Tourism Industry Insight: Should Hotel Run Every Service at 100%?

At 2:15 p.m., six speedboats reach a resort within forty minutes. Reception is busy, luggage carts disappear, welcome hosts are occupied and several rooms need final inspection. Nothing has technically exceeded capacity: every employee is working, every vehicle is moving and every desk is occupied. Yet guests begin waiting. Managers looking only at utilisation might conclude that the operation is impressively efficient. Operations research suggests almost the opposite. A service system running continuously near its maximum capacity can become extremely vulnerable to even small bursts of demand.

Waiting does not rise in a straight line

The mathematics of queues explains why.

Imagine, purely illustratively, one service point capable of handling ten customers an hour. If customers arrive at an average rate of five per hour, the system is running at 50% utilisation. In the simplest queueing model, with random arrivals and service times, the expected waiting time before service is about six minutes.

Increase demand to nine customers per hour and utilisation reaches 90%.

Waiting does not merely increase by 80%. Under the same simplified model, expected queueing time rises to roughly 54 minutes.

At 95% utilisation, it approaches two hours.

Real resorts are considerably more complicated than this textbook model. Guests arrive in groups, employees perform different tasks and managers intervene dynamically. But the underlying mechanism survives: as demand approaches maximum service capacity, the remaining margin available to absorb variability becomes very small.

A peer-reviewed restaurant operations study similarly found that balancing service quality and capacity cost could outperform treating the two as a simple trade-off.

This is why utilisation alone can be a dangerous productivity metric.

Variability consumes the last few percentage points

Hotels rarely receive demand smoothly.

Flights arrive together. Rooms are released in clusters. Breakfast peaks between particular times. Rain suddenly moves guests from beaches into restaurants and spas. A delayed transfer can shift thirty arrivals into the same hour.

The Maldives makes these interactions unusually visible because the one-island-one-resort model concentrates accommodation, transport, dining, recreation and utilities inside one operating system.

That system needs some ability to absorb variation.

The insight complements the case for cross-trained staff as hidden hotel capacity. Cross-training is valuable partly because it creates temporary headroom: when reception suddenly becomes congested, capability can move there instead of leaving the queue to grow.

Transport provides another example. The analysis of why the cheapest transfer can cost a resort more showed how consolidating passengers may improve vessel utilisation while increasing guest waiting. Higher utilisation is therefore not automatically higher system value.

The same network logic appears in better flight timing and tourism value: capacity creates more value when it fits the timing and connections of the wider journey.

Measure the queue before adding capacity

The practical response is not to keep every department deliberately idle.

It is to identify where high utilisation produces disproportionate delay.

A resort can track demand and service capacity in 15- or 30-minute intervals around critical processes: airport dispatches, check-in, breakfast seating, housekeeping releases, buggy requests or restaurant arrivals. Alongside average waiting time, monitor the 90th percentile, the experience of guests near the slow end of the distribution.

Then test thresholds. Does waiting remain stable until restaurant occupancy reaches 75%, then rise quickly? Does one additional clustered arrival produce a disproportionate check-in queue? At what workload does housekeeping begin missing room-release targets?

Sometimes the answer will be additional capacity. Sometimes it will be staggered scheduling, reservations, better forecasting or flexible employees.

And sometimes deliberately preserving capacity is rational. The option value of an empty villa makes a related revenue-management point: unused capacity is not always waste when retaining it protects a more valuable future opportunity.

Return to the resort at 2:15 p.m.

Every employee was busy, every vehicle occupied and every desk working. The operation looked efficient precisely when the guest experience was beginning to slow.

The lesson is not that low utilisation is desirable. Hotels still need productive assets and labour. It is that the final units of capacity can have unusually high value because they absorb randomness.

In tourism, where guests, flights, weather and service requests rarely arrive according to plan, spare capacity can be more than idle resource.

It can be the space that keeps the whole system moving.