Logistics congestion, geopolitical tensions, labor shortages, and increasing operational costs continue to put pressure on supply chain organizations. These unprecedented challenges have exposed the fragility of supply chain interdependencies and highlighted the importance of fast, informed decision-making in supply chains across all sectors, from pharmaceutical to FMCG. In a sector where unexpected global events or natural disasters can have huge implications, accelerating decision-making by using real-time, accurate data is a vital capability and one that organizations must prioritize.

Mark Holmes is a Senior Advisor in Supply Chain at InterSystems and Ming Zhou is their Head of Supply Chain. They each have industry experience from a broad range of companies including Tyson Foods, Ferrero Roche, TJX Companies, Hard Rock Café, Albertsons, GE, and IBM.

In this episode, Mark and Ming join hosts Scott Luton and Greg White to talk about:

• The biggest data-driven challenges facing supply chain operations today

• Three ways leading organizations are leveraging real-time data optimized decision-making to empower their team members and improve overall supply chain orchestration

• The importance of taking an outcome-based approach to strategic planning and decision making

Intro/Outro (00:03):

Welcome to Supply Chain Now, the voice of global supply chain. Supply Chain Now focuses on the best in the business for our worldwide audience, the people, the technologies, the best practices, and today’s critical issues, the challenges and opportunities. Stay tuned to hear from those making global business happen right here on Supply Chain Now.

Scott Luton (00:32):

Hey, good morning, good afternoon, good evening, wherever you are. Scott Luton and Greg White with you here on Supply Chain Now. Welcome to today’s live stream. Greg, how you doing?

Greg White (00:41):

Good to have you in. I’m doing very well, thank you. It is evening where I am.

Scott Luton (00:46):

On special assignment, right?

Greg White (00:48):

Yes. Special assignment in Europe, yes. You can tell because it’s really dark and fashionable behind me. Well, you can’t see the fashionable, you’ll just have to take my word for it.

Scott Luton (00:58):

We always take Greg White’s word for it. But hey, Greg —

Greg White (01:01):

What’s up?

Scott Luton (01:02):

We have — we’ve been on a hot streak this week, I’ll tell you. And we’ve got another great show here today. We’re focused on the power of real-time, highly accurate data when it comes to making better, faster decisions across your supply chain. We’re going to be talking about how to approach that, proven practices, as well as how to empower your team members to be more successful. Greg, it should be a good one, huh?

Greg White (01:25):

I was waiting for you to say that. Yes, I almost can’t answer until you asked that. Yes, of course. It’ll be a good one. Yes, I mean we’ve had Mark on the show before and now he brought a doctor. So, that’s — right?

Scott Luton (01:38):

That’s right.

Greg White (01:38):

We’re going for the real experts.

Scott Luton (01:41):

All the experts. We got a dynamic duo here today. Quite a one-two punch. Now, stay tuned as Mark Holmes and Ming Zao, who Greg was alluding to, would be joining us here momentarily. And folks, get ready because we want to hear from you as well. We see Juan Garza and others in the chat already in the cheap seat. So, we want to hear from you as we work our way over the next hour through this conversation.

Scott Luton (02:03):

All right. So, Greg, we’re going to bring on —

Greg White (02:05):

And on time today folks, as a bonus, right? Yes.

Scott Luton (02:09):

On time in full, just like on our guests —

Greg White (02:11):

On time in full. That’s right.

Scott Luton (02:14):

— just like our guests make happen across industry day in and day out. Speaking of, Juan, great to see you. Tuned in via LinkedIn from Dubai. I wonder if he’s rubbed elbows with Kim Winter over there, Greg.

Greg White (02:25):

We should ask.

Scott Luton (02:26):

We should ask him. And this is — hey, Robert Felix, great to have you back with us again. You were with us yesterday as well, I believe. So great to have you. And Letford [phonetic] tuned in from Zambia via LinkedIn. Great to see you. Finally, hey, Jeremy, how are you doing? It’s been quite some time. But, Greg, if you recall, Jeremy’s one of our veterans that’s transitioning into global supply chain. So, great to see you Jeremy. And Gino Pledger —

Greg White (02:50):

Yes, good looking suit there. So, he’s doing OK transitioning, apparently, yes.

Scott Luton (02:54):

I think he is managing mountains. He’s looking sharp. Looking sharp.

Greg White (02:57):

Yes.

Scott Luton (02:59):

And finally, Gino Pledger, of course, North Alabama. Great to see you, Gino, as always. Appreciate all you do out in the industry to help others do things better. All right.

Greg White (03:06):

Thanks for acknowledging our global time zones, Gino. Appreciate it.

Scott Luton (03:10):

That’s right. That is right. Evening for Greg. But hey, it’s a global conversation, right? Just like global supply chain should be.

Greg White (03:18):

Yes.

Scott Luton (03:19):

So, to that end, Greg, I’m going to bring in our two guests. You ready to go?

Greg White (03:23):

Let’s do it.

Scott Luton (03:25):

All right. So, with that said, I want to welcome in Ming Zhou, head of supply chain product strategy with InterSystems. And backed by popular demand, Mark Holmes, senior advisor of supply chain, his colleague InterSystems. Hey. Hey, Ming, how you doing sir?

Ming Zhou (03:41):

Very good, thank you. Thanks for having me here.

Scott Luton (03:44):

You bet.

Greg White (03:44):

Welcome aboard.

Scott Luton (03:45):

And Mark Holmes repeat guest, back by popular demand. One of the nicest guys in the industry. Mark, how you doing?

Mark Holmes (03:51):

I’m very fine. Very fine, thank you. Great to be back.

Greg White (03:54):

Good to have you. You’re popular and we demanded it. All right.

Scott Luton (03:57):

Nice, Greg. Nice. OK. The hot streak continues.

Greg White (04:00):

I had some seconds to thought — think about that, yes.

Scott Luton (04:04):

All right. So, we got — as you all know, we want to start with just a little fun and warmup question, right? Because that’s good to show a little personality out there. And in between me and Mark, we got it by the truckload. So, did you all know this? A famous Italian astronomer, physicist and engineer Galileo, you might have heard of him, once said that wine was sunlight held together by water. And he know — he certainly knows bodies across space, right?

Scott Luton (04:29):

So, today is Chardonnay Day in the States. Monday is going to be Chardonnay Day in other parts of the world. It happens to be my favorite adult beverage. So, with that as a backdrop, I’m going to ask all three of you all, what is your, either your favorite wine or really beverage of any sort. And Mark, I’m going to start with you.

Mark Holmes (04:44):

Yes, I would say, you know, Ste. Michelle has a really good chardonnay but also a very good riesling.

Scott Luton (04:50):

Oh, nice, Mark. Ming?

Ming Zhou (04:53):

I know chardonnay very well, but I know nothing about wine, unfortunately, because I don’t drink. So, I’m one of the worst boring company to have when people are going out.

Scott Luton (05:03):

So, OK. I appreciate that. And I love that transparency because you know plenty about what we’re going to be talking about in a second. What is — do you have any favorite freshly squeezed juices then, Ming?

Ming Zhou (05:14):

Oh, yes. Any kind of fruit juice. Freshly squeezes is good for me. I enjoy all of them.

Scott Luton (05:19):

I’m with you. Man, I love lemonade this time of year. All right. So, Greg, how about you?

Greg White (05:24):

Yes, Ming, don’t leave it out too long because it will become wine. So, be sure to drink that fruit juice fresh. Mine is a brand called Sassicaia, Tenuta San Guido. It’s a nice little blend from Italy, as it turns out. And yes, I love it. Particularly the 2009 and 2014.

Scott Luton (05:43):

Love that. OK. We’re getting —

Greg White (05:44):

How about you, Scott.

Scott Luton (05:45):

If it’s cold — no, if it’s cold, dry and dry. I love a good chardonnay. I like it, especially — I think, Chardonnay is one of the best wines to pair with food, especially fish and chicken with — especially if you’ve got like a creamy sauce to it. Man, it is like — that’s why we live and eat and enjoy good wine and food. All right.

Greg White (06:05):

So, you’re a pairing expert, too. I did not know that. That’s really — it’s good to know. Expect some phone calls.

Scott Luton (06:12):

OK.

Greg White (06:13):

I’ll need immediate decisions.

Scott Luton (06:15):

Hey, I can’t wait to hear more about your European adventures there, Greg. But Ming and Mark, again, welcome in. And I got to share this right before we get into why we’re all here. So, big show. Bob Bova is with us here. Bob, hope this finds you well. He says, you guys are still on the air? And our team at Supply Chain Now says, Bob Bova, you can’t get rid of us. I hope you’re doing well. That’s right. You can’t get rid of us.

Scott Luton (06:37):

All right. So, let’s do this, for our audience members that don’t know both of you all, Ming and Mark, I want to shed a little light on your journey. So, Mark, you know, you’ve been doing big things industry for years, especially in the retail, CPG, manufacturing, distribution, logistics, verticals. It has been quite a journey, huh?

Mark Holmes (06:55):

And — it has. I’ve been doing it for over 25 years. I won’t say exactly how many years because then it will age me. But I have a unique perspective because I’ve been with many Fortune 500 companies, either from a 3PL perspective like GEODIS or end user, which is like Dow Chemical but also system integrators and a standard setting company. So, it allows me a unique position to be able to help solve business solutions that can bring in vertical thoughts from other verticals into their vertical.

Scott Luton (07:26):

All right. And these verticals all play nicely in the sandbox, hopefully. We’ll see. Stay tuned. More to come on that.

Scott Luton (07:31):

Hey, Ming, now you’re a tech — as we’ve done our homework on you and your first time here at Supply Chain Now, you’re a technologist that’s developed a reputation as a business-oriented, result-centered, technical leader that, get this, brings an innovative, a can-do approach to problem solving. It all goes into making it happen, huh? Is that right, Ming?

Ming Zhou (07:50):

Exactly. I mean, most of my career has been focused on real world problem solving in different industries, from improving aircraft engine efficiencies to intelligent supply chain solutions. I mean, I work for small startups, you know, maybe two dozen people. Two big companies with several hundred thousand people like General Electric, IBM. So, one thing in common is I always enjoy, you know, turning technology into business outcome.

Scott Luton (08:16):

Man. OK. So, Greg, I want to bring you in on that note between as — between Mark’s journey and Ming’s journey. Man, I bet there’s lots of kindred spirits here. Greg?

Greg White (08:24):

Preach that for — say it twice for the folks in the back. Business outcomes from technology is the only reason to have technology, right? I think sometimes so many people fall into the technology for technology’s sake, right? We — oh, we need blockchains, we don’t know what for, but we need it, or you know, pick it, right?

Greg White (08:43):

I always think back to the industrial revolution. There was a purpose for the steam engine, right? It was a very clear purpose and it was mostly the speed of driving or building the railroads, right? At least in the States, that was what it was for. And we should always think of technology with that same outlook. Yes, you’re already speaking our language, Ming. Appreciate it.

Scott Luton (09:06):

This is going to be an excellent conversation. So, Mark and Ming, I want to get into, you know, both of you all traveling the world, making things happen, working with some of the leading business leaders and organizations out there.

Mark Holmes (09:17):

Mark, I want to start with you here. What are you seeing in terms of current priorities for business leaders, really, regardless of sector?

Mark Holmes (09:23):

Yes, you know, it’s interesting because I’ve been traveling quite a bit. So, I was at the Hanover Fair in Germany about, maybe, six weeks ago now. I was at the Gartner Supply Chain Symposium and then at the North American Supply Chain Summit. And you know, it’s very interesting. So, many people came up almost with the exact same discussion point that it’s all about optimizing data.

Mark Holmes (09:41):

It’s not about — I mean, one person just said to me last week, you know, it’s not about the application or the enterprise system anymore. It’s around how can we optimize that data because of all the disparate data sources that are out there, not only internally, but externally, the suppliers and all the logistics providers. But how do we harmonize and normalize to get real-time data accurately to then do something with it? Maybe it goes back into the application, but ultimately how do we optimize that business outcome into actionable insights?

Scott Luton (10:10):

Yes. So Ming, I’m coming to you next. But really quick, Mark, I love this harmonize then normalize and then maybe operationalize. And who — however many izes we can add to that? Ming —

Mark Holmes (10:20):

Right.

Scott Luton (10:22):

— what else would you add to that, Ming, in terms of current priorities?

Ming Zhou (10:26):

Yes, I mean, for the last 15 years or so, I’ve been helping our clients on digital transformation initiatives around the world, mostly around supply chain. And although I see priority changes from region to region evolve over time, a common scene still around data as, Mark mentioned, right? And the focus I see has been shifted a little bit over time, you know, or from early days, these are all about digitalizing information to drive automations, later on towards business intelligence insights. Today, I see more focuses on improving business outcome with real-time decision assistance.

Scott Luton (10:59):

So, Greg, I’m going to get you to speak here in terms of what Mark and Ming have shared, as well as what you’re seeing. I mean, right now you’re, of course, in Europe and having business conversations to Ming’s point with different, you know, different parts of the global supply chain. Your thoughts, Greg?

Greg White (11:13):

Well, I think that we’ve got so many terms for it, but again, what Mark and Ming are talking about is having technology do technology things, and even to the point of providing at least a recommendation if not a complete decision, right? I think for years we were afraid to let that happen, but now we not only should and can but have to embrace it because that’s what the coming generations in the workforce expect is for technology to do technology things and to leverage all that data to create. I wish I could come up with another eyes really quick, but all I can think of is the eyes of Texas are upon you. So, that’s as good as I got guys. Sorry.

Greg White (11:54):

But I think, you know, part of what you were talking about, Ming, is because of the expectations of the workforce. As they continue to grow, they expect that decision assistance and they’re — I can tell you — and I’m sure both — all of us can vouch for this, there’s nothing more frustrating than data that can only tell you things are — up. And you need — and we can’t give you the slightest clue what to do about it, but at least you know that they’re messed up.

Greg White (12:18):

So, that’s a very frustrating thing. I’ve always felt like that with the wealth of data that we have, there’s no reason that we can’t get to that next stage of saying, hey, by the way, your house is burning down, throw some water on it, or you know, or whatever analogy he wrote [phonetic] catastrophic than that. And we’re there now. There is enough data and with all of the izes, we can use that data to get us to what should the solution be, or at least what does the data tell us the solution should be.

Scott Luton (12:47):

That’s Right.

Greg White (12:47):

And then allow humans to use their critical thinking to determine if the data tells us enough to do that.

Scott Luton (12:53):

Excellent. And Greg, you’re capturing like major theme for today’s conversation for sure.

Greg White (12:59):

Oh, my gosh. Did I jump way ahead?

Scott Luton (13:01):

Not at all. You know, it’s one of our — it’s one of the messages we love to pound. Pound the microphone all about, right? Because there’s a massive opportunity. All right. So, really quick before we continue on with market Ming here. We’ve got a lot of folks. Jeremy’s celebrating. Arnold Palmers, which by the way, folks, will teas — tea and lemonade combined, right? Half and half. But Jeremy’s also asking a question, I bet we’ll address parts of as we get deeper, you know, talking about the impact of A.I. and how it figures into the evolution of supply chain data analytics, which I’m sure we’re going to touch on.

Scott Luton (13:33):

Let’s see here. Chris says, optionize not optimize. Hey, I like that, Chris. And what —

Greg White (13:42):

I’m thinking that’s ize, that is decisionize, basically, right?

Scott Luton (13:46):

Right.

Greg White (13:47):

Think of an ize that means decision.

Scott Luton (13:49):

And then Rod Nash [phonetic], great to have you here from Mumbai. He says, prescriptive analytics. But initially he put, perspective analytics. I don’t like that phrase.

Greg White (13:57):

Yes, I know that spellcheck got him there, but actually that’s a pretty good insight.

Scott Luton (14:01):

I’m with you. I love it. All right. So, Mark, let’s keep going with you here. I want to talk about, as we drill down a little deeper into one area in particular, what are the biggest data challenges facing supply chain operations today, Mark?

Mark Holmes (14:15):

Yes, in fact, I’ll give two examples that I think is really interesting. In fact, I love the comment around optionize, you know. It’s actually — literally a conversation that I had about two hours ago with an automotive company in Germany trying to optimize aftermarket parts into the dealerships. And it is actually that conversation, the point — the issue that they had, the business challenge that they had was that was the latency of data from all of their different systems, but enable to in real-time synchronize supply and demand, and when was their disruption between the two.

Mark Holmes (14:49):

But to be able to — they say, look, we can bring it into a control tower. But what we can’t do is we can’t move it into insights. Like so many of us have lost that tribal knowledge, the people wanting to be more productive and being able to create those insights accurately in real-time.

Mark Holmes (15:04):

But to be able to say, look, once we be able to, again, using the term harmonize, normalize, but what are the not only prescriptive insights, but prescriptive insights around options down to ROI levels. So, hey, what if I’m going to be late, but maybe what if I can move from two diff different distribution centers? What’s the impact of that? What if I can avoid expediting freight or maybe I can move it one distribution center and then expedite a small portion of another. What are all those options that have an ROI impact that’s being done in real-time and accurately assisting the line of business with that decision?

Scott Luton (15:40):

Yes, options are a great thing. And Mark, that’s a great comment. Before we go to Ming, Greg, weigh in on that because I think he’s talking some of your language there.

Greg White (15:48):

Yes, I mean all of that wealth of data allows you to understand what the options are or could be, right, or — and how to process them. So, I think that, you know, and I think the other thing we have to recognize when we talk about, as you did Mark, when we talk about what’s going on with the workforce, a lot of that tribal knowledge was in, here I go again, right? If anyone was here yesterday, you heard me say this yesterday too, it was in the baby boomer generation which despite what we may have thought initially about the great resignation. It was almost all baby boomers leaving the workforce with that tribal knowledge because they came — they started working in an era when not — when hardly anything was driven by data or even documented.

Greg White (16:34):

And so, a lot of that tribal knowledge has left the workforce at a tremendous rate and continues to until that generation is gone. And the rest of us, gen X, Y, and Z are all completely dependent on data because we were raised and, you know, and schooled in a period when you could collect and capitalize on data, even in its most rudimentary form.

Scott Luton (16:56):

That tribal knowledge piece in Mark’s response, that’s what I knew I was going to bring you in to address. Sorry, Greg we’re talking about that a lot. Ming, what else would you add when we talk about, you know, some of the biggest challenges, data challenges when it comes to supply chain operations today?

Ming Zhou (17:10):

Geez, I find — somehow, I find that’s one of the most difficult questions to answer. It seems easy now, there are many data challenges in supply chain today. It depends who you’re talking material in their digitalization transformation journey, right? For example, some people still struggle today to get the data into one place, integration-wise, right? Particularly with some legacy systems they have in place.

Ming Zhou (17:30):

Others, they may be dealing with, struggling with like a master data management. You know, they may be like one example Mark and I are talking to, they have 32 ERPs and they couldn’t get things together, you know, to go across all these different ERP systems. But if I have to pick one, I will say really it has to be how would you drive business value out of your data while you are having constant disruptions in your supply chain.

Scott Luton (17:58):

So, that’s — Greg, I think that’s a pretty good one to pick. I’m still stuck on 32 ERPs. I’m sorry. Ming, you got me stuck there. Greg, you respond, please.

Greg White (18:07):

I mean, that 32 ERPs is a very common refrain, especially in the manufacturing and brand — brands in the industry where they’ve acquired themselves to breadth of product line, right? They’ve acquired this factory or that factory or this entire company who acquired other companies, all of which had their own ERPs, or they may have all had it or many had a similar ERP, but different versions, different customizations, et cetera, et cetera.

Greg White (18:33):

So, it is a very common problem. And there are solutions built not to do an ad, but I’m wearing the T-shirt, which I won’t show you. There are many companies or solutions built to do that harmonization of data between those types of things. The old, you call it a pail, we call it a bucket of paint, whatever, or can. And those are the simple problems and those are very difficult to attack. But you know, once you reconcile yourself to however, you’re going to do that, and thank goodness it doesn’t always have to be a data cleansing effort, which takes about as long as an ERP implementation, you’ve enabled your business to capitalize, you know, as Ming is talking about.

Scott Luton (19:10):

All right. Good stuff there. Man, we’re not even a third of the way through. Got a lot of more good stuff coming. Mark, any final thoughts there? We’re talk about the biggest data of challenges. We’re going to — we’re about to move into some examples, some opportunities and a lot more. I’ll give you the final word, Mark, when we talk about what organizations are struggling with when it comes to data? Your final words.

Mark Holmes (19:28):

Yes, I think it’s, you know, Ming hit it spot on. And it’s how do we really get to business outcomes in the quickest way, the most accelerated way. So, accelerated time to decision, but also accelerated time to the right decision. Anybody can make a decision. But is it the right decision? And to what Greg was just saying, and I’ll lead this into when we talk about the ultimate control tower. But talking to a company last week, you’re exactly right, Greg, there’s so many acquisitions taking place today.

Mark Holmes (19:59):

So, this company I spoke to last week just did yet another acquisition. They have 62 different enterprise systems and 3,000 suppliers. And how — and their whole focus is how to optimize and manage data in a very accurate, real-time and effective manner, right?

Scott Luton (20:16):

Right.

Mark Holmes (20:17):

You can’t spend six months trying to put it all this together and make sense out of it. You need it done in weeks and you need an architecture to be able to do that.

Scott Luton (20:25):

You provided us with a wonderful segue, especially the first part of your response there. So, we want to talk — there’s a better way folks. And that’s the good news here. So, we’re going to talk about a couple different approaches that organizations are leveraging to find success, especially when it comes to real-time data, optimized decision making, empowered team members. And get this, one of my favorite phrases, overall supply chain orchestration.

Scott Luton (20:48):

Now the first one, Mark, you let the cat of the bag earlier. And. you know, when I see this phrase, Greg, I keep thinking of the Ultimate Warrior, if any of our wrestling fans out there, the Ultimate Warrior. But we’re not talking about Ultimate Warrior, we’re talking about the ultimate control tower. So Mark, tell us more.

Mark Holmes (21:04):

Yes, so look at it this way, from a control tower standpoint, of course, there’s a lot of control towers out there that are even either being developed within the company or purchased from an outside vendor to bring it in. And again, the same conversation this morning with this company from Germany is how do I ensure that there’s no latency and my data is most accurate coming into the control tower, number one. Especially in all the conversations that we have just had, which is so important.

Mark Holmes (21:30):

So, that data has to be accurate. It has to be real time. But the ultimate portion of the control tower comes to what do we do then with the data? We know that shipments are going to be late due to maybe it’s a hurricane, or disruption with a supplier, or simply a mismatch between the SKUs of the purchase order and the actual what’s being supplied against that.

Mark Holmes (21:50):

But the point is, what do you do with it? Because let’s say that you have on-shelf availability is key, or fulfillment in the manufacturing is key. You can’t be off when SKU, right? You can’t — it’s important. You’re going to miss a buy or you’re going to miss production.

Mark Holmes (22:02):

So, it’s important that if you know it is going to be late, what are my options? And there’s so much data and there’s so much going on with disruptions that you need assistance and be able to make that happen. Whether it’s optimized algorithms, maybe it’s A.I., maybe it’s M.L. The point is, ultimate is taking the data and providing predictive and prescriptive insights to be able to do something with that disruption.

Scott Luton (22:27):

Yes. And Mark — and Greg, I’m going to get your comment here. But you know, one of the things that Mark just shared reminds me of an old adage I learned at the beginning of my career. One of the only problems you can’t do anything about is one you don’t know anything about. But Greg, speak to what we heard there with the ultimate control tower, Greg.

Greg White (22:40):

I mean, I think we’ve all started to recognize that controlled tower — terms like control tower and visibility really mean the opposite of what, in general, they have meant, the opposite of what Mark and Ming are talking about. They show you the problem, but they don’t show you the solution, right? They — and they can’t drive you to the prescriptive or perspective or optimized insights. And I think that the value of what Mark and Ming are talking about is going the next mile to say, hey, we found this problem in your ecosystem and here’s what you need to do about it. I just think that is so incredibly powerful. And as we talked about, it’s going to be demanded by the coming generations, right?

Scott Luton (23:21):

That’s right. That’s right. All right. So, Ming, I want to shift gears here. I want to talk about Smart Data Fabrics. Of course, this is a topic that Mark and some of your previous appearances we’ve dove in to here, Ming, your thoughts.

Ming Zhou (23:33):

I want to continue from this ultimate control tower topic there. So, the control tower itself can be very sexy from outside. But really, to make it real to deliver value, it counts on many different things behind the scene, right? From real-time information, normalize consistent data, right? Different analytics tools to support whatever you need to do, business rules, you know, and all those things need to come together to help not only provide the visibility, but also help you to get the value out of the data to help your decision-making process, right? That’s the part we want to get to.

Ming Zhou (24:07):

I mean, if you think about traditionally how you can do things like that, you’re going to take a lot of different services, different part out and then try to integrate them together, have data transform from one form, one location to another form in another schema. Huge amount of effort and the resources required.

Ming Zhou (24:24):

In many cases, in supply chain, particularly, because supply chain changes so fast, you know. By the time you implement a solution using those approaches, the scenario already different? Basically, what you’re doing only give you partial value, not the original four value you’re expecting from it. So, what that really requires to make it successful is something what we call a Smart Data Fabric architecture.

Scott Luton (24:44):

And we’ve got a graphic here that might help folks fall along with you, Ming. You want me to go ahead and share that?

Mark Holmes (24:49):

Yes, please.

Scott Luton (24:49):

OK. Let’s do that. So, tell us what — tell us more about the Smart Data Fabric.

Ming Zhou (24:53):

So, I would like to use example to explain instead of talking about the technical details here, right? So, let’s give a simple supply chain problem. Say, if a warehouse manager, I get the notice and say, there’s a container ship — container delivery is going to arrive, say, three days earlier ahead of schedule. What should you do? Very simple scenario, right? But this person have to check a lot of different things to really decide whether he’s going to be able to find a space and able to receive it three days early, or maybe he’ll have to put the container, you know, put in the yard somewhere, or he have to reschedule with the carrier to say, deliver another time or for whatever reason.

Ming Zhou (25:31):

Now, before you can make that decision, he — first he will have to collect all the information, right? From — data from your warehouse management system, WMS, OMS, TMS, right? Get all the data. And then he also need to collaborate with other systems like with carriers, with the labor management systems, so on. And he — and in some cases, in order to say if he can really actually delay the receiving of the container ship, he may have to understand what’s the demand sensing look like for the product in certain regions, right? So, he may need some ML tools to help him out to understand the demand.

Ming Zhou (26:02):

So, if you think about all of these, there’s a lot of different things that you need to come together. Now, with Smart Data Fabric, all these features we talk about, you know, whether from data integration, business process orchestration, you know, all M.L., A.I. things, it’s all running on one data platform. And the beauty thing, because they’re running on one data platform, we can let the data to be shared and across all these different systems and solutions. So that way you don’t have to do huge amount of integration effort from data, from proper business process, from business logic perspective.

Ming Zhou (26:37):

And you can easily go in through the scenario analysis and come to a decision and say, hey, based on my current information through all my analysis, I can just reschedule it, the shipment for another day, another time.

Ming Zhou (26:51):

So, that’s what this Smart Data Fabric will give you. You know, you don’t have to go through all those different systems, trying to move data around. Data flows automatically through all these systems. And each of these capabilities, whether it’s M.L., integration, analytic, they just work together and share the same data to help you to accelerate the decision-making process.

Scott Luton (27:11):

Thank you, Ming. I really appreciate that. Mark, do you want to add anything to Smart Data Fabric before we move forward?

Mark Holmes (27:19):

I think — not really, to be honest. I mean, Ming covered it extremely well. I think what’s important to note too is, you know, data fabric has been around for a while, right? Forrester talks about it. Gartner talks about it. But we truly have taken it one step further to that Smart Data Fabric, so the smart section of what Ming stated, right? The embedded interoperability, the embedded analytics, the A.I. and M.L. Those two portions when they work within the platform is adding a significant differentiation but all used in that architecture, which is an ability to have a low cost of ownership. It’s very efficient and allows you to scale very quickly.

Scott Luton (27:57):

And Greg, before we move on to some of the data insights, scaling quickly in Smart Data Fabrics, your thoughts, Greg?

Greg White (28:05):

Yes. I mean, I think scaling is critical. The — even just the basics of harmonizing that data is really critical. But then being able to leverage it into those next tier types of solutions that we’ve already talked about, that’s absolutely critical for that. And, you know, it gets really technical. So, Ming, I appreciate you addressing that as an example, because this whole notion of data fabrics and all of the math and science and magic that goes into it is very confusing.

Greg White (28:34):

But I think that — you know, one of the things that’s really encouraging about the way both of you are addressing this is focused on the outcome, right? Even a Smart Data Fabric is just a hammer, right? We’re just trying to build the house better. And the way that you all have so naturally transitioned that into solutions, actual solutions, not just playing with or massaging data is really impressive. And it’s a spirit that a lot more companies need to take on. Because I think, sometimes, we fall in love with the problem and we — right, and we love to toy with the solution too much. But starting with, you know, my big motto, Scott, begin with the end in mind. I think that this is a very important perspective for people to capture as we talk about this today.

Scott Luton (29:20):

Agreed. And I want to echo what you said on the front end of your response there, Greg. The altitude that Mark and Ming, both of you all keep it at where it’s approachable for so many business leaders that may not be a technologist, may not be a big data scientist and keeping it approachable. You know, Ming, you’re a Ph.D. from MIT, if I’m not mistaken. And the approachability that you respond and share with –Mark, much like you, that’s so important in today’s age where we’re getting so much thrown at us.

Scott Luton (29:47):

But on a lighter note, Mark and Ming, free advice for the next — I know you all go to a lot of conventions and trade shows. Harmonize is something out there, you know, we hear it five times a day. We need to have a barbershop quartet talking — singing about supply chain harmony. I’m telling you all, it’ll bring people to the InterSystems booth. OK.

Greg White (30:07):

That is the worth the cost of admission, isn’t it?

Scott Luton (30:10):

Yes, which is nothing, right, or your money back, guarantee. All right. So, all of this brings us into the third thing we’re going to be talking about. Mare, we’re going to get you to start here. Predictive and prescriptive insights. Your thoughts, Mark.

Mark Holmes (30:23):

Yes, you know, in fact, I like what you said earlier, Greg, about the whole magic, right? You know, it seems like there is a lot of magic going on behind the scenes. But one of the things — we’ll talk a little bit about this in just a little bit. But I — it’s something — and I’m not going to be salesy here but, you know, it’s something that InterSystems has been doing for 45 years.

Mark Holmes (30:43):

45 years, optimizing data within a data platform. In fact, just give you two examples leading to prescriptive insights. This is a new data [phonetic], we’ve been doing this for a very long time. And one of the things that we — you know, just give you a couple fast facts. So, 80% of all medical records within North America runs through InterSystems technology. 80% of all medical records, patient medical records runs through our software. Also, you know, the Milky Way, right? The European Space Agency runs on our system to be able to map out the Milky Way.

Mark Holmes (31:17):

So, think about the billions and billions of sets of data be able to do that. But it’s leading to — so one of the things, just to go back to the healthcare with the hospital aspect of how prescriptive insights are used. Surgeons, literally, at the point of surgery on their app, on their phone and on their app, we bring, we push prescriptive insights if we think something has been overlooked that could injure that patient either before or during surgery. It goes in through that app.

Mark Holmes (31:43):

So, we do this extremely well and the insights that we bring into prescriptive is obviously used across verticals. I mean we use at SPAR, one of the largest grocery consortiums in Europe runs on our application. They run their grocery application on the technology that we have. They can sense demand patterns, right? They use heavy usage of insights behind it.

Mark Holmes (32:04):

And I think it’s good to know, I think around prescriptive and predictive insights, you know, everyone automatically goes to A.I. and M.L. driven, right? But not everything has to be A.I. and M.L. driven. You know, there can be things that you could do because think about it, to truly use M.L., you want to make sure that it’s predicting the right model accurately, right? But there’s ways of maybe relationship modeling, mathematical modeling, mathematical algorithms that we can go in and truly provide very accurately and very scalable prescriptive insights.

Mark Holmes (32:34):

So, it’s something that can greatly enhance the line of business in making the right decisions and optimize as we do existing systems. Like SAP doesn’t work really well when you get into an unconstrained environment, right? We can go from unconstrained to constrained and provide insights of how to be able to manage better maybe production sequencing. So, it is because of everything that Ming has been talking about, the technology to allow business outcomes as examples.

Scott Luton (33:02):

Yes. All right. So, Greg, I’m going to get your comments a minute. Ming, I want to — what else would you add to what Mark has shared here?

Ming Zhou (33:08):

Mark already covered the technical aspect, the algorithm piece. So, I’m going to stay away from technical side here. So, one thing I would like to add here, you know, there are many tools out there for analytics, you know, A.I., M.L. and so on. So, I’m not going to get into those details. But I see two critical things to make the successful for actual business.

Ming Zhou (33:26):

One is how easy are these tools is to be able to inject their customer specific business logic. So, people may think, all I need is a powerful A.I. tool ChatGPT, will that solve all the supply chain problems? No. If it doesn’t consider your own business logic, right? It doesn’t necessarily solve your problem, so that’s one thing.

Ming Zhou (33:47):

The other thing is, really, can a solution you get out of this tool, what was your existing system? Can that be easy to use in my control tower for example? Can I use that in my existing enterprise B.I. tool? Because I cannot say, I need to set up a whole — another B.I. system just to do one little thing, and it’s — which to use with ChatGPT, for example, right? So, really those two creditors sometimes get — didn’t get enough attention, in my opinion. But that’s critical to make the solution work for your business.

Scott Luton (34:16):

Yes. Well said, Ming. And I like how Mark took the technical side of that answer first. So, all right. So, Greg, what would — what’d you hear there and what do folks need to hear there, maybe?

Greg White (34:26):

I mean, I think it’s — I guess as technical — this is what Mark was talking about. The stunning realization that technology is guiding physicians who hold people’s lives in their hands, right? I mean, I think we all knew that to some extent. But when you hear it in this context where we have people who don’t want to let technology do their job because they know it better, this is literally someone who cuts people open and fixes them for a living. And it’s much, much more high stakes than anything we do or ever will do in supply chain.

Greg White (35:00):

So, if technology is at a stage, and I would think that after 45 years of evolving data management and use of data that you could fully expect that to be the case. If it is advanced enough to guide someone to keep a human being alive and enhance their life, in fact, then it’s something that we can use in a business environment, right?

Greg White (35:24):

And you know, to — Ming, to your point of being able to easily inject your business logic, that is basically being able to tell the system what your ecosystem rules are, your boundaries are, all of that sort of thing. And then having the system adapt to that. That is incredibly powerful. One, Mark, I have to confess, even after all the times we’ve talked, I didn’t know 45 years, I had no idea.

Mark Holmes (35:50):

Yes.

Greg White (35:50):

But that is — that was a really enlightening realization. The fact that it is used for surgery, right?

Mark Holmes (35:58):

Yes, the surgeon. The surgeon.

Greg White (36:00):

It’s incredible.

Mark Holmes (36:01):

Yes.

Scott Luton (36:01):

So, Greg, I’m going to to jump in there because healthcare was fascinating. And Greg — and Mark, you and I haven’t chatted enough about that. But what you’ve been holding out on, because you know I’m a big old supply chain nerd as evidenced by what I — my space shuttle I got back from the Smithsonian a month or two ago. I had no idea about with the European Space Agency mapping out the Milky Way. We’re going to have to sit down and dive more into that soon.

Mark Holmes (36:27):

Yes, that’d be fun.

Greg White (36:28):

Mark can really call himself a universal solution, literally.

Mark Holmes (36:33):

I like that.

Scott Luton (36:34):

So, all right. So, this is what I’ll do. You know, as many conversations as we’ve had Mark with you here, you know, going back a couple years, both in person and virtual, if there’s some things that we still new wrinkles that we don’t know about all the cool things in our systems is doing, I’m sure our listeners will have some of the same blind spots. So, I want to shift gears into, if you could tell us in a nutshell, you know, what exactly — tell us what InterSystems does, Mark.

Mark Holmes (36:59):

Yes, and if you don’t mind, maybe — you know, I’m going to call you out on this right to slide. If you don’t mind bringing that slide up.

Scott Luton (37:04):

Sure.

Mark Holmes (37:05):

Yes, because I think it’s important and it’s something that we can share. And, Ming, please jump in. I’m just going to be very brief because I don’t want to be too reiterative of what we’ve been saying. But look, been around for 45 years. We’re over a billion dollars in revenue. We are private, privately held with no private equity investment at all. Our CEO wants to make sure — and founder of the company, still here, in fact, he is giving a speech right now, is it’s all about the customer. What making sure the customer gets that business solution that exceeds expectations, and we make that happen day after day. And our platform is unique in the industry where, like, what we’ve been all talking about is that we have four embedded technologies that acts as one capability in one product.

Mark Holmes (37:50):

So, we can bring all of that data in no matter what format it, it doesn’t matter. Disparate data comes in, harmonized, normalized, it’s all in real time. It’s not another data warehouse or data lake. It literally sits at rest to use on demand. And then we can use our embedded interoperability, which is simulating work process flows and then — or embedded analytics, which can be either A.I. or M.L. or the algorithms and whatnot that Ming has been talking about.

Mark Holmes (38:17):

So, all four of those use in a Smart Data Fabric works together. And that is one of the major reasons why that we can accelerate time to value. Because you’re only dealing with one product, you’re not dealing with five, six. You’re dealing with one. And you know, that’s why we can go into Fortune 100 companies and give significant results in six weeks. You know, Ming, anything to add that I missed that you think is important to the listeners here?

Ming Zhou (38:42):

I think you covered it well. I mean the only thing I will add is what attracted me to InterSystems is the company is really focusing on bringing value to our customers. Not necessarily trying to sell just for the sake of sending technology. It’s really driving business outcome, that’s what really attracted me.

Scott Luton (38:58):

And that’s powerful, Ming. That’s kind of where Greg is kind of where we started this conversation, right? Not leveraging technology for the sake of leveraging technology. And I think we talked about — and Greg, I’m going to bring Jeremy’s comment here because it references this. Now, I’m not sure what a spanner wrench is. I’m going to find out. But I think we —

Greg White (39:14):

It’s just a ranch in England, that’s what they call it.

Scott Luton (39:17):

Oh, OK. All right. You learn something new.

Greg White (39:19):

It’s just a spanner. Yes.

Scott Luton (39:20):

OK. So, tell us — so, Greg, speak to whether you want to pick back on what Mark was sharing about the InterSystems mission or what Ming said there, which I think is pretty powerful focus simply on outcomes. Greg, your thoughts?

Greg White (39:33):

Yes, look, I think this is a model — obviously this is a model that works, 1978 to give you an idea, it was 45 years ago. We, all of us here were little kids then. So — I mean, I think it would be — it’s probably difficult for some of the younger generations to even think about the fact that people were using data in 1978. I’ll tell you kids, they were also using it in the ’50s, but not quite as effectively as this.

Greg White (39:59):

But I think the expertise, the fact that they have been evolving business model and understanding not just the data exists, but also — Mark said this, I think very diplomatically, kind of dragging their customers along to utilize this data. As he said, this whole notion of prescriptive is not new. I mean, I know companies that were doing it in the ’80s and I bet InterSystems was one of them. But we’ve been able to and because we’ve had boom times at various times. We’ve been able to ignore the efficiencies.

Greg White (40:37):

And particularly, I must let you know, not picking on any particular industry, but manufacturers and brands have such incredible gross margins and they foist so much of the risk in supply chain. For instance, off onto retailers and distributors that they haven’t had to be as efficient in their supply chain. And now the one thing that has changed that forces everyone to be supremely effective with their supply chain is visibility. And it’s not visibility in your supply chain, it’s visibility of your supply chain.

Greg White (41:10):

Children, right? Every consumer and even politician know what the supply chain is. And as all of us can relate to, we’ve all been at a cocktail party and said we were in supply chain, watch somebody’s eyes glaze over and see them go find somebody much more interesting to talk to. But now, people are aware that supply chain exists and what it — and there is no — there is nowhere to hide.

Greg White (41:30):

And now, finally, manufacturers, big companies who have grossed with the capital G margins have realized it’s not just a margin play supply chain. It is also an identity, a brand identity play for your company because nowadays people know that if Costco or Target run out of toilet paper, it’s not just their fault, right? Whoever Charmin had something to do with it as well, right? So, that’s what makes the — that along with incredible generational change in the workforce is what makes this so important now.

Scott Luton (42:11):

Well said, Greg. You spiked the football there. A couple quick comments. David Glover, congratulations on making a supply chain achievement. So, congrats to you. Jeremy —

Greg White (42:22):

So, David’s been drinking before he came on.

Scott Luton (42:24):

That’s right. He’s been celebrating. That’s right, Greg.

Scott Luton (42:27):

Jeremy points out, you know, silos and how that has been a big barrier to data analytics across bigger organizations. That’s right, we’ve talked about silo busting going back for years. A good comment there.

Scott Luton (42:39):

All right. So, Mark and Ming, you both brought some resources that we’re going to be sharing with a few folks. Is there anything else before we go into those resources? And of course, we’re also going to make sure folks know how to connect with both of you. Anything else you want to add about how critical this juncture we’re at, right? Where, you know, we have decisions to make and not making decisions is certainly that’s an option. Going back to options, right?

Scott Luton (43:05):

But man, what you can cost your team, your organization, your mission, you name it. And we talked about the healthcare industry where it’s life and death. Man, we can learn a lot perhaps from the healthcare industry. But anything else you want to add there, Mark, before we proceed to some resources that InterSystems team brought?

Mark Holmes (43:23):

Sure. Just real quick, I think, you know, some of the things that I hear when I’m traveling around disruption’s over, it’s so untrue. You know, we don’t know what the next disruption’s going to be. There are still significant disruptions, Bed Bath and Beyond’s out of business, you know. And there would be many more that would be going out of business. So, what does that do? Creates disruptions along the supply chain. So, we’re just seeing the beginning in my opinion, and we need to move along the digital transformation roadmap as quickly as possible.

Scott Luton (43:50):

Well said. Well said.

Greg White (43:51):

You know, Mark, that’s a really good point. I think because the general public lacked awareness of supply chain, they didn’t realize that that aside from the dramatic effects of COVID and the impact that it had on the labor force, which I will argue to my death, is the largest — I mean largest region — reason for impact on the supply chain. That ships got stuck in the Suez Canal before. That, you know, we had catastrophic freezes in Texas that caused the petroleum-based products to be difficult. We’ve had all kinds of disruptions on and across the seas.

Greg White (44:27):

It’s just that nobody knew about it before. That’s why I say that awareness and this visibility of the supply chain is the only change that has really manifested in a different understanding of the supply chain. The — we’ve had wars before — I mean all of these things have happened before, just nobody knew that it could impact the supply chain, right?

Greg White (44:48):

I think even, probably, 45 years ago, even supply chain practitioners — I would say, even 20 years ago, many supply chain practitioners didn’t realize that these kind of world events impacted the supply chain, at least not so broadly. But it is, it’s a very complex and delicate ecosystem. it’s really — probably a better term for commerce ecosystem that — you know, butterfly flaps its wings and tidal waves hit the west coast of California, right? So, I think there’s — that awareness is the biggest change. And the notion, I would love to meet whoever said that to you, Mark, the notion that disruption is over is silly and I’m curbing my language, right?

Scott Luton (45:32):

Right, it’s scary to have that — right now.

Greg White (45:36):

That is a terrible perspective. That is — I mean, I could really torpedo a company.

Scott Luton (45:42):

Alright, Mark, Greg, Ming, what a great conversation. I wish we had another hour to dive into some of this stuff. But you know, for the second time, I want to keep us on time and we’ve got some great, you know, going back to what Ming said all about the outcomes, right? All about the outcomes. We’ve got some great resources that will let you dive in deeper. So, the first one in our team I think is going to drop it down in the comments. That’ll be also be episode notes. Want you to check out this brief from InterSystems team on how to accelerate business outcomes with integrated decision intelligence. You all can check that out.

Scott Luton (46:14):

Secondly, I got a great — I need infographic on Smart Data Fabrics for supply chain. Going back to some of the things that Mark and Ming both shared there, trying to make things easier, which is good. And then finally, Mark, I know we wanted to drop the — if you want to learn more about some of the supply chain solutions that Mark and Ming both mentioned, you can visit intersystems.com/supplychain.

Scott Luton (46:37):

And Mark anything you want to add in terms of the good old WIIFM, what’s in it for me, right? We’re talking about these resources that folks bring. Mark, anything you want to add there as I make sure folks know the links are in the chat and episode notes? Mark?

Mark Holmes (46:50):

Yes, I think — just real quick and I’ll move it over to Ming, that for me, you know, LinkedIn. I’m a huge LinkedIn user. Please, by all means, you know, chat with me, send me some notes, be more than happy to follow up and have, you know, have a conversation, even if it’s just bouncing ideas off of each other.

Scott Luton (47:05):

Yes, Mark, you beat me to the punch. And we’re going to make sure folks know how to connect with you all. Absolutely. And Mark, as I going back to, I think how we teed you up, one of the nicest guys, most informed individuals in supply chain. You will enjoy comparing notes with Mark. I can tell you. Ming, anything you want to add about those resources?

Ming Zhou (47:23):

No, I think that’s a good coverage. And the same way, best way to reach me is on LinkedIn and I’m happy to take this conversation further with any of your interests.

Scott Luton (47:32):

Excellent. Greg, I’m going to get your final key takeaway, but Mervin is back. We want to say hello to a few folks here. Mervin, hope this finds you well. Let us know where you are now. When — Greg, if you remember when Mervin was joining us a while back, I want to say he was either —

Greg White (47:44):

Dublin.

Scott Luton (47:45):

Yes, Dublin. That’s right. So Mervin, hope this finds you well. Check out what he says. Supply chain is disruption. Managing it is another thing, said Mervin. And hey, Saad [phonetic], great to see you. It’s been forever. Best of luck to you as well. You and all of your associates there from — at BoxBrain.

Scott Luton (48:02):

All right. So, Greg, we have heard a lot of really good stuff, including some resources from Mark and Ming. And folks, as Mark and Ming both mentioned, you can find them on LinkedIn. We’ve dropped their links there. We’ve dropped the URL. We can learn more about intersystems.com/supplychain, some resources, you all checked that out. But Greg, all the ground we’ve made up here in the last hour, if there’s one thing that folks got to take away from this conversation, what would that be?

Greg White (48:29):

It’s why an outcome-based perspective is necessary because with — when you understand what your desired outcome is, then you do the right things that follow — that lead to that, right? You select the right technology, you decide — Mark, I can’t believe I missed this comment — a comment on this comment. You decide whether A.I., M.L. or just you know, straight mathematical algorithms or even just process enhancement is what or all you need to reach your outcome.

Greg White (49:00):

You don’t buy or technology for technology’s sake, as we’ve said here several times. But it’s important, not just for Ming and Mark and the folks at InterSystems to have that perspective, but for all of us who are seeking solutions to have that perspective to first, right, begin with the end in mind, but to first understand what it is we’re trying to accomplish. What is that end? And then what does it take within our entity to get that?

Greg White (49:25):

I have done, as you know, Scott, a couple thousand software implementations and I have seen some truly rudderless implementations despite that warning, you know, throughout the sales and the implementation process. And the result is always the same. I — the result of rudderless management is exactly what just had happened to Bed Bath and Beyond. Rudderless and utterly incompetent.

Greg White (49:51):

But rudderless is dangerous enough. Let’s not go the way of Bed Bath and Beyond. Let’s just — but seriously, let’s just understand our business and what our goals are and then challenge. And I know that Mark and Ming will welcome this challenge, challenge someone with a solution to tell us how they can do — how they can approach that and give us that outcome. Because it really — selecting any kind of service provider or technology company should be as easy as that. I have this problem. Tell me how you can solve it. Too many companies try to prescribe this, the methodology of the solution rather than just the outcome. Yes. And if you can train yourself just to think just about that outcome, these guys can get you there.

Scott Luton (50:33):

Yes. Greg, man, that was quite the benedictions, what we called that back in the day, going to church. I love that. You got to preach that louder to folks in the back, for sure. Mark and Ming, a real pleasure. Ming Zhou, head of supply chain product strategy with InterSystems. Ming, thanks so much for joining us today.

Greg White (50:50):

Yes, thank you.

Ming Zhou (50:50):

Thanks for having me.

Scott Luton (50:51):

You bet. And Mark Holmes, a dear friend of the show, senior advisor of supply chain at InterSystems. Mark, always a pleasure.

Mark Holmes (50:59):

Yes, thank you. Really enjoyed it.

Scott Luton (51:01):

Greg, always a pleasure. Hey, big thanks to all the folks behind the scenes, Greg, right? We’re stand on the shoulders of giants here, right? Amanda, Catherine, Chantel, Clay, Karen as well for this episode. Thanks for all you do. Thanks for all the folks that showed up. Greg, we had a bunch of comments, bunch of folks celebrating some of the thought leadership drop today. Greg, it was a good show, huh?

Greg White (51:19):

Yes, of course it was. Yes. What else do we do?

Scott Luton (51:22):

That’s right. And hey, thank you, Juan. Great conversation, as always. I appreciate that Juan from Dubai. Hope us find you well. And thanks for being here as always. And Jeremy says, one final note. Oh, Bed Bath and Beyond, you just enter it. I love that. What a great way to wrap. All right.

Greg White (51:39):

They got a sledgehammer.

Scott Luton (51:41):

Right, man. But whatever you do, folks, between Greg and Mark and Ming, we’ve tons of actionable insights here today. Deeds, not words. Do something with it, put it in a football and take action, move that mountain. But whatever you do, on behalf of our entire team here at Supply Chain Now, Scott Luton signing off. Challenging you to do good, to give forward and to be the change. And we’ll see you next time right back here at Supply Chain Now. Thanks, everybody.

Intro/Outro (52:07):

Thanks for Being a part of our Supply Chain Now community. Check out all of our programming @supplychainnow.com And make sure you subscribe to Supply Chain Now, anywhere you listen to podcasts. And follow us on Facebook, LinkedIn, Twitter, and Instagram. See you next time on Supply Chain Now.