Get a peak estimate in about 30 seconds
Smásala Staffing Reiknivél (Visitors and Þjónusta Time)
Estimate required staff for peak periods from visitor volume and average þjónusta time. Use it for a quick checkout staffing check or as a first draft for shift áætlunning.
This is a practical workload estimate, not a strict queueing-theory model. Adjust utilization and buffer rates to match your floor reality.
Inntaks, share URLs, and CSV generation stay in the browser. Nothing is sent to a server.
See the assumptions behind the number
Adjust utilization and buffer rate easily
Share by URL, CSV, or print
How to use
- Start in Peak only mode and enter visitor volume plus average þjónusta time.
- Switch to By time slot when you want a shift-ready view in 30-minute or 60-minute blocks.
- Open Settings to adjust utilization, buffer, role ratios, and small-store behavior.
- Share the niðurstaða by URL, CSV, or print, then restore the same inntaks next time.
Start with a 30-second peak estimate
The minimum path is visitor volume, average þjónusta time, and a utilization preset.
Standard 75% utilization with a +10% buffer gives a safe starting point.
If you only know daily transactions, the inline hint shows how to turn them into a workable estimate.
Áætlun by time slot for shift drafting
Choose 30-minute or 60-minute rows to match how you áætlun the floor.
Paste from a spreadsheet, then add or remove rows as needed.
See recommended staff, workload minutes, and the peak slot on one skjár.
A spike warning highlights sudden jumps that may need support staff or front-loaded work.
Options: utilization, buffer, and role ratios
Use Stafla 65%, Standard 75%, or Efficient 85% as quick presets.
Treat the buffer rate as coverage for breaks, absences, interruptions, and training overhead.
Enable role ratios when you want a rough split across checkout, sala floor, and birgðiring.
Small-store mode switches the úttak from dedicated assignments to multitasking coverage.
Niðurstaðas: recommendation and peak warnings
See peak recommended staff, the peak slot, and the assumptions in one summary line.
Use the chart and tafla as a working draft for shift coverage.
Warnings appear for very high utilization and sudden staffing jumps between slots.
Use share URL, CSV, and print to circulate the estimate quickly.
Glossary
Utilization
The share of each slot that one person can realistically spend on active þjónusta work. Too high, and waiting or congestion becomes more likely.
Buffer rate
An extra percentage added for breaks, absences, interruptions, or less experienced staff.
Workload
Total work minutes calculated from visitor count multiplied by average þjónusta time.
Calculation method
- Workload = visitor count × average þjónusta time
- Required staff = workload ÷ (slot length × utilization)
- Recommended staff = required staff × (1 + buffer rate), then rounded
Algengar spurningar
I do not know the visitor count.
Use móttaka count, transaction count, or POS order count as a proxy. If you only have a daily figure, divide it by opening hours to get an hourly average, then test a peak at roughly 1.3 to 2.0 times that average.
How should I account for breaks or absences?
Add them through the buffer rate. Use a higher value on promotion days, during uncertain weather, or when the team has many newer staff.
Can I use this for a very small store?
Yes. Small-store mode keeps a minimum of one person and changes role úttak from dedicated headcount to multitasking coverage.
What utilization target should I use?
Around 75% is a strong default. For steadier þjónusta, stay around 65 to 75%. Even in efficiency-focused setups, going past 85% usually increases waiting and operational friction.
How should I measure average þjónusta time?
Time about 10 cases and use the average. For checkout, measure from the start of payment to the end of the transaction. For assisted sala, use a clear start-to-completion definition that your team can repeat.
Notice
- This verkfæri is a practical workload estimate, not a strict queueing-theory solver.
- Real waiting time can still increase because of demand variability, simultaneous arrivals, task interruptions, or staff experience level.
- Final decisions should still reflect your store layout, number of checkout lanes, merchandise mix, and local labor rules.