Is the Internet of Things (IoT) the Answer?

While our last few blogs focused more on problems that the lack of expertise in the skilled trades is causing us; in this blog, we would like to try to find some solutions.  We will start by considering the Internet of things.  IoT is a scenario in which objects, animals, or people are provided with unique identifiers and the ability to transfer data over a network without requiring human to human or human to computer interaction.  IoT has evolved from the convergence of wireless technologies, micro electromechanical systems, and the internet.

A “thing” in the Internet of Things can be a heart monitor implant, a wild animal with a bio chip transponder, an automobile that has built in sensors to alert the driver when tire pressure is low, or any other man made or natural object that can be assigned an IP address and can transfer data over a network.  So far, the Internet of things has been most closely associated with machine to machine (M2M) communication in manufacturing and in power, oil, and gas utilities.  Products that are built with machine to machine (M2M) communication capabilities are often referred to as being smart machines.

The Internet of things revolves around increased machine to machine communication.  It is mobile, it is virtual, and it provides instantaneous connections.  It is going to make everything in our lives from street lights to seaports smart.  In fact, Daniel Burrus, in “Wired”, November 2014, said “There is no one sector where the Internet of things is making the biggest impact – it will disrupt every industry imaginable.”

The real value that the Internet of things creates is at the intersection of gathering data and leveraging data.  All the information gathered by all the sensors in the world isn’t worth very much if there isn’t an infrastructure in place to analyze and utilize it in real time.

Here’s where the problem comes in.  How do we analyze the data that is coming in from various sensors to a central location and make sense of it?  What is it telling us to do?  This is where companies must leverage the knowledge of their aging workforce before it is lost.  If we can build “rules engines” that tell us the “if this –  then that” scenarios, we can fully utilize the data.  If this analysis structure can be built, then when information is captured, then it is not only telling us what the condition is, but how to correct it.

Where is the information to build this “rules engine” database stored?  There are at least three locations.  They are:

  1. In the brains of our skilled trade employees
  2. In our existing work management systems
  3. In our equipment/ asset design documentation

Based on our last blogs, (1) we must move quickly to capture the knowledge of our skilled trades employees before they retire.  (2) Our existing work management systems can be utilized: however most have inaccurate or incomplete data.  (3) Our equipment or asset design documentation is quite often lacking because someone failed to negotiate its inclusion in the transaction when the equipment was purchased.

So, while the solutions to gathering knowledge appear to be simple, the sources are truly complex.  It is only when an organization takes a true asset management focus will they be able to leverage the value that can be provided by the Internet of Things.

By | 2017-07-20T15:26:41+00:00 March 14th, 2017|1 Comment

One Comment

  1. Cameron Wight April 12, 2017 at 6:40 pm - Reply

    This article does a good job of outlining the need to leverage failure description and effects information from the older generation before they retire. I agree, there are too many eager data collectors without a plan on what to do with that data. Maybe a simpler way of stating this is that there needs to be a framework and process along with the capacity available to analyze and specify the collected data. Overall, I believe that the connection of previously unconnected devices will provide positive outcomes for industrial applications first, but there will be growing pains during the implementation and startup phases.

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