How to align staff and demand in new restaurant locations

Introduction

When a restaurant opens a new location, the first weeks provide the most reliable data on how many guests will arrive, how long they stay, and which service patterns emerge. Traditional staffing plans often rely on assumptions drawn from existing sites or on generic industry benchmarks. Those assumptions can lead to overstaffing, understaffing, or misaligned labor costs. VideoBI replaces guesswork with measured foot‑traffic and vehicle‑arrival counts captured by the VideoBI Scout. The Scout reads existing camera feeds, extracts counts and timestamps, and stores the data locally for analysis. This article explains how to set up the Scout, collect baseline counts, and translate those counts into shift schedules that match actual customer flow.

Deploying the VideoBI Scout

1. Identify camera locations – Choose cameras that have an unobstructed view of the main entrance, drive‑through lane, and any external queuing area. The Scout works with cameras the business already has; add a dedicated camera only if a view is missing.
2. Mount the Scout – The Scout is a compact box that connects to the selected cameras via Ethernet or Wi‑Fi. It draws power from a standard outlet and does not require a network connection to the cloud.
3. Configure detection zones – Using the Scout’s local interface, define zones for foot traffic (people entering the dining area) and vehicle traffic (cars entering the drive‑through). Set a minimum dwell time that qualifies as a visit, such as five seconds for a foot‑traffic count.
4. Start data collection – After the zones are defined, the Scout begins recording counts and timestamps. Data is stored on the device’s internal memory and can be exported daily via a secure USB or network share.

Collecting baseline traffic data

During the first two weeks of operation, the Scout will generate hourly counts for each detection zone. The goal is to capture a representative sample of demand across different days of the week and typical operating hours.

Sample data set

The counts are raw numbers; they contain no personally identifiable information. By aggregating the data you can see patterns such as peak lunch periods, early‑evening lulls, and weekend spikes.

Align staffing with demand using video counts

Turning raw counts into staffing forecasts involves a few straightforward calculations.

1. Calculate average guests per hour – For each hour, add foot‑traffic and vehicle counts when the drive‑through serves the same menu. If the drive‑through has a separate menu, treat the two streams separately.
2. Estimate service time per guest – Use historical data from existing locations or industry standards. A typical fast‑casual order may take three to five minutes from order to delivery. A sit‑down table turnover might be around thirty minutes.
3. Derive labor minutes needed – Multiply the average guests per hour by the estimated service time. For example, 80 guests in an hour with a four‑minute service time yields 320 labor minutes.
4. Convert labor minutes to staff‑hours – Divide the total labor minutes by the number of minutes a full‑time employee works per shift (for instance, 240 minutes for a four‑hour shift). The result indicates how many full‑time equivalents are needed for that hour.

Hypothetical calculation

If a lunch rush runs 40 cars an hour and each car results in an average of 1.2 orders, the vehicle stream contributes 48 orders. Adding 60 foot‑traffic orders gives 108 total orders. With a four‑minute service time, the labor needed is 108 × 4 = 432 minutes, or 7.2 staff‑hours for that hour.

Building shift schedules from forecasts

With hourly staff‑hour requirements in hand, you can construct shift patterns that align with demand.

1. Group hours into logical shifts – Typical fast‑casual shifts might be 6 am‑10 am, 10 am‑2 pm, 2 pm‑6 pm, and 6 pm‑10 pm. Align the grouped labor minutes with the shift boundaries.
2. Assign roles – Break down the total staff‑hours into roles such as cashiers, kitchen staff, and front‑of‑house assistants. Use the proportion of tasks observed in the data (for example, 60 % of labor minutes are kitchen work, 40 % are front‑of‑house).
3. Create a staffing matrix – For each shift, list the number of employees needed per role. Adjust for part‑time availability and labor‑law constraints on break times.
4. Validate against budget – Multiply the number of employees by their hourly wage to see the labor cost per shift. Compare the cost to the projected revenue for the same period. If the cost exceeds the budget, revisit the service‑time estimate or consider cross‑training staff to handle multiple roles.

Adjusting schedules in real time

Even with a solid forecast, demand can deviate because of weather, local events, or promotional activity. The Scout continues to collect counts after the initial two‑week period, allowing you to compare actual traffic to the forecast.

1. Set up daily alerts – Configure the Scout to flag hours where the observed count exceeds the forecast by a predefined threshold (for example, 15 %). Alerts can be delivered via email or displayed on a simple local dashboard.
2. Deploy flexible staffing – Keep a pool of on‑call part‑time employees who can be scheduled on short notice. When an alert triggers, add a staff member to the affected shift.
3. Iterate the forecast – After the first month, incorporate the new data into the average‑guest calculations. This refines the service‑time estimate and improves future scheduling accuracy.

Integrating VideoBI data with labor budgeting

Labor budgeting often occurs at the corporate level, where planners allocate a dollar amount for each new store. VideoBI data can be fed directly into budgeting spreadsheets or planning software.

1. Export daily counts – Use the Scout’s export function to generate a CSV file with hour‑by‑hour counts.
2. Apply the forecasting formula – In a spreadsheet, link the counts to the labor‑minute calculation. This creates a repeatable model that can be applied to any new location.
3. Run scenario analysis – Adjust the service‑time variable to see how faster or slower service impacts labor cost. Scenario analysis helps decide whether to invest in additional kitchen equipment or to hire more staff.

Best practices for new‑store staffing

• Start with a two‑week data collection window – This captures weekday and weekend patterns without over‑committing resources.
• Validate camera coverage before opening – Ensure the Scout’s detection zones are free of obstructions; a blocked view can produce inaccurate counts.
• Document assumptions – Keep a record of the service‑time estimate, role‑mix percentages, and any adjustments made after the first month.
• Maintain a flexible labor pool – Having a small number of part‑time staff available for short‑notice shifts reduces the risk of understaffing during unexpected spikes.
• Review data weekly – A weekly review of counts versus forecasts highlights trends early, allowing proactive schedule adjustments.

How VideoBI supports the process

The VideoBI platform provides dashboards that visualize foot‑traffic and vehicle counts, making it easy for managers to spot peaks and gaps. The on‑site Scout works with existing cameras, so there is no need to install a separate video system, and all video stays on site, eliminating privacy concerns. For more detail on the dashboards, see the VideoBI dashboards page. Information about the Scout itself is available on the VideoBI edge page.

Conclusion

For chains opening new locations, the first weeks provide the most reliable insight into actual customer flow. By deploying a VideoBI Scout, collecting foot‑traffic and vehicle‑arrival counts, and applying a straightforward forecasting method, operations leaders can create shift schedules that match real demand. The approach reduces reliance on guesswork, aligns labor costs with revenue potential, and provides a feedback loop for continuous improvement as the new store settles into its market.