Chantal Snyders (Asito): “Data-driven cleaning is not a goal but a means”

Organizations often want to get started with data-driven cleaning very quickly; Detail cleaning of the respective building use by data. For example, with a sensor in every workplace, preferably in every room, so that they always have an overview of when the room needs cleaning. Snijders says, data-driven cleanup is not a goal, but a means.

Planning and practice

The cleaning schedule is often evenly distributed; Fixed number of days, fixed number of hours, and fixed number of people. While the use of the building can vary by day. You don’t immediately need very specific data to work more efficiently, says Snijders. If the goal is to better align the cleaning schedule with occupancy, it is better to start globally and then get more and more specific, rather than the other way around. This requires less effort, but gives a great result. Snijders advises: “Look at planning in general: What does my career do all year long? Are there differences between months, weeks and days? You can modify the staff layout according to the trends you see. One more employee on Tuesday because it’s busier that day, for example, and one less employee on Wednesday.”

In the right place at the right time

After adjusting the daily schedule, data-driven cleaning is the next logical step. Consider analyzing and optimizing data on occupancy for each floor, area, room, or even workplace. Asito is currently working on a number of pilots at office and airport locations. “We are comparing the use of the building to the customer and our employees. Together with the property manager, we discuss the activities we will be doing at quiet or busy times,” Snyders explains. “When it’s really crowded at the airport, there’s more pollution. With data, you know that you have to schedule additional employees at busy times. Together with the customer, we are also looking at whether we can better distribute the use of the buildings. ”

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