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Smart People IoT: Cleaning up with data analysis

Jan Bockholdt of Bockholdt Group

IoT: What do digitalization and data analysis have to do with vacuum cleaning, mopping floors and emptying wastebaskets? Jan Bockholdt spent many weeks and months pondering that question. Quite a lot, he decided – so he then set out to prove it.

Smart People - data analysis: Jan Bockholdt of Bockholdt Group

Cleaning up after others runs in the Bockholdt family. His grandfather founded a small commercial cleaning company in 1959 and called it ‘Blitz-Blank’ (spick ‘n’ span). Jan Bockholdt grew up “breathing hoovered air”, he jokes. Today, he employs more than 6,000 cleaners working out of 16 locations all over northern Germany, making his company of the largest industrial cleaners in Europe. Cleaning is mainly a manual job: cleaners push their carts full of mops, brooms and cleaning equipment through the corridors, stopping to complete the same set of routine jobs in every cubicle. But some offices are empty because the occupant is sick or on vacation, so why wipe floors that nobody has walked on?

Bockholdt asked his dedicated team of software developers to create an app that is now installed on a tablet fitted to each cleaning cart and to display an exact list of tasks the cleaner is expected to perform in each room. Customers can also access the system and specify exactly what they want the cleaner to do, where and when. Bockholdt junior wanted to take the idea a step further and clients can now grade the cleaner’s job performance. “This gives us a good idea of how good our own people are”, he says, “but even more important is that we know exactly how happy our customers are at all times.”


There are still hundreds of ways we can make better use of our data.

Jan Bockholdt

Once started, Bockholdt didn’t look back. There must be even more ways to put existing data to work, he thought. It occurred to him that many of his cleaners have long commutes to their workplace, often one or two hours, and twice a day. “Inefcient”, he concluded.
His programmers started comparing addresses of customers and cleaners, trying to match skill sets of his staff with the needs of nearby clients. But in many cases, it turned out to be impossible: The cleaning profession in Germany is highly regulated, and employees need to be qualified before they can perform certain jobs and in some instances, special training is required. The cleaning of wind turbine blades is required from time to time. The job requires ‘cleaner-climbers’ who, like mountaineers, can scale to dizzying heights. These specialists are in huge demand and finding them on the job market is tricky.
Again, Bockholdt asked his computer people to scan the backgrounds of his employees to see if any of them had previous job experience that would qualify them to become climbers. Anyone who used to work in high-rise construction, for example would obviously have a head for heights, so why not offer to retrain them for a job that may be closer to home – and pay more, to boot. “There are still hundreds of ways we can make better use of our data,” Bockholdt believes, “and if we can clean up by fnding them, then so much the better.”

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