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Solution · Management & planningEvery Monday the owner sees which hours earn and which merely keep the lights on
Utilisation of lanes, rooms and studios, weekly
A robot collects data from calendars and booking systems and assembles the week’s map every Monday: utilisation of every hour, lane, room and studio, with trend and comparison. Pricing, opening hours and rota decisions finally get numbers underneath.
Executive summary
Infrastructure, lanes, rooms, studios, costs the same on an empty Tuesday as on a full Saturday, but knowledge of its use ends at the impression of the evenings; nobody counts the hours, because the data is scattered across calendars.
Every week the robot collects bookings and visits from all sources and assembles the map: utilisation per hour, day and resource, with weekly and seasonal trends, in one Teams report.
Off-peak pricing, opening hours and the rota stop being guesswork; dead hours become visible, so they can be sold, and the peak can be priced as it deserves.
calendars and booking systems (Bookings, industry systems); the team rota; the report in Microsoft Teams and Power BI
Business problem
The impression of the evenings instead of an hour count
Every service facility sells fundamentally the same thing: hours of its infrastructure. A range lane, a studio room, a treatment room costs its money whether anyone uses it or not; rent, instalments and salaries flow on an empty Tuesday exactly as on a full Saturday.
Yet in most small firms, knowledge of how those hours are used is purely anecdotal. The owner is around in the evenings, sees the crowd and concludes things are fine. Nobody sees the mornings except the team, which has grown used to them. The data exists, in calendars, booking systems and rotas, but assembling it into an hour count is work there is never time for.
Without that count, decisions are made in the dark. The price list has been one for years, because nobody knows which hours would bear a higher price and which need discounting to live at all. Opening hours run on habit. The team rota is arranged around impressions, not traffic. And the investment conversation, another lane, a second room, rests on “it will probably fill up”.
The most expensive part is what stays invisible: the hours that die every week in the same cells of the grid. They cannot be recovered from memory; they first have to be seen.
How it works today
Below is what the work looks like before anything is automated.
- PersonBooking data lives in three places: a calendar, an industry system, a notebook
- Risk of errorNobody assembles it into an hour count, because that is an evening nobody has
- Risk of errorUtilisation knowledge is the impression of evenings and Saturdays
- WaitingDead hours die every week in the same grid cells, unnoticed
- PersonPrices and opening hours persist out of habit; nobody knows what they would bear
- Risk of errorInvestment conversations rest on “it will probably fill up”
Why the current process costs more than it appears
The bill that never shows up in a budget.
- Every empty infrastructure hour carries full cost and zero revenue; over a year it is usually the firm’s largest invisible line.
- One price for all hours means the peak is too cheap and the dead hours too dear; both sides of that mistake cost.
- A rota built around impressions pays people to keep watch over empty rooms.
- An investment decision without an hour count is a bet, not a decision.
Cost of inaction
The first row is the cost of unused hours existing at the model facility, not a promise of selling them; some will never sell. But the gap between 58% and even 70% utilisation is worth tens of thousands a year, and the only road to it is visibility.
The report makes no decisions: it shows the hour grid as it is. Pricing, promotions and opening hours remain yours; the difference is they stop being made in the dark.
A model organisation with realistic proportions – the numbers exist so you can run the same maths on your own data; they are not a client result.
A sports and services facility: six lanes and two rooms, bookings in Bookings and an industry system, group classes in a calendar, the team on a rota, Microsoft 365.
The owner knows the evenings and weekends; the rest of the week is guesswork. A uniform price list for three years.
A manual utilisation summary made twice a year for a “sense of control”; it takes an evening and ages immediately.
The seventh-lane decision has hung for a year, because nobody knows whether six really fill.
Every Sunday night the robot collects bookings, visits and classes from all sources, normalises them to a grid of hours and resources, and publishes the report on Monday morning: the week’s map, a four-week trend, a year-on-year comparison, the top empty and top full hours.
In the modelled case the first three decisions off the report, discounting dead mornings, moving group classes and adjusting the rota, lifted utilisation by several points in a quarter. Model numbers, not the facility’s records.
Proposed solution
We start by defining the grid: which resources count (lanes, rooms, studios, equipment), in which hours they are paid, and what counts as use. One meeting settles it; that is what makes the report speak your language rather than a system’s.
Every week the robot collects data from all sources: Bookings, the industry system, class calendars, the team rota if needed. It normalises everything to one grid and assembles the report that arrives in Teams on Monday morning: the hour map with each resource’s utilisation, the trend, comparisons, and a list of the ten deadest and ten most overloaded hours. For those who want more, the same picture lives in Power BI.
The report has one rule: no forced interpretation. It shows that Tuesday 10:00-13:00 has stood empty for eight weeks and that Thursday 18:00 turns bookings away; what to do about it is the owner’s decision. Practice shows the first decisions come by themselves, because for the first time you can see where the money lies.
UiPath Orchestrator: the weekly schedule, source collection, retries and an audit trail; UiPath Integration Service connectors for Microsoft Teams and SharePoint
The resource and hour grid definition, multi-source data normalisation, the weekly report with map, trends and extremes lists, and a live Power BI view
Sources: Microsoft Bookings directly, industry systems via export or API, Microsoft 365 calendars; the report in Teams and Power BI
How the automated process works
- AutomationEvery Sunday the robot collects bookings, visits and classes from all sources
- AutomationThe data normalises to one grid of resources and hours
- SystemOn Monday morning the report lands in Teams: map, trend, comparisons
- SystemThe extremes list: the ten deadest and ten most overloaded hours
- PersonThe owner decides: pricing, hours, rota, promotions
- PersonThe decision’s effect shows on the map in the following weeks; experiments get results
Human-in-the-loop model
Automation handles
- The weekly collection and normalisation of data from all sources
- The report with the hour map, trends and extremes lists
- The live Power BI view for those who want to look more often
People decide
- The definition of resources, paid hours and what counts as use
- All decisions: pricing, opening hours, the rota, promotions, investments
- Context: the report does not know about the room refurbishment or the school holidays
Before and after
Systems and integrations
The stack is short on purpose: one engine, one execution layer, one place where a person decides.
Inputs
- bookings from Bookings and industry systems
- class and visit calendars
- the team rota (optional)
- the resource and hour grid definition
Automation layer
- UiPath Orchestrator
- UiPath Robots
- UiPath Integration Service
- UiPath Action Center
Target systems
- the weekly utilisation report in Microsoft Teams
- the hour map with trends and extremes lists
- the live Power BI view
Human touchpoints: the report on Monday morning; a grid definition review quarterly; the rest is your decisions
Technologies used
the weekly schedule, multi-source collection, retries, a record of every run
Areport publishing and the weekly archive
Athe live hour map for those who want to look more than weekly
Abooking sources read directly
Avisit and booking sources; scope depends on the interface
BIllustrative economic model
Numbers you can check against your own data.
The report does not lift utilisation by itself; your decisions do, finally with numbers underneath. The model assumes a modest seven points of improvement in a year; volumes and rates belong to the scenario. Your own numbers go into the calculator alongside.
Run the maths on your data
An illustrative estimate based on your inputs. It models freed capacity, not promised savings.
Business benefits
- Every hour’s and resource’s utilisation is visible weekly, without an evening at a spreadsheet
- Pricing can finally split peak from dead hours on the strength of numbers
- The team rota follows real traffic, not impressions
- Experiments, a promotion, a moved class, have a measurable result within two weeks
- The investment conversation rests on a trend, not a bet
The management view
- Infrastructure stops being a fixed cost of unknown productivity; it becomes a measured asset
- The decision culture shifts from “I feel” to “the map shows”
- Another resource, lane, room or location enters the report with one grid definition entry
Board-level KPIs
overall paid-hour utilisation · utilisation per resource and hour · the week’s dead hours · bookings turned away at peak · the effect of pricing experiments
Security and governance
The automation has exactly the permissions it needs. Not one more.
- The report works on aggregated data: hours and resources, no customer personal data
- Report and Power BI access by role; the weekly archive in SharePoint
- Every run has a trail: sources, time, data completeness
- Data gaps are flagged openly, not masked; the report says what it does not know
- Data stays in your Microsoft 365 tenant; the robots run in the EU region of UiPath Automation Cloud
Why now
Facility fixed costs have risen; an empty hour hurts more than ever, and it only shows on an hour count.
Customers have moved bookings online, so the data finally exists; only the assembly is missing.
Flexible pricing no longer surprises customers; whoever does not differentiate leaves the peak too cheap.
Relevant executive roles
Sees every Monday where the money lies, and decides on numbers
Arranges the rota and promotions around real traffic; experiments get results
Works when there is traffic, instead of keeping watch over empty rooms
Common questions and objections
That is the norm, not an obstacle: Bookings and calendars we read directly, the industry system via export or API, and the notebook can become a one-minute form entry, or you simply start from the digital sources and see how much of the picture they already give. The report flags data gaps openly, so you know what it cannot yet see.
Every owner knows the evenings and weekends. The report earns its keep on the rest: on the Tuesday 10:00 that has stood empty for eight weeks, on room B empty on Thursdays, on the trend that has been falling for a month while the evenings still look fine. This is not knowledge against intuition; it is intuition with numbers.
The report shows resource hours, not people’s performance, and setting it up that way is part of the implementation. In practice the more common outcome is shifting hours to where the traffic is, and selling the dead hours, which means more work, not less.
When this is not the right solution
- One room and one calendar: a look into Bookings suffices
- Bookings solely in a notebook and no will to change: the report has nothing to be built from
- Expecting the report to set the price list itself: it shows the grid, the decisions stay yours
A question for the next management meeting
What percentage of our paid hours actually worked last week, and how do we know?
Implementation approach
Scope without ambiguity, before anything is signed.
We deliver
- The resource and hour grid definition in your language
- Automatic weekly data collection from all sources
- The Monday report: map, trends, extremes lists, in Microsoft Teams
- The live Power BI view
- The weekly archive and a definition review after the first quarter
We need from you
- Access to the booking sources (Bookings, the industry system, calendars)
- One meeting to define resources, paid hours and use
- A decision who receives the report and who gets Power BI access
Stages
Discovery
Data sources, their completeness, the resource and hour definition
Rules
The grid, the definition of use, the report format, the recipients
Build
Collection, normalisation, the report, Power BI, the archive
Calibration
Two weeks: we check the map against reality and tune the definitions
Go-live
The report enters its Monday rhythm; a review after the quarter
A quick win. The report is built on data you already have; effort depends on the number of sources and their interfaces.
Six lanes, two rooms, fourteen opening hours a day. How many of those hours did someone buy last Tuesday? That is exactly the point: nobody knows.
Describe your resources and booking sources. We return a mock-up of your Monday report and the list of data it needs.
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