Guest Podcast Episode 4
Digital Transformation: Dimensional Inventory Best Practices
Dimensional inventory is more than just a bunch of attributes at the product level.
It affects how the items are created, planned, and tracked.
See Below for the full transcription of this episode!
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?
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):
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?
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?
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?
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.
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.
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.
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.
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?
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.
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?
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?
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?
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.
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.
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.
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.
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?
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.
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.
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?
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.
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.
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.
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.
Tom Rodden (30:44):
Uh, so yeah, again, I'm not gonna claim tremendous expertise in depth here. What I saw when I was exploring this was that, um, uh, you know, you could have multiple dimensions, um, on, on one axis, um, and, uh, different, um, variations within that dimension. So let's just say you had, um, I don't know, a piece of steel, um, you bought, you bought a certain, uh, grade of steel, um, and, uh, it's, it's stainless steel. Um, and now you say, well, I, I, they're always bought in standard sheets. I dunno if, if, uh, chucker or Abu was talking about this, but somebody was mentioning, you know, you have, typically, you have certain sheet size that you buy it, maybe it's industry standard. Again, not my, not my industry of expertise. Um, but, uh, once you start, um, uh, procuring that you might say that's one SKU u that that, I don't know, eight by eight foot sheet or whatever the size of it is, um, that's my standard, my standard sku.
Now, I might for different purposes be cutting it in half, um, and using it then maybe in quarters. And, you know, and again, I'm gonna end up with different remnants or drops from the manufacturing process, and I can actually say, well, for that SKU u uh, at least one dimension is going to be, uh, the, the length and maybe another is going to be the width. Um, and, and it's a stainless steel. So I kind of, and maybe again, I, I don't know the, the, the, the, the dimension that defined it is stainless steel. That could be another dimension. You could just have steel, I suppose, but, but at some point you need to get to a common S K U definition. Um, so that's kind of what I saw. You'd see maybe, uh, a length width grid, um, but it could be color and size or it could be a lot of things. Um, again, how one would drill down even further if you had more dimensions. Um, I, all these packages are saying they can do multiple, you know, three, four or maybe unlimited dimensions, but at how you would visualize that, I'm not sure. I think it's a great question from Andrews, you know, what I saw were two dimensional grids, which really were showing only, only two dimensions per skew, uh, with a variety of different, you know, types of attributes.
Sam Gupta (33:04):
Yeah. Could not agree more. I, uh, uh, I'm actually going to provide some more historical context there, uh, you know, on this question, and typically you are right. I think there's gonna be a little problem when you are gonna have more than two or three dimensions. But you know, the reason why it is called matrix is because it's supposed to be matrix and, you know, like the, the way it is shown for the inventory, and that comes from the product data management. Yeah,
Tom Rodden (33:25):
I can only imagine that you could drill down maybe from that two, two dimension grid to see additional dimensions, but it, I don't think you can visualize it very easily, even beyond two. Now, I visualizing it as a cube of three dimensions to me, is it is kind of challenging for, for the user. Um, but yeah,
Sam Gupta (33:43):
Typically it's going to be a very similar experience as your private table. That's how, uh, you know, the, the systems that are really designed for the retail makes sense. That's how they're going to do it. Yeah. Um, so Sharon, I know you are going to be, uh, touching about number one, the, the whole matrix inventory. And I think you do a lot more work in the retail space. The way retail treats the matrix inventory is very different from manufacturing. These guys have been talking about, you know, manufacturing a lot. But you know, retail is a very different business in general. And the reason why this problem exists, uh, overall matrix inventory, because in retail you are probably gonna have a million excuses, okay? And if those million skews are going to have many different variations, product data management becomes very complex and time consuming. For example, let's say if you have apparel or shoe, and you have, let's say thousand variations of these same styles, same color, okay?
And if you have to change one property, you are actually going to those thousand variation and you are changing that. So what they do is they're gonna have a wrapper, and on top of that gonna have four different variables that is going to be your color, size, uh, season. And based on that, they are gonna have, and they have a different concept called P L U. Okay? They don't call it SQ because the pricing is going to be very different, okay. For the inventory. So they're planning merchandising is very different. So over to you, Sharon, whatever you have, uh, you know, you might be able to share related to your experience when it comes to metrics or the dimensional inventory.
Sharon Custer (35:05):
Okay? Um, I would like to share my experience from, um, manufacturing and warehousing and retail. Okay? So when, when you manufacturer something and then you have, uh, three different sizes, say, posted, um, you manufacture a big sheet and then cut as your demand comes up, you don't, you don't produce that item until, you know, closer to that month or, you know, two months, then you cut that inventory. Um, that's just rule of thumb. And I go back to Chad mentioned about, you know, numbers and things like that. When your manufacturer sink may, you know, you know, you have a three meters left, but you cannot just cut two meter here and one meter there. It really depends your, um, process. What does that make sense to your business? If it's, it spends so much time to measure it out and it cut it out, forget about it, you know, you just have to count that cost. Say if you are cutting four meters out of a five meter lumber, you just have to discard that one meter, or you can resell, repurpose it in some ways. Um, don't try to, uh, make the process too complex. It doesn't, it, it, it, it does it like profitize. It does not benefit the business. Um, so like back to Abu has, um, a, I Cher his name, I think
Sam Gupta (36:48):
Abu, everybody comes him Abu
Sharon Custer (36:50):
<laugh> Abu, um, about Bri drink and privilege, you know, like in the restaurant, the thing is like, you just have to know, like I say, if you have 10 chicken and or 10 gallons of beer, then, you know, like in the end of the day or in the end of the month, how much is actually used and how much was sold, then that difference is your waste. Then you have to manage that waste. You just have to look at the bigger picture to manage that you lost. Um, don't make it complex again. You know, it is what it is. You just have to know it is a spell or if is what happened to it. And so you can manage it. Um, so when it comes to, uh, wholesale and shipping, it is always a problem with limited space in container. So in terms of dimension, you have to consider is that bigger size of item is more profitable or smaller size of the items more profitable.
You have to find your mix that balance to maximize your container space. Um, in warehouse, the same thing, like you cannot just rely on the system. You have to look at, like say if you're cutting, I don't know, fabric, and as Chuck say that you can have like inches, meters or different type of metric me measurements. So in the end of that row of that fabric, just that much is wasted. So cutting cable is the same thing. Sometimes you cut so much and then you left this much, and then you cannot reuse it. Now, roof sum is that you have to be able to visualize, you know, the person who is managing the inventory will be able to see personally, physically in the warehouse, because sometimes you have to cut two rolls at the same time. Some are longer, some are less. And you need to know how many f full roles uncut is there. So, you know, in the future demand how to utilize the uncut roles. So you cannot just rely on that system, say there's 10,000 meters, but actually it, it does not fulfill your whole order. Does that make sense?
Sam Gupta (39:29):
Yeah, it does. Sorry, go ahead Sharon. Go ahead. Go ahead.
Sharon Custer (39:32):
<laugh>. Yeah. Yeah, so, so that you, you can, I recommend not to cut multiple rolls at the same time to waste all the, you will have so much residuals in the end of the day. So you just cut one roll, and then, you know, in the end when, when you, when you fulfill the order, you may have, you know, one meter left, you know, half meter left, and then, you know, that's, you know, then you open the second row, don't cut multiple roll at the same time.
Sam Gupta (40:04):
Yeah. So very interesting commentary. In fact, you are bringing a very interesting layer there as well. Okay. The dimensions that are going to be relevant for marketing are going to be very different. The dimensions that are going to be relevant for merchandising is going to be very different. The dimensions that is going to be relevant for a shop load guy is going to be very different, but the most important point that you mentioned is the f and the warehouse. Okay? For warehouse, it's a very different dimensions. For freight, it's a very different, so how do you manage all of this? Um, Sharon, when you are talking about sharing of item master,
Sharon Custer (40:34):
Uh, manage that. First of all, you've gotta know your, you know, uh, again, my favorite topic in your forecast, <laugh>. Um, you just have to manage that e expect, manage that expectation. You know, like I say, instead of looking at how, how much you can squeeze in that container, you have to look at how much profit you have squeezing in that container, if that makes sense. Yeah. That's the goal. Okay. Um, and then the second thing is that some of the, uh, business, they do customized order. Okay? So the customized order, you have to very be careful that, um, when, when, when you are loading the container, it could go very messy <laugh>. So you ha uh, uh, it, it would be, it would be ideal to have one supplier or, or a place, a professional loader that can load your container. That's number one. So they know where the things go.
So when you are finding your stuff, you gen you have a general idea where the things are okay. And this special, the special order, you have to make sure every steps there's a mark or, you know, <inaudible> or something to make sure that specific order is intact so you don't lose the order for your clients. Um, when it comes to, uh, the other end, you need to make sure that you lay the inventory out before accounted and before going to the warehouse, you have to have that space and time to count those inventory before you re it's receive process you receiving correctly, especially specialty order. Um, so that each step of the manufacturing and the shipping and warehousing, you don't lose your client's stuff.
Sam Gupta (42:45):
Okay? Amazing. Thank you so much, Shannon, for that. Uh, check on coming to you. Comments, over comments, any stories?
Chuck Coxhead (42:50):
Oh, I've got a story. Uh, and I think you're gonna like where this one goes. Um, and I'll save it for the punchline and the closing comments. There's a couple of things that just popped into my mind as we're going through this. And, and it's so often we tur we think in terms of arithmetic, okay? Or we think in terms of variations, and I'm using my apple pencil here, but you know, it's not just about how much I'm going to use, okay? If I need to make something that's this long and say I'm going to machine it, it's important to understand that in order to machine this, something's gotta grab it all right? And it takes x number of length for you to grab it. Now, if I make 20 pieces out of a single piece of stock, I'm going to have a certain amount of waste in that each, in that length.
Yep. If I make two pieces out of it, I only need the same amount of lengths to grab it. So I'm actually going to have a whole lot less waste depending on how I do it. That's one example. But now when we get into this matrix inventory, when the add a new dimension, there's one with which I have direct experience called, it's the dimension working in semiconductors manufacturing. It's actually extraordinarily cool when the, when the devices come out, the semiconductors are made, they kind of come out, there's kind of a performance curve, you know, like this is kind of where you think they wanna perform, but they might be over here or over here, and there are fewer and fewer that perform kind of out on the bounds, everyone. And if you make, you make these things that come on a disc, and you'll get all kinds of different performances out of one disc, out of one wafer, and then they say, okay, this one meets this criteria and this one meets this criteria, and this one meets this criteria, and it's called binning. Interestingly enough, their performance may overlap.
So what happens is normally we think, well, I need one of these, and it has these characteristics, not in this case, if I need these characteristics, I can use one of these, or I can use one of these. Either one works. And the real nightmare once you get down to inventory planning, because most of the time when we're talking about inventory planning, most, okay, we're talking about arithmetic. You do inventory planning for semiconductors, it's statistics. Yep. Is your e r p sophisticated enough to do statistical inventory planning? Because you may think you've got it all planned out, and when you get there, reality doesn't match statistical model. And now you're in an inventory stockout with a really long cycle time to produce replacement wafers, hoping you can get something in that performance range that you need. And it's a really complex model. And so that binning into performance, it's another dimension.
They're each is, they may be a single skew, okay? They're all the same dimension. The color is irrelevant, they like look the same, but they're not. It's a performance dimension. It's not physical. So there's just so many aspects to this that just can be, they just, honestly, they're mind blowing. And it, it's just really important that when you're looking at an e r P system that you tell your consultant about all your workarounds, all the things that you haven't been able to quantify on your spreadsheet, something just makes it, well, I can do this, but then this happens sometimes, and blah, blah, blah, blah, blah. Because that's where it's gonna get you in your planning. That's where it's gonna get you in your requirements. Um, you know, gathering is all these things that are come up. Oh, well, we forgot to tell you about that. Okay, Jane Jor, <laugh> gotta help you all. So
Sam Gupta (46:23):
Yeah, I was going to say mind blowing, but you stole my words. So I'm going to say speechless with that story. Thank you so much. Check for that. <laugh>, uh, Abu comments, over comments? Any stories?
Um, I mean, I think it's just dimensional inventory is, you know, always complex and it's always, um, you know, exciting to deal with. Uh, we were, you know, just yesterday we were talking to a prospect and they are in tobacco industry and they have these boxes of tobacco. Uh, you know, they, they'll bring it to the machine, use the tobacco, and then if there's leftover, they'll, you know, they'll take it back. So now they need location control, they need box and how much is in that box, right? So that becomes another aspect, um, of damage inventory. So it's always fun and exciting to deal with this new different kinds of issues. One of the things I think Tom was mentioning, uh, and in my experience, you know, uh, the dimensional in material, what I've seen is, you know, this, the groups or the dimensions, they're mostly used for reporting purposes, right?
So, uh, you know, you can have five dimensions or six dimension on a product, and they're mostly, you know, I want to see how much a certain pie thickness sold, for example, or how much did I sell red colored t-shirt? And that's where those dimensions come in rather than, you know, visually laying it out, uh, and selecting it. I, I mean, maybe Chuck's, uh, experience is different, but that's how, uh, I have seen it. And then just tying into the statistical, um, you know, planning, you want to sometimes plan that I'm going to order red color shirts of this size and this branch throughout, right? You want to look at a certain dimension of the product. And then those systems, they allow you to, you know, to do m r p planning, NPSs planning, or, uh, just purchase planning based on a certain product attribute rather than by a skew, right? Um, because sometimes if you're making red shirts, you know, it's better to make red shirts and red sweaters and red, you know, things together rather than, you know, just changing or switching, uh, that mechanism over. Um, but overall, you know, I find time eventually went a very complex, very interesting topic today. Uh,
Sam Gupta (48:32):
Yeah, could not agree more. Thank you so much Abu for that. Tom, comments over comments? Any stories?
Tom Rodden (48:38):
Um, I gave you my key story, uh, uh, more my two, my two stories, you know, with fluorescent tubes and, uh, Goodyear tires. But, um, uh, maybe two quick comments. One is, um, and it maybe feeding off of what Abu said a moment ago too, about reporting and analytics. Um, when I first started looking at this and thinking about it, uh, you know, I I was thinking about dimensions and attributes in a business intelligence sense from a reporting and analytics point of view, and that that's legitimate and it's important. Um, but what I believe is fascinating about this topic and this this form we have today is the operational integration of dimensions and attributes into business processes and how e r p systems can support that. To me, that's what's really interesting or even exciting about the topic. Um, and whether it's nps, R p, uh, atp, um, whatever the, the business process is for sales or procurement or manufacturing that's taking into account these dimensions, um, that's where I think the power lies.
And, and the second comment, well, I wanted to make was, um, this is the first time, Sam, I, I've always been a big advocate of, uh, some of the big e r P systems, and most of my experience has been with those, but this was the first time where I, I really felt like the little guys have a niche that they can exploit here. This is challenging specialized capabilities that the big guys have either just ignored or just said, Hey man, it's too hard and we're not gonna invest in that. Um, so, you know, a lot of these smaller players I think have a legitimate case. And for anybody in these apparel industries, uh, woodworking, uh, metal cutting, uh, all of these spaces that we've talked about today, they really need to good, take a good hard look beyond the big guys and the traditional powerhouses in E R P and look at people who can offer these kinds of capabilities that we've been talking about. This, this is, this is really different. Now. That was my other comment or observation.
Sam Gupta (50:57):
Amazing insights. Thank you so much, Tom for that. Sharon, comments over comments? Any stories,
Sharon Custer (51:03):
Um, comments. I will say reuse the material that, uh, you call the waste or extra, um, for example, the woodworking or metals, you can convert that dimension into from, from dimension, you know, one meter to meters to by the weight because you know, the density is the same. You can reuse it or re rebuild it and a reform it. Um, don't waste it, you know, and that's still easy to converting the system to be, you know, accurate inventory.
Sam Gupta (51:45):
Okay. Amazing insight there. Thank you so much c for that. So, um, we are going to touch on this comment. And Chuck, I'll let you respond to this. I'm pretty sure you are going to have some, uh, insights sales. I'll, I'll read it for you. Uh, saying, especially given that getting a run off chips done starts at a million dollar, uh, for the first wafer, and that's exactly right. Uh, that performance distribution analysis is going to be critical. Check over to you.
Chuck Coxhead (52:10):
Yeah, so I mean, there's, there's a couple of ways of looking that. So it, it's like everything. So your initial development cost is literally just millions or millions of dollars on that. And, um, the distribution analysis is critical. The beautiful part is, is that as the product life lifecycle matures, the yield typically, so the yield loss for a semiconductor will typically increase. Okay? It will definitely go through its ups and downs. Mm-hmm. <affirmative>, the beautiful part is it will increase and in theory it will become more predictable. So the d distribution analysis, I mean, the statistics are, are crucial, um, but they do become better over time. That's the good news.
Sam Gupta (52:50):
Okay. Amazing. Anybody else has any comments on this question by any chance? Open floor. Okay. If not, uh, abbu, do you have anything? Uh, no, I'm good. Go ahead. Okay. Uh, then we'll take closing advice, uh, Chuck, uh, closing advice please. And we have five minutes so you can take lo long closing advice. I guess today.
Chuck Coxhead (53:09):
I don't even need to be long, honestly. I know, and I'm pretty long winded. The beautiful part is all these things that I've done with you, Sam, I will say this is the first one that's illustrated this so elegantly, there has never been a topic that I've experienced with you in these panels and others that more clearly illustrates how important it is that when you're picking your system, building on what Tom said, when you're picking your system and when you're picking your consultant, how crucial it is that they are from your tribe. Yeah. That they know your language, that they know your unique set of problems. The, the, the things about food. I never even gave the chickens a a second thought for goodness's sake. Thank you, Abu. You gave me a new dimension on dimensional inventory. I mean, it's just, thank you <laugh>. Um, it's so, and, and you may nev if to the extent anyone ex understood my comments on semiconductors, you may not have ever understood that it's never been more clear in a topic. Get someone from your tribe, it will save you a ton of money.
Sam Gupta (54:06):
Could not agree more. Thank you. Second, so much check for that. Uh, Abbo closing advice, please.
Yeah, I mean, I'll, I'll echo Chuck's comments. Um, you know, it's definitely very important to choose the right e r P systems. Um, dimensional inventory is complex and you know, believe it or not, not every system can handle that for, for a specific type of industry. The other thing that I find is also do an analysis. Like, is it worth the effort to track all those dimensions or, you know, for example, in sheet metal, if the overall wastage is low or it's not that complex to, you know, um, manage some people just, you know, might like, try just the square footage, for example, right? And then they'll write up or down. So based on those decisions criticality to your business in terms of dollar value impact, um, you know, make those decisions and make sure you choose the right system and the right team.
Sam Gupta (55:00):
Okay. Amazing advice. Thank you so much, uh, Abu for that. Tom, closing advice, please.
Tom Rodden (55:05):
Um, well, one of my bits of advice I guess was about the selection of E R P, and I think Chuck built on that, as he said. Um, so that, that would be one of my comments to reinforce. Um, the other thing I think maybe again, more of an observation is that, um, I have a sense that the, the, uh, relevance of dimensional inventory is not necessarily black and white by industry. Uh, it is more of a spectrum. Um, I think it, you know, it goes to even, you know, to your point Sam, like if you wanna avoid an explosion of SKUs, um, even if you don't necessarily have, uh, dimensions that are dynamic, they're re relatively static. Um, but you, that's one of the, a key success factor for your business operations. You know, managing, limiting, uh, handling, uh, just the, the, the product master and the maintenance of the product master.
Um, then, you know, there's, there's a, there's a case that could be made to say, Hey, dimensional inventory, and this kind of, um, management of a, of a skew and its dimensions is potentially relevant. Um, even if it's not a traditional or, you know, a, a target industry for some of the businesses or these, these e r p uh, suppliers, uh, that, that they have, you know, been thinking like we've been talking in, in, in, in steel cutting or, or, um, or, or woodworking. Um, so I, I think there's potential relevance and it's kinda opened my mind up potential relevance to some of these capabilities beyond the traditional applications. Um, and so I, I think, you know, people should keep an open mind to the use of these capabilities and, and be willing to explore that even in maybe industries that haven't traditionally embraced this kind of functionality. Uh, it's, it's, I it's been a great, great conversation.
Sam Gupta (57:08):
Okay. Amazing advice there. Thank you so much, Tom, for that. Sharon, closing advice, please.
Sharon Custer (57:12):
Um, my advice is to wholesale and retail retailers. Um, there are so many E R P or inventory management systems can give you, you know, dimensional inventory management like colors and sizes and things like that. It's very, in, in my view, it's pretty much standardized for the retail and wholesalers. So what I'm trying to say is that you have to look at your business is, does that really make sense? You have so many options. Okay. Um, now it's great, you know, you can offer your customers different options, colors, and sizes, but the more dimension you have, the more management effort or resource you need to put it into, you have to find that balance to make sure that your, your business is profitable.
Sam Gupta (58:09):
Okay. Could not agree more. Thank you so much for, uh, that advice. On that note, that's it for today. If you join for the first time, this was part of our digital transformation series for which we meet every Thursday at 5:30 PM Eastern. So make sure you guys are gonna be here next week. We are gonna come back with another topic on that note. Thanks everyone for tuning in. Right.
Sharon Custer (58:29):
Okay. Thank you. Okay.