Monthly attendance summary
A monthly summary compresses thirty rows of daily marks into one line per person. Days present, days absent, leave taken, times late, minutes late. Five numbers a manager can read in a minute and act on.
Produced at month end from the daily record. Meant for reading and discussion, not as the underlying evidence — keep the daily record behind it.
Attendance summary — ____________ (month / year) Name | Working days | Present | Absent | Leave | Late count | Late minutes | On-time % Employee name | __ | __ | __ | __ | __ | ___ | __% Employee name | __ | __ | __ | __ | __ | ___ | __% Department total | __ | __ | __ | __ | __ | ___ | __% Working days exclude weekly offs and holidays. On-time % counts arrivals within the person's own shift start, not a single office opening time.
How to fill it in
- Fix the working-day count first. Everything else is read against it. Get holidays and weekly offs out of the denominator before you start.
- Separate late count from late minutes. Ten arrivals two minutes late and one arrival ninety minutes late are different problems. One column hides that.
- Compare a person to their own shift. A late figure measured against an office-wide start time punishes everyone who works a later window.
- Read the department row last. The average tells you whether the problem is one person or the schedule.
PeopleMuster computes on-time rate and late minutes against each person's assigned shift window, so a 12:00 starter is not counted late at 10:05. On-time rate and late minutes.
Questions people ask about this letter
Should a half day count as present or absent?
Neither, cleanly. Give half days their own column and half the value in the present count. Folding them either way distorts both figures.
Why does on-time percentage look worse than people feel it is?
Usually a grace period exists in practice but not in the calculation. Write the grace period into the rule, or drop it from both.
Who should see this summary?
The person it describes, first. A summary discussed with a manager before the employee has seen it turns a data problem into a trust problem.