Internet of things was hot for a few years but I think it has been passed over by the popularity of artificial intelligence nowadays. Even though the actual wide range availability of solutions has not even matured yet! I still think it is one of the most business innovative technology trends of this period. There should be a reason why so many people believe in the concept. Let us try and define what that X-factor is and let’s see if Contingent Labor can draw some lessons here.
What is the Internet of Things (IoT)?
Of course many people know what Iot stands for, both in abbreviation as in technology. But just to be sure, here is a definition and explanation of IoT.
Internet of Things is the name of everyday objects that are being connected online. Whether this is about machinery in a factory that is linked together in order to plan and execute more precisely, or it is about your car that can be pre-heated via an app on your smartphone. These are all inanimate objects (things) with sensors and embedded, small capacity computing devices that can tell you all sorts of data from it’s ecosystem and/or can follow orders that are given to it.
Basically it is the first step that follows the widespread use of smartphones. A critical mass in humans was connected to the internet first. Yes, we were already online via PC’s, but the smartphone connected us individually! Now, in an enormous worldwide pace, we connect all sorts of daily appliances to that same internet in order to use our smartphone better to enhance our everyday lives.
What is the main beauty of Internet of Things (IoT)?
For the thoughts in this specific blog I would like to focus primarily on the industrial, processing side of IoT. The personal added value of controlling appliances via your smartphone is fun and comfortable, but in business application lies its measurable advantage. And although it is always pretty subjective to assess added value of technology, I think there are three main pro’s to be recognized within IoT.
- 1. Data is highly validated: all connected machines in the flow measure overlapping data. So data sources of single measurements are continuously compared and validated against each other and thus corrected and finetuned.
- 2. Reactive correction becomes proactive action: when you have all process data non-stop and real-time and you can compare that to historic data continuously, it is immediately clear whether or not something is off from the standard. Predicting issues makes it easier to correct before things really get out of hand.
- 3. Less waste: being able to measure and steer more precisely, any flow will result in less waste, both economically, socially and environmentally.
Let us compare these advantages to your average everyday administrative contingent labor process… Data is often not correct due to many manual integration points. The invoice is the only moment in the process where the correctness of data is actually validated. You can hardly call that anything other than reactive. When you really process your data only in the last process step, it is obvious that much rework will come from that and thus much process waste is created.
How could IoT principles be applied to Contingent Labor processes?
Basically it is fairly simple to apply IoT to contingent labor. In its extreme I could say that every worker should be inserted a chip, but maybe that is pushing the innovation a bit too far. But essentially that is actually one of the first steps that is most clear. Get every worker in the contingent labor pool connected to the process and measure data as real-time as possible to make it valid. Off course no chip under their skin, but smartphones and any other decentralized data-entry might help here enormously.
The second step is most important: when data is entered, do not use manual actions to enhance that data or integrate it between systems. Make sure the data is validated and re-used automatically over and over again.
Imagine a process where time is measured close to where it is spend, the budget owner is able to validate and release that data, and all following steps in the process can use that validated and correct data to enhance their work. Basically this is what every implementation of an efficient Contingent Labor process is after. Maybe we should depict it more often in an Internet of Things setting instead of in a traditional process- and systemlandscape?
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