The concept of IoT originated in the Auto-ID Lab at MIT in 1999 and was based on RFID, but has been expanded since to include sensor devices that enable machine to machine communication and autonomous event-driven decision making. Sensors are appearing in traffic signals, factories, distributors, homes and even the weather. A while back I reported on XML in Your Weather. While I was primarily focusing on the METAR coding scheme to send weather data around the World, the "Weatherman" told me a lot about Big Data and things like "smart" rain gauges.
Not like they sprung these sensors on us overnight. They have been hinting" for a while. Even the movies picked up on them. The 2002 movie 'Minority Report' shows a person receiving sensor-based individualized advertising messaging as he walked through a store. Director Steven Spielberg consulted numerous scientists in an attempt to present a more plausible future world than that seen in other science fiction films, and some of the technology designs in the film have become true.
Internet of Things can connect millions of devices via a network, like vending machines, heart monitors, trucks, appliances and buildings — nearly anything with sensors. They report device-specific information back to other devices or applications that analyze the event and take further action. For example, a vending machine low inventory level will create a refill order-to-delivery event.
The amount of data that could be accumulated is HUGE! Businesses must put into place the infrastructure to handle this data and the analytic tools to do something constructive with it.
Let's stick with the vending machine idea. It presents loads of opportunities for the distributor if they think of EVERYTHING. Or it could become a nightmare if they don't.
McKinsey set up six, what I like to call "buckets", where this data will join with other data and optimize the business of vending machines. Below is my take on how I would fill the buckets
(1) Information and analysis: Tracking Behavior: Let's start out with what candy brands are bought, what time and which machine.
(2) Information and analysis: Enhanced situational awareness: If a machine is in an office, and it rains at noon, how much does consumption spike up?
(3) Information and analysis: Sensor-driven decision analytics: If I am going to service a specific machine because some products are getting low, why not check the other brands too? Automatically calculate the tradeoff between inventory dollars and fulfillment dollars.
(4) Automation and control: Process automation: "Conversations" between the machines and the warehouse will initiate a loading plan for each machine and plan the route from machine to machine. Then we just show the driver his route and what he loads UNTIL they start using robots, then you will have a depreciation schedule on the robot (but no wages, pensions and health care to pay!)
(5) Automation and control: Optimized resource consumption: Fill your machines with the optimum amount of product and lower your investment in inventory. Replace slow-moving items.
(6) Automation and control: Complex autonomous systems: I would call this the scheduling system that causes each machine to be filled in the most efficient manner. Your delivery truck will not be running hither and yon filling random orders. That would be the "nightmare" I alluded to.