Revenue at Risk From Missing Timesheets

Every services firm knows some hours go unlogged. Almost none can say what that is worth, which is why the conversation stays a complaint about timesheets instead of becoming a decision.

A revenue at risk dashboard closes that gap by doing one piece of arithmetic: for each person, the hours they were expected to log minus the hours they actually did, priced at their own rate. The output is money. The point is not precision, it is that a currency figure gets acted on and a compliance percentage does not.

The direct answer: Revenue at risk is the expected-versus-logged hours gap, per person, multiplied by that person's rate, summed across the period. It is an upper bound on exposure rather than a receivable, because some of those hours were never billable and some will be logged late. Its value is that it converts a compliance number nobody acts on into a currency number somebody does.

What Does the Revenue at Risk Dashboard Actually Measure?

Three inputs, and the second one is where most implementations quietly go wrong.

Expected hours, resolved per person. A firm-wide default is the starting point, but the resolution walks an override chain: an individual setting beats a team setting, which beats a role setting, which beats the org default. Part-time staff and a four-day week break any model that assumes one number for everybody, and a dashboard that reports a three-day-a-week consultant as permanently 40% behind teaches people to ignore it within a fortnight.

Exemptions, applied before the arithmetic rather than after. Someone on parental leave, a new joiner in their first fortnight, a role that does not log time at all. In Onplana these are either permanent exemptions or date-bounded exceptions with a start and end, and a person covered by one is removed from the roster rather than shown at zero. That distinction matters: excluded and behind look identical on a chart, and only one of them needs a conversation.

The rate, from the rate card resolved for that person, walking the same user, role, org-default chain.

How Is the Gap Turned Into a Number?

Take a week for one consultant. Expected 40 hours, logged 31. The gap is 9 hours. At a resolved cost rate of $85 an hour, that week contributes $765 to the figure. Repeat across everyone not exempt, sum across the weeks in the period, and report per currency rather than converting, because a converted total hides which office the exposure is actually in.

The dashboard then sorts by the largest exposure rather than the largest percentage, which is a small choice with a real effect. A part-timer 50% behind on a 16-hour week is 8 hours; a senior consultant 20% behind on a 40-hour week is also 8 hours, at roughly double the rate. Percentage sorting puts the first at the top; money sorting puts the second, which is the one worth the conversation.

What the Figure Deliberately Overstates

This is the part worth reading twice, because a dashboard whose limits are undocumented gets quoted in a board pack and then quietly distrusted.

The calculation prices the total-hours gap at the cost rate. It does not isolate the billable portion of the gap. For a consultant who spends the week on client work, that is close to right. For someone who splits their time between client delivery and an internal platform project, it is not: some of their missing hours were never going to be billed to anyone, and the figure counts them anyway.

Isolating a billable-only gap would need per-project allocation, meaning a claim about which project each missing hour would have gone to. That is a guess dressed as arithmetic, and a wrong guess in a number people make staffing decisions on is worse than a known overstatement. So the simplification stays and it is labelled, in the product footnote and here.

Read it as a ceiling. A firm whose revenue at risk is $4,000 for the month has at most $4,000 of exposure and probably less. A firm whose figure is $40,000 has a problem whose size does not depend on the caveat.

How Do You Get the Number Down?

Not by turning on enforcement first, which is the reflex and usually the wrong order.

The figure is normally dominated by a handful of people, and the reason differs per person. Someone consistently nine hours short every week is often working on something that has no project to log against, which is a setup problem rather than a discipline one. Someone who logs nothing for three weeks and then a perfect 120 hours is reconstructing from memory, and their data is fiction whether or not the total matches. Neither is fixed by a stronger reminder.

The order that works is: make sure every real piece of work has somewhere to be logged, make logging take seconds rather than minutes, then let visibility do its job for a month before adding a nudge. Timesheet compliance without nagging covers that sequence. Escalation chains and hard-lock exist in Onplana for firms whose obligations require them, but a firm that opens with hard-lock is applying a discipline tool to a visibility problem, and it will get compliant timesheets full of round numbers.

Where It Sits Against the Other Utilization Metrics

Revenue at risk looks at hours that were never logged. Every other metric in this family looks at hours that were.

That makes it the complement to the ratios rather than a competitor.

Metric	Which hours it sees	What it catches	Blind to
Revenue at risk	Hours never logged	Time that vanished before it reached a timesheet	Everything after logging
Utilization	Hours logged, against hours available	An unfull week	Whether the work was billable
Billability	Billable hours, against hours logged	Too much internal work	An empty week
Realization	Revenue invoiced, against standard-rate value	Discounts and write-offs	Everything upstream

Utilization vs billability explains why a rising utilization figure can hide falling revenue, and both of those ratios are computed from logged hours, so both are silent about the hours that never arrived. Revenue at risk is the only one of the four that measures the absence. Realization sits at the other end of the same pipe, catching revenue lost after the hours were logged and invoiced.

Run together, they cover the whole path from an available hour to a paid one, and each one names a different place the money leaks. In Onplana, Revenue at Risk is part of the Enterprise enforcement layer, alongside escalation chains and audit-grade evidence export; the timesheets and compliance visibility it reads from are Pro, and the rate cards that supply the rate are on every plan. For the capacity side of the same question, resource capacity forecasting covers projecting the expected-hours denominator rather than reacting to it after the week has gone.