TECHNOLOGY & DATA
Digitise the assets, then unlock their value
Digitising a fleet starts with something simple: know every asset you have. Alexander Manafi, CEO of ToolSense, on why that first step already pays for itself, and how the data it produces unlocks proactive maintenance, tighter processes and real margin.
2M-Gruppe, a German cleaning and FM company, spent a year looking for a glass cleaner’s ladder. It vanished from a site, the client was accused of taking it, and the loss was written off. A year later, it turned up at the same client, still in use. The company had no dependable way to know where an asset was, or whether it was even still theirs.
That’s the kind of loss a QR code fixes
on its own, but 2M-Gruppe kept going. Managing Director Oliver Majowski dug into the data his newly digitised fleet was producing, and found something bigger: a €35,000 loss on a single contract, the kind of leak that only shows up once you know what you own and what it costs to run. The ladder was the easy win. The margin was the real one.
Digitising an asset starts simply: put a QR code or a tracker on the scrubber, and you always know where it is. That already solves real problems, lost machines, disputed sites, hours chasing equipment down, but it’s also the foundation for something bigger. Once every asset carries a record, that record accumulates: age, condition, service history, running cost, utilisation, all tied to a real object instead of an ERP module, a GPS system or a spreadsheet somebody updates when they remember.
Digitise the processes too, and the numbers move fast. At ISS, one of the world’s largest facility services groups, a single reliable data set changed what the operations team could do with it. Excess machines in central warehousing got reallocated to new contracts instead of triggering new purchases, and PAT checks compliance is made sure by ToolSense, with the system keeping track of compliance dates, sends reminders and holds certifications for all assets.
Karen Fink, a Senior Project Manager at ISS, said: “When we have the right data, there are so many options. Procurement, health and safety, finance: they’re all ready to use it.”
None of that requires buying better machines – it’s what the data makes possible once it exists. The same visibility which catches a margin leak also books a service at the first sign of trouble rather than after a breakdown, worth around
44 | TOMORROW'S CLEANING
80% less unplanned downtime and a fifth off machine costs over an asset’s life.
This is why we built ToolSense as a neutral platform, tied to no single manufacturer. We integrate directly with the brands already in most UK fleets, including Numatic, Kärcher, Nilfisk, Pudu, ICE Cleaning Equipment and others, so a mixed fleet becomes one data model instead of a login per badge. More than 200 companies now run their operations this way: digitise the asset, and the value keeps compounding.
The next step is teaching that data to talk back. We’re soon releasing an AI Fleet Assistant Manager and an AI Support Manager. The Fleet Assistant Manager takes on the optimisation ISS was doing by hand, matching underused machines to new contracts and flagging what’s worth repairing versus replacing. The AI Support Manager pulls an asset’s full asset context (IoT history, troubleshooting guides and documentation, and many more) providing possible fixes for anyone on site, so issues resolve faster. Neither invents new data: they act on the margin already sitting in the fleet.
Tag the ladder, and you’ve fixed the ladder problem. Keep building on that data, and you fix the margin problem too. That’s the real opportunity in digitising a fleet: not a single win, but one that keeps paying out as the data behind it grows.
www.toolsense.io
x.com/TomoCleaning
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60
