Finance teams do not reject automation business cases because the savings look small. They reject them because the assumptions are invisible. The fix is a calculator where every input is labelled, every number can be changed, and the case is presented as a range rather than a single confident figure.

Why does a hotel automation business case need a calculator?

Because the value does not sit where the procurement conversation sits. Licence cost differences between platforms are usually a few hundred euros a month. Coordination time, re-cleans and delayed room readiness are worth several times that, and none of them appear on a price list. A reusable calculator moves the conversation from anecdote to evidence, and it survives the question every CFO asks: what happens if you are half as right as you think?

The placeholder inputs used in this worked example

Read this table before the arithmetic. Every figure in it is a placeholder chosen to make the maths legible. None of them is a Sweeply benchmark, none is a result we are promising, and the model is only useful once you have replaced each one with your own number.

Two of those deserve a warning. “Coordination time removed” and “re-clean rate after inspections” are the assumptions that move the answer most, and they are the two you have least evidence for on day one. They are placeholders. A pilot replaces them with measurements, which is what step six is for.

The six-step calculator, with the arithmetic worked through

1. Set the baseline. Coordination minutes per day equal (10 housekeepers x 25 minutes) + 60 supervisor minutes = 310 minutes. Across 30 days that is 9,300 minutes, or 155 hours a month. In money: housekeeping is 250 minutes a day x 30 = 7,500 minutes = 125 hours x EUR 22 = EUR 2,750. Supervision is 60 minutes a day x 30 = 1,800 minutes = 30 hours x EUR 26 = EUR 780. Baseline coordination cost = EUR 3,530 a month. Capture this before you change anything, using the same definitions of clean, inspected, guest-ready and out of service that you will use afterwards.

1. Choose the levers and the reduction you will model. Live room status removes calls to the front office. Auto-prioritisation removes manual reshuffling when demand shifts. Inspection templates and photo notes reduce misunderstandings. For the worked example we assume those levers remove 25% of coordination time. That 25% is an assumption you replace, not a figure Sweeply is claiming. EUR 3,530 x 25% = EUR 883 a month.

1. Add rework and hard costs. Cleans per month = 84 rooms x 30 days = 2,520. At the assumed 4% re-clean rate that is 101 re-cleans. Assume inspections take the rate to 2.5%, giving 63 re-cleans, so 38 fewer. 38 x 18 minutes = 684 minutes = 11.4 hours x EUR 22 = EUR 250 a month. Add EUR 60 a month of paper and printing tied to daily boards. Total monthly benefit = 883 + 250 + 60 = EUR 1,193.

1. Add the cost side. Licence at the placeholder EUR 480 a month. Onboarding at EUR 100 per property, once. Training at 20 people x 2 hours x EUR 22 = EUR 880, once. Upfront cost = EUR 980. Annual cost = (480 x 12) + 980 = EUR 6,740.

1. Work out payback and simple ROI. Monthly net benefit after the licence = 1,193 - 480 = EUR 713. Payback on the EUR 980 upfront = 980 / 713 = 1.4 months. Annual total benefit = 1,193 x 12 = EUR 14,316. Annual net benefit = 14,316 - 6,740 = EUR 7,576. Simple ROI = 7,576 / 6,740 = 112%. Add net present value and internal rate of return only if your finance team asks for them.

1. Run scenarios, then replace the assumptions with pilot measurements. Change one input, the coordination time removed, and show the range rather than a point estimate.

Take the conservative column as your official business case and keep the other two as upside. A case that clears approval on its worst column is a case nobody has to defend twice.

What should the pilot actually measure?

Pick one or two floors, or one building, with a typical mix of room types and a typical crew. Train supervisors first, then line staff, then run a shadow period where the old and new processes run side by side for two weeks.

Measure four things weekly: minutes saved per shift on coordination, re-clean rate, work order close time for issues raised by housekeeping, and room readiness time. Normalise for occupancy, stayover mix and event days, and use alternating weeks as control periods where the rota allows it. Log exceptions daily with an owner and a due date, and resist fixing everything at once. Two high-impact fixes beat twenty small ones in a four-week window.

The benefit lands in different places by property type. An airport hotel with heavy same-day arrivals tends to see it in prioritisation, while a serviced apartment operator tends to see it in inspection quality and the guest credits that stop being issued. STAYERY is a worked example of the starting point: an aparthotel group that moved stayover scheduling off spreadsheets and onto an automated schedule.

What does the approval pack need?

• An executive summary with outcomes, risks and mitigations

• The calculator itself, editable, with inputs, formulas and all three scenarios

• An implementation plan with named owners and dates

• Support commitments and vendor contacts

• A post-go-live reporting cadence with a named owner

Name three risks explicitly, because the reviewer will find them anyway. Partial adoption is the first, so model savings at low, medium and high adoption with a training plan attached to each. Integration surprises are the second, so check the event coverage your PMS actually publishes, for example in the Mews Connector API documentation, before signature. Change fatigue is the third, so phase the rollout and pair experienced users with new ones, which the change management guide covers in detail.

How do you keep the gains after go-live?

Report weekly for the first month on sync stability and adoption, then monthly with a quarterly deep dive to catch seasonality. Share the dashboard rather than describing it: minutes saved per shift, re-clean rate and work order close time are numbers a housekeeping team engages with once they can see them.

Sweeply’s published results give you a sanity check on whether your model sits in a plausible range. Íslandshótel, 18 properties in Iceland, recorded 6,400 hours saved in four months. The Views Hotels in Madeira, 750 rooms, recorded EUR 5,370 saved per month alongside a 60% reduction in housekeeping activities. Independent reviews sit on Hotel Tech Report. All of those are outcomes rather than inputs: use them to check your answer, not to fill your assumptions.

Where this calculator is wrong

It ignores revenue upside entirely. Faster room readiness can protect rate and reduce compensation, but attributing revenue to an operations tool is an argument you will lose in a finance review, so the model leaves it out. Treat any revenue benefit as unmodelled upside.

It assumes coordination time is measurable. In a property under roughly 30 rooms where one supervisor coordinates everything informally, the baseline may be too small to survive a time study, and the honest answer is that the licence costs more than the problem.

It assumes a working PMS integration. If your PMS cannot push reservation events, the coordination lever largely disappears and you should rebuild the model without it. Check the integrations list or the benefits of integrating your PMS with task management first.

And it cannot model a staffing shortfall. If rooms are late because you are three housekeepers short, this calculator will show a saving that never appears in the rota. Fix the establishment first.

Get the licence figure for your own model

The one input you cannot estimate is the licence line, and it is the one that decides whether your conservative column clears approval. Send your sellable room count, your PMS, the number of properties and the modules you want (housekeeping, maintenance, guest connect, AI agents) to hello@getsweeply.com. You will get an exact quote, with the EUR 100 per property onboarding fee itemised separately, in a form you can paste straight into the calculator. No demo needed to get the number.