Subscribe
Share
Share
Embed
This podcast offers business solutions to help listeners develop and implement action plans for lean process improvement and implement continuous improvement projects, cost reductions, product quality enhancements, and process effectiveness improvement. Listeners come from many industries in both manufacturing and office applications.
John Broadbent 0:00
Proof of concept is a technical question. Can we, for example, take data from a machine and stick it on a dashboard? Well, the answer is yes, we'll be doing it for over 30 years. So can we just get over ourselves? What we need to be able to do is say, if I get that data, not can I, can I put that in a form that's going to show me an opportunity for financial benefit. And so the proof of concept becomes a proof of value, and by changing our thinking to proof of value, we can then put a case together, because the CFO is going to be the one signing off on it, and he wants to know if I spend his money. You
Patrick Adams 0:50
I love that you started with doing basically a cost justification, because you're looking at how much are you spending today in comparison to what it's going to cost you to incorporate some of this into your your operation. And that's a big deal, because we know that executive decision makers are going to that's, that's the language they speak. So if we can show them how much we're spending today on manual spreadsheets, and then, you know, prove the ROI out, because I know there's applications out there to your point. Andy, I think machine metrics is one of them. There's a couple. There's many different companies out there, but they can come in on older equipment, and they can tap in and and start to pull data from that equipment, utilizing, you know, whatever it is, specialized bolt on, you know, applications and things like that, so that that would be the, basically, the first two steps, right? John, I mean, if you're, if you don't have anything like that, I mean, that's kind of what it's what you're doing in those first two steps, is that, is that accurate?
John Broadbent 1:51
And also to get rid of paper, ultimately, yes, because a lot of that information and the testing of those machines and calibration of those machines and all that would be recorded on paper somewhere, and challenge tests that they do in particularly in food and beverage, is recorded on paper. And then if there's an issue, if there's a recall, you've got a team of people spend three weeks go out to the back, back, back lot. They find the paperwork in the shipping container that's been archived somewhere or off site in Iron Mountain, whatever it is, like a storage system they got to recover it, retrieve it, put a team together, lock them in a room for like three weeks, then work out, what the hell happened that we had a recall, right? Whereas the system, yeah, once that data is digitized, you can mine it for whatever you want, and it's not that expensive to digitize these days. Yeah, if we now trust the data, the first step is then to run something like a statistical process control channel, like an SPC chart, which monitors the variability, which you can do in an Excel spreadsheet, monitors the variability of your process. And now we know if we're starting to get signals coming from the process, that we're heading north to an overweight situation where we're going to start making reject product. We can tell we can tell someone who cares, and they can go and make an adjustment to the filling process as an example. But the Holy Grail is getting into step four, where we now close the loop that we have a supervisory system that monitors those weights getting heavy, heavy, heavy, and then it automatically goes back upstream to the filling process and says, Hey, back off a bit, and then we sample again for a period of time. And that's where the closing of the loop in step four is where the real benefit finally comes. And it's also the role now of machine learning and AI, because they now have trusted valuable data on which they can work. But what I'm seeing, sadly is manufacturers, CIOs and CTOs focusing on the AI end, but more for the clerical realm and manufacturing is not getting the funding that it needs for it to be able to explore that. But they want to start at step four without doing steps one, two and three.
Andy Olrich 3:58
Yeah, they're ripping back to that step one around. And like any sort of process improvement project we jump into, it's like, well, what's the baseline? What is the cost of poor quality? Now, instead of, hey, I've seen this flash new thing here, and it's 100 grand. Can I have it? No, we haven't got 100 grand. And not another system. Whereas, yeah, going in with that, actually, it's costing us $250,000 and one of the things I love about the work that you do, John, and that was probably the first takeaway when I saw you speak at a conference a few years ago, was helping companies understand the risks around technology, debt and end of life strategies for equipment, or having every color but pink, when it comes to PLCs or things like that, you're really part of the offering there was. And being an ex PLC person, yeah, you've got all these different pieces of hardware and things, and it's they can talk to each other. But yeah, I've found that again, if you haven't got those. Foundational things in place, and, yeah, you can help, you really help open eyes on, well, that's great. You've got that piece. You've got, you've just spent $5 million on this whole automated system, but you've got an old windows, whatever PC at the back that holds the whole thing up. So I think you're really great and pragmatic about calling at risk as well as reward, but in a way that, yeah, you just kind of break it down into a place that many can understand.
John Broadbent 5:24
So the average CIO Andy knows that they're carrying technology debt at their server levels and laptop levels and desktop levels within their organization, but CIOs are very oblivious to the fact that they may be carrying technology debt on their factory floor, and that's because hearing people don't know they're carrying technology debt, or the extent of the technology debt, and then a piece of equipment 25 years old will fail, and now it's a catastrophic loss, and everybody looks around and points the finger and going, why didn't we know that we have no spares? Can't get parts. I know organizations whose automation departments are buying stuff off eBay because that's the only place they can get end of life components. And so many manufacturers, certainly in Australia, are sitting on what I call ticking time bombs in their industrial automation layer, and they're not doing the risk assessment that okay, it's still a risk. But we all know electronic components, old PLCs and sometimes dedicated control cards will fail and it's, well, what are you going to do when it does better to think about it now and have a contingency plan than not think about it and get blindsided and be down for three months?
Andy Olrich 6:33
Yeah, and that's that's what I appreciate here, is if we're sitting here thinking it's all about new things, or from now on, there's definitely a lot of if you're moving to 4.8 or you may already be, and you just don't realize is, is that piece around? Well, we may just have to correct and just standardize what we've got here in, you know, to reduce that risk, just to keep the lights on. So I thought that was a really great case study that you shared. And of course, we went back to our organization and had a look around, and we're like, four, there's a Windows 10 PC sitting at the back there. What does that do? Whereas we just kind of were looking at what's the next version of PLC that's going to come out that we like? So, yeah, Patrick, any anything
Patrick Adams 7:13
on Yeah, one question that I had just going back to, kind of what, what you were asking, Andy about just the steps that someone might take if they if they are not a Coca Cola or whatever would So, would you suggest, like, if I'm a plant, if I'm a Director of Operations, VP of Operations, and I'm looking at my facility? Should I, should I look at deploying this across all the equipment in my facility at once. Should I pick a group of machines? One machine? I mean, what's the what would be your recommendation on the approach from, from that aspect? And why small?
John Broadbent 7:50
Because you're going to learn a lot. If your culture is risk averse, you're going to struggle, because it's a finger pointing style culture, and it's very, very hard. Automation and engineering people are their own worst enemies. We love the idea of a proof of concept. And I say to organizations, can we please get over ourselves? A proof of concept is a technical question. Can we, for example, take data from a machine and stick it on a dashboard? Well, the answer is yes, we'll be doing it for over 30 years. So can we just get over ourselves? What we need to be able to do is say, if I get that data, not can I, when I get that data, can I put that in a form that's going to show me an opportunity for financial benefit? And so the proof of concept becomes a proof of value, and by changing our thinking to proof of value, we can then put a case together, because the CFO is going to be the one signing off on it, and he wants to know, if I spend this money, what's my return? The challenge for manufacturers, where the it part of the business hasn't kept pace in the factory is a lot of factories don't have decent Wi Fi. They don't have fiber backbone running through the place to carry large, large volumes of data. So if a if a manufacture, head of manufacturing or an operations manager goes to the CIO and says, I need 100,000 bucks to light up the factory and get a network there so I can start mining for data, and the guy says, Well, what's my return on investment in the first year? Well the answer is zero, because you're not going to get much back in that first 12 months. But without it, you don't have the foundations in place to then capitalize on that into the future. So we have to start educating CIOs that, yes, it's an investment, but without it, you're flying blind,
Patrick Adams 9:41
right, right? Yeah. It makes me. It makes me think about it. There's a company in Texas that I was working with, and their equipment was very old, and it just, it makes me think like, it's almost like they're driving a car with no dash to your earlier example, and have no idea what's really happening. Inside the vehicle, and it's like to your to also to your point, like, get over it, like we need to spend the money to get the data that we can make data driven decisions so that we're putting out good quality at the best level of efficiency and at the best cost for the company. I mean that it just seems like a no brainer to me and and I I hope I'm not offending anyone that's listening right now, but I'm feeling like I'm in the same boat that that you are with that one. It just seems like, do you do you find companies push back a lot on this and say, No, we can't do that. Or it's interesting.
John Broadbent 10:34
It's interesting you use the car analogy, because I use that exact analogy. I say it's like driving a car with no dashboard, the windows are blacked out. The view in the rear view mirror is where you were last month, and the managing director sitting in the passenger seat asking, Are we there yet? And that's like, you know, it's crazy stuff. In fact, I, years ago, I brought a OEE overall equipment effectiveness in a box from a company in Canada. I became their agent in Australia. I thought it was a license to absolutely print money. We had a little briefcase that was set up to go and stick in a switchboard. You could set it up within a couple of hours, we started recording data off this packaging line for a vitamin supplier. And I went back three weeks later, got the data home, pulled out all the reports, had a look at it. Went, wow. Look at this. They're running 25% OEE, what a great opportunity. I put together what I thought was the best PowerPoint deck I've ever put together. I went in there to present their results back to them, and I thought that I was going to walk out with a purchase order for five lines, sure. Part way through the presentation, I've shown they're doing 25% OEE, a young Production Manager, looks at me and goes, Oh, we don't need this because we're not capacity constraint. Said, What do you mean? He said, Well, we finish around midday Friday. I said, Well, that just means your standards are soft and you need to stretch yourselves to have higher standard production rates and stuff like that, so that you never finished your production run by lunchtime Friday, I said, but correct me if I'm wrong, you're a two shift operation. You run day and afternoon shift. You said, Yes. I said, so what if we got your OE from 25 to 50 and we dumped your entire second shift, right? And at that point I was escorted off the premises.
Patrick Adams 12:18
Oh, geez. Well, it, I mean, it's an accurate statement, but the other way to think about it too is, what if we could free up more capacity, enough to bring in another production line, or, you know, now, now we put the pressure on sales to to come in and fill the capacity, right? I mean, there's, there's a bunch of different ways to look at that, right? And I think to your point about getting rid of second shift, not necessarily laying people off or firing people, which is necessary sometimes, but, you know, shifting them into into roles on first shift, or support staff, or whatever it might be. Yeah, it is it, it's, it's very interesting to think about that. And you know, really, you know, it is it, is the the goal is to make decision, data driven decisions, and that's basically what you're talking about, is, let's, let's establish ourselves as an organization that allows data that we have, the data we've mined it, we have the data to actually make decisions. And I think that's another thing, like we just talked about the people that maybe aren't taking the step toward getting the data. But you also have the other side of this too, where you have organizations that have spent the money for the right type of equipment that's giving them all the data that they can possibly imagine, but they're not doing anything with it. It's just sitting somewhere and they're not actually using it to make data driven decisions. Do you come across that sometimes too? A lot.
John Broadbent 13:40
We built a ready mill factory. It was Australia's first purpose built ready mill factory, little packet meals you get from supermarkets. Family business that recognized an opportunity, they built the ready meal plan, and from day one, we put in that factory what's known as a process historian. So we've been recording everything that opens and shuts, room temperatures, set points, machine speeds, everything for the whole period of time. And two years after that factory opened, I got a phone call from the strategic transformation manager who gave us the job in the first place, and he said, got a problem. He said, we have these pasteurizing retorts. They're big, long torpedo that hold ready meals and you submerse them in water. Once they're packaged, submerse them in water, cook them at about 90 degrees for, you know, certain number of minutes to pasteurize the product so you've got long shelf life. And he said, we're not getting the number of cycles through these things that we need to get through per shift. And I said, Well, if you had a look at the processes store in because you'll be able to see the trend of temperature, pressure, and then a gap while you unload and reload, etc. So I showed him how to do that. He went and had a look, and he came back to me. Said, really weird. He said, I've got, you know, a 13 minute gap best case, but, like, an hour and a half worst case. I don't understand why I've got this discrepancy. I said, I was on site. Let's go down and have a look. So. And we go down to the factory floor, we grab one of the operators, and we said, when you run this product, it's like 13 minutes changeover. But when you run this product an hour and a half, he goes, Oh yeah. For this product, we have enough trucks and trolleys and trays that we can pre stack and we just do a swapper roomy. He said, but for this product, we don't have enough trucks or trays. That was it. So they'd run for two years at that capacity constrained simply because when they originally bought trays to put the product on stainless steel, perforated trays, they just didn't buy enough for that particular product.
Andy Olrich 15:32
Oh, wow, it's fascinating. And the case studies, John, like I said, You've been doing this for a long time. We could, yeah, there'd be anyone that's out there listening and has a particular challenge or thinking? I wonder if John's had some experience with that fair chance he has. Yeah, we've got all this data now, what and did we trust the data is the dashboard telling us we're doing 60 when we're actually doing 90. However, that would be another discussion for another day. I think so we'll, we'll wrap this up now, John, it's, it's been fantastic to have you on if there's one hot tip 30 Seconds to anyone listening out there, regardless of where they are on their 4.0 journey, what would it be?
John Broadbent 16:11
Look at what paper you're collecting, whether the information you're collecting is important, and what do you do with it? And is there a way that you could collect that, say, on a tablet, into a spreadsheet or some other form where it's actually digitized and stored, because then you can mine that data in the future to do some
Patrick Adams 16:28
really clever things with it. I like I like that as a close i i would love to maybe have you back on another time, even just to talk, because in the Lean world, a lot of times we talk about the power of paper to pen, and the importance of operators actually being the ones that are doing that. And I think there's probably people listening that are thinking to themselves, are we, are we saying that we need to stop doing that? And is it not as important? I guess I know we're closing we're wrapping up here, but John, just any, any comment on that, because I'm sure there are people that are listening that are going, I don't know, like,
John Broadbent 17:01
yeah, organizations that do that, well, Patrick, still have their handwritten scoreboard in the factory, which is on public display for everybody to see. And they'll take a number from a digital system and they'll go and write it, you're absolutely correct, they'll go and write that on a board somewhere. So there's still some ownership of the number, and I'm a great fan of that. The alternative is that everything is stored on paper, and then things are missed and human error, garbage in, garbage out, stuff. So the hybrid model of digitizing collection, but then recording a key KPI on a vision board where everybody can see it is the best outcome.
Patrick Adams 17:37
Perfect with you. 100% on that. I love it. Great answer. So John, it's been great to have you on if anyone does want to reach back out to you, have any any other questions or anything, what would be the best way for them to connect with you? They can find
John Broadbent 17:53
me via the website at realize potential. That's an S for Australian spelling, realize potential.com.au, and I'm also
Patrick Adams 18:01
glad you made that point, because I definitely wouldn't have used it.
Andy Olrich 18:05
So sorry, John. Is that the correct is that the correct way to spell it is that I have this American friends that's like, why do you use that? Sorry.
John Broadbent 18:17
And they can find me on LinkedIn under John s for Sam. It's actually Steven, but John s Broadbent is my moniker on LinkedIn, so easy to find perfect
Patrick Adams 18:28
All right, we'll put those in the show notes. So if any, if anyone listening, wants to reach out to John, just go right to the show notes. You'll find the link there to connect with him, John. It's been great to have you on again like, just like Andy said, we could probably go on and on with the case studies, the stories, the history that you have. I think it would be great to have you back on and maybe dive into a couple other, you know, down a couple other pathways. But John, it's been great thanks again for coming on and just sharing your wisdom with our listeners.
John Broadbent 18:55
Thank you, fellows. Appreciate the opportunity.