**When an attendance record has a start and no end, the hours worked that day are
not data any more. They are somebody's decision.** In our own production data that
happens on 6,110 of 40,970 records, which is 14.9 per cent, or about one shift in
seven.
The number that makes it interesting is the other one. Records with a clock-out
and no clock-in: zero.
Why the asymmetry matters
If terminals were dropping punches at random you would see both kinds in roughly
equal quantities. Some shifts would lose their opening punch, some their closing
one. That is not what the data shows. It shows every single incomplete record
missing the same end.
So this is not a hardware problem. It is a behavioural one, and once you see it
that way the cause is obvious.
Clocking in is load-bearing. It is how you are recorded as present, how the shift
starts, and in many workplaces how the door opens. Skip it and something
immediately does not work.
Clocking out is the last act of a shift, performed by somebody who is already
leaving, and nothing visible happens if they skip it. The consequence lands two
weeks later on a payslip, on somebody else's desk, in a queue.
Every incentive points one way. One shift in seven is what that looks like at
scale.
What it costs
The hours become an opinion. Somebody in payroll decides what time that
person left. They will be reasonable about it, and they will use the roster, and
they will usually be close. But the number on the payslip was produced by a
judgement, not a measurement, and the employee has no way to check it.
It concentrates in the wrong places. Missing clock-outs are not evenly
distributed. They cluster on shifts that end at awkward times, at sites with a
single exit point that gets congested, and among people whose shift end varies.
Which means the same individuals are repeatedly having their hours estimated.
It undermines everything downstream. Overtime is computed from hours worked.
If one shift in seven has estimated hours, then one shift in seven of your
overtime is estimated too. In our data 15,651 overtime hours were recorded across
the period, at real cost, and a share of that rests on inference.
The part that matters in a dispute
This is where an incomplete record stops being an inconvenience.
If an employee disputes their pay, the employer's position rests on the
attendance record. The strength of that position depends almost entirely on one
question: how hard would it be for the employer to have changed this?
A record captured by a biometric terminal, where manual entries are visibly
marked as manual, is a strong document. In our data 140,764 of 140,775 punches
came from a terminal and 11 were entered by hand. That ratio is itself the
argument. Hand-entry exists, because terminals fail and people forget, and it is
rare enough and visible enough that its presence is meaningful.
The failure mode to avoid is the one that feels most helpful: automatically
closing an open shift and writing the inferred time into the record as though it
had been captured. It cleans up the report beautifully and it destroys the
evidential value of every record in the system, because from that point on nobody
can tell which times were measured and which were assumed. A tribunal does not
need to prove your specific record was wrong. It only needs to establish that
your records generally cannot be distinguished from estimates.
So: infer freely, and never disguise an inference as a measurement.
What actually reduces it
Make the last punch unavoidable, if the site allows it. A door that requires
a badge to exit produces complete records without asking anybody to remember
anything. This is only available at some sites and it is by far the most
effective option where it is.
Prompt at the right moment. Not the next morning. A notification twenty
minutes after a scheduled shift end reaches somebody who is still nearby and can
still fix it themselves.
Give supervisors a fast close. If the fix takes four clicks and a reason
field, it gets done. If it takes a form and an approval, the record stays open
and eventually somebody guesses in payroll instead.
Watch the concentration, not the total. The total tells you the size of the
problem. The concentration tells you where it is. If a quarter of your missing
clock-outs come from two sites, those two sites have an exit problem and you can
solve it physically.
Where the gaps actually cluster
The total tells you the size of the problem. Where it sits tells you what to do
about it, and in our data the concentration is not random.
Shifts that end at a handover. When one group leaves as another arrives, the
exit is congested at exactly the moment everybody wants to use it. Our busiest
single hour across 274 days is 18:00, carrying 11.1 per cent of all punches, and
a queue at a terminal at the end of a shift is the most reliable way to make
somebody decide it is not worth waiting.
Sites with one door. A single entry point that also serves as the exit
handles double the traffic at both ends of a shift. The busiest terminal in our
fleet has recorded 11,805 punches while the quietest has recorded two, and the
top five carry 27.2 per cent of everything. Those five are where an exit problem
becomes a payroll problem.
Shifts whose end is not fixed. Anybody who finishes when the work finishes
rather than when the clock says so has no cue to punch out. There is no
five-o'clock feeling to attach the habit to.
None of those three is solved by reminding people. Two of them are solved by
changing the physical layout or adding a second terminal, and the third is solved
by accepting the gap and making it cheap to close properly.
The rule we would give a supervisor
Close the record the same day, and write down why.
Same day, because the information decays fast. Today the supervisor remembers
that the shift ran late because a delivery arrived at six. In a fortnight, when
payroll asks, nobody remembers anything and the roster gets used as a
substitute for what happened.
Write down why, because the reason is what makes the closure defensible. "Closed
at 18:30 per roster, no exception reported" is a position you can hold a year
later. A time with no explanation is indistinguishable from a guess, and a year
later that is what it will be treated as.
The honest position
We have not solved this. Our own data is the 14.9 per cent quoted at the top, and
it is the strongest argument we can make for the design principle underneath it.
The principle is that a system should be very good at recording what it observed
and very explicit about what it assumed, and should never let the two look the
same. That is an unglamorous property. It costs you a tidy dashboard. It is also
the only thing that makes an attendance record worth anything on the day somebody
disagrees with it.
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