Guest Podcast

Sam Gupta (00:23):

Hello everyone. Welcome to today's show. And if you're joining for the first time, this is one of our digital transformation series for which we meet every Thursday at 5:30 PM Eastern. We pick one topic related to digital transformation, and we always have an expert panel for today. We have a very interesting topic. It's, uh, called dimensional inventory or matrix inventory, or there might be too many dimensions, I guess. You know, Chuck is going to be talking about that. Uh, and everybody's going to be building on that. So we are gonna have a lot of fun. We are going to start with everybody's intros, and then we are going to go, go right to the topic. Um, so if you don't know me, I am your host, Sam Gupta, principal at Elevate iq. Elevate IQ is the independent E r P and digital transformation concerning firm. On that note, I am going to move to Chuck for his intro.

Chuck Coxhead (01:18):
Hello? Hello. My name is Chuck Coxhead, and I'm director of Sales and marketing for Turnkey Technologies, a full service, uh, Microsoft Dynamics, e r p consultant and 35 year manufacturing, sales, marketing and organizational professional. And I'm gonna go back into my, before the 35 years started with the story today,

Sam Gupta (01:39):
And this is gonna be so much fun. Thank you so much for being here, Chuck. Um, Abu, uh, can I ask you to introduce yourself next?

Abu (01:48):
Sure, absolutely. My name's Abu. I'm the president and founder for Pan. We are a CX three partner, and we have been helping companies transform their distribution, industrial manufacturing, food and beverage business for the last 12 years.

Sam Gupta (01:59):
Okay, amazing. Thank you so much for being here, Abu. Uh, Tom, can I ask you to introduce yourself next?

Tom Rodden (02:05):
Gladly, Sam and hi everybody. My name's Tom Rodden. I am a longtime IT professional, uh, now about, uh, 25 years. And, um, about half of that was consulting. And I'll be drawing upon maybe like Chuck and, and others, I'll be drawing upon my, my consulting days a little bit more than my, uh, most recent, uh, position as CIO at Vary Medical Systems where it was a life science, uh, med device company. And this topic of dimensional inventory was not relevant, but, and some of my other consulting engagements, it was. And, uh, I think this is a fascinating topic, as you said, Sam. Um, so looking forward to it.

Sam Gupta (02:45):
Okay, amazing. Thank you so much for being here, Tom. Sharon, can I ask you to introduce yourself next?

Sharon Custer (02:50):
Hi, uh, glad to be here. My name is Sharon Custer. I'm an inventory optimization consultant in Inventory Optimization Pro, and my mission is to help businesses to manage their workflow inventory and increase their cash flow and profitability.

Sam Gupta (03:13):
Okay, amazing. Thank you so much for being here, Sharon. And if you're in the audience and joining for the first time, make sure you guys posted your questions and comments. We typically try to cover them during the show if in and out of time, our panelists are going to make sure that you receive your answers. On that note, Chuck, I am going to start with the first question, and as we were discussing in the pre-show, there are many layers to dimensional inventory, and every industry is going to have very different understanding and expectations, uh, of the matrix inventory. Now, I have seen crazy scenarios when it comes to the real execution, okay? Mm-hmm. <affirmative>. Um, so if you are going to have a system that is going to be compliant with the inventory that you are planning, that you are carrying, that you are manufacturing, distributing, detailing, whatever you are doing with it, uh, you know, if you have that system, then you are probably going to be okay. Otherwise, you probably need to work very, very, very hard <laugh>. So,

Chuck Coxhead (04:11):
<laugh> <laugh>,

Sam Gupta (04:13):
I'll let you start, you know, whatever stories or uh, commentary you have, Chuck.

Chuck Coxhead (04:17):
Well, yeah. So it was interesting when we first we heard about this topic, my brain went two completely different directions, right? The first dimension, dimension of inventory, we think about, um, we think about mechanical dimensions, okay? Yeah. In the air piece, you have an inch, you have an inch, you have a foot, you have a pallet. Okay? You have a length, okay? A length could be 20 feet, one piece, or you could have one that's a length. Uh, it's of 10 feet. It's still one piece depending on how the vendors might sell it. And I've experienced all of those things in my career. And then there's also another layer in that is de inventory dimension groups, which starts to look at other aspects of dimensional inventory. It's a different dimension of the different definition of the word dimension, okay? It, it's, it's a, it's a, it's a modeling word, okay?
(05:03):
It could be configuration color in addition to size, okay? You might create a de an inventory dimension that is around information that's pertinent to your warehouse, okay? Or pertinent to whether you might track the inventory or something to that effect. I don't wanna go too far a field because there's so much to talk here, and I'll go on forever. So I'm just gonna start by talking about something that happened before I even got into the career world. Believe it or not, I had a summer job, and my job was to program a system to optimize the use of a sh of pieces out of a sheet of plywood. They used to build cabinets. And then what they would do is I, I built a system, a, a, a program to say, okay, I need these pieces of this dimensions. They're coming, gonna port my four by eight sheets, what's the maximum utilization I can get out of this piece of plywood and minimize waste?
(06:02):
And this is a big problem in dimensional inventory, okay? You come with one sheet of plywood with standard dimensions in the US of four feet by eight feet, okay? Um, but you, you need to minimize your waste. And so the only way to do is either sit there and put a whole lot of work into it with a, you know, pen and paper and draw on that sucker, or you figure out some set of calculations, which by the way, there's no way in heck I remember, okay? Um, in order to do that. And of course that's doable, but that's some sort of add-on optimization, specialized for that cabinet making industry. And of course, it'll apply. Likewise, you know, there are, there are things where they might sell lengths of tube and they sell them in 20 foot pieces. Okay? What do you do with the leftover when you've taken six, three foot pieces?
(06:53):
You now have a two foot, how do I put it back in inventory am by putting it back into the foot, but it came in and it came in in 20 foot lengths, but if I just stock it by the number of feet, well then I'm gonna end up with this leftover that I can't use. I can use it, but how do I account for that? How do I know what's actually in my inventory? And is it cost effective for me to actually know, or is it more cost effective for me to throw it away? As much as I hate that for sustainability purposes. So it's just this amazing topic, not even getting into colors, right? Different wire colors, right? If they're different skews, no problem. If somebody's lazy and gives you a wire color and a skew, and they go, this one's red and this one's blue, this one's yellow. Now you've gotta look at a different dimension of inventory. It's just such a complex topic. And, and honestly, I'm, I'm looking forward to hearing the expertise and the stories from the other panelists because this is really cool stuff to me. I guess I'm just a geek and I love being one.

Sam Gupta (07:49):
Okay. Amazing. Uh, you know, set up there. I absolutely love these story about the lumber, and I think we are gonna have a little debate there, you know, from everybody's perspective. So, number one thing that you mentioned, and, and I am looking for everybody's opinion, uh, here in terms of what they think, whether they relate this with the dimensional or matrix inventory, or are they thinking about some of the process? So when you talk about lumber industry and lumber industry is very different in general, the way it works, okay? So if you look at the kind of e r P system, it has the kind of e-commerce, um, systems, it has the kind of, you know, it does not have sort of the CAD because it's probably going to have the b i M system, which is going to have very different bomb that comes out of it, that goes to your E R P.
(08:34):
And typically the challenge in that industry is that you are probably not going to have as fixed skew that you can carry in your, uh, you know, e R p. So some people might call it as configured to order, but that's not necessarily true either, because each of the dimension that you are gonna get, it's very, very, very different the way that the, the bombs look. Okay? Mm-hmm. <affirmative>. The second point I'm going to make Chuck on your comment is the process that you were describing as part of your summer's job. In my mind, that's slightly more nesting and I don't know how nesting is going to sort of overlay with the dimensional and matrix inventory. So, and the third thing that you had mentioned about the left over inventory, typically that is called the piece inventory mm-hmm. <affirmative>, but again mm-hmm. I don't know whether that correlates with the matrix inventory. So any follow-up commentary there by any chance?

Chuck Coxhead (09:24):
Uh, yeah, it depends on how you set up your system, honestly. You know, when you look at it, it would just go simple. We look at a length of tube, yeah, okay. Very often that's Stockton feet. Yep. Okay. But if you have something else that's in a length and it's stocked in eaches, okay, you need to make the conversion in your system. Yep. You're going to use it, you're going to use it in something that it may not be purchased in and your initial inventory, and otherwise you have to convert it when it comes in, when you receive it, you have to, you have to change it, which is of course, extra work. Yep. When your system can do it on the fly, the, the trouble becomes, depending on how it comes in, you then have these leftover pieces, and that's where less sophisticated systems and less sophisticated companies get into trouble.
(10:09):
They get into trouble because they now have to do a workaround. The system doesn't accommodate that. Okay. And it is usable. Okay. But not for everything. Now what, so it, does it become a di a different skew or do we put it in another, another variable in the system? Yep. Okay. That allows us to identify that as something else, and then we can use the piece parts are from this dimension, okay. As opposed to, we think only in terms of a single skew. So it really comes to what's the capabilities of the system and how sophisticated you are in your setup and anticipating these problems, honestly. So it's, it's not co-products, but it's of flavor of that, you know, it's a byproduct of waste, you know, it's waste, right? Frankly, so that you wanna make usable.

Sam Gupta (10:58):
Very interesting commentary there. Thank you so much. Uh, check for that. So Abu, I'm coming to you and, uh, the industry that you operate in, okay, that's a very interesting industry as well. And, um, the reason why I find that is specific industry, and this is going to be your food and beverage, agriculture, not so much, I guess, um, you know, as well as cannabis. Uh, and the reason why I find this interesting is because number one, there are two overlaps, okay? Number one is going to be slightly more overlap with the retail business. And retail business in general are very different from the ER p perspective. In fact, one of the LinkedIn comment recently, I was talking to one guy and he's like, I have never seen any e r P working in the retail situation over a billion dollar. They explode, it doesn't work.
(11:41):
Okay? So the only thing you can do from the a r P perspective is going to be you are simply going to be sending your GL entries. You cannot really have that m R P costing process, uh, because the transaction volume is so high. Uh, even systems such as, you know, SAPs for Han cannot scale that. Uh, okay. So typically they have very different retail system and you know, Chuck is gonna kick, uh, get a kick out of this one because, you know, those systems are very different, okay? They're gonna have your OMS layer, they are going to have your wms, they are going to integrate with tms, but no accounting embedded integration, okay? <laugh>. So it's not a real <inaudible> in my mind, okay? <laugh>, and, and Sharon is probably enjoying this because how do you sort of measure the inventory <laugh>, but that's how those businesses are running, to be honest, okay? So they don't know what inventory they have, but they are, they have been running for last 500 years. So Abu to you, uh, <laugh>, do you agree, disagree? Uh, what, what, what are your thoughts? Uh, you are on mute. Sorry.

Abu (12:45):
Yeah, so some of the challenges you described, you know, they're relevant challenges. Um, like I used to work, we did a project for a large scale Canadian retailer, and they said if the system say it's below hundred as you outta stock, right? So they, their system became inaccurate after a certain defined number. But I'm not sure how that relates to, um, dimensional inventory per se. I mean, I, I guess you can bring about, you know, for example, you have each is, you know, at the retail level, but you know, when you are talking, for example, juice, for example, right? So you are, you're measuring it in lits or, um, you know, a volume or sometimes kilogram and then you're packing it into units, um, and you're reporting that sale as well. Um, but typically dimensional inventory comes into play more outside of food and beverage, uh, business, you know, and in food and beverage, you know, cash weight with, um, is more of an issue, right?
(13:41):
You can perhaps call it dimensional, um, where, for example, you have a unit of, let's say you're going to the market and you're buying steak, for example. And you know, the unit, it's the product skew is, is a stake, but you know, it can have 9.5 grand or 10 grand or 10.1, and you value it based on the weight. So that that concept, you know, becomes a bit more complicated. Um, in the food and beverage, it's in the meat, in the poultry business. That's, uh, really where it becomes an issue, um, especially when, you know, you're reconciling afterwards. You buy a chicken and it's more of a reverse bomb, and you have to reconcile afterwards what's the actual weight, how much you're going to pay, uh, your vendors based on the weight while you're buying eaches of chicken, right? Um, that's where it becomes more complicated, uh, in the food and beverage industry.
(14:34):
Um, the other thing you've described are more of technical challenges. You know, it's a high volume. So you, you know, so you perhaps need, uh, different systems to deal with it, you know, retail business, it has its own very specific, uh, needs while you, you know, and if, do you, are you a retail only business? Do you own the warehouses? Um, you know, is it a vertically integrated business or that's where those challenges come in, uh, in terms of a systems perspective. Uh, you know, retail users system, they, they generally want to do very simple, right? They're the way the head office looks at data and how the retail clerk is looking at it is, you know, two different things, right? So that's where the, uh, complicated comes in. But in general, you know, when we hear dimensional inventory, when we are trying to do business transformation, you find like scare, right?
(15:19):
Cause it always has it, uh, complications. But some of the interesting, uh, scenarios I've run across, you know, it's sheet metal. Um, and typically these issues come more downstream in the industry rather than the company actually making those sheet metals, right? Because for them, they're probably doing in typical sizes, you know, they were fixed size for them, it's more of a weight issue, right? So you're buying 10, 10 eaches of sheet metal, for example, but they're charging you in weight of the sheet metal. It's when you come downstream, now you're using that sheet metal to make, you know, a car, for example, any other equipment or machinery. Now you have to start tracking that sheet metal. So the most interesting problem, for example, comes in, you only need half a sheet metal to make something, right? So how do you track that inventory? You're going to have each, the standard is, for example, let's say 40 square inch of sheet metal, you only use one third of it, right?
(16:14):
So now that's where the complexity starts coming in, uh, to manage it. And you need to talk about nesting software. The other in, um, you know, the interesting application comes in is, um, you know, cable rolls. So you have a huge cable roll, 500 meter cable roll, or 500 feet cable below, and you have retail customers coming in and you're selling 10, 10 feet each, right? So now you want to track each is, and how much of that cable roll that each has. Um, you know, that becomes tricky and you, you know, you typically need a specialized software, then you have additional characteristics to track as well, right? So for example, in your payroll industry, color, yep. Uh, size, um, manufacturer brand and so on. Um, other interesting examples are for them in the pipe industry, we have internal pipe sickness. You have what sort of coating is around the pipe?
(17:06):
What's the length of the pipe? Then they also define pipes by ranges, right? So they call a range one pipe, which is bigger in land than range two and arrange three pipe. So those are of things really make it complicated to, uh, track and measure it. Sometimes if you try to track too much, then it's just too much work for the company itself, right? So they initially they'll want all the tracking system and then they, it's too much to do it, right? So, so those are the more complex scenarios, you know, that we have come across. So,

Sam Gupta (17:36):
Yeah, so very interesting. Sorry, Chuck, do you have a comment? No. Okay. Um, so the reason why I had br brought, uh, the whole, uh, retail conversation, I guess, and I completely agree with you, uh, with your comment that the, the way the retail organizations are going to look at data is going to be, uh, the headquarter is going to look at the data versus your, uh, the actual store or the p os, uh, is going to have very different perspective. Uh, but in the retail organization, some, somebody has to plan, somebody has to ship those pallets. Those pallets need to go at the right time so that you have the right quantity in your retail store. And that's where the whole planning, uh, you know, component comes in food and beverage. There are some businesses that are going to have very retail flavor if they are very retail centric, if they are distributing in the retail fashion, but not everything. So I guess, you know, for manufacturers, uh, it may not be as big a problem. So I don't know about if you have any other follow up comment by any chance.

Abu (18:33):
Yeah, we, for manufacturers, typically they're supplying to retailers, um, right, so like Costco in the Walmart of the world, and typically they will drive the ordering system, uh, compared to the manufacturer itself. So, you know, so, so it is, the data is coming from them for the manufacturer, it's not, you know, as complicated I guess for them compared to, for example, Walmart where they have thousands of aisles and thousands of products that they have to manage and the space requirements and their ability to communicate with all the vendors, that's where the complexity is, rather than with the actual manufacturers.

Sam Gupta (19:08):
Okay, amazing. Thank you so much Abu for that. So Tom, I am coming to you, um, and, uh, I don't know, I mean, see if you're gonna have any sort of insight with this business. I know, um, even your, um, in your vertical, I mean, you are probably gonna have similar challenges, so whatever insights you might have related to this or any experiences, Tom.

Tom Rodden (19:28):
Yeah, no, I, I do, I really think this is a fascinating topic and, uh, I'm, I'm very happy to be here with others who have more maybe direct experience than I have. So I, I've, I've approached this maybe from a little bit more of an academic, uh, angle researching, um, how this, uh, concept has been defined and how it's been applied. Um, and, uh, you know, maybe a couple of thoughts just on, on fundamentals and then, uh, a couple of, uh, experiences, um, in terms of the fundamentals. Uh, you know, when I first was thinking of this, I said, well, you know, there are, there are aspects of dimensional inventory management that, uh, I've seen in even the big e r P systems. But, um, this is really way beyond, I think, or it can be way beyond what the, the classic SAP or oracle or, uh, big e r p systems are, are able to handle, handle well.
(20:23):
Um, you know, one, one example from my experience was, um, when I was a ge and we were in the lighting industry and we were handling, you know, product that was small incandescent lamps, maybe stacked on a pallet, but you could create a conventional size pallet that could go into a bin in the warehouse and, you know, into a truck and you can ship. Um, and, uh, and then there were the long fluorescent tubes. They had to be handled differently. They had to be, uh, stored differently. There were not the standard bins that could handle those. Um, they had to be stacked differently because they were long and fairly fragile. Uh, so you couldn't, you had to be careful how you stacked them. And so, um, this was handled though within a, a conventional E R p, um, by simply defining the warehouse in a certain way and classifying these products in a certain way.
(21:10):
And so put away strategies when you'd receive them into your warehouse could be handled without truly complex dimensional inventory capabilities. So that was kind of one of my exposures. And I said, oh, this, this is an interesting topic, but, you know, is it all that complex? And then I started to understand some of the stuff that Abu and Chuck are talking about, where it goes or can go way beyond that level of, you know, just one physical dimension that drives a certain way of handling inventory. Um, where, you know, you get into, whether it's the lumber industry or it's the apparel industry, or it's the, the, the steel industry, and again, I agree violently with Abu as well. It's not really the, the, the, um, the people all the way up the value chain, creating the sheet metal or creating, uh, the lumber that is used then in, uh, cabinetry.
(22:07):
It, it is the cabinet makers. It is the suit makers in the apparel industry, in my mind. Not not the end retail business. It is the, uh, the, the cutters and shapers of the steel or wire or whatever those, those original raw materials were that have been provided to these intermediary stages in the supply chain. I see that, I see it as the intermediary members of the supply chain who have to deal with these raw materials that need to be converted from a rather, uh, generic form, um, to something, uh, sellable, um, to potentially an end consumer. Um, whether that's a, a door or that's, uh, a, a a a a a piece of metal, a table, uh, from a lumber, uh, supplier, uh, or who, who has received lumber, uh, as converting that into a, a finished product. It's those people who have the challenge of taking that raw generic material in sheets or in pipes or in wires and cable or whatever it is, and converting it into a finished product, and then handling the remnants or drops, as I learned in some people's language, they'd call it, um, how did I do I do, I put it back into inventory and how do I, if I do, how do I manage that?
(23:26):
Right? And so I started to look at different, um, different vendors and what they actually offer. And you used the term matrix a couple times, Sam, and, you know, uh, I, I, I looked a little bit Epicor, I looked a little bit at Microsoft Dynamics, right? Uh, I looked a little bit at Infor Visual, and a lot of these people have, you know, a very fascinating concept where an S K U is is kind of a generic. Um, and then you can have a matrix of all the different dimensions, maybe lengths of pipe, uh, or shapes of material that are either what you bought or what you ended up with after your manufacturing process as remnants that can go back into inventory. And you don't create new SKUs. You don't multiply, uh, the number of stock keeping units. You simply have the same unit with maybe 3, 4, 5, 10 or more different dimensional attributes that you can see when you just say, I wanna see S K U abc.
(24:31):
And then you see in a matrix form, um, whether you're looking at from a selling point of view for, for May, maybe selling to, uh, uh, uh, uh, uh, a, an apparel or a retailer, um, you, you know, how many he, he wants to buy, you know, a hundred suits, uh, and stock that for the coming season. And you have all this raw material. How do you take that and convert it into efficiently into your production orders, and can you reuse remnants from prior production orders and integrate that into the process? So it was fascinating to see that, uh, this is, uh, really been a developed technology, whether you're running mps r p in order to consume more efficiently all of the different forms of dimensions for a particular stock keeping unit, or you are offering that in the sales module of a lot of these e r P systems, et cetera.
(25:25):
Uh, or it's just, you know, inventory visibility, uh, for maybe procurement and reordering that you want to be able to see which, which, uh, which types of inventory in terms of the attributes or dimensions you want to keep. Um, so, and, and I, I found it fascinating as well where, you know, Chuck brought up, uh, how, how some people are using the dimensional inventory capabilities just because they want to have, uh, maybe a smaller number of stock keeping units, and they want, you know, distinguish still by color or by size, but they're not gonna call that a different S K U, so they're gonna use this dimensional inventory feature to be able to handle that in, in that way. I, I, I kind of think that's, that's interesting. It's a good application maybe. Um, but those features or those attributes are relatively static. You could probably call that red sweater, uh, in size, medium, uh, a completely separate SKU u from the red sweater size large from the blue sweater in size, medium, right?
(26:28):
You could tho those could be, and maybe in many cases are for some businesses, different SKUs, but it looked like, from what I could read, some people are using the dimensional inventory capability to keep the number of skews limited and be able to have this variety that they can manage. Now, that's, that's a, that's a good application, I guess. But what I thought was really powerful was where you had the possibility of, you know, maybe post manufacturing remnants where you can't, you know, you can't say it's always gonna be two foot of pipe left, it might be four foot, it might be one foot, it might be, you know, half a foot and you need to make decisions. Am I gonna keep that in inventory if I think I can reuse it or not? And so you can upfront predefine what are the different dimensions that, you know, you would find, or that you would be willing to retain an inventory.
(27:19):
Maybe it's, you know, you buy, you know, 10 foot of pipe, uh, and you're gonna say, I'm gonna keep, keep everything from one to 10, but if it gets to be only six inches or something, anything that small, I'm just tossing, I'm not gonna even keep, but you can, you can actually make these decisions in a fairly dynamic way. So it's not like color necessarily. Uh, it can be even more dynamic than that. So this was, this is what I found fascinating. And, uh, and so I started thinking, you know, it was a little bit like configured to order for a lot of these businesses. They're, they're kind of a make to order business, but the, the way that they configure is not with this static set of materials. It's not just, oh, I've got, uh, this software module that I could use, or you could use this other one.
(28:02):
Uh, it's not, you know, it's not all fixed and predefined, even if that, you know, a lot of configural businesses, they're, they've got this complexity, this is now even, even the inputs are not necessarily all predefined. Um, they, they could be of all different types. So to me, that was, this was a fascinating, um, exploration. And, and I'll give you my example just to wrap up. Um, I was, I was working as a consultant, um, and it was on a big S A P E R P implementation, and it was at Goodyear. And, uh, I was brought in as a WM expert. I was gonna figure out how to solve this problem. And their problem was that when you stack tires, um, you know, you can stack 2, 3, 5, 10 high at a certain point, and I don't remember what it was, but at a certain point, the tire stack collapses its own weight, creates a smaller stack that would then allow you to stack more into the same space.
(29:09):
So at like 10 high or 15 high, you know, the stack will collapse about half its height, and, and then you could put more into your truck if you were transporting it or into your warehouse. Um, and so, you know, there, there we were saying, well, boy, this is a fascinating kind of, uh, dynamic dimensional inventory management issue. Um, and we had to cut, uh, ultimately code a custom solution. There was nothing in the e R P solution that could handle something like this. Um, but that was one of my exposures to what I thought of as a, a dimensional inventory challenge. So, but anyway, I love this topic and thanks, thanks for inviting me.

Sam Gupta (29:50):
Yeah, so thank you so much. And I love your example about tires and, uh, you know, yesterday I learned that the packaging process for tires could be even different. So what you are talking about is you are doing the, uh, space optimization because of its weight, but one more thing that they can do in the entire industry is that there's gonna be a vacuum in between. Okay? So they actually like to put stuff so that they <laugh> the, the freight cost is going to be far cheaper. That's even more so that the whole packaging pro process is, uh, completely different. But Tom, do you want to touch on unders comment? Uh, you know, I'll read it for you. So he's saying Tom's comment on dimensionality for a single skew that might be three to 10 dimensions, clearly two dimensions can be shown on a grid. How are all these ERPs displaying four plus dimensions? Tom, uh, you wanna test that? And then I'll probably provide some more context there as well.