, and they’re accountable for hitting targets.

Corporate can set the framework and review the numbers, but the people closest to each restaurant do the actual forecasting.

Track forecast accuracy and adjust

Measure how accurate your forecasts are over time. If you’re consistently off on Wednesday dinners at Location B, figure out why and recalibrate.

Improving forecast accuracy by even a few percentage points translates directly to labor savings and better inventory management.

Turn your restaurant projections into smarter schedules

Forecasting is the foundation. The real value comes when those projections drive your scheduling, inventory, and staffing decisions across all your locations.

The restaurants that get this right spend less on labor, waste less food, and stress less about coverage. Their managers aren’t guessing. They’re planning.

Ready to connect your forecasts to your schedules? Start a free trial of 7shifts and see how real-time labor cost tracking can help you staff smarter across every location.

Also read: Why 7shifts is the best scheduling software for multi-location restaurants

FAQs about forecasting for multiple restaurant locations

How far in advance should I forecast restaurant sales?

Most multi-location operators forecast one to two weeks ahead for scheduling purposes. For budgeting and staffing plans, quarterly projections work well. The further out you go, the less precise your forecast, but even a rough quarterly projection helps with hiring and expansion planning.

What is a good forecast accuracy target for restaurant sales?

Aim to get your forecasts within a reasonable range of actual sales consistently. If you’re regularly far off, review your data inputs and adjust your method. Perfect accuracy isn’t realistic, but consistent improvement is.

Can I use the same forecasting model for all my restaurant locations?

You can use the same method, but each location needs its own data inputs and adjustments. A time-series approach might work for all your restaurants, but the specific patterns, local events, and seasonal factors will differ by location.

How do I adjust my forecast when a location consistently underperforms projections?

First, check whether your historical data is accurate and complete. Then look for local factors you might be missing, like new competition, road construction, or changes in nearby businesses. Recalibrate using the location’s actual recent performance rather than company averages or older historical data.