Case study · Singapore · Mews · 3 properties

Hours moved from empty mornings to the evening crush

A three-property Singapore group cut average check-in wait 40% while reducing front-office hours 7%, hourly arrival forecasts from Mews staffed the 17:00–20:00 peak properly for the first time, and a cross-property float pool moved capacity to whichever desk peaked first. Hiring stayed capped throughout.

−40%average check-in wait vs baseline
−7%front-office hours across the group
81%occupancy while it happened

Measured results, properties anonymized at client's request. Metrics verified against an agreed baseline.

The problem: the roster ignored the arrival curve everyone could see

Arrivals in Singapore business hotels cluster hard after 17:00, late flights, late meetings. The group's desks ran six agents flat from 08:00, so mornings idled while the evening queue regularly passed fifteen minutes at the flagship. Guests reviewed the queue, not the lobby.

What changed

Mews arrival data became an hourly staffing forecast per property; the roster followed the curve, and cross-trained floaters covered whichever property's wave arrived first. The cross-property view that makes this possible →

Demand-matched rosterStaff required (from arrivals)Old flat roster
051008, Demand-matched roster: 310, Demand-matched roster: 312, Demand-matched roster: 414, Demand-matched roster: 416, Demand-matched roster: 617, Demand-matched roster: 918, Demand-matched roster: 1019, Demand-matched roster: 920, Demand-matched roster: 722, Demand-matched roster: 408101214161718192022 08, Demand-matched roster: 3 · Staff required (from arrivals): 3 · Old flat roster: 6 10, Demand-matched roster: 3 · Staff required (from arrivals): 3 · Old flat roster: 6 12, Demand-matched roster: 4 · Staff required (from arrivals): 4 · Old flat roster: 6 14, Demand-matched roster: 4 · Staff required (from arrivals): 4 · Old flat roster: 6 16, Demand-matched roster: 6 · Staff required (from arrivals): 6 · Old flat roster: 6 17, Demand-matched roster: 9 · Staff required (from arrivals): 9 · Old flat roster: 6 18, Demand-matched roster: 10 · Staff required (from arrivals): 10 · Old flat roster: 6 19, Demand-matched roster: 9 · Staff required (from arrivals): 9 · Old flat roster: 6 20, Demand-matched roster: 7 · Staff required (from arrivals): 7 · Old flat roster: 6 22, Demand-matched roster: 4 · Staff required (from arrivals): 4 · Old flat roster: 6
Flagship desk, typical weekday: demand-matched staffing (bars) follows the arrival curve (amber); the old flat six (dashed) idled mornings and drowned at 18:00.

The data behind this chart

HourStaff requiredOld rosterDemand-matched roster
08:00363
10:00363
12:00464
14:00464
16:00666
17:00969
18:0010610
19:00969
20:00767
22:00464
“Reviews stopped mentioning the queue. Same team, same buildings, the hours just moved to where the guests were.”
Group Operations Director
How can wait time fall 40% while hours fall 7%?

The old roster was both overstaffed and mis-timed: six agents all day meant surplus at 10:00 and a queue at 18:00. Staffing the arrival curve put nine or ten agents on the evening peak and three on quiet mornings, fewer total hours, far more of them where guests actually arrive.

What is the cross-property float pool?

Three properties within walking distance share a small pool of cross-trained agents. Because all three run Mews, one hourly arrival forecast covers the cluster, and the scheduler assigns floaters to whichever desk peaks first, capacity moves instead of headcount growing.

Why not just hire more agents?

Singapore's labor shortfall is projected to shave up to 1.4 points off sector growth, the hiring cap was a market reality, not a choice. The group ran 81% occupancy; the only lever left was pointing existing hours at the peaks.

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