In this episode, we have our guest Martin Cloake, who discusses different barriers associated with Artificial Intelligence and Industry 4.0 adoption. While technology has come a long way, he shares his insight into how behavioral challenges impact manufacturers to operate with a continuous improvement mindset. Finally, he discusses why siloed systems result in companies operating on two sets of KPIs and ledgers and the impact on the growth of this siloed mindset.
- [0:17] Intro
- [2:08] Personal journey and current focus
- [4:02] Perspective on growth
- [5:14] Why is data necessary for industry 4.0 adoption?
- [9:10] Key barriers for the industry 4.0 adoption
- [12:08] Industry 4.0 adoption challenges for manufacturers
- [20:03] The role of financial data with industry 4.0 adoption
- [21:46] The implications of siloed financial and operational data
- [24:39] The role of different data sources with industry 4.0 adoption
- [27:47] The challenges of integrating operational and financial data
- [33:24] Closing thoughts
- [37:03] Outro
- One of the things when you begin to capture the simple stuff, that’s really the first effective step on your digital journey, know what is happening, know what has happened. And then the next phase, once you’re confident that you have those, you can begin to understand why did it happen, which is kind of the next level of analytics, and then the next, the final step is to predict what’s going to happen and avoid bad things.
- A continuous improvement comes from a realistic and true view of where you currently are, which is a struggle and challenge for many organizations. I think our true view of where we are we typically rely on our feelings and instincts, which absolutely have value, but for the most successful organizations, they combine their instincts about where they are with data to support those instincts.
- The age of being able to rely on instinct alone is going away quickly in manufacturing. In some ways, I would say we’re often behind the times a little bit, but things are changing.
- It’s key for leaders to recognize that there’s a shift that’s accelerating right now and to take those same skills that made them successful in driving their businesses to this point and use that to find the right technology partner and service partners to help them jump to the next level.
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Martin is an experienced executive and award-winning technology entrepreneur with a background in manufacturing, hardware development, and operations management. Martin holds multiple patents and is Mechanical Engineering and business graduate from McGill University in Montreal, Quebec.
Martin Cloake 0:00
Integrating new pieces of technology also requires that we change our behavior, and we don’t change as quickly as technology. You can buy a new piece of software, but to have that actually influence how you run your business is slower moving. So for leaders today, it’s important that they recognize that this change is coming.
Growing a business requires a holistic approach that extends beyond sales and marketing. This approach needs alignment among people, processes, and technologies. So if you’re a business owner, operations, or finance leader looking to learn growth strategies from your peers and competitors, you’re tuned into the right podcast. Welcome to the WBS podcast, where scalable growth using business systems is our number one priority. Now, here is your host, Sam Gupta.
Sam Gupta 0:53
Hey everyone, welcome back to another episode of The WBS podcast. I’m Sam Gupta, your host and principal consultant at digital transformation consulting firm ElevatIQ.
Data is the new oil, but collecting data could also mean distracting you operators from focusing on jobs. Moreover, just because you collected millions of data points, drawing insights or meaning could be challenging. This is especially true if your systems are siloed and don’t communicate with each other. Finally, the data quality issues may result in poor sales and operations planning, impacting your bottom line and growth.
In today’s episode, we have our guest Martin Cloake. He discusses different barriers associated with artificial intelligence and industry 4.0 adoption. He shares his insights into how behavioral challenges impact manufacturers to operate with a continuous improvement mindset. Finally, he discusses why siloed systems result in companies operating in two sets of KPIs and ledgers. Let me introduce Martin to you.
Sam Gupta 2:08
Martin is an experienced executive and award-winning technology entrepreneur with a background in manufacturing, hardware development, and operations management. Martin holds multiple patents and his mechanical engineering and business graduate from McGill University in Montreal, Quebec. With that, let’s get to the conversation.
Hey Martin, welcome to the show. Hey, Sam.
Of course, just to kick things off, do you want to start with your personal story and current focus?
Martin Cloake 2:39
Absolutely. So I work at Raven AI. We serve manufacturers helping them to improve performance with data. But maybe I’ll just describe sort of what got me into this. So my background is in high tech. I worked in telecom in sort of the boom of the 2000s. But when I graduated, I got recruited by a company called BlindsToGo in Montreal, which is a custom blind manufacturers.
So I was high tech, I jumped into manufacturing, which is where I got my start in this field. And one of the things that were most interesting was that my impression of manufacturing. And my actually seeing what it was in reality, once I got there, was completely different.
Martin Cloake 3:16
And one of the biggest challenges that I found was that the impression is from an engineering perspective and a technological perspective. It’s a series of processes and machines that have to be optimized with math, where we’re effectively manufacturing today is still very much a people-centric organization.
So that’s how I got into manufacturing. Then from that is really what triggered my observation that manufacturers struggle to use data in an effective way. And I found that Raven or co-founded Raven, partly based on my experience in manufacturing. The desire to make it so that manufacturers could spend the time they need with their operators to drive those kinds of behavioral changes to improve while getting access to all the real-time insights needed to guide their actions in the most effective way.
Sam Gupta 4:02
Okay, amazing. So obviously, there’s going to be a very exciting discussion here since you are doing some cutting-edge stuff. But there are going to be some foundational elements that you need to take advantage of this cutting-edge technology.
But before we do that, we have one standard question here. And that is going to be your perspective on the business growth, Martin. What does growth mean to you?
Martin Cloake 4:24
I would say that it means evolution and positive evolution in some ways in business and personally. In order to be successful, you need to have a growth mindset. And you can constantly be looking for opportunities to improve, so I associate growth with continuous improvement in a business setting.
And I would say in a business context. A continuous improvement comes from a realistic and true view of where you currently are. It is a struggle and challenge for many organizations. We typically rely on our feelings and instincts, which absolutely have value. But for the most successful organizations, they combine their instincts about where they are with data to support those instincts. And I think the first step is to know where you are and where you’ve been. And for those who are most successful, they’re able to take that information and change their behavior to continuously improve.
Sam Gupta 5:14
Okay, amazing. So this is a very interesting point. And that’s probably sort of truth if you ask any organization, they are going to say that I understand my business very well.
But in reality, they don’t have a thorough understanding because they don’t have either enough data or enough quality data. So in your opinion, Martin, what are some of the key growth barriers? And the challenges that you are seeing in the market at this point in time?
Martin Cloake 5:48
Well, and I don’t want to discount the power of instinct. So many say medium-sized organizations have become very successful based on the instinct of people in leadership positions. And I think there’s there’s a lot of power there. But growth and continuous development are slow as they are dependent on the instincts of leadership.
One benefit of a clear view is: you can share the burden with others because others don’t need to rely on instinct. Also, one main benefit of new technologies that are making it easier to see what things are happening is. It’s about making it easier to perform at that transformational level that typically only the top can do.
Just imagine, like a chess master, it took us a long time to be able to beat that chess master with software. If you compare today, if you take an average chess player and give them average chess software, they’re going to beat that chess master. It shows that a combination of good skills and good software can outperform skill alone. And that’s really the potential here where you can perform at a chess master level by combining your instincts that with software,
Sam Gupta 6:58
Yeah, and I completely agree with respect to your comment about these instincts. But you must have the quality instinct. If you look at some of the successful leaders, the reason why they have successful instincts is that they have lived through the quality information.
The way our human bodies and brains work is, the people who are really good at judgment, they are able to filter out the information, really quality information from noise. And that’s why they are so good at instinct, and they can make quality decisions.
So even our human bodies and brains do require quality data, a quality environment. A positive environment for us to be able to make decisions. What would be your thoughts on that? I mean, see, instinct is definitely foundationally reliant on the quality of data. And obviously, when you talk about the infrastructure that has to have the quality data as well,
Martin Cloake 7:45
And I would say that the age of being able to rely on instinct alone is going away quickly in manufacturing. In some ways, I would say we’re often behind the times a little bit, but things are changing.
And COVID has only accelerated that was for people to rely exclusively on instincts today to guide their business is a risky proposition. And those are the kinds of businesses that are not growing and are not evolving and will struggle to maintain competitiveness, as others are, as everybody’s diving into the bringing in technologies to support them here.
So I think it’s key for leaders to recognize that there’s a shift that’s accelerating right now and to take those same skills that made them successful in driving their businesses to this point and use that to find the right technology partner and service partners to help them jump to the next level. Because if they wait too long, at some point, they’re their competitors will be ahead.
Martin Cloake 8:36
And being ahead isn’t simply the fact that you do have software or you don’t have software. One of the biggest things that we often discount in this phase of growth is that adopting or changing our organizations. integrating new pieces of technology also requires that we change our behavior.
And we don’t change as quickly as technology. You can buy a new piece of software, but to have that actually influence how you run your business is slower moving. So for leaders today, it’s important that they recognize that this change is coming and don’t discount the fact that change management within their organization is likely going to be more difficult than simply purchasing or partnering up with a technology partner.
Sam Gupta 9:10
Yeah, I agree. And I’m actually going to go back to your comment about manufacturing being a people-centric organization. So when we think more from the data perspective, the more manual intervention that we have in the process, it’s likely that we are not going to have the data that either the machine or the software is going to require to be able to process so obviously, humans are very good at instinct.
And that’s what they should be looking at. They should be acting on the data. They should be making decisions on the data. So when you look at the current landscape of manufacturing, what are some of the key barriers that you see on the shop floor with respect to the system in having a lot of manual intervention in the process, and because of that, the barrier to the key insight that businesses can really utilize for their competitive advantage.
Martin Cloake 9:57
So I think there’s. I don’t know if you’ve heard the term data is new oil probably yes. So this is a very dangerous statement with regards to its impact on people on the shop floor. So by making the statement data as the new oil, what has happened is that many organizations are treating their operators on the shop floor as a source of data.
And to your point here, there’s a lot of data that we can get from them. But the reality is that by asking your operators to provide and feed these data systems, it is, by definition, a source of waste. So these systems are often set up in a way that discounts the fact that what our operators need is to be left alone to run their process. And they need a lever to apply pressure on the organization to get support, what they don’t need us to be constantly distracted by data.
Martin Cloake 10:38
So I think one of the fundamental mistakes is that organizations are having redefined what success is. And collecting data is not a success. Success is the better product, your customers better profits, better jobs for people on the shop floor. So I would say that one of the biggest challenges on the shop floor is just the whole frame of these transformations, where it’s centered around data and not centered around continuous improvement, which goes back to your comment about growth.
So the way to view this change our view here is I’m not sure if you’re familiar with just the concept of servant leadership. Yeah, sure, you are so. So effectively at a, if you are in a manufacturing organization, and you’re not standing in front of a machine, it is your duty to do whatever you can to help them be more effective in their job. And that could be to help them figure out how to not spend too much time doing setup and spent help them run their machines more effectively.
Martin Cloake 11:26
So what we don’t need to do is to slow them down with data collection. So I think one of the things that I question that people should ask is when and whenever we’re being asked operators to input data into the system, we should question how many questions we’re asking of them.
And we should question whether or not we’re asking dumb questions. if you’re asking a dumb question frequently, you probably don’t have the right system. So on occasion, you can ask questions to provide context if the purpose is to apply pressure to the organization to fix important problems.
I think that if you ask that question is that, are we asking too many dumb questions, it sort of frames kind of the technology solutions you have on the shop floor in a bit of a different way. And that’s the biggest challenge. So when people say data is the new oil, they are pointing themselves in the wrong direction.
Sam Gupta 12:08
Yeah. And those dumb questions are not fun, even for humans. I mean, they don’t appreciate procedural stuff. That’s data collection for the sake of it when they don’t know what you are going to be doing with this data.
So it’s an interesting dynamic there. So now what we are going to do, Martin, we are going to take the human side of the picture from the equation because obviously, people’s issues are slightly harder to solve in my experience. Okay, so let’s talk purely from the software and machine interaction perspective.
So again, going back to the landscape of manufacturing, typically small to the medium-sized manufacturer. So let’s say keeping the human element aside, what are some of the challenges that you are noticing in the market at this point of time, when you look at the kind of software these manufacturers may be using at this point of time, that kind of machinery they may be using at this point of time, and the challenges that they face? Number one with respect to data collection, and the key insight that can be that they can really utilize for their competitive advantage?
Martin Cloake 12:59
Yeah, I think in many cases, the story of digital transformation in industry 4.0 adoption, that’s being told by service providers and technology providers is not the story that’s most important to most manufacturers. And what happens is that manufacturers get overwhelmed with the idea that they need to digitally transform and with industry 4.0 adoption and perform predictive analytics and all this where effectively the kinds of things that they would benefit from now are much, much simpler.
There are many manufacturers today that have combinations of equipment that are legacy equipment from 30 years ago, equipment that they just got recently, and many manufacturers don’t have a clear view of what has happened recently. So and when I say what has happened recently, if we asked a manufacturer to reconstruct the timeline of what happened on a given station yesterday, they will struggle to do so. They will struggle to identify that they can capture how much it’s run.
Martin Cloake 13:51
But it’s difficult to understand why it wasn’t running or why it was slowing down. And this is kind of going back to the previous comment about capturing data from people like the context that people can provide to those systems is critical to understanding how they’re spending their time.
So the narrative should start off with the first thing is, Do you know what’s happening right now? Really? Do you know what’s happened recently? many manufacturers aren’t at that level because they don’t have that reliable true data set, even capture those most simple things. And one of the things when you begin to capture the simple stuff, that’s really the first effective step on your digital journey, know what is happening, know what has happened. And then the next phase, once you’re confident that you have those, you can begin to understand why did it happen, which is kind of the next level of analytics, and then the next, the final step is to predict what’s going to happen and avoid bad things.
Martin Cloake 14:36
But I think people don’t recognize that you need to go through those steps in that sequence. And one of the benefits of starting in that sequence is that it’s really easy to get operators to understand the first level; we just want to know what happened yesterday and then operate and understand. Okay, what happens is that vendors are pitching analytics tools and predictive tools which are jumping to the end, which are typically based on trying to squeeze value out of bad data and Be nobody understands in the shop floor because we’ve introduced this tool that’s not needed at the wrong time.
So you create disengagement in a system that doesn’t work here. So I would say that it’s shocking that too many manufacturers that they can start so simply and get so much value. And this is what we’ve done for Danaher and various other organizations. And Sanofi, the first steps on this journey don’t have to be that hard. In some ways, leaders should be discounting all the jargon-filled posts with a perfect hashtag on Twitter and LinkedIn and have a conversation about continuous improvement and see what technology is available to help them accelerate that.
Sam Gupta 15:34
Okay, amazing. So let’s start with some stories. And obviously, I mean, you are going to have tons and tons of fascinating stories, were using data, probably you have some sort of insight, and that actually rocked the world of, let’s say, manufacturers.
So do you have any specific stories that you would like to share? And typically, what I like to cover in the story is the previous situation. Whatever they were doing, it could be some sort of challenge that triggered what actually happened. And because of pledge, probably you were brought into the equation, your team analyzed the system, and then finally, some sort of outcome. Right. So do you have any stories that you would like to share?
Martin Cloake 16:07
Absolutely. So I have a great story about a Danaher plant in California we were working with for a while. Yeah, so Danaher is a multinational manufacturer with multiple different sectors. What they’re known for, they’re known for being experts in, in operational excellence, right? Their culture, continuous improvement culture, their team is extremely, extremely talented.
And it’s always amazing to go visit any of their plants here. So we started serving a plant in California, making small metal components, and beginning to sort of try and figure out how to allow for them to keep up with demand, and they’re there in the dental industry. So how can we help them keep up with demand and what their hypothesis was in, as we began to work with them was that they had issues with machine reliability, which is often what triggers a conversation with a technology provider, where we think our machines aren’t reliable, we’re spending a lot of time fixing them, let’s get some more details and data from the machine?
Martin Cloake 17:10
So we set up our technology with them collaboratively and simply did that first phase that I just described earlier, what is actually happening at the machines, and there were issues with machine reliability. But that wasn’t the main cause for lost productivity. when a machine breaks down, there are actually multiple segments of time that capture the different kinds of losses.
You can imagine that you and I are working at a machine and the machine breaks down. So the first time segment is the machine’s broken down. How long does it take for us to actually call for maintenance? So there’s a little segment there, which is we’re trying to figure out, and then the next segment wants maintenance has been called, How long does it take for them to arrive? And then once they got there, how long does it take to fix?
Martin Cloake 17:52
And then once they’re done, how long does it take for us to start up again. So when the time was split up into those segments, what we found out was that they were losing 600 machine hours per month waiting for maintenance to arrive.
So a machine our stay is worth $1,000 a product that is a tremendous amount of lost time. And now, if you were to chat with their maintenance team and a supervisory team, there wasn’t a lack of goodwill towards solving this. It was just there was a little tweak in their process that was making it, so maintenance wasn’t arriving on time. So with these insights, the next step was to how do alert maintenance when this has happened?
And how do we show them how well they are doing at solving this particular problem. So, in this case, your problem was how do we increase improve maintenance response time to service requests, and it sounds so simple.
Martin Cloake 18:40
So they reduced that loss from over 600 per month to under 100 a month in six months, resulting in millions of dollars of additional production and reduced costs. And it’s. It was shocking how simple it is.
So like when people begin the conversation about we want to predict when the vibration of this motor is going to get to the point where we’re going to it’s going to break, that is such a small of an opportunity, where if the goal is simply to continuously improve, provide products to your customers faster and reduce costs, and actually create a good job here because nobody wants to be waiting.
The first things that you’ll see are shockingly mundane. And I think time and time again, when we begin to work with world-class companies, we see the same thing where leaders are almost surprised to see how simple the first thing is.
Martin Cloake 19:24
And then, from a practical standpoint, I find that many organizations try to do too much at the same time. So if you’re running your operations, and if you were to focus on one or two things at a time, it’s way easier to drive gains and actually get your team on board with the initiative because it’s easy to wrap their heads around.
So now the machine is at that station. They don’t want to be waiting if you think of the system. The goal here is to get where maintenance comes sooner to fix their machines. There’s no operator that’s gonna fight that because it’s solving a specific problem. So that just, I would say a great example of how the narrative of the kinds of things that manufacturers need and the way To drive these massive gains is not as complex as people believe.
Sam Gupta 20:03
Yeah, it’s kind of interesting because of the kind of space that I operate in the SMB space. Sometimes they are not even tracking these machine hours or the product costs, they just don’t have a sense of how much time they might be wasting in between runs, and almost a product may be costing.
And sometimes that cost is not really counted towards the product. So they don’t really have a true sense of their product costs. So in this particular case, did they have a true sense of how much the time was wasted?
Can you talk a little bit from the financial data collection perspective, then what were their processes? And how did they recognize that they were wasting this time? Was it because just from the conversation that they felt that they were wasting 600 machine hours? Or were they did they have actual tracking of financial hours as well?
Martin Cloake 20:46
Well, so maybe I’ll also talk about it from an SME context. Danaher is an organization that has a very good sense of the connection between their operational data sets and their financial data sets. So like that’s something that, but I would say for many smaller manufacturers and some larger manufacturers, it’s always surprising how it seems like plant management has two sets of books.
They have the set of books that is looking at operational metrics, and then they have the financial metrics. And the connection between those two is often tenuous. And it’s almost like you see a plant manager with two ledgers on their desks. It’s always a bit of a joke here where, when you’re trying to prove the value of a system, they have to somehow map between the two, and they always get frustrated.
Sam Gupta 21:30
Yeah. So typically, I’m the finance guy on the floor. And I remember a lot of times I got kicked out from the shop floor because the team felt that I didn’t belong there.
What were you wearing, a white shirt and shiny shoes?
Sam Gupta 21:43
I have to. I’m an ERP consultant, brother.
Martin Cloake 21:46
There you go. See that as a problem? If you go on the shop floor in a white shirt and shiny shoes, then people will look at you find it easier. You have to dress for the right spot.
Sam Gupta 21:55
I was a rookie back then. And then I figured out I had learned my lesson. That’s good. Okay, Martin. So yeah, so you were talking about the financial and operational data? So do you want to touch a little bit on that from the SME perspective?
Martin Cloake 22:06
Yeah, so I think the, and maybe this is leading into a conversation just about how organizations tend to have data silos. One of the most obvious Data Silo is that between the planning and financial data and the operational data, and this is very much a function of how our systems are set up.
I know your space is kind of the ERP space. ERPs were not designed to facilitate connections between data in different sections. They were designed to have been a one-place store for all this different data. One of the challenges with the supply chain operates on a daily basis, or on a whereas an operation side. It’s in real-time. So you have these two different systems that are often we’re trying to optimize them in isolation, where they are absolutely connected. And I think the plant manager recognizes that but often that the technology solutions that we’re providing to manufacturers don’t play well together.
Martin Cloake 22:59
And if you have these two systems connected together that are being optimized separately, you are not setting yourself up for success to be efficient; you’re almost baking in thrash into your system that is going to make it inefficient. And I think that’s one of the reasons why many manufacturers are struggling to see the benefits of tweaking one aspect of their system, because optimizing supply chains are great, but if your operations can’t keep up, there’s not going to work.
And I have an example here, which is right now more and more people are consumers have a desire for quick delivery of customized goods and manufacturing has is way more efficient when you have slow delivery of the same kinds of things. The consequence of this shift to quick delivery of customized goods is there’s a lot more pressure on manufacturers to basically make up for mistakes in the plan.
It’s almost like the approach to come up with a plan for production and then execute that plan is failed from the start. What I see happening on the operation side is you have a production plant with a certain sequence that may be optimized.
Martin Cloake 24:01
But then, every day, there’s a backlog of orders that didn’t get completed the previous day. Then, because you all have a duty to your customers, you jam those orders back in your production lines, and you basically blow up whatever plan they add. Manufacturers have to switch from one job to the next to the next in an extremely inefficient sequence that results in very low performance and that this is not the fault of operations.
It’s not the fault of the planning is the fault of the fact that the two systems are not connected. The model of trying to plan a week out when that’s not how consumers want to consume is resulting in a lot of thrash. And I think the real opportunity here is to connect those two systems in a more practical way.
Sam Gupta 24:39
Okay, so let’s talk about the system landscape a bit more. You mentioned the operational and financial systems not being connected in most organizations. My assumption here is going to be, and you can correct me if I’m off here. So my understanding of the operating system that you are referring to is going to be some sort of MES system that actually talks to the machines, but there aren’t going to be many different data sources that are going to be there on the shop floor.
I don’t know how many data sources you typically utilize in the kind of data gathering that you guys do and data analysis that you guys do to be able to make the decisions for your engagement. Typically, what kind of data sets are going to be relevant? Is it going to be just the ERP data? Is it also going to be MES data? Do you acquire data sources from there? Do you look into the engineering data from the CAD system perspective? So what are the data sources that are really relevant from the operational and financial planning perspective?
Martin Cloake 25:34
Yeah, and I’ll talk about that from a practical perspective right now. And then also maybe touch on kind of how I see things changing here. But from a practical perspective, the most basic information is how much of a certain product is trying to be, are you trying to build, and when and that’s typically sitting on the side?
The second one is, what is the build standard? What is the standard cost for this product in the ERP? So, what do you need to build for the customer? That’s generally pretty clean data. And that’s easy to map. One of the biggest challenges is that the standards that sit in the ERP to describe how long it should take to produce products are typically a disaster. And because they’re done so infrequently and changed infrequently.
Martin Cloake 26:10
They’re frequently changing because it is disruptive on the financial side to be changing standards, but the fact that we have a disconnect between the capability that we believe we have and that we’re using for our plants and our actual capability is almost making the planning pointless.
We see cases, and one of the first things we do with our clients is a little bit of validation of their performances per SKU compared to what they think it is. And that first glimpse of that is just shocking. Just even the way that these standards are come up with, if you send a call up to the shop floor to perform a time study, really a time study shouldn’t be a one-time thing, you should have a 24 seven-time study that’s making sure that you have a clear and true view of what your actual capabilities are. So the two most simple things that we connect to is the MES. What are you trying to build? When is it due?
Martin Cloake 26:57
And we have examples of clients in the pharma space where delivering on time can mean the difference between getting a $40 million contract and not getting it. So it’s critical for us to understand what is their goal because we want to make our clients achieve those specific objectives.
Now, we’ve always set up our model, like we are a company, we work on a month to month basis here. So at some point, if we’re not earning our keep, we don’t get to stay around here. So yeah, like, what is your goal? What do you need to present products for when? And then what is your actual capability? And then with those two, I would say that those that are like no, as I mentioned before, with the example in the plants, and simply getting maintenance to be more responsive to these mistakes, I would say that if you just connect the plan to your build targets, to the shop floor and have a true view of your actual performance, that is a very big step for most manufacturers, and they and that that will take them a long way.
Sam Gupta 27:47
Okay, so do you have any stories around the challenges associated with integrating the financial and operational data? The people who don’t have, let’s say, software background, sometimes it’s just harder for them to understand why is integration so difficult when we are talking about two systems, and sometimes these two product could be from the same vendor? So why is integration so challenging? When we talk about two systems, let’s say operational and financial?
Martin Cloake 28:13
Well, I think there’s this bit of a mindset change where it’s hard for people to recognize the value of connecting these two systems. So there’s First off, it’s a mindset, I think, on the technical side, and I know consultancies that are aware of both datasets are aware of the planning data set, and the operational data set can connect these datasets in an effective way.
So I would say it’s the main challenge is for organizations to recognize the power of connecting planning to their true operational data set, and then find the right kind of service partner to connect those two, because at this point here are I would say the biggest challenges in our industry are no longer technological is behavioral that we’re not quick to jump on to new things.
And in some ways, we’re looking for that model here. So I would say it is just awareness, because technology is available both in service their service providers available that can create that strong connection so that you do not have to have two sets of books, you can understand what your operational performance means on the financial side.
Sam Gupta 29:09
Okay, so do you have any stories that you might be able to share around the integration challenges that you have seen in your space?
Martin Cloake 29:14
Often, when we begin to chat with clients, the first thing that they request is, let’s connect the data set to SAP, right? And what we can cover is part of that, and consultants can come in as well. I think organizations don’t often have an awareness of what’s required on their side to actually create those connections.
So often, what happens is that there’s a motivation to make the connections. Many organizations don’t have the internal, so one of the things that I’m sure you’re aware of here is you can’t create a live connection to SAP it wasn’t designed to do that. It’s designed to have intermittent connections from a data perspective.
Martin Cloake 29:49
So this is something that we come across again and again, and in some ways, the need to push to connect these two data sets and maybe just and I’m not even sure if the past That we will end up is one that goes through the ERP players that are currently in place. Because of the way that the ERP systems have been designed, the way that MES has been designed and these operational systems have been designed is with minimal interconnectivity.
And maybe this is getting into a bit where I see things going. But you know, there’s a challenge here where we have a bunch of legacy software systems, and even more importantly, legacy behavior that is struggling us to switch from the old way of doing things to the new way of doing things, which is to optimize for one thing, and that’s what I said on the top here, which is optimized for our ability to deliver value to the customers to maximize profits, and to make jobs better on the shop floor. To do that, you need to optimize one equation and not optimize these data silos. Yeah, so one of the things.
Sam Gupta 30:45
This is not really related to one specific vendor, in my mind, and again, when we talk about SAP, SAP has many different products, and they all have different versions. And that is good with any vendor out there, right.
So some ERP systems may be able to provide live connectivity, some ERPs may not be able to provide, some may be able to connect with the system. But again, the capabilities that some of these companies may require could be different. So there are a lot of different variables when we talk about the software landscape as well. And that is something I think everybody needs to keep in mind. So do you have any other stories that you could not cover as part of this episode? Well, okay, so maybe this goes back to sort of my experience in manufacturing as well.
Martin Cloake 31:00
But one of the consequences of not having a strong connection between the operational data set and the financial data set is you have many engineers, and I was one of these engineers when I was working in manufacturing, struggle to make a compelling business case to financially motivated leadership to get them to invest in improvement projects.
And what happens is that so this is something where many engineers struggle early on in their careers. And I think your background is one that as background in finance, which I think would benefit many engineers early on to understand how to follow the dollars all the way from the money coming into those projects here.
So I think a lot of projects start with best intentions, but the fact that engineers don’t have the skill sets to understand how to create that translation is making it difficult for them to be internal advocates to drive these improvement initiatives. So I think at some point, it’s, in order to unblock, we need to be able to change the way that leaders view operational performance and have a much stronger tie to financial data.
Sam Gupta 32:00
And one of the ways to do this is to technology. Absolutely. But I think this is something where there’s a bit of a shortcoming, on how engineers are trained, and even in organizations, how the engineering side of organizations are run, kind of as they’re project-based versus continuous improvement focused.
And I think that mentality, which has been in manufacturing for a long time, is kind of created this idea that engineering provides value by executing projects versus providing value by accelerating continuous improvement. So it’s not a specific story here.
But I think there are a bit of mindset changes that that’s needed in manufacturing, where if you are an engineering, you are almost, by definition, linked to continuous improvement and continuous improvement culture. Yeah, but that mindset of being project-based disconnects them from that because a successful engineer is somebody who executes projects on time and on budget versus who provides more impact to the business in a way that’s measurable financially.
Sam Gupta 33:24
Yep, completely agree. So that’s it for today. Martin, do you have any last-minute closing thoughts?
Martin Cloake 33:28
Yeah, no, I think just as far as how things are going, I mentioned that earlier, in some ways, the ERP is not in the middle of these challenges that we’ve been discussing, a lot of the data that we are of massive value sits there, the operational data that sits next to it would naturally flow into it. I think what’s going to transform the industry is by finding ways to leverage that data in a holistic way. What I mean by that is that to track, we do a lot of work in manufacturing to track how value is provided from the person at the machine to the customer, but to actually map that financially. And then optimize. That is really where I believe our industry is going.
Martin Cloake 34:07
One of the neatest things is that when and maybe this is only here in Canada, but when you Google manufacturing, the first company that pops up is Shopify, which is kind of shocking that Shopify has so, and I’m in Ottawa, Shopify town, and that sort of I think that is sort of pointing in the direction of where things are going here, where we’re going to have these systems.
And maybe at some point, your ERPs and operational data systems will simply be apps off the Shopify store, and why I see this happening is that the way to create really this to optimize manufacturing and achieve all the promises that industry 4.0 adoption requires is to connect that so if you are an app on a Shopify store, by definition, you have access to data on the sales and marketing side, but you also have access to influence on the sales and marketing side.
If you have access to the ERP data and costing and planning and operational data, you basically are creating that full data connection from the person to the customer.
Martin Cloake 35:02
And if we think about technologies that have been massively disruptive over the last ten years that the companies that come to mind are Uber and Airbnb. And if you think about Uber, it’s the first that you wouldn’t say it’s necessarily similar to manufacturing, but you have a person, and you have equipment providing value to a consumer. Now, in that case, the reason why it works, it works so well is because there is a direct connection between operational data sales and marketing. It’s all in one.
And they’ve cut out all the inefficiencies. And I’m not speaking about whether or not they’re, they’re profitable or not here, and there’s competition in that space. But to date, if you think about what Amazon and Shopify do, they don’t complete that loop from the sales portion of things all the way down to the person providing the value here. And the way to complete that loop is to connect those systems to the operational data systems.
Martin Cloake 35:50
And I think that is when we’re all looking for that Uber moment for the industry for auto, and the Uber moment will happen when that is connected, when it is so clear and simple for SMB leaders to see how they might be using this system can provide more value to their customers, more profits and better jobs for their teams.
And I think that’s completing that loop is what’s going to give our space that Uber moment where everybody will flip over. But until we do that, as long as we continue to optimize these different systems in isolation, there’s always gonna be thrash, and there’s going to be, and we’ll continue going through pilot purgatory, and where we install something, we think it should work.
Martin Cloake 36:27
But at the end of the day, we don’t see those financial benefits. And I think that’s kind of the big challenge for us that we need to now think horizontally and not vertically with regards to data. That’s an amazing and very interesting perspective. And my personal takeaway from this conversation is going to be everybody knows that data is the new oil.
But in the new world, I think connectivity is probably going to be the new norm. So the more connected we are, the better and more competitive we are going to be as a society and also as an organization. So on that note, Martin, I want to thank you for your time. This has been a fun conversation and very insightful. Awesome. Thanks for your time. Thanks for the invite, Sam.
Sam Gupta 37:03
I cannot thank our guests enough for coming on the show for sharing their knowledge and journey. I always pick up learnings from our guests, and hopefully, you learned something new today. If you want to learn more about Martin, head over to raven.ai. Links and more information will also be available in the show notes.
If anything in this podcast resonated with you and your business, you might want to check out our related episodes, including the interview with Dave Griffith, who discusses why manufacturers must look for low-hanging fruits when exploring the path of industry 4.0 adoption. Also the interview with Susan Walsh, who discusses how to normalize your product, customer, and vendor data to avoid planning and forecasting issues with your inventory.
Also, don’t forget to subscribe and spread the word among folks with similar backgrounds. If you have any questions or comments about the show, please review and rate us on your favorite podcasting platform or DM me on any social channels. I’ll try my best to respond personally and make sure you get out. Thank you, and I hope to get you on the next episode of the WBS podcast.
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