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Manufacturing Intelligence and the Skills Gap

November 4, 2015

This blog originally posted on Jim Gavigan's LinkedIn page here.

 

I have been working in industrial automation for 20 years now and I have seen a lot of changes. When I first started, I remember carrying a beeper for emergency calls and getting messages from vendors and customers on slips of paper when I came back to the office. 

I remember the walkie-talkie style cell phones and dedicated car phones. The internet was still in its infancy and AOL was ruling the day. I remember there being engineering departments at the plants that I worked in with engineers from many disciplines. Usually, there were "old hands" teaching the younger engineers about the processes and the machinery. It seemed that the aftermath of Y2k into 9/11 changed everything, although signs of changes were already out there. In the late 90's into the early 2000's, I saw much offshoring of manufacturing. I saw a decline in students studying engineering. I saw a decline in the numbers of people within the engineering ranks. What used to be a full department of 5-15 engineers was now a group of just a few people. I saw many plants close and never re-open.

 

In the mid 2010's, there was a change in manufacturing. Manufacturing jobs were coming back, "re-shoring" was taking place. More students were going into engineering studies and things were looking brighter for American manufacturing. However, there is still a significant issue that we must figure out how to face because of past decisions. We now have a large number or people within the engineering ranks approaching retirement age, and a large number of engineers that are in the field with 5 years of experience or less. It seems like there is less engineering talent in between these two age groups and it is plausible that the late 1990's through the mid 2000's had a significant impact on the number of engineers in manufacturing as a whole. I haven't seen any studies on this topic, but I have had a number of informal conversations with many people and we all notice this pattern to some degree - that there are more retirement age and more entry level engineers than those with 10-20 years' experience.

 

 Regardless of the actual numbers, many companies are facing heavy retirements in the next 5-10 years and they also have a significant number of engineers that have less than 5 years of experience. So, how can we get the knowledge transferred from these soon to be retirees to these young and eager engineers and somehow sustain the knowledgebase we have enjoyed for so long in manufacturing? I think a key ingredient is Manufacturing Intelligence.

 

I remember walking on to a plant floor in my mid-20's and it seemed liked the veteran engineer or operator I was with could tell something wasn't running right just by the sound of the machinery. Nothing sounded out of the ordinary to me, but almost inevitably, we would find a problem somewhere. I think that kind of instinct and tribal knowledge is all but gone. However, it doesn't have to be. We just need to translate what that veteran used to hear into data and information that can notify the proper people that something is going awry with the machinery. Instead of it being a pattern of sound waves that is recognized by the engineer, we have to translate the data into information that mimics those sound waves.

 

What we used to almost think of as an art form (the sound of poor functioning machinery being recognized by a wily veteran of that machinery) is really a science. I will give you a case-in-point. I was at a well-known alcohol manufacturer a few years ago and while touring the facility, I could tell that there was tremendous pride in the product as well as tremendous pride in the legacy of how it has been made for a long

time. They truly treated it as an art form. Tasters at the end of the process would let operations, engineering, and maintenance know when things needed to be changed and where. The company described to me how it took operators a minimum of 2 years to become proficient at the process because of all of the nuances within it. I told them that what they considered an art, is really a science. There is a certain chemistry that makes their product taste a certain way and if they had data that was turned into information, they could shorten their training cycle for operators and I also stated that the tasters' palettes could be backed up by data (I would NEVER see this company getting rid of the tasters nor am I an advocate that they should).

 

We somehow have to digitize what many engineers and others in manufacturing have

seemed to know by feel, instinct, and experience, and turn this into useful information for this next generation of engineers. After all, many of the young engineers coming into the workforce have grown up around computers, tablets, and phones and are used to all kinds of data and information in other facets of their life being at their fingertips. We now have the challenge to do the same with manufacturing data for them. What used to be a pattern of sound waves that was recognized by veteran ears has to be turned into a pattern of data that is recognized by relatively inexperienced eyes (and ears). The way that young engineers will learn the manufacturing processes will be different than the way I learned them and drastically different than how the generation before me learned them. The only way that manufacturing will continue to thrive in this country is if we can make this translation of information from tribal knowledge into repeatable information, and we need to do so now. Otherwise, we are primed for a dip in efficiency simply because most companies are stretched much thinner than they have ever been and they could have much less knowledgeable and experienced engineers that they are relying on to keep them on track.

 

I recently read another perspective on this topic and it can be seen here. It is written by @Gerhard Greef, who is worth a follow.

 

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