The industry is evolving again; by industry I am referring to the internet production infrastructure known as IT. By evolving I mean it is shifting again. If, you consider IT as the internal only production infrastructure. Part of the evolution going on is the expansion of IT to the personal portable device. Beyond the personal portable device to the IoT infrastructure.
Recently I saw an interesting post about the concept of applying IoT to the restaurant business. An interesting question – what should a supplier tag with IoT devices and what should manufacturer have report back to the main organization about the statue and capabilities within devices sold. The question could be as simple as fryer oil. Every time you use a fryer (turn it on etc) it destroys the integrity of the oil. So you have to, from time to time, change the oil. You open the shuttle cock at the bottom of the fryer and let the old oil out. (then properly dispose of it).
You could, build an IoT oil life sensor (older oil has a different consistently and response to heat than newer oil does) and report back to the manufacturer. Older oil has cooking sediment in it that can cause the machine to require maintenance more often. If I use a sensor to dispatch the tech before there is a problem, it is cheaper both for me (panicked customer phone call) and for the customer (machine doesn’t break down during production time costing lost revenue). The same is true for the restaurant as a whole, for example shutting off lights when the restaurant is not open, shutting off systems when the restaurant is closed and so on. Oil life sensors could also let the restaurant know when the oil was well past its peak, food doesn’t taste the same with older oil as with new oil. Once filtered cooking oil can be sold to people that utilize biodiesel technology so you can recoup some of the cost of oil.
The value of IoT for restaurants then is as a cost reduction, system maintenance and quality control systems. Just those three things would help a restaurant stay functioning and reduce downtime. Ever been to a fast food place without French Fries? You could be amazed how many people hear no Fries and just walk out of the restaurant. The same is true for Doughnuts (or Donuts) and just about any food item. We go to someone else’s place (a restaurant) because we want a specific food item.
Yesterday I outlined a system for road monitoring. In that system, like the restaurant system above there are tiers of data and connections between groups or organizations and the data tier. If we consider the impact of data, it has two distinct impacts. The first is, who needs to know the information we have, and what of the information we collect do they need.
In the road hierarchy I laid out yesterday we collect the following information:
1. Citations issued by Police or Law enforcement officers
2. Citations issues by red-light, speed and other automated traffic systems
3. Weather data is collected
4. Traffic flow data is collected (although not all roads at all times)
5. DUI’s by geography and frequency
6. Accidents
Let’s take a view then of each of these and begin to break down who needs what data. Accidents are something that insurance companies and enforcement agencies need to have. If there is a stretch of road that consistently has a high rate of accidents, law enforcement officers can be stationed near that stretch of road for faster response. Or they could bring out radar and manually track cars. There is a natural slowing down of vehicles when there is a police run speed trap. The data collected first is where do the accidents occur. We take that information and rework the deployment of law enforcement officers. Insurance companies then can take the data and notify their customers about risky roads. Putting a device into the management port of the car, the insurance company can offer discounts to drivers that do not use risky roads. Pushing the data up one level we can also consider at a regional level the why of bad traffic areas. Too many cars in a specific location or area may require additional roads to better support traffic.
DUI enforcement can also be, like accidents tailored to data collected. If we find a specific area that has a lot of DUI’s – keep enforcing that area. In fact, if there is a stretch of road with a high DUI rate let’s push enforcement to all the side roads and the main road in that area. We can then push data up (effectiveness of reducing DUI) and respond to changes (people are avoiding these three routes, move the checkpoints to other roads nearby).
Effectively we create a data tier that allows us to better interact and respond. The enforcement system for DUI could also leverage IoT devices to increase the protected areas. Place IoT laser sensors on straight path lines in the road. Put an easy pass sensor at the beginning of the section of monitored road and tag each car, how many times do they cross the lane lines? What is the average time (easy pass to easy pass) for cars in that section of road (watch for impaired I.e. slower drivers)? The data collected allows the police to modify checkpoints, on a nightly/hourly basis.
The increased efficiently at the regional level allows for even great flexibility in enforcement.
More to come…
.doc
IoT evolution futurist
Docandersen's Blog 