Industrial Automation – It Doesn’t Have To…

Industrial Automation - It Doesn't Have To... Be Myopic

November 10, 2020 elliTek, Inc. Season 1 Episode 5
Industrial Automation – It Doesn’t Have To…
Industrial Automation - It Doesn't Have To... Be Myopic
Show Notes Transcript Chapter Markers

Myopic is being short-sighted -- unable to clearly see things that are far away. 

elliTek's mission is one of empowerment. We want manufacturers find a way to better position themselves moving forward. Our hope is that this podcast provides helpful perspectives.

This episode is about the tendencies we can have as human beings to focus on one piece of the puzzle but miss the picture as a whole. 

Brandon shares a customer experience where the customer's focus was on the "squeaky wheel".  You'll be surprised to hear what happened.

You'll also hear the important Key Performance Indicators (KPIs) used  in manufacturing and why they're important. Key Performance Indicators are anything that has to do with the performance of your process and gives an indication of that.

Brandon gives helpful definitions and examples for some industry terms listed below. 

  • Process Data - A machine, cycle, process, or manufacturing process. Processes have faults and those are called downtime events.
  • Production Downtime
  • Production Volume 
  • QA - Quality Assurance. Where quality and production come together.
  • Production Costs - The cost of raw materials and labor, plus electricity, cleanroom environment (if necessary), and the equipment being used.
  • OEE - Overall Equipment Effectiveness. Process based indicators that assess the scheduled efficiency. OEE basically decides how many good parts can be made across a time.
  • OOE - Overall Operations Effectiveness. Operational efficiency KPI that decides how many good parts could have been made.
  • TEEP - Total Effective Equipment Performance. Uses OEE and OOE to track the overall effectiveness of whatever it is being measured. TEEP measures the performance across all time, 24/7. It's used to make decisions such as adding another production line, identifying if a line is being underutilized. 
  • OA - Overall Availability. This indicator is part of OEE, Overall Equipment Effectiveness.
  • Product Data - Any data that has to do with the product being made. Traceability ties into product data. Used to improve your process
  • Traceability - It's like a VIN on a car. It's used to retrace - see every process the product went through.
  • Six Sigma - A quality program. A perfectly quality built and fully assembled ready to ship part. 
  • Defect Flow Out Prevention - Eliminating the opportunity for the defect to have ever occurred.
    • Zero Defects - Part of Defect Flow Out Prevention. Zero Defects is removing the possibility of building a perfectly bad part. 
    • Process Skip Check - Ties in with traceability.  The next machine process is alerted as to whether or not  the part passed, failed, or didn't even go through the previous process. If the part failed or didn't go through the previous process, the next process is not given permission to stat.
      Process Skip Check is a method of Defect Flow Out Prevention. This leads to a cost savings, because a perfectly built bad part still uses resources and materials and ends up in the scrap bin. If the bad part is caught midstream, it can minimize the amount scrapped out.

elliTek's Workflow Manager  which is part of the IIoTA™ Platform allows users to easily build their processes, connect them for certain part numbers, and do one to many - many to one relationships. The Workflow Manager gives users the ability to do Traceability.

Dashboard tools to create drag & drop web-based dashboards to view the KPIs listed above for a specific assembly line or lines are included in the IIoTA­™ platform.

Lastly, there's no need to fear databases! Listen to the full episode to find out

Brandon Ellis:

Hello, guys, this is Brandon Ellis, the owner of elliTek and the host of "Industrial Automation - It Doesn't Have To". I'm here with Beth Elliott, our marketing coordinator.

Beth Elliott:

Hi guys, how you doing today?

Brandon Ellis:

Hey, listen, what's the... I'm gonna let you say today's topic because I don't know how to pronounce it right. It's a smart people word.

Beth Elliott:

I had to look it up. No. It's called "Industrial Automation - It Doesn't Have To... Be Myopic. And that means short sighted. It's more about about, you know, focusing on one thing, but before we get into that, what have you been up to lately, Brandon?

Brandon Ellis:

Well, not my, I don't know if I've been myopic or not. Um

Beth Elliott:

Well, hope not.

Brandon Ellis:

Well, ya know, we just came through an election. That's been interesting. I've tried to keep that turned O-F-F. And just separate from that too much stress. I'm ready for positive, this 2020 has been a challenge for positive and we need more positive. So I've been trying to just be positive.

Beth Elliott:

That's a great, yes, we all need to stay positive.

Brandon Ellis:

Well, yeah,

Beth Elliott:

I mean, the sun always right, the sun will always rise. Ya know. So we just got to.

Brandon Ellis:

It doesn't matter how many good or bad decisions we make. The sun will always come up the next day. We can't we can't stop that. So, what about you?

Beth Elliott:

Well, I just wanted to talk about what we're doing with these podcasts. And we're just trying to go over the things for manufacturers to help benefit and empower them. And that's my focus has been on this.

Brandon Ellis:

Beth's whole world has been the podcast and mine too. Yes, yes. Well, that is our mission statement as a company. So elliTek mission statement is to empower our clients to do that through a few ways. And we hope that this podcast of course is part of that, which is, is to provide more insights to provide hopefully helpful perspectives. Some that come from experience some that come from book smarts, I guess. But not every solution is the best solution. But sometimes it's the best for the situation. And I think that's a little bit different of our perspective is, everybody wants the perfect solution for the perfect amount of money to last, you know, the perfect amount of time, and which is usually forever, with no support. And so you can't get to all those things. But sometimes the best solution may not be the one that's marketed the best it may be the one that actually fits your needs. And so we want to be talking about those types of solutions. And like you said today is the meaning of myopic, is to be able to see things clearly. Especially things that are farther away. And so sometimes, manufacturers today, especially in this 2020 environment, boy I've talked to some companies, as we get the opportunity to go out and talk to them that some are having unfortunately, furloughing people, they're laying people off, their business is way off, and then some are just, they can't find enough people to work. They're, the employees are just worn out from all the overtime because of all the orders that - so it really, 2020 has just been a really crazy, crazy year for manufacturers and and I think is is going to give way too, and even crazier 2021. And so, you know, notwithstanding election years, and things of that nature and pandemics and all the other stuff that you can't, well, we can plan for an election year, but we can't plan for a pandemic. It's been really interesting to see what's going on. So I think being able to clearly see things that are far away is a good theme as far as empowering folks, because there's a lot of people that are worried either because they're going to miss the truck from all the orders or they're worried because there are no trucks and they have to find a way to position themselves in a better position.

Beth Elliott:

Okay, so today's topic is about perceptions and how these beliefs or opinions are based on how things seem. The perceptions that some folks have won't allow them to see the forest for the trees. Brandon, you describe Can you describe to the for the listeners a time when a customer was focused on one piece of the manufacturing puzzle, like sensor and what implications if any that had on the project?

Brandon Ellis:

Well, you're right, I mean the forest for the trees, forest for the trees. We know That's an old old adage, that was the one that comes to mind was a situation where the, I'll give a perfect example, this leads into what we're going to be talking about just a minute on the next next subject, which has to do with process data, which means a machine or a cycle or a process, a manufacturing process. And processes have faults, we would call them or downtime events.

Beth Elliott:

Okay

Brandon Ellis:

So they were checking, counting the quantity of downtime events at certain machines. And based upon the quantity of downtime events, how many occurred across multiple machines, and they weren't checking it wasn't just one machine, they're checking multiple processes or machines, machine processes throughout the manufacturing cycle or process. They were checking them all. And if they had one specific, they were counting the quantity of these defects. And so they're budgeting their cap, capex schedules for upgrades, maintenance upgrades, and in you know, countermeasures and things of that nature was based upon the quantity of defects and had been for a number of years. And then suddenly, in this case, they were blessed with what's called a Data Commander manufactured by elliTek. And they were able to get to other to do other things. And in this case, they were able to actually, when those defects occurred, before they had a sensor that would just say it was running or not running, it was up or down.

Beth Elliott:

On or off. That kind of thing.

Brandon Ellis:

Very discreet.

Beth Elliott:

Okay,

Brandon Ellis:

With the Data Commander, they were still seeing the discrete up or down, but now we could mark time, and they began to overlay the number of defects on top of the total amount of attributed downtime. So if it was this defect, how much downtime did we realize versus if it was another defect. I'm not sorry, not defect machine fault. That's important. I did I misspoke, not defect, because defects have to do with products. And faults have to do with processes or machines. And that's a very important thing when it comes to data, and interpreting the data. So but in this case, they had been setting all of their budgets based upon the squeaky wheel.

Beth Elliott:

The squeaky wheel gets the grease, huh?

Brandon Ellis:

That's right, the more downtime events that you had just on - off discreet count, the more we would focus at that. And what they found was their largest downtime event, if they made improvements, was only responsible for about 1% of their total downtime.

Beth Elliott:

Oh, my goodness.

Brandon Ellis:

And the fourth, so the largest number of fault, machine faults, and then the second and then the third, and then the fourth, which didn't have many, but attributed to nearly 90% of their downtime occurrence. So by putting, they shifted all their budgets then to that number four defect that they had never paid attention to, because the quality, you could say the quality of the improvement had a larger impact, much, much larger impact because it now was a 10% improvement across 90% of your downtime versus a 10% improvement across 1% of your downtime.

Beth Elliott:

That's amazing.

Brandon Ellis:

Yeah, and it was a it was a fantastic way. And so it just leads us into our next subject, which is KPIs, Key Performance Indicators. Because one of these, the number of defects is a single, I'll say indicator,

Beth Elliott:

okay, okay.

Brandon Ellis:

And so, Key Performance Indicators are anything that has to do with the performance of your process, and gives an indication of that. So we call them Key, because there s a lot of indicators that don t really mean much. You know, y u can look at the refrigerator a d say, well, the refrigerato s running, that doesn't me n there's food inside, it doesn t necessarily mean the food insi e is good. It just means t e refrigerator is running. S it's probably cold. So but it ay be perfectly cold, bad fo d. So some performance indi ators aren't as impactful as o hers. And so we refer to those s key performance indicators. An that said, In those cases, we h d two indicators. We had the qu ntity and then the amount of dow time, which would be the quality I would call that the quality r the impact. How much impact di each of those those machine do ntime faults -- I may have sai defect again, just now if I di Forgive me, I mean to say mach ne faults when it comes to t is example. So

Beth Elliott:

So what are the important KPIs in manufacturing that help corporate meet their objectives?

Brandon Ellis:

Well, now we're stepping into a, I think more into the quality department, the QA department - quality assurance. And so this is where quality and production kind of comes together. So production, let's start there. At the end of the day, we want to produce good parts or pieces or widgets, or whatever. And so things like production volume, just how much are you producing?

Beth Elliott:

That's, that's fairly straightforward.

Brandon Ellis:

Yeah. So today we did 100 widgets tomorrow, we hope to do 110. You know, that's just how much volume. Downtime we just talked about is just how much production downtime. How much were we not producing, how much time of the time we're counting. And we'll draw back to that in a minute. But of the time that we're keeping that we're saying is production time, how much of that was spent producing, we call that up time versus not producing, which is downtime. And then what's the real cost of production. So production, usually, on the most basic level, we think of production costs being this is the cost of my raw materials, this is the cost of my labor. And put those two together, add them together, that's your cost of production. But anybody that's a production engineer knows there is a lot more that can be figured into the cost of producing something. Just having lights on over your head, have an air conditioned space, a cleanroom environment, those kind of things, and then the equipment that you're using comes into play as well. And so now we begin to slide out of production specific indicators and move into more, I guess you would call them I think of them as quality, but it's probably more production. But it's process based things. So things like OEE, overall OEE that's what I said, because there's an OOE. OEE, Overall Equipment Effectiveness is usually our efficiency is what we refer to that as. OEE that basically assesses the, the scheduled efficiency, and that's important scheduled efficiency of our equipment. So your equipment can be - I can have equipment that can make tons of parts, but if there's a bottleneck upstream or downstream, and that equipment setting idle, it creates an idle condition, it's not really downtime. It's not broken. Downtime events happen for machine faults.

Beth Elliott:

Oh, okay

Brandon Ellis:

It's not broken. It's just being underutilized. So how effective across the scheduled efficiency of the equipment. So if we're expecting that machine to only run 50%, and it runs 100% of the time during that 50% of the shift, then it's perfect. But if you're expecting that equipment to run 100% of the time, during the shift, and it only runs 50%, then now you're got half of your OEE. And so everything's a bit of a an equation.

Beth Elliott:

What are the other KPIs that manufacturers should look at?

Brandon Ellis:

Well, there's, there's a few others. OEE is probably the most marketing friendly term of any. So unless you've been living under a rock during this IoT movement, OEE is what everybody's after or so they tell you, you know, the people, they're trying to sell stuff.

Beth Elliott:

Okay. Okay.

Brandon Ellis:

But any, again, going back to anybody in production, there's there's other things you need to look at to get to the bigger picture. And so one of the other ones that's probably lesser known is Overall Operations Effectiveness. OOE and that's where we start looking at operational efficiency. So basically, your OEE decides how, how many parts, good parts you can make across a time. The OOE really looks at the efficiency. So how much, how many good parts were made versus how many could have been made.

Beth Elliott:

Oh, okay.

Brandon Ellis:

So again, that's where we're getting into some of the use efficient use of the equipment, which really now expands into really the entire process. So once again, if you have one, you have an operation that's either really, really fast or really, really slow compared to the upstream or downstream processes, then you get to a point where you have what's called bottlenecks in that it's either being starved, or it's starving something downstream. So now we, the way we kind of look at the whole thing all together, they refer to as TEEP.

Beth Elliott:

TEEP, what is that? You gotta explain that for our listeners.

Brandon Ellis:

So that's an acronym. T-E-E-P - TEEP, Total Effective Equipment Performance, and that tracks the overall effectiveness of whatever it is you're, you're measuring and, and so a lot of this is subjective. I said earlier, we talk about solutions, not the best solution, but solutions, because a solution may be fantastic for one manufacturer and mediocre for someone else. So really, you have to come down everything subjective. We're not all making the same part.

Beth Elliott:

Okay, so you got to take into account all the different parts and equipment

Brandon Ellis:

Processes and the whole nine yards.

Beth Elliott:

Okay. Wow.

Brandon Ellis:

Because because what if someone's making I don't know, tractors, there's more involved. So the pieces and parts are probably a lot heavier than someone making printed circuit boards. Or you know that we've got a great company around here locally that that makes medical kits. And so if you ever seen in the movies, that's where I would have seen it, or if you've ever had surgery, hopefully you haven't had to deal with that too much, but the nurse basically brings out a tray and lays it out. It's, it's pre covered, it's sterile, and they open it up. It's a single use, and it has all the surgical equipment there laid out really nicely on the tray. Well, that's done by a company. But that for the most part is done manually, still, believe it or not. Because there are so many variations, and so many differences in the instruments and stuff like that. It's very expensive to try to automate that. So it really comes down to what you're doing. But you can measure these things, because remember, we're measuring sometimes we're measuring equipment, but sometimes we're measuring just the operation, which is people,

Beth Elliott:

Okay, okay, okay.

Brandon Ellis:

So now all of a sudden, TEEP, basically TEEP takes into which again, is Total Effective Equipment Performance. And this is an equipment based KPI, that's we're going to be focused on that. But people come into play, people come into play, because there's things called walk time.

Beth Elliott:

Walk time?

Brandon Ellis:

Yeah. So if I'm, if I have a machine where I put the stuff in all the load in all the pieces and parts and hit the start button, and this was all this stuff, you walk, I walk away, and it's working on its thing. And it gets done. It's like, okay, here's the finished part that I've just, you know, the robots finish it or whatever. If you're not there to take it out and put another one in. So you sometimes you have to pull that out, walk it over, put it into a box or the next process or something, then come back, grab the next pieces, parts, load it back up. So that's what we call walk time. The machine's not running during that time, but it's not broken.

Beth Elliott:

So what would that be then?

Brandon Ellis:

Well, that that comes into the Total Effective Equipment Performance.

Beth Elliott:

Okay. Okay.

Brandon Ellis:

Basically, TEEP measures, TEEP measures your your performance across all time. So 365 if you were able to produce 365 days a year, 24 hours a day is the way that fundamentally TEEP works. And then you have to subtract or considering that all the, you know, the unscheduled time the not the not planned or the not scheduled time. So, time, that's just not scheduled. So we're closed on Thanksgiving Day. If we were manufacturer, then that means Thanksgiving Day, we are not scheduled to work. So But TEEP doesn't, you subtract that from your TEEP because TEEP decides again, what's the what's the best case - what's the what's the biggest potential, the largest potential that you could get to with the equipment that you have?

Beth Elliott:

Oh, okay. Okay. And then from there, you can decide whether or not what needs to be fixed? Or if you need better equipment, or if you could utilize, or something's being underutilized, then you know, that that needs to be focused on that. Is that what

Brandon Ellis:

Yeah, exactly, exactly. But in my opinion, it all boils down to TEEP. So all these other things, and the one thing we haven't talked about is availability.

Beth Elliott:

Oh, the overall availability

Brandon Ellis:

And availability comes into play with Overall Equipment Effectiveness, it comes into play with with TEEP, it comes into play with all these things. And availability, it's how availability is measured. That's, again, this is subjective thing, it's you. At the end of the day, you have to know your production process, you have to know what you're doing. Because the TEEP then ultimately, that we look at the TEEP and we can make decisions like Do we need to add another line another production line? Or is our line just not being utilized efficiently? Of course, it's a lot better to increase the efficiency on equipment that you already have. It's already in a depreciation schedule, or whatever. And you're already got an ROI underway return on investment. Once you have equipment, once you spend that money, you're looking for a return on your investment. Well, going out and buying another one has its own return on investment. So if you don't have to add equipment, you're better off to not but the TEEP is, is where we all boil it down because it encompasses the effectiveness of the equipment that you've got. It encompasses the overall operations, effectiveness, all those things, and then the availability and also the quality. So what's your actual output? And what's your good part percentage of that? Those kind of things all come into play. A machine can produce parts very efficiently, but if you don't take the quality aspect in it may be efficiently producing bad parts. Which is totally counterproductive. So KPIs, Key Performance Indicators, require us to to look at multiple ones, we have to begin to put them all together. I fly airplanes.

Beth Elliott:

Yes. You mentioned

Brandon Ellis:

Not professionally.

Beth Elliott:

You mentioned that in the last podcast. When I asked you about the pitch, yaw and roll.

Brandon Ellis:

It's one of my expensive hobbies that I don't get to do very often, which saves me money, I guess. But in an airplane, of course, today nowadays we have glass panels and that kind of thing, it makes it really nice. But before and many, many people still fly what we call a six pack. And so that means we have our, our, our directional gyro, our artificial horizon, our airspeed indicators, our vertical speed indicator, all these different things. There are six instruments.

Beth Elliott:

Are those KPIs?

Brandon Ellis:

Well, they kind of can be, yeah, if one of those is off, it can change your day, if you if you're not seeing it, you know, measuring it correctly. But we have to look at each one of those, and derive meaning from each one, but not but one on its own, will not give the entire picture - of what's going on with the airplane. Now, again, this is kind of getting into where if you're flying just with what we call it instruments, instead of looking out the window. I mean, certainly you can look out the window and tell if the planes upside down, or if it's going up or down those kind of things. But when we're flying by instruments, those six instruments are what we have to rely upon. And so in manufacturing, it's a lot the same. If you have no instruments or your instruments are not reading correctly, you have to be able to determine that and act on it or know to ignore that instrument and rely on the others, you're not getting the complete picture if you don't have all the instruments, but you can get a pretty good picture from some of the instruments. But there are key instruments, there are instruments that are more important have a larger impact than others. But you should be able to derive anything, even if you lost one or even two of the instruments. If you lose all the instruments, you have no idea what's going on with the airplane. If you do not have or are not utilizing any KPIs in your manufacturing process, you have no clue what's going on. You may have a hunch, or a feeling, but you probably get surprised often. Or you have questions like What happened? Why did that occur? What's that stuff over there? Why don't we have more of this and none of this. That's where KPIs come into play. I said, when we were talking about our IoT stuff, we talked about IoT, cyber security in previous podcasts, we talked about IoT in general. If you're not planning on making business decisions, educated business decisions based upon the data coming from your production processes and manufacturing processes, then you don't need to invest in IoT. That's what it is for, to be able to derive these things. And you can derive this manually, you don't have to have an IIoTA or a Data Commander or anybody else's product out there doing automatically. It's more accurate, I think it's easier.

Beth Elliott:

You get a bigger picture, don't you?

Brandon Ellis:

You get a bigger picture, because you're pulling more data. There's less mistakes, so you can trust your instruments more, those kind of things. But you, you can still go out with a stopwatch and measure your up times and your downtimes, and take those indications. The point is - Do It. You need to do it because honestly, it does me very good from a moral standpoint, and also hopefully from a business standpoint, if you succeed as a manufacturer. And especially here in this United States. And so so these Key Performance Indicators are really about that. But they're again, they're more about process data.

Beth Elliott:

Okay, so you mentioned process data and product data earlier, can you tell what the difference is between the two or go into a little bit more detail on that?

Brandon Ellis:

Sure, processs data, again, and I alluded to this earlier, has to do with the process or the cycle, usually machines or equipment.

Beth Elliott:

Okay. Okay. The processes. okay, okay.

Brandon Ellis:

Yeah. So when an operator is standing in front of - oops, standing in front of a machine, sorry, I hit my microphone, hope that wasn't loud out there. If they're standing in front of a machine, that machine that's doing whatever it does, that is the process, okay? It may be part of a larger manufacturing line. (I did it again, sorry, I'm going to sit on my hands. I'm talking with my hands if you can't tell.) It may be part of a larger manufacturing line. And each one of those is a process. So in some manufacturing worlds, it's what I call semi automated, which means there's a person controlling one process, but then that person passes it down to the next person, and they have their sets of processes. And that's the way it is in automotive. We've seen the cars coming down the line and they started out as just a chassis and then by the time they go out the end, they're finished car. And so each point, whether it's robots or people or whatever, there are processes going on, as they're adding and building and assembling this, this and that's, of course, usually final assembly is what we call that. So those are all processes, machines, equipment, that kind of thing. Product would be the car.

Beth Elliott:

Oh yeah.

Brandon Ellis:

And so any data that has to do with the car, so every car every automobile in the United States has a Vin, a Vehicle Identification Number. And if you go back and look at that VIN number, if it's a Ford, Toyota, Honda, whatever, if you go back to the factory with that Vin, they probably, they should be able to see every process that it went through, what was applied to it. And we also call that traceability so retracing, we're keeping up with the product as it comes through. So the data that has to do with that product, so as it goes through each process, what it went through maybe quality checks, maybe what parts it got, what settings were flashed in those kind of things. Those are all that's data that's specific to that product. And it could be different for the next product and different for the next product. So that's product data. And also, you could have data associated with that product that says during during its time at this process, there was a process fault or machine fault. And it did not result in a part defect, a product defect, which would be a bad part that resulted from the machine fault. But it's nice to know that it happened for a lot of manufacturers if they get a return or something.

Beth Elliott:

Okay.

Brandon Ellis:

And so again, the way they use, it's just information, it's knowledge. So if you find out every time this fault happens, we don't think it hurts the part. But we're getting these returns, and we realize that everyone that's coming back had that happen. So now all of a sudden, we realize it's causing a defect down the line or something like that. That it's just it's just improving your process. And that's where the quality aspect of continuous improvement comes about.

Beth Elliott:

Okay. Okay. So what is defect flow out prevention? And how is it different than Six Sigma?

Brandon Ellis:

I think, now I am not a Six Sigma, black belt guy. There are many out there. And I want to encourage you guys to chime in on the comments on what I'm saying here, I'm doing my best. Six Sigma is is a quality program. It's a quality standard. And so Six Sigma, I think to actually achieve Six Sigma is to achieve perfection, which means no quality,

Beth Elliott:

No defects, nothing

Brandon Ellis:

Well, well, no, yeah, you've attained perfec ion. I don't know that you ca get it. I think it's it's u attainable. It's like perfec ion, which means your proces , your product, whatever it is, 100%, no defects, nothin . When it's built. When it's d ne. When it's finished.

Beth Elliott:

Okay, so there's... manufacturers are striving for Six Sigma.

Brandon Ellis:

Right. So, so usually, from my experience, it's so many so many defects per some quantity. The illustration that I've used in the past in the past is, is your drinking water. And so all drinking water has some level of contaminants.

Beth Elliott:

Don't want to think about it.

Brandon Ellis:

Yeah. But through I guess, the FDA through whoever

Beth Elliott:

Trial and error.

Brandon Ellis:

Hopefully, we've got it right, that there's an acceptable level of lead and whatever, bad stuff in the water that we drink every day out of the tap. Apparently, people don't buy into that too much, because they still buy water filters and all that kind of thing. But nevertheless, there's an acceptable level. And so it's usually parts per billion, I guess, or something that so many parts per billion. So that, Six Sigma is similar to that in manufacturing and that if we're going to have bad parts or defects, we can only allow a maximum of this many to occur. And then just your quality processes wants to catch them and keep them from from going out. But that is not that is not defect flow out prevention. It is but it's not so defect flow out prevention from my from my experience really has to do with a movement or a philosophy called zero defects, I guess no defects is zero defects, which is six sigma, right? And so but the the concept of from my experience, the concept of that is to prevent the assembly. Remember I said earlier, Six Sigma was making a perfectly quality built and fully assembled ready, ready to ship out on the truck part. Zero defects basically says we want to remove the possibility of building a perfectly bad part, a defective part. So we want to stop a defect from ever happening versus the more traditional quality stance of we want to catch the defect after it's occurred. And so that's a little bit different perspective, if we're trying to change the processes so that the process is not allowed to occur if a defect is imminent.

Beth Elliott:

okay,

Brandon Ellis:

Versus a process is going to check the part to see if a defect occurred at the previous process. And if it did, we need to arrest that part, but now we've allowed the defect to occur when we're checking for it. And the defect Flow Out Prevention says we want to eliminate the opportunity for the defect to have ever occurred again.

Beth Elliott:

Okay, now traceability ties into that defect flow out prevention?

Brandon Ellis:

Exactly, and that's I mean, that's largely with our IIoTA. You know, our IIoTA is the derivation of our Data Commander. So the Data Commander was designed in 2014, develoed in 2014, and its only job was just to connect the plant floor PLCs, and manufacturing robots and all that kind of stuff to the upstairs database systems, or the cloud base systems nowadays, not so prevalent, but still, the capabilities can be there, or vice versa, just moving data, maybe doing some mashups, but just moving data. The IIoTA is what the Data Commander grew into the IIoTA platform, MES platform, and the primary difference is the IIoTA, first of all, is more edge based, the Data Commander's edge based as well, but the IIoTA is pure edge based, because on the IIoTA side, you can have your database server inside, it's in there.

Beth Elliott:

Oh, you don't have to go to the cloud, or the enterprise?

Brandon Ellis:

Or the upstairs systems, you want to, but you don't want to have to rely upon this. And so we call that edge base, meaning that it's on the edge where the production process is. So if the upstairs system goes offline, the production process can continue on and we're still working off of that edge based system. And so the reason that's important is, when we're doing traceability, keeping the data is one thing, but telling the next machine process that it passed, or or maybe didn't even go through the previous process is what we call process skip check.

Beth Elliott:

Oh, okay.

Brandon Ellis:

But it's not just skipping the process. It's did it go through the process and fail. So it's both of those things I just said it's, saying if it goes to the next machine process, it checks back to see if it went through the previous process. But then also did it pass, if there was a quality check there, did it go through correctly, and if it didn't, it does not give permission to that next process to start. So it's a method of defect flow out prevention, if it's caught within the process. Now, there's also a cost savings there. Because a perfectly built bad part, still took as much time, resources and materials to build. And if you get it to the very end of the line, assembly line, and then they look at it and say this is bad, throw it in the scrap bin, scrap it out, you scrapped out all those parts. Whereas if you're mid process, this is bad, you have the opportunity to rework it probably better to fix whatever didn't happen on the previous process or rerun it on that process. Or if you have to scrap it, you've only scrapped a percentage of the time, the labor times what I mean. So the labor and the parts, you've only - you've minimized, hopefully, the amount that you scrape out. So we refer to that as defect flow out prevention, trying to stop the defect before it happens - before it starts.

Beth Elliott:

So are there dashboards and stuff that people at the plants where they can see what's going on in their processes?

Brandon Ellis:

Right. And that's in the IIoTA as well. So what I was previously talking about is our IIoTA Workflow Manager, where you can basically design your processes and connect them for a certain part number, or you gotta you got to have a means of tracing the part. So you've got to have some type of, I don't know, a QR code or barcode or something that RFID are something that allows us to know, to identify specifically that part to follow it through your process or a lot of parts or you know, a lot not meaning many, but you know, a pallet of parts or something like that. So you have to have the ability to do that traceability, but that's our Workflow Manager. And it's part of our IIoTA process. And o it's very easy for anybody o put together to build the r processes, connect them f r certain part numbers, and do o e to many, many to o e relationships and those kind f things. But then there's t e dashboard tools, which a e included with all of our IIoTA s. It's a drag and drop dashbo rd tool, again, very easy to u e. That's the theme, of course of our IIoTA platform is asy, empowering our customers to easy programming and setup. But that allows you to create a web ased dashboard or multiple dashb ards so that you can see the KPIs that we're talking about here for a specific line or line and things of that na

Beth Elliott:

Okay. And you had mentioned databases before. And have you noticed that there have been some negative perceptions about databases where - I don't want to deal with it's too hard can Are there any examples that you can give that maybe there were misperceptions that slowed or even stopped a project because they're like, Oh, I'm not a database person. I don't want to deal with that.

Brandon Ellis:

Right? Yeah. Well, that's where most all of us controls engineers were and probably a lot still are. Database. Database database programming is is different. We don't typically think in terms of that we think still in terms of comma, what we call comma separated value type files, or, or CSV files, which is really more spreadsheets in Excel. And so, I don't want to say misperception. But the the comment I get from a lot of folks is, we don't need to deal with any kind of a database, just - can you just get this to show up in Excel. And there's ways to do that. A lot of the PLC manufacturers, some of the PLC manufacturers have the ability to interface directly with Microsoft Excel, specifically, um, we don't, I don't typically encourage that. Because Because if you're using our Data Commander or our IIoTA, you don't need to be fearful of databases anymore. yYou can interface with databases very easily. It's very much point and click - that was the point of the Data Commander and the IIoTA was because in 2014, I was looking for a new way, because I was not a database person, I was not comfortable with databases at all, I had this horrible, horrible thing called OPC that we were dealing with and screaming about, Oh, please connect and all that kind of stuff. And then it was all in a PC based platform, on a SCADA system. And we had firewalls and viruses, virus engines, and all the stuff that IT was pushing down. And it was just annihilating our communications paths and our productivity, or so we felt like. Today 2020, I have a totally different perspective of that, and have gained a huge respect over the last six years for what the network and IT people do in terms of security and cyber security. But we, controls engineers, still are more comfortable about Excel. Because we use Excel, we don't use databases. And databases are kind of magic. However, you should not fear the database, the database is built to do one thing, and that is to house data. Excel is built to be a spreadsheet, it's different - different tools, you wouldn't use a screwdriver to drive a nail, it just wouldn't work as well.

Beth Elliott:

You could turn it around and bang it.

Brandon Ellis:

You can bang, you can use a hammer to drive a screw, but it doesn't always go well. So getting the right tools in play and in the hands of the people. Because my theory is this, once once you take the data out of the PLC world, the I shouldn't even say PLC, but the OT the Operational Technology - manufacturing floor once we can get it from that world into a database server, there is all manner of easy to use programs out there, Excel is one of them that can easily connect to that database. And now if you want it in Excel, you can do that. If you want it inside of another SCADA system, you can do that. If you want it inside of another database, you can do that. There's there's all these hooks that are already in place once it gets into that database world. But as long as that data is sitting in a PLC or a robot or a CNC controller, there's not a good connection. And that's what the IIoTA does is makes that connection for you.

Beth Elliott:

in there. If it's still sitting in there, there's no way to measure the KPIs?

Brandon Ellis:

Well, that's where your data is coming from. I mean, we're everything here we were talking about, there's people involved, but there's also equipment, there's always equipment involved in production. And so that's where your key data is.

Beth Elliott:

Is in the equipment.

Brandon Ellis:

Yeah. And having people do tick sheets and write down end of shift reports and stuff like that is certainly fine. But it's prone for errors.

Beth Elliott:

Yeah, you can fat finger something in.

Brandon Ellis:

Well, you know, we were we were talking to a potential customer just just the other day. And they have within a single process they need to for for their KPI structure, they need to take multiple data measurements all at the same time. I mean, within the same second.

Beth Elliott:

Oh, wow.

Brandon Ellis:

And that's hard. But they also need to do it across multiple lines. That's really hard for for a human being to try to write down and we're talking about five to 10 indicators, not a huge amount, but still to try and take a look. You know, if you had a dashboard, I guess you could snap a shot of it with your phone, and then write it down. But, but you can't snap a shot across five lines, you're gonna need five people. Well, that's expensive. And so being able to do that, in an automated fashion certainly makes all the difference.

Beth Elliott:

Well, that's why Industrial Automation - It Doesn't Have To... Be

Brandon Ellis:

Myopic.

Beth Elliott:

That's right, myopic - short sighted.

Brandon Ellis:

That's right. Sorry, I turned away from the microphone there for just a second. That's a great word. And this was a great topic. I appreciate you putting that together.

Beth Elliott:

Well, thanks for doing your research on it because I know you had to look up some stuff.

Brandon Ellis:

Well, like I said

Beth Elliott:

You know, the process date and the product data up and down. traceability and defect flow out and the quality and stuff like that, but Thanks for looking into the TEEP.

Brandon Ellis:

Well, the I am not - not - not - not a quality engineer, and I am not a production engineer. But I have had the privilege and the honor to work with quite a few that are fantastic. And so they've taught me a lot of stuff about that. And hopefully, we've passed on some, some morsels of goodness to our listening audience.

Beth Elliott:

So please, like us, share us, follow us and give - give us your comments.

Brandon Ellis:

Absolutely. So these podcasts again, every other Tuesday, they release and we're going to keep going. We'll even go through Christmas. So stick with us and on into 2021. So we want to thank everybody for listening. As Beth said, keep telling people about us. The best way to in the easiest way to find us is just a search in your favorite podcast app, just search for the name elliTek, e-l-l-i-T-e-k and we should pop up Industrial Automation - It Doesn't Have To. So we look forward to talk with you guys in the comment section. Take care.

Why elliTek is doing these podcasts & why Myopic is the topic
An example of focusing on one piece of the manufacturing puzzle
What are Key Performance Indicators (KPIs) and what are the important KPIs in manufacturing?
What is TEEP?
Example of using KPIs
What is the difference between process data and product data?
What is Defect Flow Out Prevention and how is it different than Six Sigma?
How traceability ties into Defect Flow Out Prevention
Don't fear the database