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Lisa Ryan: Hey, it's Lisa Ryan. Welcome to the Manufacturer's Network podcast. I'm excited to introduce our guest today, Jonathan Porter. Jonathan Porter, a renowned supply chain professional, is the founder and CEO of PorterLogic, the supply chain stack for ambitious brands. As a Georgia Tech alumnus, Jonathan's warehouse management and industrial engineering expertise allow him to help businesses navigate supply chain and inventory complexities and achieve their full potential. Jonathan, welcome to the show.
Jonathan Porter: Thanks so much. I'm excited to be here.
Lisa Ryan: Share a little about your background and what led you to do what you're doing with Porter Logic.
Jonathan Porter: Sure. I started my career at Manhattan Associates, which is probably the top w m s provider in warehouse software. I spent five or six years implementing warehouse management systems, so I got immersed in detail with supply chain and warehousing. But I just found it fascinating. Honestly, warehouses are some of the coolest things.
So many processes must come together for that box you ordered online to show up at your door. And so many people need to understand how much goes into modern e-commerce. So yeah, I saw a lot of opportunities for efficiency improvements and ways to improve things.
I started the company coming up on about three years ago. But I came from a super entrepreneurial background and knew I would always do something. Of course, I had side businesses in high school and all that. But yeah, the timing was right, and the market opportunity was there, and we are excited to be doing what you're, what we're doing.
Lisa Ryan: It is fascinating. From a consumer standpoint, I drive by some of these huge Amazon warehouses in my area and wonder. How do they find the coffee I order can be there within a couple of hours if I order within the next 31 minutes?
Jonathan Porter: Yeah. It sometimes floors me what folks like Amazon and others are doing. Yeah, you can order in the morning and get it delivered a couple of hours later, which we used to think two-day delivery was remarkable. But no, now they're, like I said, Amazon and others, but many people are pushing the bar even higher.
Lisa Ryan: It was interesting, too, of the subject, but I was in the car with an Uber driver the other day. And Amazon has an Uber-type app for their drivers for delivery. We are seeing so many changes that even a couple of years ago, we would've never in a million years thought about using people in their cars instead of our beautiful company trucks to deliver products and get them there that quickly.
Jonathan Porter: Yeah, no, it's fantastic to see, but simultaneously, it's incredible that the technology is powering much of this, right? You could only do this with a lot of the underlying technology that's powering somebody to log onto an app and receive a task to do a delivery. And it just routes them directly to where to pick it up and where to put it out.
And that all has to seamlessly integrate with the warehouse management system and your order management. And it's just this orchestration of a lot of data moving back and forth that then is powering this consumer experience that, most people I've just mentioned, like most people, don't realize what's going on.
You order something, you click to buy, and you're mad. Now if it shows up two days later, you expect one day. But yeah. And. The number of people has ended up touching that box. It must go from a warehouse to a truck, train, or ship to another warehouse.
It is a; it's amazing what's going on in the background, and in a lot of ways, we now see the pandemic has shown us just how critical the supply chain is. Everybody realized very concretely we couldn't get toilet paper; we couldn't get proteins in the grocery store. And it's now.
Very much in the limelight or very much in the foreground. The supply chain is critical, and the technology is critical.
Lisa Ryan: Oh. How do you see the role of digital transformation in advanced technologies like IoT and AI impacting the supply chain?
Jonathan Porter: Sure. I'll be the first to say that I was a naysayer on AI until ChatGPT, as much as I hate to say that I'm a technology person. I've coded most of our products, and even I was saying, oh, AI's way overhyped. So, yes, there's maybe something there, but I. We've seen it happen with several technologies in the past where people say they're going to change the world. Blockchain's a great example. And then, there are some interesting use cases around blockchain. Don't, no doubt. But it has yet to have this profound impact outside of crypto and that industry. But AI and things that are happening with generative AI are transforming the way that we do business.
A lot of, even our marketing materials, right? We're using chat g p T to at least write a draft. There's still usually a human layer that interacts, but it's just. It's enabling things to happen much quicker and much faster. And then, especially, you layer that into the supply chain. There are just so many untold efficiencies that are coming out of automating things that people previously did.
IOT's also interesting when you get into the supply chain on the transportation and warehousing sides. Things like temperature sensors have been around for a long time. Companies are now starting to make those enabled to communicate with other systems. That's interesting.
But also things like pallet tracking, so in specifically in a warehouse, having digitally enabled pallets that know where they're moving, instead of people having to go and scan a pallet off, tell the system it's in a particular rack location, or instead of a conveyor having to constantly scan goods as they go around.
You can now have connected pallets and connected chips that go onto pallets so that you know where those are at all times. A plethora of use cases have started emerging because of things like 5G that's enabling connectivity in places we didn't have before.
So it's several factors that are coming together. AI is one of the most exciting. Flashy ones come to the front, but it's taking multiple technologies all coming up simultaneously to work in concert. That's then empowering some of these new digital transformation initiatives.
Lisa Ryan: And just the speed of technology. I remember when Google started coming out, and we're like, oh my God. Ask a question, and we can find the answer. And then when one of my friends got G Ps for his car, the time and the car was talking to us, and now that, just within the last couple months with chat G P T. One of my friends was like let's ask it to write this, whatever it is. And yeah, we have the human to it, but you're no longer; you no longer have to look at a blank piece of paper. So yeah, you can have something that gives you that head start. And so the user-friendliness of technology is what's helping people to get over their fears and try things that, even a couple of years ago, we would've never, ever thought that we could do as much as we're doing.
Jonathan Porter: Yeah, it's outstanding you say that. I was reading an article recently from somebody at OpenAI. It might have been Sam Altman. They're like c e o, but somebody was talking about how they had the actual model and underlying technology for a long time, but then they had to put a lot of forethought into the user interface.
What would it look like for a person to interact with this technology? And that's part of why chatGPT has explicitly catapulted. There are a number of these AI projects going on. Several generative ai or companies are doing AI art and all kind of stuff, but that person and where the person interacts with the software and the technology is critical. And it's also part of why so many people fear AI taking over the world. And I don't think it will take over in many ways people expect because there will always need to be some human input.
That point at which humans interact may change. As a result, we may have to learn how to work with technology differently or in a different capacity. But at the same time, it will take a while for an AI to fully replace that human decision-making or input.
Lisa Ryan: What are some of the strategies that you see that manufacturers can implement to ensure seamless end-to-end visibility throughout their supply chain?
Jonathan Porter: Great question. Visibility is interesting, especially over the last few years, because visibility first meant knowing what was in your warehouse. That was step one, right? Or knowing what was in your supply chain. We saw, though, over the last couple of years what happens when there are disruptions farther upstream. And now, the conversation is about tier two and three supplier visibility. Not only just seeing what you have, but what your suppliers have, what have they, and what their demand even look like, right?
Will they be able to fulfill your demand given everything else they have going on? You see some interesting things happening with predictive. Say you have a supplier in an area of the world where an earthquake just hit, and some systems can monitor things like news outputs and start giving you predictive alerts around.
If a supplier is going offline soon, you should redirect some of your demand. And it's, it all comes back to visibility, though. And what we mean there is just pulling data into one place. It's as simple as that. So you need something that's going to pull that together.
You'll hear words like control tower specific; it's talking specifically. A control tower is often a system that will sit on top of your and other systems to pull data together. You can build control towers with tools like Power BI or Tableau; that's just a system to pull data together and do some aggregation.
So then you can see whether it's in a dashboard or something visual. Okay, here's all my inventory; here's inventory that's in other places. Here's inventory that's on the water, right? You may be pulling in container data or something to see what's on a vessel, what's at a port.
Being able to paint that whole picture is an important piece of it. You can see everything you have on order, what you have in transit, and what's shipped, but you must get to your customers. So a piece that's flourished is pulling all that data together in one place, whether a control tower or another reporting tool.
There's a part about data. Of course, we need data to figure out all this stuff, but on the other hand, it's almost overwhelming as far as what data is the right data to have and how you determine that. So what are some examples of how Porter Logic has helped manufacturers optimize their operations and data collection for inventory management?
Yeah, that's a major piece of what we do. We are a warehouse and inventory management system and a native integration layer to pull data from other systems. Specifically, we have a customer that is a retailer. And they have 12 different fulfillment partners.
So they're not doing any of their warehousing or fulfillment. They're relying on third parties three, pls. And they needed a way to pull all this data across company lines into one place. So that's where talking about tier-two supplier visibility or The challenge; it's the cross-company lines.
How do you do that exchange of data? And you had technologies like EDI, which unfortunately are still around. They're antiquated at this point. But it is being able to pull all that data from various sources. So doing things like APIs, we read APIs from a couple of different systems, but also being able to pick up emails like, I know that's crazy and simple.
It could pull an automated email or receive an email with an attachment we could process. That's something that our technology can do. And that allows us to pull in data from all kinds of sources. For that particular customer I was referencing, all of their three, please send inventory data into Porter Logic.
And then, they can log their inventory system again into Porter Logic and see all their inventory in one place. The other major component of our platform is a low-code UI builder or a user interface, a screen builder so that you can then build grids, charts, dashboards, and interactive screens to tell the system some data. It can be making decisions on that.
For this particular customer, we built them a network-wide and then a buy warehouse view. And then, they have views into their kits and their finished goods. And so once you get the data in, you can slice it in many different ways. You can interact with it. We do things like, push Slack reports to their marketing team so that their marketing team gets visibility into inventory, but they can have partial access to the system.
They need primary inventory data to run promotions and things like that. So we send preemptive alerts around low inventory, and we do threshold checks to make sure if they are running low on particular items, that maybe they have that in a different warehouse, and they didn't know that.
And so we're correlating a lot of that in the background. Then we're presenting this concept of managed by exception. Your users don't have to look at everything. It's what we're calling out; here are the five things you must focus on today. And that allows companies to repurpose a lot of the time previously spent, combining over data spent all in Excel sheets and just trying to wrangle all this together.
Now you can do value-added work. So here are my five problem areas, where I should focus, and where I should be putting my time. And yeah, it allows for all kinds of efficiency gains and just improvements in operations.
Lisa Ryan: When it comes to this, if you have a manufacturer that's been around for a long time and may need to have all the bells and whistles and technology that they need, how would they even know where to get started?
Jonathan Porter: Yeah, that's a great question. And much of the advice we give to companies we're talking about is to start small and hacky. Start in a spreadsheet. Start with something simple, quick, and dirty because you need to validate or try something new to see if that will even work.
For example, say you're a manufacturer and do you have a legacy ERP or some legacy system you know may not. Exactly optimal. But yeah, you're at that point of how you even get started, right? It's this behemoth of a project. Our advice often is to find a painful tiny sliver.
It could be the processing of your return or your food waste if you're like a food manufacturer or something. But pick one very painful but contained section to dive into and then try things in ways that might not scale. That's one of the things that you learn as a startup.
But more prominent companies and more established companies can take advantage of is do some things that aren't going to scale Initially. Experiment and try some things and do it in Excel, do it in Slack, do it in just basic systems. And from there, if you find something that works okay, it's time to automate it and find a system.
But initially, many companies spin their wheels by saying, okay, we know returns are a problem, so we need to find a returns management system. And they spend months pouring through every RMS out there and trialing and proof of concept. Then, when their problem may be something completely different, that's manifesting itself in return.
But they'd done a lot of experimenting and trying. In that case, they might have identified that the problem was they needed a system that they needed a customer experience system to manage the customer side of it, less the warehousing side of it. In any case, just an example of where you can click in on one area, do a lot of trialing and experimenting to find out what the actual root cause is, and end up saving probably a lot of time than just trying to layer on and integrate some other system that you don't know if that's solving your actual problem.
Lisa Ryan: Wow. How would you recommend that manufacturers approach demand forecasting and capacity planning so that they could manage some of those fluctuations in production?
Jonathan Porter: Sure. Yeah. Demand planning is an exciting topic because, historically, the vast majority of demand planning and forecasting was based on sales and previous sales data, right? So we would look at the last couple of years, and we would run an average or a median, and you would add some growth multiple, but that was heavily predicated on what your sales were over the last couple of years.
That doesn't work right now because of Covid, and the pandemic demand has been everywhere. And supply chain disruptions have caused demand patterns that don't accurately reflect consumer demand. So your sales data may be low, but that might be because you were out of inventory for six months.
And you can only rely on a few historical models to do accurate demand forecasting. So the gist is you have to figure out a way to. Accurately figure out what that true consumer demand is. And so one of the tips that I give people is to look at competitors. For example, say you're trying to launch a new blue T-shirt and want to figure out how much or how much you should order now, given that you're calling six months later.
That's another piece you constantly have to factor in lead time. You always have a factor in manufacturing time. So from that point, look at a competitor also selling a similar blue shirt and extrapolate how much they are selling. How much and what is their demand for it to give you some of the true consumer demand right now?
There's another try. Things you can try throwing up a fake product on your website like that also sounds crazy, but if you want to sell a new blue shirt, Go ahead and put it on your website and see what kind of demand you get in. You can always cut it off after a hundred orders.
But see what time it takes you to get a hundred orders of this new product, and that can help you gain what the genuine demand is right now because that is what you should be planning on. So I advise companies that if you're still trying to use historic sales, some industries could have been more effective that would still work. But, still, most industries have been through some upheaval throughout the last couple of years, which makes your sales data less accurate right now.
So yeah, get creative, but look for that true consumer demand number, and that's what you should be planning and forecasting around.
Lisa Ryan: But it's something that just when you were talking about that, it just reminds me of in my industry speaking in the training of how many times we develop this fantastic training program and finds people don't necessarily see it as fantastic we do. And so just coming up with an I,