Intro/Outro (00:08):

It’s time to wake up to TECHquila Sunrise. I am Greg White, your supply chain tech advisor, with more insights into what you need to know to succeed in supply chain tech startup, growth, investment, and transformation. So, let’s tip a glass to another enlightening TECHquila Sunrise.

Greg White (00:36):

Hey, in supply chain transportation, reporting analytics and visibility leave a lot to the imagination. And, frankly, create a lot of frustration for the people who use them. So, I’ve experienced this a bit myself in supply chain planning, a little bit different industry, but we had the same issue. And as a practitioner in supply chain and a solution provider, I’ve seen it firsthand and it is incredibly frustrating. My goal for this episode is that we alleviate that frustration and risk to help you optimize your transportation effectiveness, your spend, and, of course, visibility. But even more, by the time my guest, Shannon Vaillancourt, and I are done today, you’ll know why you should expect more than just visibility, what a better alternative is, and also how to convert that visibility into meaningful and impactful vision.

Greg White (01:35):

So, let me introduce Shannon and what he’s all about. He’s the CEO of RateLinx. And it’s a transportation management visibility audit and analysis provider. And he’s not new to supply chain either. Shannon started RateLinx in 2002. In fact, we started our companies at, roughly, the same time. He’s often published and, in fact, is one of the few on the invite only Forbes Technology Council. Did I get that right, Shannon?

Shannon Vaillancourt (02:07):

Correct. Yeah.

Greg White (02:09):

He’s been proving it with solutions and improving businesses longer than most TMS and visibility providers have even existed. So, Shannon, thanks for joining us. It’s great to have you here again.

Shannon Vaillancourt (02:23):

This time we’re not quite at sunrise, so I’m a little off the TECHquila Sunrise. I think we will adapt and move on. But thanks again for having me on. I appreciate it.

Greg White (02:38):

Yeah. Of course. It’s great. You know, I think you and I share a lot of similar philosophies in terms of the why of our business, right? You are big on not just visibility, not just transparency, and not just the availability of data, but all these prescriptive insights that you can get from that. And instead of sharing kind of incomplete or even inaccurate or untimely data and analytics, you guys make sure that it’s all of those things, of course, timely and accurate. But then, you tell people what to do with it. So, can you share a little bit about that philosophy?

Shannon Vaillancourt (03:16):

Well, I think a lot of that just comes from trying to solve the problem. At the end of the day, when we look at our customers, really, what they’re trying to do is solve the problem. And they either have inventory issues, they got shipment issues, costs are up. Something is happening that’s causing them to go out and look for a solution. And rather than just giving them pieces where they then still have to do a lot of work on their side, we try to actually get to the root cause and go, “Okay. What problem are we really trying to solve here? And then, let’s make sure we put in the right solution for it.” And I think that’s where, you know, a lot of times people miss that mark. And, instead, what they do is they try to deploy.

Shannon Vaillancourt (04:05):

And that’s where we talk about a 3D approach. The 3D’s, where it’s diagnose, develop, deploy. And what we see is a lot of, not only providers, but also customers always try to deploy first. And then, they come back around and they diagnose the problem. Then, maybe they exacerbate it or amplify it. And then, they develop another solution. And that’s why a lot of these deployments of your off the shelf software that’s supposed to solve the problem takes so long is because they’re not deployed from the perspective of “Will it solve this problem?” I think it’s because they just don’t determine what the problem is. And then, the next step, really, is saying, “What’s my definition of success?” So, if you don’t have those two bookends, it’s pretty hard, I think, to get what you’re looking for in the market and actually solve that problem.

Shannon Vaillancourt (05:13):

And that’s kind of what I’ve learned from being on the implementation side on the early part of my career. You know, coming out of school with an electrical engineering degree and being thrown into implementation of these software products, that’s what I learned, is, you would see a lot of times where you’re like, “Why did you install this thing? Why did you buy this piece of software?” And they’re like, “Well, because I thought it would solve this problem.” And it’s like, “Huh. Well, now you understand why it’s not solving the problem because you really need this other piece”. Or you really can’t even solve it because you’re missing a fundamental piece of data or fundamental processes is just wrong within your organization. That’s the real issue.

Shannon Vaillancourt (06:05):

So, that’s why when we started RateLinx back in ’02, I just wanted to do it a different way. You know, I enjoy understanding how customers do what they do and make sure that we are actually delivering value. Because at the end of the day, if we’re not delivering value, what’s the point? I mean, we’re just a cost, and I just didn’t like that. You know, I like to actually make sure we’re helping move them forward. So, that’s why we’ve taken the approach we have.

Greg White (06:35):

So, you said a couple of things there that really hit home. One is, so many technologies are a hammer looking for a nail. And I think my experience, Shannon, has been that’s why some of these technologies get deployed first and the problem diagnosed second, I think, too often, when you have technology, you just want the customer to buy in because most companies make their money on selling the technology. And they’re selling that technology then they’re onto the next deal. They’re not worried about delivering on the value.

Greg White (07:10):

And the other is, you mentioned, they didn’t get the result they want. Often, what they really got was acceleration of the problem. Because if you automate a bad process, all you do is fail faster. And that has been an issue in the industry. But, you know, I think you guys take a little bit different approach, not only from the fact that you diagnose first, but also your implementation, it’s not Big Four-ish type implementation. It’s very light. More configurable than customizable. Right. And so, I mean, I feel like I’ve always used that methodology myself. And I feel like that takes a ton of risk out of the implementation process. And, yet, can help the solution attack the diagnosis.

Shannon Vaillancourt (08:08):

Right. I mean, there’s two things that we do very differently, I think, than most. One is, just our business model. We don’t charge for professional services, which is odd.

Greg White (08:22):

Again, a good way to keep from piling professional services on too.

Shannon Vaillancourt (08:26):

I mean, because if I’m charging professional service time, the only way I make money on that is I need to take time. It behooves me to be slow. It behooves me to not really quite solve the problem. And if I am giving you advice on, “Hey, I think you should do this or do that.” If I’m the customer it’s like, “Should I really? How much is that going to cost me in time?” for example. And so, we try to do very quick implementations because you actually can do very quick implementations. Because we’re not charging for professional services, it doesn’t impact us because that’s not our model. So, that way we can deploy quick and get a short time to value.

Shannon Vaillancourt (09:15):

And then, the second thing that I think is different for us is, our software is what we call extensible. So, we can extend its capabilities to really fit the need of the customer. And that’s where I’ve found, in my past, that a lot of times we would get to about 80 percent of what the customer needed. And we just couldn’t take it to that final 100 percent, and that’s because the amount of customization – because that’s all that you could do in the past – was way too much. Whereas, we extend its capabilities. There’s an architecture built for this. So, that way, the standard product piece, which is doing 80 percent of the heavy lifting, can get upgraded and updated, new features can get added, new technologies can get added. And it doesn’t impact the “customization” that we did.

Shannon Vaillancourt (10:13):

In fact, we talked to a customer yesterday, and they’re going through asking a bunch of questions about the product and how long has it been installed. You know, it’s like, “Oh, it’s been there since 2006.” And they’re like, “What about the amount of customizations?” And I’m like, “Yeah. It’s doing all these pieces that are custom to you. And I don’t think we’ve changed it since 2007.” And they’re like, “But wait, we’re on the latest version of your product and you haven’t touched that.” And I’m like, “No. Yeah, you’re on the latest version, we haven’t touched it because of the architecture and how it’s built.” And it’s like, there’s some peace of mind to that.

Greg White (10:57):

Because usually the opposite is the case, right? If you have customization, you often can’t upgrade because of those customizations, right?

Shannon Vaillancourt (11:05):

And can’t you? Or is it because you can’t because there’s no professional services?

Greg White (11:13):

Maybe. I mean, yeah, that’s true.

Shannon Vaillancourt (11:15):

It behooves you to not do it in a way that allows customizations if you’re charging per hour. So, that’s why, again, I’m here to help drive value for the customer, not get in the way. And I think that’s what caused a lot of friction out there. And, you know, we want to partner with the customer and really collaborate. And I think that’s missing a lot because collaboration is really built on two parties having the same goal. And some of the providers out there and customers, they don’t have the same goal. So, that’s why you feel some friction from time to time. Whereas, for us, we have the same goal. You know, we want to help remove exceptions, help deliver value, make sure we’re solving the right problem.

Shannon Vaillancourt (12:04):

And we get a lot of requests from customers that, we ask them, “What problem is this going to solve?” And they’re like, “Well, I’m hoping it’s going to solve this problem.” And it’s like, “Well, it might not. Maybe you should actually do something different.” And then, they may end off going and doing something with another provider because that’s really going to solve the problem and it’s not really in our wheelhouse. And I think that’s a unique thing in working with us and how we work with a lot of customers out there [inaudible].

Greg White (12:38):

Because that’s a refreshing way to do it. You know, that’s a really refreshing approach because, particularly in technology, most companies, they just want to get the subscription or license or whatever at any cost. And, honestly, I think that has been a big impact on the inefficiencies in supply chain. Companies like ERP, which are essentially souped up finance systems, a mile wide and an inch deep, getting into supply chain solutions has been hard on the industry because the solutions are – I wouldn’t even say they’re minimum viable product. They’re very, very so basic in so many of these companies. And I see it every day.

Greg White (13:25):

I came from the automotive industry where volume and specificity was very unique in that industry. You know, you might have a part that only fits one car out of one million, so you have to be very specific. And when I see, for instance, some manufacturers and transportation providers, the state of their technology and what they’re accepting as solutions, it’s eye opening. Because we solved so many of these problems in finished goods, in retail, and distribution. We solved so many of these problems decades ago. And by decades ago, I mean, two or three decades ago. And I mean – and I know you’ve seen this – solved in a way that people in some manufacturing companies and others can’t even comprehend. You know, it’s like magic to them. And I think that has a lot to do with the deployment model and the diagnosis model of traditional technology. And it’s refreshing to see companies that are both willing to say, “No, that’s not us.” But also when they do say, “Yeah, we can do it,” they can really do it, like really approach the problem and, not just provide visibility as we talked about, but real prescriptive insights.

Shannon Vaillancourt (14:53):

And I think sometimes the problem with visibility is, it’s not really solving the problem. It’s just giving you a more automated way to handle the exception. Or it’s giving you a software way for the person to click a button instead of them having to deal with a piece of paper. I won’t even say automated, they’ve digitized the poor process that the customer was doing. And thinking that it’s being delivered through a computer screen to a human is their way of automating it. Because they’re like, “Hey. Now, you just click the button instead of having to write this on a piece of paper or staple it and file it.” And it’s like, “No, that’s not really.” It’s just, you know, showing them all of their information on a computer screen. That’s visibility. And a lot of people out there, they have data. They have a lot of data. But like we talk about on the data quality side, is it accurate, complete, and timely?

Shannon Vaillancourt (16:01):

And sometimes people forget what the complete part is. Because we talk about data, and it’s like, “Well, is it the right data?” I don’t know. And that’s the complete part. So, are you missing a component of it? Yeah. It’s accurate. You know, the number is five, but it’s five what? Five what?

Greg White (16:24):

Right. $5? £5,000?

Shannon Vaillancourt (16:27):

Yeah. And think about if I’m storing it that way or – that’s a good one, you leave currency out. It’s like, “It’s five.” And it’s like, “Oh, that’s good.” And it’s like, “Well, is it £5? Can$5? US$5? ₱5?”

Greg White (16:45):

So, particularly in transportation, this fascination with visibility is, I would say, relatively new. I know it’s been around forever, and in as much as you started RateLinx in 2002, and you have evolved to this prescriptive model, I mean, this is just my perception of what many people mean by visibility. And that is, visibility is kind of a new age term for reporting, or analytics, or business intelligence, or whatever. It’s not always that. But whatever we have evolved to try to upgrade the name of that over the years. But I think, you saw something when you started RateLinx, and I’m curious, one, why do you see so much momentum around visibility in the last several years? And what did you see then that allowed you to get to where you are now? That, in my estimation, seems to be so far ahead of just plain visibility. It’s actual prescriptive insights and recommendations. What to do with the visibility is what I perceive that you do. Is that fair?

Shannon Vaillancourt (18:07):

Yeah. I mean, it’s exactly –

Greg White (18:08):

Okay. How did you get there?

Shannon Vaillancourt (18:11):

To me, it just makes sense. Part of it is, I’m wired to be pretty lazy. And, you know, there’s a good lazy and there’s a bad lazy.

Greg White (18:26):

I’ve heard you get up at, like, 4:30 or 5:00 in the morning, so you can’t be that lazy.

Shannon Vaillancourt (18:31):

I’m an early morning guy. But I’m about efficiency. That’s the lazy part of me. And there’s one thing I hate doing, and it’s the same thing again. I hate it. That is the one thing I will not do. I won’t do something twice. Why? And it’s like, well, the reason why you had to do it twice or three times is because you didn’t do it right the first time. So, you think about data and if it’s showing me something and then I’m always doing the same action, what’s the point? I don’t understand. You’re not adding any value to the organization. And that’s kind of how I’ve always thought about this stuff. It’s like, “Man, wouldn’t it be nice if when you see this value, you don’t have to go through that thought process again of, ‘Oh, what do I do?'”

Shannon Vaillancourt (19:20):

Because I’ve also learned that depending on the person that you put in front of it, they may interpret that differently. And, again, this is the engineer side. Every time you map out a process, you circle the parts where there’s a person who has to make a decision and that’s a risk point. So, it’s like, let’s just remove the risk points from the process and make the computer do it. And that’s where I think the prescriptive insight comes into play. I think that’s where, now, what companies are realizing is, you talk about and you read about all the talent gaps. “Oh, my God. We’ve got this big talent gap in logistics. What are we going to do?” And there’s no talent gap for execution. You can get anybody to click a button, but is that the right button being clicked at the right time doing the right thing? That’s why there’s a talent gap because all of these solutions out there are all about executing, not about making the decisions. And why would I have to ask you to click the button if you clicked this button 100 out of a 100 times when this data looks this way? Why would I do that? That makes no sense to me.

Shannon Vaillancourt (20:35):

So, that’s how we’ve built our system, where the prescriptive insight can not only tell them what to do, but it can actually do it for them. And then, just tell them it did it. “And by the way, it just updated your routing for you because it hit this condition.” You know, this carrier was not on time, or there’s a new carrier in the mix that fits the model better, or the product goes better on this carrier, or goes better in this mode. The system can see that. It’s the same decision making that we’re making.

Shannon Vaillancourt (21:11):

What’s interesting is, when it comes to making decisions, what’s fascinating about the human brain is, the human brain is the greatest pattern recognition computer in the world. So, what’s held computers back from the AI and machine learning is their capacity to learn. It’s just capacity. It’s computing capacity. The more patterns that you can recognize, the computer, the more like our brain it gets. Think about the earliest form of pattern recognition is knowing who you’re talking to. So, it’s like I see you on the video call and it’s like, “Oh, that’s great.” I recognize a face. That’s the pattern I’m recognizing.

Shannon Vaillancourt (21:54):

So, now, think about in your daily job what you’re doing. You’re getting all these inputs and you’re recognizing the pattern, “Oh, this input is bad. I need to now take this action to it.” Now, imagine I’m giving you bad data. It’s not complete. Isn’t that when you make a wrong decision? It’s not because you’re a bad decision maker. It’s because you didn’t have complete data or the data wasn’t accurate. That’s what causes it. You know, think about our kids. Kids are the greatest form of no data quality. They always leave something out, don’t they?

Greg White (22:36):

Yeah. Well, and they learn, right? I mean, they learn based on the data that they’re presented, whether it’s right or wrong.

Shannon Vaillancourt (22:45):

Well, they recognize the pattern. They’re like, “If I tell dad this, there’s no way he’s going to let me go. Because the last time I did it, he said no way. I’m just going to leave that part out.”

Greg White (22:57):

So, it’s interesting you say that because there are a couple of things that I recognize. One is, I think people misunderstand AI, and machine learning, and technology logic. And that is really what we are doing with technology is, we are imparting the knowledge of humans. We’re validating the accurate data is always there. I mean, think about how many times, Shannon, you have experienced this. Somebody has to sit there and press the button because they know some element of the data – and it’s always the same element of data – is inaccurate or unavailable. And this person has to make a discernment based on that. So, there’s a couple things, obviously, we need to do. We need to fix the data and provide that better data to that human. And then, the other is, when that becomes repeatable, we need to impart that knowledge from that human into the machine. That’s why it’s called machine learning and artificial intelligence, because most intelligence is human intelligence imparted as a start. And then, that pattern recognition occurs from learning from that.

Greg White (24:12):

The difference with technology is, technology is never emotional. It never forgets the solution because of the stressful event during the decision-making process. It never forgets to apply all the data that’s available because it’s programmatically developed. And its decision-making is more consistent because of taking out the frailties of the human condition, those things like emotion, forgetfulness, stress, whatever that causes to make those mistakes even when we are as equipped as the AI to do it. And it takes a lot of burden off of human beings to be able to do that. And when we can provide a technology that better guides the human when they’re necessary in the process and gives them all, and complete, and accurate data that they need, and when they need to look for exceptions and things like that, the prescriptive insight or the recommendation, it becomes the recommendation unless I know something that the data doesn’t reflect. And it’s pretty much as simple as that. Do you think?

Shannon Vaillancourt (25:36):

I think so. I mean, to me, it’s like when we talk about prescriptive, I always think about medicine and going to the doctor. And it’s like, you know, I went in last week, it was for my physical. And I had lots of visibility to all of my numbers. I don’t know what it means. They take three vials of blood and they run it through everything. And they’re like, “Your whatever number is 21.” And I’m like, “What does that mean?” I don’t understand. But I had visibility, so isn’t that what you’re supposed to have? I don’t understand.

Greg White (26:16):

That’s a really good analogy. Just because you have that visibility, doesn’t give you any ability to do anything with it.

Shannon Vaillancourt (26:24):

When I think that’s where there’s this knowing versus understanding. Visibility is knowing. Knowing is a bunch of static backs. Understanding is more of an active process. It’s the ability to take these facts in context to build the big picture. So, you take all of my numbers, you put it together, and they’re like, “Yeah. You’re okay. You don’t need anything. You seem to be healthy. You’re doing well.” And then, they show you your ranges based on your age and all that stuff. And it’s like, “Oh, I’m right in the middle or I’m near the low end of stuff, so that’s good.” And, again, that’s understanding. That’s putting it into context and getting the big picture. And that’s ultimately the gap that you have with visibility. You’re giving visibility to people and, now, you’re relying on them to actively take that and build the big picture. What if they build it wrong because they just don’t understand? I mean, that’s what you always hear from somebody, right? And they’re like, “Oh, I didn’t understand what that meant. But I saw it. I knew it. I didn’t understand it. And that’s the prescriptive insight. It bridges that gap.

Greg White (27:45):

Why do you think it is so rare to take this approach of going from just visibility to a prescriptive insight or recommendation?

Shannon Vaillancourt (27:56):

It’s understanding.

Greg White (28:00):

It’s hard too.

Shannon Vaillancourt (28:02):

Well, a difficult thing to do. And it requires a lot of experience. So, if I’m a pure technology person, I could come in to this industry, and I can connect data, and I can collect data. But what I don’t know is I don’t understand it. I just don’t. So, it’s like, how would I give you a good suggestion on that data if I don’t understand it? And I think that’s what makes it hard, is, there’s a lot of technology people who came into transportation who are like, “Oh, heck. I can just connect all this stuff together. I mean, here’s an API.” That’s when you start hearing all the buzz words. That’s kind of my red flag. When you’re hearing technology buzz words in the marketing, it probably means they don’t really understand the industry because that’s not how you would think if you were in the transportation department or on the supply chain side. You wouldn’t care if it’s an API, or if it’s EDI, or if it’s Jaison, or whatever the heck else they’re using. You just want to know, “So, is this good or is this bad? And if it’s bad, what should I do about it?” That’s all you care about.

Greg White (29:20):

Yeah. That’s right. That’s the problem and the solution. And, to me, you’re right. The technology itself, it’s the hammer. It really is. We’re trying to build a roof better and faster. And the technology is a better hammer. So, you’re right, when people start focusing on what the technology is rather than what the technology does or what it means, I mean, those are principles that I like to live by. There’s what it is, what it does, and what it means. And the what it means is what really matters. You’re going to make millions more or whatever in your company, or say, millions, whatever. The what it does is, it gives you these educated recommendations that allow you to make better decisions. And what it is, is, that’s kind of the due diligence aspect of it. “Oh, AI helps with that.” Or, linear technology, or APIss, or EDIs, or whatever, all those I’s and whatnot, all the acronyms. So, we probably ought to tell people who you work with at RateLinx. So, who are you helping to solve problems for with the tech that you guys have?

Shannon Vaillancourt (30:43):

So, from a vertical perspective, pretty much every vertical out there, whether it’s retail, manufacturing, e-commerce, you name it. And we’re typically in the supply chain area and we’re starting to leak over into the data area, because we’re finding that a lot of companies now are starting to create data organizations within their group. And, actually, we’re starting to leak into that area now that it’s becoming more prevalent out there. Because a lot of companies now are looking for – what they call – an integrated platform is what we’re running into a lot. And, really, again, it’s just another word. If we go back ten years ago, they were looking for big data. Then, five years ago, they were looking for blockchain. Now, it’s called an integrated platform. It’s the same thing. They’ve been looking for the same thing forever. They just keep calling it different names.

Shannon Vaillancourt (31:39):

But that’s really what they want, is, they want to grab all this data, integrate it together, and that’s where we look at it as your IQ. What’s your data IQ? Is it integrated? And what’s the quality? Again, IQ stands for the integrated and the quality, and that’s really what everybody’s looking for. Because if I can take my transportation, what did I execute on? How did I do it? If I match it up to the tracking so I can see what service I got from it? And then, you match it up to the invoice. What did I ultimately pay for it? You pull those three pieces together and then you start adding in order information, products, customer IDs, things like that. Now, you’ve got your whole picture. That’s an integrated and a high quality amount of data because it’s accurate, complete. And then, make it timely so that way it’s accessible to the user. That’s how you’re going to make decisions. And that’s where you’re going to get the best, I think, prescriptive insights, the most valuable ones. And that’s really what we’re running into out there.

Intro/Outro (32:58):

How can I help you improve your shot at supply chain tech success? Four ways. One, subscribe to TECHquila Sunrise wherever you get your podcast to make sure you’re notified of my new episode every week. Two, follow me on LinkedIn and see my supply chain summaries every weekday. Three, if you’re a startup founder or growth stage leader and you need an active advisor to propel you through your supply chain tech journey, I’m currently considering select strategic advisory roles. Or four, if you need an incubator or investment for your supply chain tech, reach out to me on LinkedIn and let’s talk.