Revenue can exceed Forecast while workload productivity falls, because revenue also reflects price and sales mix. Compare workload and hours separately, using the same period and hour basis, before deciding whether staffing needs to change.
Check what the productivity measure represents
Identify the cost driver and productivity measure used by the department. Depending on its configuration, workload may be represented by room nights, covers or another driver. Use the report’s definition and distinguish operational productivity from measures that include other hour categories.
For a measure expressed as workload units per hour, productivity depends on both the volume of work and the hours used. Revenue also depends on price and mix, so revenue growth alone does not show that workload grew by the same amount.
Distinguish workload productivity from RevPOLU
Workload productivity, such as covers per productive hour, measures an operational volume against its defined hour base. RevPOLU is Revenue Per Operational Labor Unit: it relates revenue to the applicable paid labour hours, including productive and nonproductive hours. Check the selected report’s scope and department inclusion settings before comparing values.
A higher selling price can improve a revenue-based measure while covers per productive hour declines. These measures answer different questions. Do not label a covers-per-productive-hour result as RevPOLU or substitute one measure’s hours for the other’s.
Compare volume and hours separately
Illustrative example: a restaurant’s forecast assumes 1,000 covers, an average check of 200 currency units and 100 productive hours. Actual figures are 900 covers, an average check of 240 and 100 productive hours.
| Measure | Forecast | Actual |
|---|---|---|
| Covers | 1,000 | 900 |
| Average check, currency units | 200 | 240 |
| Revenue, currency units | 200,000 | 216,000 |
| Productive hours | 100 | 100 |
| Covers per productive hour | 10 | 9 |
Revenue rises from 200,000 to 216,000, an increase of 8%. Covers per productive hour fall from 10 to 9, a decrease of 10%. In this example, a higher average check explains why revenue and productivity move in different directions. This compares actuals with Forecast. A separate Target would be needed to establish a Target miss. The example does not establish whether 100 hours were operationally appropriate, and it supplies no nonproductive-hour total from which to calculate RevPOLU.
Is revenue down because of fewer guests or lower spend?
Use a volume measure that matches the revenue being analysed. For restaurant revenue this may be covers; for rooms it may be sold room nights. Do not divide restaurant revenue by hotel guests and assume the result is average spend per restaurant cover.
For matching data, revenue equals volume multiplied by average revenue per unit. Compare the same outlet, dates, service periods, revenue basis and baseline. Check whether guest or cover counts are complete before interpreting a lower volume.
Illustrative restaurant example: the comparison period has 1,000 covers at an average spend of 200 currency units, giving revenue of 200,000. The current period has 900 covers at 190, giving revenue of 171,000: a shortfall of 29,000.
One consistent way to split the change is to value the cover change at the comparison spend, then value the spend change at current covers:
- Cover effect: (900 − 1,000) × 200 = −20,000 currency units.
- Spend effect: 900 × (190 − 200) = −9,000 currency units.
- Total: −20,000 − 9,000 = −29,000 currency units.
State the decomposition convention when presenting the split. Average spend can change because of prices, discounts, purchases per cover or sales mix; the calculation identifies the contribution but does not prove which of those caused it. Investigate the relevant transaction or mix evidence before recommending an action.
Investigate the remaining drivers
- Check that the workload driver is complete and appropriate for the department. Arrivals, departures, events or service complexity may matter even where they are not captured by one headline measure.
- Review whether productive, nonproductive, transferred and outsourced hours are being compared on the same basis.
- Compare the timing of demand with staffing. A period total can hide busy days with insufficient coverage and quieter days with excess capacity.
- Check fixed duties, minimum coverage and unusual activities before treating every hour above a reference as avoidable.
An HOD can then review the affected days and planning assumptions. A GM or controller can check whether the same explanation appears across departments without averaging incompatible productivity ratios.
Act on the explanation you confirm
If lower covers are confirmed and the demand outlook has also fallen, the department head should review future staffing by day and service period. Reduce or redeploy capacity only where coverage and other duties permit. Review covers, productive hours and available service indicators after the affected period.
If workload is understated because covers were not imported or were mapped incorrectly, resolve the data issue and repeat the comparison before changing the staffing plan. If price or mix explains the revenue improvement while workload is unchanged, review the revenue and margin opportunity separately; higher revenue alone does not justify adding or cutting hours.
Ask PIA a focused question
“Productivity is below Target but revenue is above Forecast. Using the supplied workload, hours and revenue figures, explain what is supported and what we still need to check.”
If only revenue and a productivity percentage are available, PIA can explain possible mechanisms. Confirming the cause requires the corresponding workload, hour and comparison data.