LNS Research Principal Analyst, Tom Comstock, answers the 5 Most Asked Questions from his Digital Readiness for Industrial Transformation: Success...
As a longtime speaker and adult education instructor, one of the first lessons I learned about communicating with business people is that to educate them you must engage them, and the easiest way to do that is by entertaining them. Most of us would challenge the idea that prime-time network TV is educational; dominated by with reality competitions, whodunits, and comedy programs. So, you might be skeptical when I claim that Asset Performance Management (APM) professionals should treat a show like MacGyver as “educational TV.” Whether one harkens back to the original series from the late 1980’s or the rebooted version that started just last year (2016), MacGyver has become something of a legend in the TV world.
MacGyver, a genius working for a secret government agency, was/is known for his ability to improvise creative solutions from everyday items to extract himself and his colleagues from dangerous situations. In fact, the Oxford dictionary has added the term “MacGyver” as a verb used to describe the act of improving in an inventive way. As an APM professional what does MacGyvering look like and how can you MacGyver better APM performance?
The Essence of MacGyvering a Solution
Angus MacGyver is supposedly a high IQ individual who works for a secret think tank which may or may not be a government organization. It seems he constantly get into difficult situations each week as he carries out the work of the Phoenix Foundation, this shadowy organization. MacGyver’s approach to problem-solving, of which there were always many, almost always involves either the combining of several household items to create a tool or non-lethal weapon or by using something to amplify the effect of a tool or weapon to make it far stronger than it normally would be. A few simple examples:
- Using a fire hose that is tied off to block flow and pressurized to shift a steel beam enough to move it out of the way using the hydraulic pressure in a fire line
- Using a chocolate bar to plug a sulfuric acid leak
- Mixing acetic acid and ammonia to create a fog to obscure an infra-red detector
All of these improvisations were tested and validated by others after the show although many of MacGyver’s hacks were proven to be infeasible later. The common thread in all the improvisations, however, was the combination of elements or the repurposing of a tool to accomplish the task at hand.
Big Data Analytics is APM’s MacGyver Toolset
The challenge that 80% of LNS Research survey respondents cite as a top three objective for pursuing APM is “improving operational performance.”
Companies' number one use of information leveraging the Industrial Internet of Things (IIoT) for is remote monitoring with reliability the top application for that data according to our IIoT surveys. Most manufacturers, particularly in asset intensive industries like oil and gas, mining, or utilities have a tremendous amount of data regarding what is happening in the plants already. With the IIoT, almost every device will be able to send large amounts of data about that asset and its performance to any other node that needs it. When you add in the other data that is typical in the APM space such as vibrational spectrums, thermal imagery, and other non-structured data, you end up with Big Data. The challenge with Big Data is that it requires analytics to get something useful from so much data, and existing tools may not be up to the task. Enter Machine Learning and artificial intelligence (AI); Machine Learning and AI are shaking up the APM space in multiple ways.
Start-ups are entering the market with specialized functionality and displacing older and expensive dedicated analytics solutions that were affordable only by the largest companies and those with very expensive assets that posed high economic risks if they failed. This next generation of APM focused and generalized analytics tools are changing the rules when it comes to APM and predictive analytics.
If APM Professionals Don’t Learn to MacGyver Others Will
Newer analytic technology and Machine Learning are democratizing predictive analytics. Deep domain expertise or data science skills are no longer required to solve many APM reliability problems. Like MacGyver, just about anybody can use these readily available tools, be they part of the maintenance organization, the IT organization, or just process engineers. A little bit of this and some of that and the results can be as impressive as many of the hacks MacGyver pulled off. For APM professionals to avoid becoming obsoleted by others in their organization that take the MacGyver approach, they need to do a little MacGyvering on their own.