Overview
The Smart Cleaning Solution View aims to help cleaning and operations managers understand which desks and spaces need cleaning, what’s being cleaned, and what occupant sentiment is, so they can make better decisions around the scheduling of cleans and improve staffing decisions.
Filter Controls
Just like the other solution views, you can select different buildings and spaces at the top as your data source, and then choose the timeframe to view data for.
At the top, view live data for the selected sensors providing an overall overview of live air quality readings.
Charts and data visualizations
There are 4 visualizations in this solution and they are grouped in sections based on the questions that they answer:
How much money or time did we save with Smart Cleaning?
Estimated time saved on cleaning
Which were the most used restrooms and when?
Which restrooms were most used during the time periods selected
How many spaces needed to be cleaned?
Desk and space cleaning
When did cleaning happen?
Total Cleans
How did occupant sentiment change over time?
Occupant sentiment (feedback panels)
Generating reports from Solution Views
You can now generate reports for specific visualizations found in Solution Views by simply clicking on 'Export Table' or 'Export Chart.' Doing this will generate a report that will be dowloaded to your device.
How much money or time did we save with Smart Cleaning?
This visualization shows the user how much time the cleaning team is saving through usage-based cleaning.
You can filter to select which labels or spaces you would like to view.
You can see the estimated daily average for cleaning time saved and the daily average number of desks that did not need cleaning. Cleaning time saved is calculated when the daily cleaning report is sent out.
Definitions
Cleaning = everything cleaned during the day and night
Time saved = what the night cleaners do not need to clean, per the daily cleaning report
Total time saved for each label = (number of assets with that label - number that need cleaning) * time savings
Breakdown for each label
Time | 30 Seconds | 3 Minutes | 5 Minutes | 8 Minutes |
Space | Desk Area | Bathroom Cubicle Accessible bathroom cubicle Single-user bathroom Vertical circulation point Other | Meeting room Communal area Reception area Other
| Kitchen Men’s bathroom Women’s bathroom |
How estimated time saved on cleaning is calculated
This is calculated by estimating how long a desk or space takes to clean, and then multiplying by the number of desks or spaces that did not need to be cleaned during the selected time period.
Calculations for cleaning time
Cleaning time saved is calculated when the daily cleaning report is sent out.
Total time saved for each label = (number of assets with that label - number that need cleaning) * time savings
So if there are 100 desks, but only 80 were used, time saved is (100-80) * 30 seconds = 10 minutes
Total time saved is desks + meeting rooms + bathrooms, etc summed
The breakdown for the different labels are:
30 seconds- Desk area
3 minutes - Bathroom cubicle, Accessible bathroom cubicle, Single-user bathroom, Vertical circulation point, Other
5 minutes - Meeting room, Communal area, Reception area, Other
8 minutes - Kitchen, Men’s bathroom, Women’s bathroom
Which were the most used restrooms and when?
This visualization shows what the most used restrooms are during the chosen time periods. There is the option to filter by space type, or by grouping spaces, floors or buildings and lastly you can select the time period.
Right under the filter options you will see the 'Busiest day and time,' the 'Average busiest day' and the 'Average busiest hour' based on your chosen filters.
The visualization gives you a good overview of where the most used restrooms are, how many uses, if there are any changes from the previous period and what the busiest day was for each restroom. The data can also be viewed as a table or as a chart.
How many spaces needed to be cleaned?
This visualization provides visibility into cleaning needs over time by showing how many desks and spaces needed to be cleaned, out of all usable desks and spaces for the selected time period.
The dropdown on the right hand side of each space type shows a 7-day breakdown.
Sensors that have not been installed yet (and therefore have not recorded any data) are automatically excluded from this table ensuring that your data accurately represents the cleaning requirement of your installed sensors.
To filter your data by working hours, simply select whether you would like to view your data by "Weekdays", "Every day" or the working hours for your building. If you haven't set your building working hours, you can do this from the Estate Management page.
When did cleaning happen?
This visualization shows the number of daily cleans over the selected time period.
Cleaning is recorded in four ways:
when cleaners acknowledge alerts
when cleaners “confirm clean”
when cleaners “reset all”
when the overnight auto-reset triggers
To filter your data by working hours, simply select whether you would like to view your data by "Weekdays", "Every day" or the working hours for your building. If you haven't set your building working hours, you can do this from the Estate Management page.
How did occupant sentiment change over time?
The occupant sentiment visualization shows occupant sentiment over time and the relationship between occupant sentiment, cleaning, and usage to help optimize day-time cleaning activities. For example, cleaning bathrooms at the right times throughout the day.
You can choose to overlay cleaning events on the chart as well as space usage to understand the impact on occupant feedback.