The Next Big Thing
Posts about next generation technologies and their effect on business.

Why is the IoT viewed with such potential and confusion?

Internet of things.pngThere is a fundamental shift underway from dumb devices where organizations guess about how their being used, when and by whom. Now a physical product (from almost any industry) has the potential to be a first-class participant in its own value chain. It can talk back to its creators in engineering and manufacturing as well as those who service it, cutting downtime and improving its use. It has the potential to talk with (where everyone seems to be focused) those that actually use it, making their life better and more productive. There is also the potential to collaborate with sales and marketing to share what users are thinking based on where, when and how it’s used. Devices/products are becoming members of an environmental view of the context that surrounds them. Although it involves information technology, it is about a shift in business value.

 

This challenges the foundations of many of our existing products and services. Devices can have an active role in CRM and marketing. We can shift the analytics view from the past to the future. We can use the information to gamify processes and shift behaviors. As this understanding increases, what is measured and the decisions made will shift.

 

As I mentioned last month, the impact on our definition of services will shift as we understand and embrace the potential. This change will shift much of what exists (people, products and services) in our environment/industry.

Automation prioritization

 

Since I have been putting out some posts on automation, I’ve been getting some feedback from coworkers. One was a post titled: 8 Questions to Ask before You Automate. It holds some useful perspectives to evalutate if automation is even applicable to a situation.

 

Back in the early 90s, I led a project called Knowledge-based Tool Design focused on improving the productivity of tooling designers for automotive manufacturing. We used the capabilities of CAD tools to try and automate as much as we could, related to the creation of the machines that facilitate car assembly. This was a high value effort, since late stage engineering changes in the car parts themselves have implications on the tooling and can delay the startup of manufacturing. Anything we could do to address the reengineering of tooling had a direct effect on time-to-market.

 

We would load the car component models that need to be clamped and welded into the CAD system and try to create automated techniques to define and design the tooling needed. After firing rays all over the place to determine entry routes for robotic arms holding the clamping and welding tools, it became clear that people can look at parts and tooling and determine routes for entry very easily compared to doing this programmatically. Creating these designs well definitely involved creativity and intuition.

 

What people could not do reliably was define the underlying Bill-of-Materials need to create that robotic assembly, physically. So yes, I learned back then that it is very useful to understand what people and/or computers are good at, when defining the right approach to address repeatable, higher-value, computationally capable tasks with automation first.

 

Whether it is designing tools, answering calls or writing software – even though the automation capabilities are radically improving, this assessment is still required. I usually think of it as a 3 dimensional matrix and the further away from the origin, the more likely the automation effort will be effective.

 

 automation axis.png

Just because we can do something, doesn’t mean we should do something, especially when there is a constraint on the effort available to tackle a set of tasks. We need to prioritize.

 

 

The multi-dimensional value of IoT

dimensions.pngThe value and inevitable nature of the Internet of Things can be hard to quantify.

 

It has value in the vertical dimension based on what it can do for a particular industry. For example being able to understand the materials on hand, the machine capability and performance and the product location all can fit together to provide much greater insight. This is one of the reasons the manufacturing industry was an early adopter of IoT techniques.

 

From a breadth perspective, we’re seeing more devices with connectivity as well as more wearables and other ways to communicate. I can easily see a day where my oven reminds me of a meals status much more effectively than the kitchen timer. Or even the act of entering the garage can get dinner started because that’s what would be next on my agenda. Essentially it leads to a much broader range of devices working in collaboration to meet my needs.

 

In a depth sense, various devices that are doing their own thing, for their own reasons can provide a much greater contextual depth of understanding that any single view could provide. This is where the contextual understanding that is derived from multiple pieces of information comes into play.

 

I am sure there are more dimensions beyond these three… What are they for you?

 

New HP automotive industry e-zine

Lately I’ve been blogging quite a bit about the Internet of Things. Few industries are so permeated with IoT activities (both in production and in their products) than automotive. Periodically the HP Enterprise Services team focused on automotive create an e-zine and the new one just came out. At least I think it should be out there by now. If not, it will be soon. Here is a brief video about the effort:

 

 

You can see the latest edition of the e-zine here. The previous edition of the e-zine is located here. If you are really interested, you can sign up for a subscription service so the new information is pushed to you directly.

 

You can also download the latest digital edition!

 

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Where did the IoT come from?

I was talking with some folks about the Internet of Things the other day and they showed me some analysis that made it look like it was relatively recent.

 

where did the IoT come from.jpg

 

My view is that its foundations go back a long way. I worked on (SCADA) Supervisory Control and Data Acquisition systems back in the 80s, which were gathering data off the factory floor, analyzing it and performing predictive analytics, even way back then.


In the 70s, passive RFID came into being and one of the first places it was used was tracking cows for the department of agriculture to ensure they were given the right dosage of medicine and hormones – since cows could talk for themselves.

 

In the late 70s and early 80s barcodes become widely used to identify objects, allowing greater tracking of manufacturing lines as well as consumers in stores.

 

In the 90s, higher speed and greater range allowed for toll tags to be placed on cars, allowing for greater ease of identification but still very little use of sensors to collect additional information.

 

At the turn of the century, the military and Walmart required the use of RFID to track products and that caused significant increase in their adoption. About the same time, low powered sensing capabilities were developed since RFID only provided identification and the scanner provided location, people began to look at other information that could be collected like temperature, humidity as well as ways to gather information remotely like smart metering in the utilities space (although even that started much earlier).

 

Most technology adoption follows an S curve for investment and value generation. We’re just now entering the steep part of the S curve where the real business models and excitement is generated. It is not really all that new it is just that the capabilities have caught up with demand and that is making us think about everything differently (and proactively).

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About the Author(s)
  • Steve Simske is an HP Fellow and Director in the Printing and Content Delivery Lab in Hewlett-Packard Labs, and is the Director and Chief Technologist for the HP Labs Security Printing and Imaging program.
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