Small Talk: Data-driven cleaning

In a recent podcast, Nancy Farrell of Tork, an Essity brand, talked about the latest trends in the commercial jan/san space, with a particular focus on data-driven cleaning.


Cleaning post-COVID

Nancy Farrell: The world has changed so much over the past few years. Coming out of the pandemic, hygiene is still paramount; a lot of tasks related to disinfection and high-touchpoint cleaning – although not as prevalent as during the height of COVID – haven’t gone away.

For example, 73% of people want to use paper hand towels to avoid touching different surfaces in public. They also still have high expectations for places like washrooms and restrooms in terms of hygiene and feeling safe in those spaces.

Just because there are fewer people in the office doesn’t necessarily mean there’s less to do

Hybrid working impact

NF: Hybrid working has definitely caused a change in the cleaning market. We all know that in an office setting, no two days are the same, but this is now much more pronounced due to fluctuating visitor patterns. Cleaning teams have a hard task knowing when people might be in the office, even if companies have set days of the week for staff to come in.

On the flip side, just because there are fewer people in the office doesn’t necessarily mean there’s less to do. One thing we have seen over the past few years is that the sheer volume of what cleaning teams have needed to do has increased.

Yet, we want to create a great experience for people who are returning to the office and other public spaces, so they feel a sense of having a safe environment they will want to keep coming back to.

Moving to a data-driven model

NF: I believe data-driven solutions, such as Tork Vision Cleaning, represent a paradigm shift for an industry that has been relatively slow in the adoption of digitalisation. While we have seen developments in automation, such as cleaning robots, we are now talking about using software as a digital tool to fundamentally change the way of working.

Cleaning has traditionally worked in a pattern of cleaners coming in and following a set schedule of tasks they might have written down on a clipboard. Data-driven cleaning is all about eliminating guesswork by taking real-time data collected throughout a facility. 

Tork Vision Cleaning does this in a couple of ways: one is connected dispensers that measure product levels; the other is through people counters which monitor visitor traffic in a building. When you pair these together, you can start to inform and empower teams to clean in the right way. 

Therefore, instead of having a static way of working based on assumptions, cleaners go directly to the locations they know need the most attention. It could be because a certain number of visitors have passed through so it’s a high touchpoint area or due to dispensers being almost empty. 

Once customers have used data-driven cleaning, they can’t imagine how they could ever go back because it’s like having an extra set of eyes

Feedback suggests that, once customers have used data-driven cleaning, they can’t imagine how they could ever go back because it’s like having an extra set of eyes. It helps cleaning teams and facility managers optimise their resources, tackle labour shortages in today’s marketplace, create efficiencies and – ultimately – deliver the best quality.

Not only does data-driven cleaning use real-time information, but it also leverages historical data. A lot of our clients will use real-time information to act in the moment – this helps avoid complaints and drives better quality throughout the day. 

Then, at the end of a week, month or quarter, they’ll look back at the different analytics and reports that are included in our system. This enables them to continually develop and improve the cleaning KPI set.

Over time, as you collect more data, you can start to do more with it. For example, during the pandemic, when visitor patterns were fluctuating so much, businesses were able to close off sections of offices or floors. Imagine how that impacts the bottom line in terms of electricity usage, climate control, etc. 

Data has also been used to support capital expenditure projects. If customers want to refurbish an area, they can actually choose how to spend their investment based on the most trafficked places and other data insights.

The biggest barrier, as is so often the case, is the onboarding phase – learning how to use the system and changing the way of working. We have a programme where customers work with a dedicated team so we can understand what a client’s particular challenges and KPIs are, and what they’re looking to solve in the longer term. This is something that will be different for each client.

Gradually, it’s about helping them to make corrective actions so they can really understand how to use the technology to drive value. The software is not a ‘one-and-done’ solution; it’s something which should and can evolve to meet greater needs – as long as you’re using it correctly.

The onboarding phase can take anything from a few weeks to several months, depending on a number of factors, including the digital maturity of the company, the cleaning teams themselves, and the size and complexity of the facility. 

Better data = less waste

NF: An obvious benefit of real-time data is the ability to reduce waste. For example, traditionally, cleaners on a scheduled round may change a toilet paper roll even though there is product left. This is because they don’t know if they will make it back to that restroom in time before the dispenser is empty and they want to avoid a potential complaint. It’s a normal reaction, but chances are the remaining product will get thrown away.

What we can now do is accurately indicate when a dispenser needs refilling or replacing. As such, cleaners can go to the restroom the moment action is needed. Consequently, less waste is generated.

Listen to the OPI Talk podcast with Nancy Farrell