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Guest Podcast

Inventory Replenishment Strategies Best Practices | Microsoft Dynamics 365

When it comes to mastering inventory replenishment, most companies default to overstocking.

But why?

See Below for the full transcription of this episode!

Sam Gupta (00:24):
Hello everyone. Welcome to today's show. And if you're joining for the first time, this is part of our digital transformation series for which we meet every Thursday at Y 30 pm Eastern for today. We have a very interesting topic. It's called Replenishment Strategies. There are gonna be so many different names there for that, so we are gonna have a lot of fun discussing that. Before we do that, we are gonna start with everybody's intros. I am going to start with my intro. If you don't know me, I am your host, Sam Gupta Principle 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 Chris for his intro.

Chris  (01:04):
Thanks, Sam. Chris Gard, any, uh, owner and CEO of Turnkey Technologies. Uh, we're a 28 year Microsoft Dynamics implementation partner. Again, lots of involvement with distribution, manufacturing companies, a lot of different strategies. Look forward to discussing some of those. Thanks.

Sam Gupta (01:18):
Okay, amazing. Thank you so much, Chris, uh, for the intro, uh, nla, can I move to you next for your intro?

Nirav Shah (01:24):
Yeah, absolutely. Nasha ceo, founder of Answers c r p, we implement Acumatica, um, you know, is planning and replenishment systems is near and dear to my heart. You know, a lot of implementations that we do, uh, revolve around proper planning, proper demand forecasting, proper supply planning, so a lot to talk about today and, uh, love to share that.

Sam Gupta (01:45):
All right, amazing. Thank you so much for being here. And, uh, Abu, can I move to you next for your intro?

Abu Asif (01:49):
Sure. My name's Abu Asif. I'm the founder here at Pan. We are CX three partner, uh, been implementing r p demand planning in a wide variety of industry, from food and beverage to distribution to just general industrial manufacturing. Glad to be here and excited to talk about it.

Sam Gupta (02:07):
Okay, amazing. Thank you so much for being here, Abu. Mark, can I ask you to introduce yourself next?

Mark Lilly (02:11):
Yes, of course. Good afternoon. Um, mark Lilly, president and c e o of Lilly works, um, here at Lilly works. We have a long history with, uh, traditional M R P and traditional scheduling and, uh, we've come up with and also use, uh, different methodologies, uh, to replace some of those. So I'm very excited to be on to talk about how those might apply to inventory replenishment today.

Sam Gupta (02:34):
Okay, amazing. Thank you so much for being here, mark. 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. They fill an out of time. We'll make sure that you receive your answers. Uh, on that note, Chris, I am going to start with the first question. And in my mind when I look at different implementations, uh, everybody's gonna say, and you know, obviously have the 70, 60% whatever failure rate we have, e r p. Some people recognize their implementation is a failure, but even among the successful ones, okay? Uh, if you look at their M R P maturity, you, they're gonna say, you know what? My E R P is working. Everything is working. But when you look at their planning maturity, they sometimes don't even do p they don't really understand how to do Mr. P, they don't understand how to do scheduling. So that's the kind of, you know, maturity that I have seen with the businesses. So let's say, if you were to sort of, uh, you know, provide the context in terms of why the inventory planning matters and what is going to be the scope of the inventory replenishment, let's say if somebody wanna start on this journey, press over to you.

Chris  (03:41):
Sure. I, I mean, it's a great topic and replenishment can go deep. And again, we start, and I use that crawl, walk, run. So if you're just getting started, there's basics. I mean, you know, the primitive company, they run out of stuff, they order it. The problem with that lead times customer service, again, you're generating lots of X U pos. So there's, that's a reactive replenishment method. And it's terrible cuz you're gonna, you're gonna lose a lot of efficiency and time in business. So again, as you get into strategies, um, everybody hears about MinMax, what's the easiest replenishment to use? Hey, when I get to this level, buy more, buy more because before this level gets to zero, the vendor can ship me more. So there's a real principle there, right? Gets here order more then. So again, you get a little time factor there.
Time is a big factor in calculating and, and planning these things. And so the devil's in the details. And so if you think about MinMax as a standard replenishment, there's a lot of companies that can manage a large portion of their inventory. Non-manufacturer items, for example. Or maybe with the MinMax, they're finished goods, they buy 'em, they resell 'em right? Now the other thing you rationalize there is if you look at what's the revenue, what's the dollar of capital to put in a warehouse if everything's at the max, okay, if somebody needs to know how much money is that? That's 50 million, okay, lower my maxes or whatever. And then you end up buying more frequently instead of stocking and having longer lead time. So again, there's a lot of math, there's a lot of graphs and charts here. As you look at one item, and actually, you know, the future, the new tools are showing graphical images and using AI to start helping you make these decisions.
So the other thing they'll do is surface the top items. Hey, you need to go fix these cuz they're way off. And as you tune right, you lose all your exceptions. So that's the future state. But getting there. So MinMax, pretty simple PO generators, they can generate POS based on MinMax. So if you step forward into when you need r p and you think about manufacturers, what r p does is a much more invasive tool and that's why it's more complex. And again, you can segment your inventory and manage pieces grow and do more and refine. So just understand it's, you can't get to a hundred percent instantly. It does take time. But in the, in the r p world, if you think about replenishment and just strategies, and I'll be specific. If I'm manufacturing a finished good, maybe it's per customer order I make to order, okay?
My replenishment method means you get an order for one, you make one. Great. Now, below that, I've got a sub-assembly. That sub-assembly could have a different replenishment strategy. Maybe that when I build a stock, why? Because when I get an order for that one finished good, I don't wanna have to build everything to make that one product and have, it'll take six months. So maybe that second item down is a build a stock. And so it could have a different replenishment strategy, like a fixed order quantity. When I need to make that I make 50, I always make 50 or I make increments of 50. That's another concept of replenishment strategy called fixed order quantity. So again, that first one is like lot for lot. I mean, I need one, I make one, I'm done. Third level down, I got another sub-assembly. Guess what? That one I only make it if I need it.
Again, maybe a different strategy there lot for lot, it's very quick. That means I need a hundred, I make a hundred need 103, I make 103. So there's no rational. But, so again, different strategies as we look at different inventory items and how do I manage 'em. So M R P again looks at what's on order, what's the demand, what's the supply, what's the component usage. So now I've got sources of demand from a manufacturing order and a sub-assembly and maybe a sub sub-assembly. So now M R P is like, well how deep does it go? Right? Can it get down eight, nine levels? I may have, I may do an engineering bomb for an aircraft and it's 18,000 lines. And you're like, wow, you can't humanly manage that. So this is where those tools come in and differentiate companies that are using 'em well, because people can't manage these processes efficiently, you'll end up with longer lead times, et cetera.
So again, r p um, you crawl, walk, run, you can segment your inventory, you can have a whole lot of it managed with this lot for lot or mid max PM generator if you have that tool. But M r P really helps you. And again, it also tells you based on vendors, when to create the po. And again, I can use a, a one year horizon, I can use a two year horizon. That's the other thing. What r p does, they can get way out. Cuz again, long lead times or maybe it takes me 18 months to make this and I don't want everything showing up on day one if I don't need it until this 13th week. So you're hearing me describe complexities and again, as we go deeper and deeper into this. So, so I wrote a few things down. I think that I've talked about a bunch of 'em. So, uh, you know, warehouse architecture is another one. I'll, I'll leave that for somebody else. But, uh, questions.

Sam Gupta (07:49):
Yeah. So great start. And I really like your approach of crawl, walk, and run, and obviously that's how you should be starting. But typically, you know, when I work with my customer and, uh, you know, their approach is always very binary when we, they think of E R P. So either they don't want to do any planning, everything needs to happen in the spreadsheet, or E R P should do everything for themselves. And one of the comments that I typically get, and they're always probably gonna get kick out of it, and this is not the first time, uh, I heard this one, so this is going to be, okay, hey, you know what? I don't understand all of these MinMax access, safety stock. My planning is very simple, okay? What I really want is can I get this inventory for three months? Can I put three somewhere, put months somewhere? And can system not order that? Why is it so difficult? Um, so <laugh>, I don't know.

Chris  (08:40):
And again, what we do to help educate consumers is, is create illustrations. And you look at, okay, if we've got a three month horizon and you're like, what's three months of supply? Will you define that? You look at your vendor relationship, okay, when do I need to order to have that show up? And again, when I cover the three months, the order shows up. So again, there's some math that you articulate where you look at the min maxes and if that's the strategy or you say, Hey, I'm doing a fixed order quantity, but there's some trigger that times it. Now the other thing we didn't talk about is introducing a forecast. So where does my demand come from? So now I'm gonna take a sales forecast, maybe the forecast is quarterly and I drop in, I need a hundred units every three months. And guess what?
There's no min-max in the system. The item has nothing, there's no sales orders. And guess what, that thing's gonna say, bump time to order 300 or at least keep me at that level. So a forecast can drive demand in addition to a sales order. And even an M R P I could be manufacturing, I have sales orders, I can have min maxes and I can have a forecast. It's all contributing to calculating. And it is a complex calculation. But for simple, you gotta start simple and show the math. People have to understand, well, what's an economic order quantity When I need that, it's gonna look at the vendor and what's it gonna order? Well, he says a hundred increments of a hundred, maybe that's a pallet, for example. But you start with one example and you start simply illustrating. And I think that's the best way to describe it because there is a lot of math in there. And even the computations I've done to rationalize the cost of a MinMax or a volumetric expansion of MinMax. Well, how much warehouse cube do you have? You can't put all that in there. It doesn't fit right? If I've got a, I got a guy that makes blown injection molding bottles, we rationalize MinMax totally different based on a available to store the product. So

Sam Gupta (10:15):
Let me's think you so much Chris for that. So I'm coming to you and um, uh, obviously I want you to provide the priorities that you wanted to talk about. And that's the another layer on top of what Chris already described, that the formula could be very complex. Uh, but in general terms, when you talk to customers, I mean they, uh, you are going to talk about AI formulas, but for them it's a, it's a big transition curve overall in understanding how this planning works. And in most cases, they just don't get it right. Uh, <laugh>, you know, because it's very, very, very hard to sort of calibrate these things, uh, unless you have deep understanding of those things and, and really try to use it as a feedback loop as opposed to, uh, simply taking as, you know, very simple formula that I'm going to put some numbers and my planning is going to work automatically. So, n over to you, uh, you know, in terms of the priority that you wanted to talk about, any other, uh, context that you might have. Uh, n you're on mute if you're talking, sorry. Yeah.

Nirav Shah (11:17):
Do that 10 times a day. Um, yeah, I, uh, you know, you hit the nail around the head, right? It all starts at exactly, you know, what do you need? A lot of this before getting out to new r p is done by one person or two people. It's travel knowledge, it's all up here right now. And then now you're supposed to extrapolate that into an M R P and NPSs system. It's, it, it gets a little difficult cuz someone in their head is thinking, oh yeah, it's three months that, you know, we need to supply, but they're doing other calculations in their head that it's almost working like an M R P engine, right? Yeah. Uh, so it's getting that information, getting that data, you know, data's important here. Data's good. You're gonna get good results. What I see a lot of time is we get the data in, especially during the implementation, we'll get the data in.
We have, we have M R p NPSs working, right? Everything is okay, but then three months down the road, four months down the road, next thing you know, r p starts kind of breaking down and like, well, you know, this is not how we expect to plan or our order is labeled what happened here. Maybe a, a vendor opened up close by, he was able to get you that material a little closer. Oh, we forgot to update the lead time. Um, so now we're getting bad, you know, bad m r p data coming out here, right? It's important to understand M R P is as good as the data you put into it, essentially, right? You have to maintain that data garbage and garbage out, essentially. So if you maintain that data and don't forget about it, it's not a sudden and forget it thing.
Just cuz you put an item in a system doesn't mean you can't, you have, you, you don't need to go back into the item and update what's the, what, what is my safety stock for this item now? What is my lead time for this item? Now? What are my reorder points, my reorder quantities, right? What are these different things you have? This is a continuous loop like you mentioned. It has to be a process of continuous feedback, right? Because if you set it and forget it, Mr. P's gonna break down very quickly and all of a sudden those numbers aren't gonna be as accurate anymore. Cause Mr. P, right? Think about it. There's so many different, there's so many, so much happening there from a demand and supply standpoint, but just that demand and supply, break that down a little further. You have demand that's coming from dependent demand and independent demand, right?
Dependent demand. You're coming from sales orders. Yeah, we have lines already booked. We're ready to go ahead and order these things. Let's, let's pop it through the r p engine. But what about forecast, as Chris mentioned, yeah, right? Forecast is looking somehow somebody's done a historical analysis on your inventory to say, we need to go and stock this item for this quantity for this day, quarter, month, year, right? So now you're planning either to forecast or to customer orders. Now what about a layer underneath that? What about replenishing other areas in the warehouse? You might have a combine system where if a certain bin goes low, you need to kick off a specific bin, you know, replenishment from another bin itself. And if that bin doesn't have inventory, you need to kick that off into the m r p engine to go ahead and make it, right?
So there's all these factors in how you want M r P and MPS to run, essentially, right? The order increments of how you're gonna order with, uh, vendors, right? The vendors, they'll want you to place a order every single day for a piece of one. They're gonna say, Hey, we wanna order once every two weeks and it has to be a minimum of a hundred, right? That's our, that's our minimum quantity. Well, you need a good planning engine to be able to provide that type of feedback and get that, you know, purchase order available for this. So that purchase, so that vendor's gonna do business with you essentially, right? Internal transfers. If r p is set up correctly, you could set up where you go ahead and create a certain situation where you stock everything in a main warehouse, but now you have offsite warehouse and where you also need to stock stuff, uh, stock inventory because it's easier for you to go ahead and geographically manage your customers and get inventory to, to your customers.
So now, not only do you have to have r p run to make product in your source warehouse, but also transfer product to your target warehouse, right? R p will do all this for you if possible. Taking out that human element, taking out that tribal knowledge that a lot of people rely on day-to-day right now. But if implemented properly, Mr. P is a very powerful tool, right? A lot of customers, you know, initially they might be scared of Mr. P and NPSs Apex, right? Type of philosophy. American production inventory control system. I took a, you know, a number of facets around that on the supply chain management side. But you know, it's really not that difficult. You need to really take a step back, understand how your data is, do your data need a little clean up? Let's get that data cleaned up. Let's keep that loop continuing.
Get that data, uh, you know, properly maintained every month, every quarter, every year. So as you run r p and nps, you're just gonna get the best results out of it. And you're managing exceptions at that point. You're not managing the static data, you're managing effect the dynamic data through Mr. P and NPSs, right? The system is telling you, hey, uh, demand due data has changed over here, so go ahead and update the supply due date, right? Try to be just in time, try to be lean. You're not carrying too much inventory. Maybe that's the goal. Well, you know, Mr. P will help you get there, right? It's supposed to adjust to moving due date shifts in demand quantities, right? Uh, when you should reschedule, when you should not reschedule. It's supposed to do all these things for you so you could concentrate on the business and make better decisions on which vendors to source from better quality for your customers and deliver on time.

Sam Gupta (16:06):
Okay? So a lot of layers there. Obviously I can head it on, uh, a lot of different things, but the follow up question that I'm gonna have for you is going to be related to bins. Um, so I some e r P systems have bin, some don't, uh, you know, some sort of pretend to have bins, uh, <laugh>, you know, uh, they are probably not gonna have as much functionality. So let's say if you were to talk about bin level strategies, and you sort of referred the CanBan term there, right? Some companies might be using that. So what is going to be the difference? So typically if you look at the distribution systems, and I don't know, some distribution systems have bin, some don't, uh, you know, so when you look at the warehouse location, and then you are gonna have bin as well. So obviously warehouse location level, you are probably gonna have this, uh, replenishment strategy. Are you also going to have at the bill level, do you have to have that? So maybe, uh, you wanna touch on, you know, what are the best, best practices because you can go overboard with this. Sometimes we have seen processes where it's just overly complex that they are not able to manage, uh, totally up.

Nirav Shah (17:07):
Yeah, absolutely. Right? And you don't wanna overcomplicate it. Where you gonna slow the, the, the, the warehouse workers down, right? You're gonna slow the warehouse down, right? You have to look at what is the benefit of the transaction. You have cost benefit analysis of the whole e r P system, right? Does the benefit outweigh the cost of doing these additional transactions? Right? Now, if you have a big, big enough warehouse and now you have specific people managing specific areas of a warehouse, right? And you know, you're able to only pick from certain areas, you're stocking certain areas, right? You're shipping from certain areas, but the warehouse big enough where, you know, what we call that is being able to replenish from source and target bins, essentially, right? Because those areas that maybe the material handler for the shipping department is picking from, those are only picking bins and we wanna make sure that they're always replenished because we have a high volume moving parts.
So we wanna make sure those are replenished. So we set up like a, a, a what we call like a MinMax for bins, essentially. Maybe there's also priorities for bins, right? Uh, which bins you pick from first from other bins, or which bins get replenished from, from other bins. You set this whole kind of, uh, uh, permutation up from all the bins that you have, right? That are dependent on picking bins to stocking bins. So you wanna set up the best, most efficient flow. So where you're picking from, right? Has inventory, and if inventory is not available, you're gonna go to the next available bin that they could pick from replenish, essentially, and replenish that picking bin. So inventory could be shipped to the customer. You don't want those picking bins to go low on inventory, slow down shipping, slow down invoicing at the end of the day. So yeah, that's a whole strategy on its own outside of M R P, but that feeds into M R P, right? If set up properly, you want that independent demand to kind of work, uh, continuously to to pump out product, to purchase product. So those source source bins that you're gonna get from are always gonna be available.

Sam Gupta (18:49):
Okay? Amazing. Thank you so much, uh, Nira for that. Um, so Abu, I'm actually coming to you. Um, and, uh, I don't know if you wanna pick the topic related to warehouse architecture that Chris wanted to, uh, touch. Um, so I don't know if you wanna take on that and maybe you layer in your, uh, you know, experience with bins, uh, how you would like to layer in bins, uh, as part of the replenishment strategy,

Abu Asif (19:12):
Uh, leave, uh, to Chris to talk about architecture. But I think one, uh, <laugh>
<laugh>, one thing, you know, I just, it's funny, I just got a text from one of my clients, you know, uh, what's the best replenishment strategy that best fits organization? Like, it's just like <laugh>, that sounds weird, just as we're doing this. So, I mean, there's just so many layers, um, you know, in m Mr P, right? The, the hardest part is create, like having the data to run a proper Mr. P, right? Most e RRP systems have Mr. P, but the real challenge is having the data, right? So if you have 4,000 stocking units in your organization, for example, you know, trying to figure out mid Mac for each level, which supplier to order, what's the minimum model quantity for each supplier? What's the pick picking time? What's the manufacturing time? What's the lead time? It's a lot of data and you can put in a lot of effort and there's so many variables around it that becomes a big exercise to, you know, if you're not doing it, Mr.
P right now, just to get the point of doing Mr. P and then just keeping on maintaining that data. The other point is, you know, whatever you're doing in MRP is based on historical data, right? It's not, it's not live data. As of example, you put in six months project, you gather all kind of combinations, permutation, you have MRP data, and you are in a food and beverage industry, for example. Guess what? The drought happened and there's a grain shortage now. And all the supply chain, all the lead time factors, all the pricing, they're all outta the window, right? I mean, in the last four or five years, suddenly covid hit, right? So what happened to all the Mr P data rights, all of the window, right? It doesn't matter anymore. Whatever work you're doing or maintaining it, covid ends. Now you have shortages on port, you have labor shortages, you have, you know, you know, container short, all of that.
Now suddenly, what happened to all those lead times, all those, you know, data, you know, it's out of the window. So being to maintain that data, and that's where a lot of time these MR P tools, the shortcoming around these tools and, you know, people get frustrated around it, is maintaining that data accurately all the time is, is a full-time job depending on the number of queues for a number of resources, right? And that's where I feel a lot of companies miss out. You know, when they start thinking about, uh, running these MRP processes and when they, you know, when they start looking at it, it's like either squares them off or, you know, they go half-heartedly and it doesn't work, and then they get frustrated afterwards. Yeah.

Sam Gupta (21:49):
Could not agree.

Abu Asif (21:51):
Yeah, go ahead.

Sam Gupta (21:52):
Uh, could not agree more. Uh, Abu in fact, I mean, I'm probably gonna have a follow up story there, uh, you know, on your story. Um, so this is the story related to a customer. They called us, you know, they had a problem, uh, with the Mr P planning, obviously because of all this covid situation, uh, they were really struggling. They had to do a lot of manipulation, uh, in the spreadsheet. And then we go there and supply chain people are saying, you know what, we want this to be fixed. And we are looking at the system, okay, what the hell is happening, uh, with respect to planning? So then we look at all of their data sets and you know, we look at their bombs, bombs were all over the place. Uh, you know, they we're supposed to be more of the maketo stock business from the business model perspective, the way they should have configured it.
Uh, but since the er p system was really off, I mean, it didn't really do very good sort of the, the transfer, the point that, um, <inaudible> was talking about, especially when you are talking about warehouse architecture for the distribution business, it's very different, right? You, uh, manufacture or assemble in one, and then you are probably gonna be shipping to, uh, not shipping, transferring to the other one. Um, so in this particular case, the e r P system was very limiting. So obviously they had to take a lot of different shortcuts. Their bombs were like, you know, they were designed as more of the make to order, and they were thinking that the problem is really in their entities, okay? So in the entities, I think they had something like nine months lead time for some reason, and they were thinking their products were coming like, you know, in a year. So there were a lot of different complexity, but the underlying conclusion of the story was that the data was, it was just completely off. It just did not connect from the business perspective. So I don't know if you're gonna have any sort of follow up comments there.

Abu Asif (23:28):
Yeah, I mean that's, that's the core reason why Mr. P's projects are either successful or fair, right? It's the core underlying data. You know, we find either, you know, case either company can spend enough time to gather it or they spend too much time together and it's become like a one year project, right? So in maor, you know, whenever we go in, you know, it's what we advise is take up your critical items first, right? It's, uh, it's that approach that matters, right? Is it, you know, what, what items have the most business impact? So that can be dollar value, that can be from a customer experience perspective, and then, you know, start building that MRP engine, uh, down to, you know, to your last item. And, you know, there are also ABC classification strategies on the distribution side of business where, you know, you can classify your product by demand, by, by dollar value, by impact and so on. And, you know, it's building that, you know, it's, it's more of a live thing, right? It's, it's a running thing. It's, it's not, you know, you set it up and then forget about it, right? It's, you know, while the time you're setting it up, it may also change, right? So, so it should be chunk based in business impact, focus based, and then you build your rt, uh, supply chain, uh, with the business.

Sam Gupta (24:39):
Okay? Amazing. Thank you so much Abu for that. So Mark, I'm coming to you and, uh, obviously you are going to, um, say that, you know what, whatever you guys are talking about, that's 1980 because in 2022, you should be thinking about M R P 10.0, I guess <laugh>, and then you, it gets really technical when you look at, you know, different, um, uh, algorithm of M R P and how much they have evolved, uh, over the period of time. So Mark, over to you in terms of whatever context that you might be able to provide.

Mark Lilly (25:08):
Yeah, I think it might even go back to the sixties and seventies, Sam. Yeah. So, um, please <laugh>, uh, what we're talk, look, um, you know, uh, compared to how things were done in the fifties and forties and before then, I mean, M R P was definitely an improvement, right? So, yeah. Um, we, especially with the, the ability to traverse multi-levels of a bill of material and, and come up with some idea anyways of when you might need those sub-assemblies or, or down to the lowest level of components. So, so by all means, it was, uh, it was an improvement, perhaps a dramatic improvement to the way, you know, things were being done manually. And for a lot of companies today who are doing things manually, it, it is an improvement. On the other hand, um, folks who have experience with M R P and the folks who, who, um, have used r p a lot, um, experience, um, a lot of the, the side effects and the, and the, um, the, the headaches you get with, with M R P, um, one of the most, um, uh, that, that we see most often is, uh, the, the bull whip effect, right?
Is, is having, having too much of certain items, right? Um, uh, and, and just too little having, having stockouts of a number of items, either it's, it, it, the mr the M R P functionality tends to, um, encourage kind of a feast or famine type of thing. And this is, this is true through even in the four walls of an organization. And certainly when you bring in the, in the supply chain, and we, we, you heard some of the evidence of this in, in this conversation, right? You have a, you have a need for three parts, right? But, but no, we have a policy here where, where we don't want to order just three for, for cost purposes, and that's where we're planning and costing can often butt heads, but for, for costing purposes, we don't want to order less than 20 of those, right?
And, but then when we get to the supplier, the supplier's insisting that, that we only order in lots of 50, for example, of this sort of thing. And that, that's just a simple example of how, you know, a little butterfly flapping its wings turns into this tsunami of inventory that that ends up in your plant, or, or, or not, right? So, um, when you get down to the tactics of it, um, where, where Abu is going, right, right at the end, that he's, he's right. You, you want something that's dynamic, and this is one of the, um, you, you want to, you want to be on top of this information almost constantly. And this is one of the other problems folks have with M R P is, is that a lot of the parameters that you've set are quite static in nature. Um, lead time, for example, okay?
Just take, take the example of traversing that bill of material, right? To, in order to try to figure out or calculate when you're going to need, uh, a certain item, sub-assembly or raw material, you know, six levels deep, right? Well, what are you doing? You're, you're looking to the item master at each level to look for a static number, a static lead time to say, how long is it gonna take me to get this sub-assembly? Right? It has no idea of how many you're producing. It has no intelligence about the fact that if I produce two of these, it's only gonna take me three hours, but if I produce 200, it's gonna take me three weeks, right? It, it has no, it's just one static lead time value in your database for that part. So, so this is another area that skews Mr. P's results tremendously.
So, um, a a few years ago, um, a a couple people took a really hard look at this, uh, Carol Patak, who was a, um, an apex, uh, national president for a number of years in the two thousands, uh, together with a fellow named Chad Smith, who was a T O C lean type expert. Um, and they came up with an entirely different approach. Um, that kind of was the, is the antithesis kind of an opposite view of traditional M R P, and they call it demand-driven Mr. P. Um, and what demand-driven M R P is, instead of, instead of trying to guess the timing, and that's one of the things with M R p, you're, you're always trying to guess the timing and if things change, you've gotta rerun that program, and then the timing is different again. And so you're constantly doing pullins and push outs of, of work order dates or purchase order dates and this sort of thing, okay?
So instead of looking at the timing of it, um, what they decided, what, what they, the model they put together is to look primarily in the past for usage, for, for historical usage, and based upon the historical usage and certain variability factors. And this is, this is key today too, cuz we can talk about M R P and ex, the, the static parameters and the, and the fact that that M R P is driving you towards zero inventory, right? That is the M r P ideal as free, uh, aside from safety stock, the ideal in, in M R P is that your supply matches your demand perfectly and you end up with zero inventory. Well, you don't want zero inventory, right? You want enough inventory in for any SKU that you have. Okay? So, so you can introduce the topic of safety stock in that regard, fine.
Um, but there again, it's a static value. It's not changing with the, with the changing nature of your demand, uh, or supply. So, so that's what these folks put together for, for every sku you establish what's called a planning buffer. So you're looking at not only what you have on hand, but also what you have coming in, okay? And when that coming in may be purchase orders could be work orders, right? And then there's a reasonable order quantity on top of that to establish what's called a planning buffer. And the what's neat about this planning buffer is it's like a shock absorber, and it literally flexes and changes as your demand changes. So if you get a, so it's the, the, the nature of the values, there's a number of values that, that support the, the size of these buffers, right? And, and you can look into the, look into the e R p, anybody's E R P has these values, their usage values, some of its variability values.
And so these buffers are established, and then as time goes on and demand changes up or down, these buffers flex accordingly. So if a, uh, so if usage goes up, for example, then the buffer's gonna get bigger. If usage goes down, the buffer's gonna get smaller, and these buffers also trigger replenishment orders. So it's, it's really simple. The, the entire buffer is looking into the future for you. Okay? So it's looking in the past for historical usage, but it's also looking into the future in terms of what you have coming in. Um, and, and possibly forecast too. You, you can incorporate forecasts in this model as well. Um, and in some cases you have to like, for, uh, a new product introduction, for example. Um, but we've been, um, there's, there's a, a number of case studies. So you can go to the Demand driven Institute. They're kind of a, you know, a, a generic site where you can learn about these concepts, lots of videos, lots of case studies. Um, and it, it's, it's really fascinating some of the results that, um, that folks are getting with this approach to replenishment.

Sam Gupta (32:46):
So, awesome details there. Love it. Uh, you know, obviously for the customers who don't even understand M R P and I included, I guess, you know, it's probably going to take one more life, uh, to understand the M R P completely. And now you have introduced some more variables there. Um, so I don't know when I will be able to understand those, but the point, I mean, uh, there are some very, very, very interesting layers. And I wanna touch on one thing, which is, uh, the assumption that you are going to have historical data that you are sort of using to come up with your planning buffers. Now, the challenge with that is, unless the system is sitting outside of the LP system, okay, then you are probably gonna be okay, maybe it sit in the SNOP P system, because typically in the SNOP P system, these things are gonna be easier. Your historical data is going to be easier to maintain. If you had to get your historical data inside the LP system, good luck with that. Unless you are using the same e r P system for 20 years, which nobody uses, you know, you are not gonna have historical data. So how is the altham going to work again, mark, uh, let's say if they don't have the historical data, what enough historical data.

Mark Lilly (33:53):
Yeah, so great question. I mean, yeah, yeah. You're, you're in a bind there. If you don't, if you don't have any historical data, if they haven't been on any e r p ever at all, then yeah, you're kind of, you're starting out a little bit blind with this method for sure. Um, I think typically the, you know, the, the average span of usage you wanna look at is probably minimally three months. And in some cases it kind of depends upon the, the lead time for the part, right? And the, and the demand for the part, how far back you want to go to look at the usage window. Um, I think nine months is probably as far out as it as you typically want to go. So consider, you know, yeah, once you're on your, your E R P and you have, you have that usage information for at least nine months, you'd be good to go on an approach like this.

Sam Gupta (34:37):
Okay? Amazing details there. Thank you so much, mark for that. Uh, so Chris, I'm coming to you. Uh, comments or comments? Any sort of follow up stories? Sure,

Chris  (34:44):
Lots. So one of the things that everybody's hovering around in, you know, the static dynamic values in the r p, right? Well, these aren't working anymore. Business intelligence, so we haven't said it bi. So the analytics on top of what we're doing is imperative. And, and the, the real cool thing is if you could take the analytics and update the values in your system, so look for it because that stuff is showing up, but that's a big deal. And if you think about fill rates and, you know, you talk about warehouse architecture, heat maps, do I have things in the right place? Well, how does that help? Well, that's velocity, but the Rob nailed it. You know, this replenished the fulfillment bins and the two tier warehouse structures where, you know, and again, what's r p do? It tells you what to buy, when to release the po, what to make, when to release the production order, what to transfer from where to where, when to transfer it, what to cancel.
Oh, that's important too, isn't it? What to cancel. So a lot of stuff M R P can do for you and the bend to bin transfer is, and again, as you get more elegant, and, and this is velocity, right? So not everybody needs this. Not everybody has a hundred thousand or more SKUs. And so the manageability of this, right? And again, you're still segmenting and a heat map is, well, what do I need to manage first? Meaning, what are your fast moving items? So if you're looking at your top 50, right? Mr. P, start there. That's the most important stuff to manage the old 80 20 rule. So again, how do you cart, you know, crawl, walk, run, but analytics are gonna help, you know, what are those top items? Focus on those. And again, the refinement process, everybody said it. What are my lead times?
Does that impact cost? Is you get it, you gotta get analytics. You gotta say, okay, if I do this, how much more can I save? How much room do I have? How much cash do I have in the bank? What's the economy doing? I've got guys in the H V A C industry that said my inventory went from 12 to 38 million. Okay, now they're trying to bring it down. What's that tell you? They bought everything they could. They've, they violated all the replenishment rules and it's a grab. So that takes it off the systems capability, right? So now human intervention, right? We're in a precarious supply chain world, so that kind of throws replenishment, you know, somebody said that the other day, just in time wasn't meant for this time folks. So j i t doesn't fit anymore. Nobody said that either, but it's very interesting.
So the analytics depends all good stuff. What else did that jot down here? Again, the electronic updates. So again, you think about how you get started. Again, I'm a technical guy. I've got a client now and I do SQL updates to their r p data cuz they don't use it today. Actually they do. They make all their appeals manually. I had a session with 'em last week. But they have, you know, very complex bills and materials, probably 50,000 SKUs, okay? More than you can even, hey go update all the lead times. Well, what we do is, again, technique list of vendors, lead time by vendor, update every item for that vendor with that lead time. There's a quick way to paint lead time across a vendor. And to Mark's point is you're analyzing, right? Average, average performance, you gotta look at the performance. Maybe the number you put in there isn't, right?
Okay, that's gonna make a big deal because these lead times offsets, right? It's all about what's the capital float that's on balance sheet inventory versus cash. You know, we have conversation about how do we get more cash? Will you manage inventory better? That's, that's a big part of it. So again, I'm not gonna under simplify the process, but analytics are imperative. And again, pick, pick what you can start with and get that going. Is there anything else? Uh, I think that's, oh, warehouse architecture. We started talking about that. And as you look at fulfillment, again, you're figuring out how much do I need? How much room do I need? Yeah, where's it at? So again, we talked about logistics, we talked about heat map, but again, even replenishment. What are the movements receiving? Okay? Is that part of replenishment? Yeah. Okay, so <laugh>, does the truck come in?
Does it go to the dock? Where does it go? Do I do a putaway? Yes. Can I fulfill off the dock? So there's a replenishment can. And again, now we go back to the sophistication of your system. And again, I didn't mention so warehouse management, not just warehouse architecture, but the elegance of the warehouse management. Most systems don't elegantly create transfer orders. I mean, Mark's probably mine does. And Dynamics f and o does BC doesn't. So some do, some don't. But, but again, the sophistication of your, your mobility device, right? Because again, you can have the system and tell you to do this, but how much does it automate the work? Does it create the pos, does it create the production orders? Does it update automatically? This update automatically? That, and again, we're in your high volume numbers of skews. You can't manage that by people. So again, the sophistication of that mobility absence, hey, I need to go here and get this and move it over here. I gotta go in manufacturing. I got a picking order to pick to a cart. Okay, that's a replenishment in essence, but you get it. So

Sam Gupta (39:09):
Yeah, let missing, uh, details there. Uh, and the, uh, one thing that I want you to touch on is going to be what to cancel. Uh, I guess that's a very interesting layer. It is interesting. So how, how, how does that work? I mean, describe the process a little bit there.

Chris  (39:21):
Okay, customer calls in, ah, canceled my sales order. It was a make to order. Oops, okay. The ripple effect of a make to order. I don't stock that item. I don't build that unless I need it. You think I wanna build this a hundred thousand dollars product and put on the shelf, said okay, hopefully I'll sell that thing he wanted. Okay. That's an example of a customer cancels an order. First example to trigger it. I mean, that's the best case that I have is the customer order is canceled or something, something happens on the sales side where I don't need those or it was a mistake. Whoops. And so what happens? I I, you know, I had a fat finger in the, the sales forecast and I had a hundred thousand instead of 10,000 <laugh>. Sorry guys. There's a PO out there for a hundred grand of these and I don't want it.
I had a client, I kid you not, they ordered a half a million pieces and this was a total fat finger. Cause again, you go back to, oh, they were looking at historical usage, it was wrong. There was a bad entry in historical usage and they picked it up and said, oh my god, use a lot order half million. They didn't need a half million, they needed 5,000 <laugh>. And so there's a perfect example where, and again, the other thing we're talking about is the analytics on watching this stuff has gotta be more real time. So that you're looking for exceptions. But two horror stories were, I needed a cancel order. Like real quick it's like, oh, you already shipped it, refuse it at the dock. I mean, there's a lot going on there. We don't take receipt of that shipment. So cancel order.

Sam Gupta (40:37):
Okay. Amazing details there. Thank you so much Chris for that. <inaudible>, I'm coming to you. Uh, comments or comments? Any follow up stories?

Nirav Shah (40:43):
Yeah, no, I have a, actually a pretty interesting story and I think, I think Mark touched on it a little bit, um, talking about it doesn't forward plan, right? Yeah. That, that is one of the issues with traditional M R P and nps. So what we did for a customer, actually there were a big make order, uh, customer. They had, you know, 10 or 15 level deep build materials. A lot of the lower level components were made to stocks. So they're common across a lot of their items. The top level, maybe the first four or five levels were made to order. So when they knew they had a project, they're getting close to kind of, you know, uh, that project being approved, but it's not really approved yet, right? Things had r p once they get into a true demand line demand, which is either a job project, sales order, right component lines, that becomes true demand, right?
Or you got the independent demand like I talked about, but this was none of that yet, right? We had engineers still creating the bill material for the top floor, but they knew what the lower level assemblies looked like. There a lot of those make to stock apps. So we then leverage, uh, as uh, we call simulated production order or a what if scenario type of production order where we brought in the bill of materials, right? The top level, all the way to the bottom level, even though maybe it was like 80% ready to go, but we're concentrating on the lower level stuff, right? Mainly maybe, maybe one or two of the make to order items. But essentially we're able to check off which assemblies that we wanted to go ahead and see or prioritize in the planning run to bring bubble up to the surface.
We could stay ahead of the project, right? Um, and get those things on the production floor to start manufacturing, start purchasing because there was a high likelihood that the project was gonna come, you know, into fruition, right? And get approved. Versus if we didn't do that, we waited until the project came in, we would've already been late based on the due date that you know, that the sales reps were already trying to hit. Right? Uh, cause that never gets communicated back, right? Remember the due dates is whatever the sales rep thinks that they're gonna, we're gonna be able to deliver without a lot of internal conversation with the manufacturing team. Exactly. What is our capacity? What are our Bob bottlenecks, right? When actually are we gonna be able to go ahead and, and uh, get, get this item completed? So, you know, we were pretty creative there and we're able to identify, you know, without having it look at the full structure of a bill of material where we pick and choose assemblies from a full bomb and integrate that into the Mr.
P and NPS run and identify those specific sub assemblies or make to stock from a priority level, bring 'em up and re release them directly from Mr. P to go and get a headstart on it. So I thought that was a big, big win for this customer. They're able to kind of control their manufacturing process a little better and have better visibility on those projects coming in, right? Uh, on the manufacturing floor. So I thought, I thought that was good. And I always tell my customers when we're, when we're trying to implement M R P and NPSs, um, M R P and NPS is more of an art than a science, right? Every company's gonna be different. You, you're the way you interact with your vendors, the type of materials you buy, right? How far you are from your vendors, right? What's the seasonality of your products, right?
We're, you know, all these different things come into play and creating that perfect Mr. P engine, lemme tell you, it's never perfect, right? We can get close enough and we get close enough where we manage that 80 20 rule that Chris was talking about, right? Where we are able to manage a business with the high likelihood that we're gonna be able to complete on time, get our inventory and on time, um, go through the standard routine of manufacturing and, you know, stocking inventory and putting inventory and shipping to the customer. Cuz then that comes down to the warehousing lead times and all that stuff. But it, it's more of an art to the science and iterative. You know, you have to have patience. You have to be as, you have to have the continuous loop feedback to make sure it's humming. Um, and that the proper, proper departments have visibility into the r p thing.
That's a big mis mis misconception that only the mps, the master production schedule, only holds onto that schedule. That's not true. That should be visible to everybody. That should be visible to the buyer. That should be visible to the sales rep that's putting in the orders, right? They should be able to see, oh wait, you know what Mr. Customer, we have this coming through on our planning side, we might think that might be ready on this date because we see it being planned out. You know, let's put a tentative date, date in there that should also be visible right upstream and downstream. It just shouldn't be a silo. That information is very beneficial throughout the organization if yous properly and maintained.

Sam Gupta (44:55):
Okay. So some very, very, very interesting details there. So I am going to have one follow up question for you. And I don't know how many people really understand the difference between, you know, the role of the forecast, the role of mps, and how that is going to impact your, uh, M R P processes. It could be sometimes all over the place. Uh, you know, because, uh, all of them are sort of going to be impacting each other. So let's say if you were to describe the difference between mps as well as, you know, forecast and what the boundaries are going to be of each of them when you are sort of planning, uh, for your supply chain over to you.

Nirav Shah (45:31):
Yeah, I have a very simple example for that. Think of a seasonal business. Think about a toy manufacturer, right? Toy manufacturers will not have customer pos January, February, March, April, may, June, right? They're gonna get their POS at the end of the year. They're gonna get their pos that they have to, uh, produce to the different toy retailers, right? Amazon, whatever it is, it's gonna be October and over over December. Right? But they need to put a forecast in because they need to start making the millions of toys, right? Beanie bags was, was a big one, I think in one year. Uh, but that's the only one that's coming to my mind. But they had to start making that upfront. They had to start buying material January or February, manufacture them March, april, may, right? Get the containers ready, wherever they're gonna ship it to so they could start shipping with anticipation of those pos coming in at the end of the year.
Now, if they don't have the actual POS at the beginning of the year, so they're using a forecast with the high likelihood that they're gonna be able to sell the material right? Through historical sales. So they're gonna implement a forecast to get the planning engine to run so they could get the components right, get the inventory stocked so when the POS come in in October, November, December, they're able to just quickly ship out the door. Right? That is a, a real good example between a forecast and nps. NPS is gonna be telling the system, Hey, you got the, you got the, you got the purchase order now, but we have it in inventory, let's ship it out. Versus the forecast for the same example is gonna be telling the planner, Hey, we don't have a a PO but we have a forecast or we have to make it. Either we're gonna make the customer orders or we're gonna make the forecast.

Sam Gupta (46:55):
Okay. Amazing. Thank you so much Ara for that. Uh, abbu, I'm coming to you. Um, comments over comments? Any stories.

Abu Asif (47:03):
Yeah. I mean, you know, we had a client where, you know, they were at excess inventory and, um, you know, they were, you know, blaming the MRP system. And what we found were they had over enthusiastic sales, uh, uh, demand forecast, right? The sales that's always thought they can sell more, you know, hunt, they can always beat their quotas, but they never ended up beating it. So, you know, that's a very important factor is, you know, especially when sales side of the forecast comes in, you know, how can you accurately, you know, predict your sales, right? And that, you know, even if you're off like five, 10%, it can just throw off your entire material requirement planning, right? So, uh, again, forecast, you know, as Nira was saying, can, you know, vary by the season? You know, you know, you may have business model where you sell more in Christmas and then less afterwards, and then you have to build all those trend profiles and, and all of that into the system.
Um, but you know, I just common one of the comments, you know, um, Chris was mentioning, you know, about BI systems and the ability to, you know, automate the data piece. So in my opinion, that's the easier part, right? Because you can build some sort of system to automate some, you know, automatically update something. But the point I was making is just to gather the updated information, right? So if you have a thousand suppliers, and certainly world market condition has changed, that's going to affect five, you know, uh, hundred suppliers. Even just the effort of reaching out to them and figuring out what's the new lead time going to be is, is a huge effort on its own, right? And that's where the MRP systems often lack, is that dynamic data feedback from the market forces back into your own system, right? And that's where the most complexity of the, you know, MRP system, whether it's on the demand forecast, you know, summer went longer than usual, right?
So I worked for a big company, um, in the apparel business, and they spent a lot of money forecasting how, you know, how the, you know, how mild the winter is going to be, how strong the Canadian based company, right? How strong the winter is going to be, and based on those forecasts, then they order the inventory, the number of jackets, when is the winter going to start, right? But guess what, you know, the forecast change, you know, instead of, you know, we had a very mild October here, no one was even looking for, you know, winter jackets. So it certainly just changes everything, right? And it's that market forces, you know, now the winter can say, you know, either you take up the order or you don't. And you know, it's getting that market data into your MRP system, which I find is the most challenging aspects of development of good MRP systems. And where a lot of companies, uh, you know, fail, right? Uh, BI system, again, they're mostly focused on historical data, right? The reports built up on your e r P system, on your databases. But the BI systems, very few. I mean, even if it's out there just getting runtime market intelligence, right? They're not connected to any intelligence databases or things like that, right? So it's a process, you know, it's a complicated process. It requires, uh, you know, a lot of manpower to maintain it, uh, and you know, to run it efficiently be my point.

Sam Gupta (50:18):
Yeah. So some amazing details there. And I like a bit about, you know, uh, correlating your market data, obviously with your, uh, your internal data. Uh, but the challenge that you are gonna get, obviously with e R P system, as we know, as I commented, uh, about the tightness of the E R P data model, uh, any data that you are gonna store inside the E R P system is always going to be harder. Uh, the e r p data itself is very, very, very hard to manage. Now, if you are trying to bring additional data elements that sort of don't belong to E L P, uh, it could be even more problematic in general if you are going to keep that. And that's why, and you are absolutely right, uh, about the BI system work system of the other system. And that's why we have BS and OP system.
They are really designed for that purpose, and they typically, the output of that is gonna come to your e R P system. And they, you know, they manage all of their complexity where they are going to be overlaying a lot of different data sets. Uh, and they are also be going to be correlating with your data, but obviously your data has to be right. Your integration flows have to be right, otherwise your planning is going to be probably all over the place. Um, so I don't know if, if you have any sort of follow up comments there, agree, disagree, I

Abu Asif (51:27):
Dunno. Yeah. I mean, so I agree with you. I think, again, you're, you're pointing to one is a technical challenge of maintaining the data, you know, system, system type and how to manage integration. And the other challenge is getting the market data from your own market, right? Yeah, yeah. Getting actual data from a thousand suppliers, right? So that's, those are two different things, right? So it's getting that real time market data is, is a harder challenge in my opinion. Yep. Rather than figuring out the technical piece, uh, to get it into your system. Yep. And that's where, you know, people get frustrated with Mr. P is it's that market data. It's not a feedback from the market into the system. So,

Sam Gupta (52:08):
Okay. Could not agree more. Thank you so much Abu for that. Uh, mark comments? Over comments? Any stories?

Mark Lilly (52:13):
Yeah. Um, I want to talk about a, uh, a a subtle point. Um, Chris actually talked about in his opening comments and he was describing, you know, kind of down into a level of bill of materials and, you know, the idea of, Hey, I've got this sub-assembly, um, you know, it takes me, it's gonna take me four weeks to build it, or, or do I want to decide to put that in stock so I can save that four weeks of lead time, right? And, uh, and be able to get my end product out, what, however many levels up that is out, out to my, to my client, um, my customer much sooner. So that, um, that's a whole topic within demand driven m r p and, and they call it strategically positioning inventory, um, and strategically positioning, not in the sense of, Hey, I'm gonna have a warehouse in Milwaukee and one in la Right?
It's more, it's more positioning it in, in terms of the structure of the bill of material, right? So you, you do some analysis with your, with your bill of material and see, you know, how many, how many days of lead time it takes across through, through and down each level, right? And then you can, can kind of see and, and get an i uh, an idea of where, where you have the opportunities, if I s stocked this part, how much would the, would the entire lead time shrink, right? In order to offer? So, um, we, we recently, um, engaged with, uh, a couple, um, D D M R P experts, uh, that brought a, brought a client, um, to us and, uh, working, working together in a situation where they have, uh, they, they have an end product that their, their full bill of material structure and their full manufacturing process is about a 12 to 14 week lead time today.
And because they understand the demand driven principles and concepts, they are, they are so confident they're going to be able to have enough of certain sub-assembly in stock, they're going to be able to reduce this entire lead time down to a guarantee of less than two weeks to have that product. Okay? Now, obviously the balance here is right, you, I mean, if you have a gazillion dollars and, and as much space as you need, you can, you can go ahead and, um, and stock as much as you want, right? So, so the, the challenge is of course, yes, having enough of those sub assemblies at all times, cuz it'll, it'll cost you dearly if you have a stock out cuz that lead time's gonna come right back at you. Um, but at the same time, not too much, right? So you're, you're controlling how much, so you're making sure you don't have excess as well. And that's one of the big, the big principles and the balance points of, uh, of this demand driven, demand-driven r p approach I've been speaking of is having enough of all, any SKUs that you need. So almost 99% sure that you're going to have that, but yet at the same time, not having excessive and not having too much, so you're not tying up all that cash, um, in, uh, in what could be very expensive sub-assemblies.

Sam Gupta (55:26):
So, so there are some very interesting details and I guess, you know, one of the details that you just mentioned, there are subtle nuance there and, uh, that can answer a lot of questions and solve a lot of problems that we have been talking about. So are you saying that, let's say if I have my data off and, uh, you know, I maybe doing make to order for one of PSK right now, but let's say if I want to know whether they, that should be probably stored in the inventory as opposed to making when I get the order, uh, can the Mr. P system tell me that rather than make to order, you should be treating, treating that as more off made to stock? Can the M Mr P and then make those recommendations as well, mark?

Mark Lilly (56:06):
No, no, there's not that I know of. Um, that I know of. There's not a computer program that's going to, that's gonna do that for you and give you a definitive analysis. Someone may have it. I don't, I don't know of it, but typically it's, it's a more, um, it, it's a, it's, it's frankly a, a spreadsheet analysis of looking at the structure of the bombs and their overall products to see, to see where those opportunities are.

Sam Gupta (56:29):
Okay. Amazing. Thank you so much, mark for that. So the only thing we can take right now is going to be closing advice. Chris, what would be your closing advice, please?

Chris  (56:36):
So we're not gonna be afraid of this topic, and if we're manually generating POS and production orders today, and you have a tool in your system, look at your inventory, find your most important revenue profitable items, you can put settings in there and run master planning and see what it tells you to do. You don't have to do it. Go manually, create your stuff, run it again and see if it you missed anything. But that's a way to climb into this thing and start getting comfortable. And a lot of the old school guys were like, eh, well when you show 'em the report, he's like, okay, that's what I would've done. That's how you build adoption and you move this thing forward. So,

Sam Gupta (57:06):
Okay. I'm amazing advice. Thank you so much, uh, Chris for that. And then closing advice, please. Uh, you're on mute enough. Uh, 11 time

Nirav Shah (57:15):
<laugh>. 11 time today. Yeah, sorry about that. Yeah, I would say, I would say, uh, companies that are using r p and NPS are kind of, you know, feeling a little down and, and low about that's not working well for them. Look at your data, right? You need that continuous feedback loop. Let's look at, you know, try to create some BI around that to update that data. You know, what we haven't even talked about is other technologies within the warehouse that could also make your r p runs even a much more efficient R F I D tags on inventory, right? That the feedback into M R P and NPSs. So there's a lot of opportunity there. Don't shy away from it, right? Um, you know, o o open, open the door there and you're gonna see a lot more visibility into your business as you thought you knew.

Sam Gupta (57:52):
Okay. Amazing advice. Thank you so much na for that. Abu, what is going to be your closing advice, please?

Abu Asif (57:56):
Uh, yeah, I'm going to act for Chris's comments, right? Start small, look at your, you know, most impactful items and then start building Mr. P system, you know, from, from that point onwards. So

Sam Gupta (58:09):
Could not agree more. Thank you so much, uh, Abu for that advice. Mark, closing advice please.

Mark Lilly (58:13):
Yeah, if your, uh, experience with Mr. P, if you're not happy with the results for whatever reason, take a look at demand-driven Mr. P, demand-driven, r p uh, I'm sorry. Demand driven is the generic site that'll, um, give you a lot of education, a lot of videos about the concepts.

Sam Gupta (58:30):
Okay. Amazing. Thank you so much, mark for that. So that's it today. If you join for the first time, this was one 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 tonight.

Chris  (58:47):
Thanks everybody.

Mark Lilly (58:48):
Thanks all. Thank you. Thanks Sam.

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