DigitalNOW Podcast | Utilizing predictive supply chain analytics

In this episode of DigitalNOW, guest host Reilly Whiting is joined by Mick Wagner, Senior Solution Architect with Logic20/20, to discuss supply chain analytics; the importance of data within supply chain analytics, and how Logic20/20 is helping our clients solve problems.

Transcript

Matt Trouville: You’re listening to DigitalNOW, an original business and technology podcast by Logic20/20. I’m your host, Matt Trouville. Each episode, I’ll be interviewing a new expert to learn more about industry trends, fascinating new tech, shifting customer expectations and the steps every business can take to stay ahead.

Reilly Whiting: Hello and welcome. You’re listening to Logic20/20 DigitalNOW. We’ve replaced Matt Trouville. I sent him outside and told him to count to 500 and find me, so I think that should give us enough time to do this interview. So, we thank Matt for being a participant. I’m here with one of my fantastic partners in crime, who I have the pleasure of interviewing today. I’m here with Mick Wagner to discuss supply chain analytics. So, Mick, why don’t you tell the audience a little bit about yourself?

Mick Wagner: Yeah. I am a senior solutions architect at Logic20/20, and I focus primarily on advanced analytics solutions. I cover all sorts of different industries, spanning both telecom, high-tech, healthcare, and utilities.

RW: That’s fantastic. And I don’t know much about supply chain other than it’s, you know, 8% of the national, gross domestic product. It’s one of the largest, oldest industries. It’s virtually undisrupted by technology. What would you say, fires you up most about supply chain?

MW: That’s a really good question, Riley, and I think you are understating the importance of supply chain analytics, and I think what most excites me about supply chain is that we’re reaching a point in time, now, where we’re able to combine new development technology, and more data is becoming available. So there is such things as a sensors generating IoT, and actually having the cloud computing power to really start, you know, applying more data science approaches and algorithms and machine learning. It’s really a more predictive and improved supply chain analytics compared to years past. Before it was a lot more challenging.

RW: So supply chain analytics help companies understand their costs, and how long it takes to execute critical business operations. Can you tell us a little bit more about that?

MW: Yeah, that is absolutely right, Reilly. So, when we think about supply chain analytics, it’s truly more of just the management of logistics, right? So, it’s being able to move key components, key people, key items, and even code, to where they need to be. I think, once you look at supply chain analytics on a kind of broader lens you can see that most companies are some form of this, whether it’s companies in I.T., utilities, telecom, finance. Even software companies are kind of involved in supply chain analytics.

RW: Mick, could you please talk a little bit about predictive maintenance, and where you see the evolution of it heading. And where are we today?

MW: Oh, absolutely so predictive maintenance is a very broad term, and it can really mean a lot of different things. So when we’re kind of looking at it under the supply chain analytics lines we’re really thinking about a handful of different business use cases that really help drive at this. So we’re really kind of thinking about, how do we deploy repeatable solutions? We’re going to use data and essentially run different algorithms to predict what we think will happen, right? So, in the maintenance area this is specific, in a way, in a couple of couple of areas.

One is investment planning. So thinking through, you know how long you’re going to see those benefits of the investment that you’re doing, and what the integration cycle it’s going to look like. The second component is risk management mitigation. And the third one is more of a kind of predictive maintenance optimization. And that’s really more around…one really good example of talking about that we held our lines with. This is how do you automate asset inspections, right? And so every company has, you know, an asset to some level or degree depending on what they call it. In this case we were actually talking about, You know, physical, you know, devices and assets that will that will wear down. And so this was a case where again, we were kind of using the advantage of drone video footage, combining that with all the different kinds of big data processing and machine vision techniques to essentially be able to identify components that were damaged, right? And so what this actually end up doing was we were able to identify things that were having issues ahead of schedule.

And so these parts need an additional inspection were being identified three months earlier than the prior method – which is actually pretty big when you think about the risk that a you know, asset failure could cause and so this really helps our client increase safety for the different business components and also reduce the inspection time and cost. People aren’t going out looking at things that were completely fine. They could know they were going in and looking at things that had issues.

The one kind of really fun one I want to talk about, too, is investment planning. And so it’s a really cool concept going around now called the digital twin. It’s this idea that you are, recreating – we’re essentially recreating a physical kind of concepts digitally, right? So that way you can run different experiments. Different kinds of scenarios, different kind of simulations, same kind of understanding how it all works. And so we partnered up with Microsoft, and actually helps the Las Vegas Raiders take a look at their new you know, gorgeous football stadium and think about what the impact would be from a sustainability component for optimizing their HVAC systems and really kind of providing that real time. you know, situational awareness based off, you know, different components of their stadium, different sensors, weather, and all sorts of other factors that impact that area.

RW: That’s incredible. And I think it’s incredible, because the digital twin concept is like a staging environment like before you go to production to make sure things are working, and then being able to, you know, asset management at a high scale sounds a lot like what Kubernetes is doing with APIs. So again, everything seems to be pointing in this direction of more data, but a simpler method, simpler path to it. So, thank you for sharing that very insightful.

Awesome. So, supply chain is one of those things that it’s kind of mirroring software development and portable workloads is the answer right? Being able to move data and have transparency. And so, what is the importance of data within supply chain analytics kind of high level? What would you say are the most important factors?

MW: Yeah. So, I think it really boils down to – You can’t really manage something that you can’t measure. And so supply chain kind of gets its kind of roots and strength in being able to let you measure and monitor, and therefore better manage different activities. My background is actually in industrial engineering. A lot of the supply chain background actually comes from that which was really kind of centered around the idea of kind of optimizing work and trying to figure out the best ways for planning machine activity or looking at different kinds of industrial settings and trying to eliminate waste from value, added business activities.

So when we think about data and supply chain analytics, the first question you should always ask is, “why is it important? How is it going to really differentiate and make our business better at what we do”? Well, the biggest thing is, we’ve seen, in various studies and what not, the companies that really excel and generate, higher profit margins, and kind of dominate our competitors in the marketplace are actually really good at supply chain analytics. So a couple of examples that I come to the top of my head is Amazon. A lot of people argue that Amazon is, truly, more of a logistics company, you know they do everything from not only being able to optimize their own warehouse and shipping, but they’ve gotten so good that you know that a big chunk of a business is going through the third-party suppliers where companies realize they cannot compete with Amazon and are giving them money to essentially manage their business. So everyone’s heard that one, that one is kind of fun.

One I really like to share is about Zara. So Zara – people listening to this call will know exactly what they’re all about, or they’ll have no idea. But Zara is one of the fast fashion companies that has just really dominated the last few years out of Spain. So much so that one of the wealthiest people in the world is their founder.

And so how do they find so much success in the fashion world using supply chain analytics? They have a very well-oiled machine that can, you know, attend a fashion show, understand what is hot and what’s going to be popular, and where it may take other companies, and I can’t remember the exact number, but anywhere between, you know, three months to get that manufactured, and out to sell, Zara is able to push this significantly faster? I think that they’re even now getting it down to weeks for something to on the runway, and then they’ll have it out and selling it in Zara stores way before their competitors.

RW: That’s incredible. I mean. In the time I worked in supply chain, you know, to move 80,000 units end-to-end takes about 6 to 9 months of setup right. Get the warehouse established, get it staffed – and it’s mostly because the tech right. The technology is not up to distributing computational standards. There’s no cloud instances, on-prem offerings, and that’s a really juicy, exciting opportunity for improvement. And what do you think, if you think about supply chain analytics, you know, trying to emulate Zara and Amazon is a huge component of that – But how else are we deriving what we should measure? What do you think are some ways that we’re coming to those conclusions?

MW: That’s a great question Reilly. And what we’ve done is we’ve identified a lot of common pain-points across our different clients. And these are areas that we’ve focused on helping bring other solutions. So these kind of boil down to kind of three go-to-market solutions.

One is labor forecasting. The second one is inventory management and optimization. And the third one is predictive maintenance. And so all these are kind of covering a little different area of supply chain, and we begin to kind of scratch the surface. These are just the three areas we’ve kind of really kind of focused in on with our clients. And so, the first one, when you’re thinking about labor forecasting, what does that mean? It’s really about utilizing statistics, and machine learning to understand the different kinds of business constraints and tying that back to the data and having a plan to operate around that. And so when you’re doing labor forecasting correctly you’re going to be looking at a couple of different elements that will be applied. One of them is event modeling, and so, whether these are very common events or very extreme events, it’s something that will have a direct impact on what you’re doing.

Another one is scenario planning. So this might be, you know, looking at the ups and downs of demand throughout the day. With that you’re going to have to start thinking about workforce planning. So, who’s actually needs to be there? You want to make sure you’re not understaffed because you could lose a lot of business that way. Likewise, you don’t want to have your team standing around and not really being productive – all that ties into the broader scheme of workforce management. So how do you tie in all these different scenarios – event modeling, scenario planning, and all the different kind of day-to-day workforce planning to come up with a broader plan, and with that comes a vital piece, the schedule and potentially inspection optimization.

And so, we’ve touched on a lot of these different components in the past. And so, I think, telling a story about these really kind of connects it a lot better.

So, we’re working with a retail company to understand their demand and labor forecasting better. We were able to take in their historical data and all sorts of other elements. Apply some machine learning products to them, and actually look through the different algorithms and efficiently plan, their labor, not only across, like a company in this place for thousands of locations, but also down to the region and down to the store. So the reason why this was really important was to allow them to kind of forecast demand. And there we even got down to an hourly increment so they could figure out when they’re bringing people in when they need a surge group when they needed smaller groups, and could, you know, adjust these things to the season, the region, and you know, different sales patterns.

So, the whole idea of this is, if you’re doing this right, you’re essentially lowering your cost by not having too many people there. But then also, you know, being a retail story, you’re not having that customer experience with someone walks in and they want to buy your product, but they see a line 10 deep, and they go, “it’s really not worth my time. I’ll just go to the competitor across the street”.

RW: It’s incredible. What’s funny is, you know, before the pandemic, you basically are as good as what your inventory looks like, and you don’t want too much or too little right on your shelf. However, we learned that having toilet paper, toothpaste, and water is very helpful, and I remember hearing a stat that a water bottle company ran through all of their surplus for the following year in that short time. And so, there’s like a mad shortage. And it reminds me a lot of, you know, distributing computing when we, when we turn PET servers into stateful machines that can distribute and scale workloads, and that’s kind of what’s happening now in supply chain, which is fascinating and in terms of the schedule inspection, optimization, example. Do you have any kind of examples of something that we’re offering and doing that you could talk about in terms of at Logic20/20?

MW: Yeah, absolutely. And so, we think about some of the different solutions that we deliver and we, you know, we talk a lot about technology. It’s fun, and it’s sexy, but really it boils down to, “how is this changing the business? How are they going to make decisions better? And how are they going to do their jobs better”. So, we look at our utilities clients. Their biggest focus is on safety and lowering risk. And so, we’ve worked with them to build an end-to-end solution that allows them to do better vegetation management. So, this is using technologies such as machine vision coming from drones, and then being able to optimize and understand – What are the different types of vegetation growing? What’s the rate they’re growing at – all with the end goal, to be able to give a very intelligent list to their inspectors that go out to do their second mobile inspections of these high-risk areas. And so that all sounds nice. But the awesome thing we’ve been able to do is from a labor planning perspective, this client has now gone from only really being able to plan in four months in advance, and now being able to plan eight months in advance.

And when you think about the high demand there is for people these days, and just providing a better experience for your employees, that’s just significantly better.

RW: Forecasting, if done properly. Makes everything a better situation. Scenario right across the board, and that’s pretty fantastic. So, are you, would you say that you were smokey the bear?

MW: Only on the weekends, only on the weekends.

RW: That’s cool, because, being able to prevent forest fires using technology is a pretty sexy thing in my eyes, and I feel like it’s making a huge impact. So that’s exciting that we get to be a part of that.

MW: Well, and it’s funny, Reilly thinking about kind of that comment you made about the pandemic and the mismatch between inventory and what was available. It’s a very, very classic kind of issue, right? So, there’s a book that came out a while ago called you know the Black Swan. It looks at these very extreme events that essentially cause wide disruptions. Now, unfortunately, it’s pretty easy to ignore those in your modeling, because they’re kind of expensive, and they never happen. It never gets angry at you because you’re paying too much of inventory in place. And so the real goals: How do you optimize this? How do you make sure that you don’t have too little, but you don’t have too much. And so, this is another thing that we’ve helped a lot of our clients with. And so, what we do is we actually pair a lot of the modern management techniques with statistical modeling and just an important different data, visualizations and dashboards that way. The business can really get the right kind of insights they need to manage their data better.

And this is actually a very, very complex space. So, I made a very quick look – I hit on what seven of the key business use-cases that are in here, and I kind of tied back to a few of the examples where we’ve helped out. So, when you’re thinking about inventory management. Right? It’s all about your material.

So, you’re ordering your allocation. How do you essentially match these concepts? For supply with the demand forecasts, and part of that demand forecast is understanding your sales, your current inventory management, and then identifying any process bottlenecks.

So, you know, if your lead time is really high, that then puts a lot of strain on your system. The way to get things ordered in time and process quickly. So, it’s just all these different kinds of components, whether you’re looking at processing or a demo, or reviewing processes, or doing reengineering, it’s scenario planning and it’s really with some of the other firms describe it as that control tower management, where you want to be able to provide the business the ability to understand the current state, and be more predictive with all the different components of their supply chain.

So, what does that look like in reality on different projects? We’ve worked on one of them, which is an interesting project. We built a custom solution for our telecom clients that essentially automated their BOM process. So, if you’re not familiar with a BOM, BOM is a bill of materials. When you’re thinking about, in their case, deploying networking or network sites for essentially 5G and telephone servers and everything else, you have to think about all the different bits and pieces you need. And so, you go through this whole process of you know, engineering a design, and that engineered design, the engineer will go through, and they’ll start to put that into the system we built for them.

It’ll automate the BOM process to make sure they don’t miss any parts, and it increases the visibility and gets that planning cycle into the system. And then with that you get, you know, increased oversight or controls around procurement, better visibility and critical deadlines and everything else. And so, when we did this, this was a very, very short description of it. It was a very complex multiple year project. We were able to help our clients say millions of dollars between, you know, having the wrong stuff and not having enough of this stuff. but they essentially needed to get their antennas and insights developed.

RW: So, it sounds like there’s a lot of information flying around about who wins in today’s day and age is that person that can take away what information is relevant, and draw it together. And we’re using technology to do that, it sounds like, and what do you think companies should be thinking about heading into 2023?

MW: Yeah. So that’s a really good question. I mean, I think there’s still a lot of sore points coming out of the pandemic, and people who’ve learned a lot of lessons from it. I think one of the things we’re going to see is that technology is going to remain a really critical component.

And so there is a lot of hype around 5G, and really its low latency, its ability to, you know, provide everything that can be connected right? So, I think the Internet of things, or IoT is really going to become more real as a 5G, as it is looking at connecting everything? You’re seeing a lot of cloud improvements, for what they’re calling edge computing. So how do you really move that computing power closer to where it needs to be. And Google has built some fascinating ways to essentially do the machine learning work closer to the site, instead of losing a lot of time, setting up to the cloud and all the processing and things like that. So, what you’re going to see is, the companies will need to really make sure they’re ready for this. Because the companies that are ready for it do have the correct cloud data systems in play, and they’ve also just importantly thought through their business use cases. They have their modern data stacks ready to go. They have the right people to implement these AI and ML systems will really be best able to take advantage of this.

And so, I think we’re going to continue to see in the top performance of businesses for the companies that are, you know best able to leverage data, are going to continue to gain advantages when it comes to, not only revenue, but the profitability.

RW: Right, and in terms of where edge computing is now, it’s the same thing kind of recycled right? It’s like, “where do we put the memory, where do we allocate it? What are the trade-offs? How do we simplify things like that”? And it’s fascinating that we get to be a part of that in the consulting world, and it’s one of the, I think, magical things about consulting is having a pulse on the market. Where is it going? And being able to see that and help everybody is so very fascinating.

I wanted to ask you, Mick, are you a pineapple on or off pizza, kind of person?

MW: I think any ingredient can find its place on a pizza…If it’s done right, if you really kind of change the way you define pizza, there is. There’s room for everyone at the party.

RW: That’s amazing, is the calzone pizza, or is that a pizza sandwich?

MW: I think a calzone could be a dumpling.

RW: Oh, it’s a huge dumpling that’s fantastic. And real quick question – If somebody was interested in Logic10/20 and being in consulting, what would you say – If you’ve been thinking about consulting, what are some things that you should look for in terms of clear indicators that you might be made for consulting?

MW: Yeah, it’s a really good question, and I’ve actually had some of my favorite employees and teammates have asked that question. What is the difference, what is the differentiator? I look at two things that are going to mark success among our consultants.

So one is intellectual curiosity. It’s that combined with just general kind of creativity and looking at these different things. So what our clients really appreciate about us is that our people will work to different industries, so I can take something that I learned from a high tech client, and it might be directly applicable to a utilities client. And they’re hiring people with deep utilities experience, and they really don’t have that.

The second thing we look at is, when people hire consultants, they tend to do it because they need help right they can, for a varied number of reasons, but usually it’s not going to be easy. So I’d say, if you enjoy solving difficult problems, and really kind of have that, you know, kind of grittiness to adapt and adjust, and really try to figure out how to get to the right answer, then consulting can be a very rewarding career path for you.

RW: I also noticed there’s a lot of talkers here, and team players, and it goes hand in hand, would you say?

MW: Well, no, I think that there’s a role for everyone in the company. I don’t think you need to be the loudest person in the room. I always think it’s fun to go to our different happy hours that we have as a company and I see other different groups interact with some groups that are very excitable that it’s high energy. Other groups are sitting quietly discussing, you know, books they’ve read, or some articles. And so yeah, it’s consulting. It’s one of those things. It does help if you’re comfortable having more of those discussions. But yeah, there’s a place for everyone in the profession.

RW: That’s great. And of course, the talky, loud recruiter is going to, you know, claim that everybody’s a talker.

Well, Mick Wagner, thank you so much for your time. I really appreciate this episode. I see Matt still outside looking for us. I’m probably going to go let him know he can come back inside, because that was pretty cruel.

MW: We told him there were kangaroos out there. Because he gets excited, about wallabies, kangaroos…

RW: And he’s going to say, that was  a ruse of kangaroos.

Thank you for joining us. We’re not funny, but we’re very serious businesspeople. In closing we talked about supply chain tech, the pain-points of it, and also the relevant technology that is being used. If you’re interested in this, please visit us online, Logic20/20.com, if you’re interested in the company, please reach out to me, I’m Reilly Whiting, I’m a principal talent advisor at the company, and I’m happy to chat with anybody. So, Matt is going to take us out as always, we got him recorded. Mick, thank you so much for your time, until next time, thank you so much and have a great day.

Matt: You’ve been listening to Logic 2020’s podcast Digital now. To learn more, visit our website at logic2020.com or follow us on social media. See you next time.

DigitalNOW is an original business and technology podcast by Logic20/20 that is released on a monthly basis. In each episode, host Matt Trouville interviews a new expert to learn about industry trends, fascinating new tech, shifting customer expectations, and the steps every business can take to stay ahead. Check back here for future episodes, OR you can find us on all major podcast sites, including Spotify, Apple Music, Pandora, and more.