I wrote a post a few months ago about IoT (internet of things) and how it was “big data” with a new suit. I also read a tweet that gave me pause for thought by Simon Jacobson that raised the question about IoT use cases –
It is an interesting comment. IoT right now, much like Big Data, is all about the ability to collect data from more and more “things” in our universe. The ability to collect the data is becoming increasingly powerful. Whereas in the past you would rely on your plant supervisor to visually monitor the performance of a printing press, now you have hundreds or thousands of sensors that can communicate in real time with monitoring software to ensure the health of that machine. Companies like Caterpillar are putting sensors on a greater number of their products, allowing consumers and Caterpillar, to monitor their earth movers, trucks and other machinery. For a more current example of how IoT is everywhere, all one has to do is look at the tragedy of Malaysia Air flight 370 – it was the GE produced engines that were constantly pinging the maintenance servers that gave investigators a better sense of the direction of the plane’s flight. But these examples reinforce with Simon was saying – these are all about remote monitoring.
When will our supply chains see IoT moving beyond “just” remote monitoring and asset management examples? That is where M2M (Machine to Machine) will begin to play a larger role. I spoke earlier this year with Toolsgroup, a supply chain solution vendor, and they spoke to me about their working with Costa coffee and their coffee distribution machines. So how does a coffee vending machine have a role in all this? Initially this story is all about improved monitoring for improved inventory management. How Costa Coffee dispensers are being “smarter” in how they communicate to ensure more efficient fulfillment. Up to this point it is really about monitoring. But let’s take it to the next level.
With greater machine learning, could the dispensers become smarter with marketing to the individual consumer? You purchase a soy milk latte from the machine. That machine can now learn your preferences if that is what you have been purchasing at other machines. Could you imagine that their is data being exchanged between other systems? Maybe where Costa coffee materials are sold or maybe partner goods are offered. Machine to machine learning, could lead to greater machine to machine communications. Now the machines could communicate and proactively push coupons or deals to your mobile device about a soy milk and coffee bean bogo offer. Yes there is some big brother aspects to this…but get used to more of that with IoT.
We are just beginning to see how IoT will impact supply chains. What we know it is doing is bringing a greater amount of information from a larger number of sources that we otherwise had access to. What we are still determining is what kind of data we will be getting and how we can use the data – for more than simply monitoring.