TECHNOLOGY & DATA
The data hiding in every wipe
Ian Adams, Owner of Speciality Wipes, reveals the importance of information which you can gleam from a less-than high-tech source.
When people talk about data-driven cleaning, the conversation often moves quickly towards sensors, dashboards and smart buildings. I think there’s a simpler place to start, however: look at the waste bin.
The number of wipes discarded after a task, the amount of chemistry used and the frequency of repeat cleans can reveal a surprising amount about how well a cleaning process is working. These aren’t sophisticated data points, but they sit much closer to the task itself than many of the metrics usually associated with cleaning technology.
What wipe consumption can reveal
Suppose two operatives clean the same type of surface using the same procedure. One routinely completes the task with two wipes. The other needs five.
That doesn’t automatically mean one person is working incorrectly. Contamination levels may vary, the wipe may not suit the surface, or the amount of chemistry being applied may be inconsistent. Even so, the variation gives you something worth investigating.
I see wipe consumption as a process signal. If a production area normally uses around 40 wipes per shift and suddenly begins consuming 60 without any increase in workload, something has changed. Repeat cleans are even more revealing. A surface which needs a second pass isn’t simply using another wipe: it’s also consuming additional labour, chemistry and disposal capacity.
Controlled inputs create better data
Measurement becomes less useful when the cleaning process changes every time it’s performed. Take manual chemical application: one operative might apply two sprays to a wipe; another might use considerably more because they prefer the surface visibly wet. Both can record the same completed task, yet the quantity of chemistry reaching the surface can be very different. From a measurement perspective, that creates noise.
Precisely saturated wipes can remove part of this variability by supplying a more consistent amount of chemistry. They won’t guarantee the final result, because technique, contamination and contact time still influence performance. What they can do is make one important process input more repeatable.
For me, this is where consumable specification starts to overlap with data strategy. Reliable measurement depends on having enough control over the process to make comparisons meaningful.
50 | TOMORROW'S CLEANING The cheapest wipe isn’t always the
cheapest process Wipes are often assessed on unit price or case cost. That’s understandable, but it can disguise inefficiency. Imagine Wipe A is cheaper, but regularly requires three sheets to clean a defined area. Wipe B costs slightly more per unit, but completes the same task with one. Looking only at purchase price could point you towards the wrong conclusion.
“Reliable measurement
depends on having enough control
over the process to make comparisons meaningful.”
The more useful measure is cost per successful cleaning outcome. That means looking at wipe usage alongside chemical consumption, repeat cleans and time spent on the task. Waste can tell a similar story: if oversized wipes are routinely thrown away partly used, the format itself may be poorly matched to the application.
Start with a baseline
Data-driven cleaning doesn’t always require another digital platform. Define the task, establish typical wipe consumption and record how often the job needs repeating. Then look for sustained changes, rather than reacting to one unusual shift. Over time, those patterns can expose differences in training, wipe suitability, contamination levels or process design.
Data-driven cleaning is often presented as a technology story. I see it just as much as a process-control story. Better information only becomes useful when the underlying task is consistent enough to compare. Sometimes the most useful data point isn’t coming from a sensor: it’s already sitting in the bin.
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