Name: Beyond Excel & ERP: How Successful Manufacturers Plan & Schedule
Uploaded: 2025-05-08T18:30:02.800Z
Duration: 55 min 34 s
Description: Beyond Excel & ERP: How Successful Manufacturers Plan & Schedule

Transcript for "Beyond Excel & ERP: How Successful Manufacturers Plan & Schedule": Well, good afternoon, everybody. We really appreciate you taking some time out of your day to join us here today. We, we have an exciting topic. I think it's a very relevant topic to talk through today, considering all the different things that are happening in the manufacturing world. This concept of how do we optimize and and manage our our environments, to get the most out of our production environments, I think, is critical. So I've got, my partner here with me, John Rogelstad, who's the managing consultant at The Logic Factory. The Logic Factory specializes in these kind of conversations. And John's gonna take us through some of the thinking, that you guys might, be able to use, as you try to handle, the the changes that are occurring in the environment today. So, John, I'll turn it over to you. Alright. Thanks, Darren. So, yeah, Darren's with the GoEngineer. We're with The Logic Factory. We're both partners of Dassault Systems who we'll be introducing in a minute if you aren't already aware who they are. I, my background, just a little bit about me. I've been working in manufacturing pretty much since I was a teenager doing assembly on the floor and working my way through college, and I have somehow or another been involved with either direct, plant planning and scheduling or plant management or quality control or just working on the floor. Like, I've I've done all those things out in the real world, so hopefully I can relate to, some of the folks here working in manufacturing. But I've also spent a lot of time on the IT side, managing IT groups, and then, probably about a third of my career, working specifically in ERP. And in the last ten, eleven years, I've been working, exclusively in the domain of planning and scheduling software with the, product set that we're about to talk you through. So just a little overview of what we're, about here. We'll do an introduction on, on the the the larger topic here, and then the overview about, a specific brand of Dassault product called DELMIA and talk about how DELMIA's AI, technologies support the plan, do, check, act cycle, which is a a kind of a standard continuous improvement process that a lot of companies, in in lean manufacturing use these days. So kinda tying it back to lean manufacturing principles, and then we'll summarize, what we've gone through, after we go through the the the details of how, the toolset we've got here can support all of those steps. So, again, about who we are. So GoEngineer is the one hosting this event. They are the largest partner of Dassault Systemes products. Among the more popular products you've probably heard of are, like, SolidWorks and CATIA. These are the CAD system flagship products of Dassault Systems. Goengineer has offices all over the country, and, and a pretty good sized staff. So a big support partner there. I am from the The Logic Factory. We are also a Dassault Systems partner. Although our focus, we are more focused less on, say, the CAD and and some of the related, product life cycle, solutions, and we're more focused on the planning solutions. So that's, why I'm here. We've got this picture of a race car we like to frame ourselves as the pit cruise. So while, Dassault Systemes makes the race car and you're out, you know, running the race that you're trying to win in in your space, we're the pit crew that helps you get that car tuned up, for optimal performance so you can win that race. And if you know anything about pit crews, they have to be fast, responsive, and know their game very well, and, that's how we like to see ourselves in in in the space that we play in. And then finally, Dassault Systems. Probably a lot of folks on here. If if you've been working with those CAD products I mentioned that you would already know, who who they are, because those are pretty ubiquitous products in the manufacturing world. What a lot of people don't know is just how big the footprint of Dassault Systems is. 23,000 employees, over 300,000 customers. I like to highlight this part that 41% of the, 20 plus thousand employees are working in r and d. So this is a company that invests heavily in being on the bleeding edge of technology, which kinda makes sense when you think CAD software, and you're designing cutting edge products. You need cutting edge, tools to do that kind of design work. And it's not just making drawings. It's it's all kinds of simulation work, and that's a lot of what we'll be focused on today is bringing the, you know, simulation, down to the execution level of manufacturing from from just engineering. Alright. So let's talk a little bit about the tool set. So a good tool, I like this quote. A good tool improves the way you work. A great tool improves the way you think. Right? So we're talking about maybe changing the way you think about how your your business works. The the big trouble or or challenge these days, it just seems to constantly be escalating is is the disruptions. We had, you know, a pandemic a few years ago. You know, there's tornadoes and hurricanes hitting and earthquakes, here and there. We've got world conflicts. Technology is a big disruptor. You know, we got consolidation centers that are completely automated coming online everywhere now. What does that mean? Picture of the robot with the box. Who knows? I might have robots delivering your own pretty soon. The markets are swinging up and down, which can affect, you know, the your your demand cycle and your planning. And then you have the political disruptions. Right? You know, we've got this big thing with tariffs in The United States now, and and a lot of folks are trying to figure out, okay. How do we how do we need to change our strategy quickly to adapt to, you know, that and and everything else going on here? And, you know, back in the day when lean was really starting to catch on in The United States, and, you know, it kind of originated in Japan, although interestingly enough, some folks from even from The US, who I'll talk about in a second, started that. We had maybe let's just call it a more stable environment, and these sort of new inter processes of continuous improvement were introduced in most, you know, top tier manufacturers these days have some kind of process for continuous improvement. But those processes kind of assume a certain level of stability. And the problem is with all these changes I just mentioned, it it makes it hard to continue to use those processes, and and keep up, so because things are moving faster. So the question is, do you have a process to adapt to rapid change where your supply chain can literally change overnight. And most people don't. Right? It's, we we're kind of, like, just reacting. We get tough management, questions from management, and, you know, we go try to just figure it out because because our our continuous improvement cycle wasn't really meant for this sort of instability. And and the trouble is, this is a quote from, one of my favorite folks in, quality, w Edwards Deming. If you if if you can't describe what you're doing as a process, you don't know what you're doing. Well, you know, I think a lot of folks don't think you know, you might have some kind of disaster recovery process, but most people don't think of their continuous improvement is really having to deal with all this. But what we're trying to, kinda get out with this webinar is that, there are tools that can help you adapt more quickly, to these kind of environments and and fast moving situations. So, one of the things we'll start with is the idea that, a lot of the continuous improvement is not necessarily, system oriented. It's kind of changing your company culture. But, nevertheless, there's no doubt everywhere you go, process control within electronic systems, you just can't get away from it. There's gonna be some kind of process control. And, you know, a few decades ago, the the big idea was, we're going to, have all the things, all the processes that we need to manage in one system, you know, now it's called the ERP. So that has the accounting and human resources, got your engineering information, can do project management and things, all kinds of different things, and it would just all be there. And yet, even though pretty much everyone has an ERP system now, we find that it's still sort of impossible to get everything in there working well. Good example is the SolidWorks software I just talked about. That's that's designed software. Well, it usually isn't really in the ERP except maybe referenced, as as as something in the the product master record, but, of course, there's no, like, drawings in the ERP. So they kinda live in their own world somewhere else. Lots of folks have adopted manufacturing execution systems that layer on top of the ERP. Why? Because, we'll talk a bit more about sort of the the whole idea behind ERP and why it doesn't do well in some of these, some of these domains. But, really, the need there is, a lot of folks need operational feedback that's hooked directly into equipment. So the machines are literally reporting what's going on on the floor, how the machines are behaving, the quality of the product, how fast it's moving. And then even when people are reporting, you kinda wanna streamline interface, so that you're not having to click through 16 screens, to report something happened on the floor. So folks have those. And then because we have all these we're starting now to have all these different databases, we end up with data lakes where people try and put everything, all their data into some kind of central repository because it doesn't just stop at the ERP and the MES. There's often other subsystems kind of supporting where the ERP is weak. So then we we say, well, let's just put all the data in this data lake, and then we'll get all kinds of wonderful reporting out of it. And then, unfortunately, what happens in reality, pretty much everywhere I've ever seen, is that all these systems are hard for certain people to get their jobs done with because they're very rigid. And, ultimately, there's a lot of time still spent working in spreadsheets. 85% of companies still do major scheduling and planning and other kinds of operation activities in spreadsheets. So we still rely on Excel, and we just haven't got to where we thought we'd be with the kind of one one system that does everything. So let's let's talk about the problem with ERP. So, I like this old saw, this adage, when you're hammer, everything's a nail. So, using that analogy, what would we say that ERP is really good at? Because it's not like ERP systems aren't any good. They they do they're quite good at a a number of things, and and that's why everyone has one and should have one. But what they're really good at is accounting. Right? The accountants are often the final decision makers in choosing an ERP. Oftentimes, the implementation, this is sort of the number one concern. And other concerns, like planning and engineering and so forth, kinda take a back seat to what the needs are of accounting, often to the great frustration of, the folks involved who were hoping it would do a lot more than just that. And so subsequently, if you need like, the hammer isn't good at turning bolts or driving staples or hose clamps or screws and and all these other things that are shown here, the ERP is also not necessarily really good at some of the other things that it is sold to be able to do. And then turning towards spreadsheets, everyone kind of, like, scoffs at them. They sort of the love hate relationship. You know, we all say, ah, we shouldn't be using Excel so much, except we kinda have to. Because if we can't get our job done in the ERP, it's like the next best tool to do sort of ad hoc, manage data. But it's it's kind of actually really unfortunate and sad that, a lot of, high level executives and managers sort of just accept this reality, because, like, if we thought about engineers, that's what we're trying to point out in this slide, there's no way anyone would accept the reality that an engineer should do automotive quality drawings in PowerPoint. Right? You know, like, silly little drawing of a car here that's just no one would even think of that. And yet even though supply chain planning is pretty much on par in terms of complexity with engineering problems, this is what we expect supply chain people to do, to use a tool of that caliber. And, again, Excel Excel is not a terrible tool. It's a great tool. But what is really good is ad hoc things. You wanna do something quickly and just sort of off the cuff. But, like, trying to run a business with that, or, like, do supply chain planning and and engineering with it is, maybe not such a good idea. And just to kind of show or or play out, like, why how this kind of stuff works out in real life, we're gonna paint a a kind of a user story. So we're gonna talk about Ashley and Ashley's story. Ashley is a a a sourcing manager. Right? She works in supply chain and sourcing. And, of course, we've all seen the news where now there's some tariffs that just got implemented, and all our managers are asking, what are we gonna do about this? How does it affect our costs? Should we change our sourcing strategy? What should we do? Ashley's under a lot of pressure. So as it stands, she's she's in particular trying to source this valve pictured in the lower left corner, and they have been sourcing it from Asia. They can also get valves, with comparable specification in Mexico and in The United States. But up till now, Vietnam was the primary source simply because of the cost advantage. Her situation is, having erratic demand. So kind of while demand swings here, these this chart kinda shows her up and down undulations of demand. We need a whole bunch, then we don't need anything. We need a whole bunch more. So that's significant to her thinking, because it means she's gonna need to carry stock to deal with the kind of wild swings here. It's not enough time to order this. You gotta keep stock. And here's kind of a quick table of the the situation she's facing. The, she's got three suppliers, one in Vietnam, One in Mexico, and one in The US. Vietnam supplier has by far the lowest unit cost. And even before the tariffs, it wasn't quite, as obvious of a cost advantage by the time it showed up on the in US shores because there's transportation costs and customs and and even before there were some some tariffs, and all this. So but it there was still even with all that, there was a cost advantage before. But now with this 10% tariff added, the cost advantage is, somewhat erased. So that makes us now or or Ashley have to now question, well, should we still be sourcing these from Vietnam since it's not really the cheapest option anymore by the time you consider all that? So she's got a couple other options. She as I said, she can source them from Mexico. The Mexico landed's cost is is slightly less than Vietnam and, while The US cost is one might expect is the most expensive. But it's noteworthy that the lead time is longer the farther away she has to order because of a transportation and lot sizes. Minimum order quantities are larger, and that's a significant thing to think about. In The US, you can get it in a couple weeks, and you can order a small quantity. So there's some kind of advantage there that we might need to consider, especially because cheaper valves, might have lower unit costs, but carrying inventory also costs money. It ties up cash. And she's been given an annual goal on top of all the chaos she's trying to deal with to lower her inventory and increase returns. So it's not then with that sort of countering goal of faster inventory turns and lowest cost. It's not quite clear to her, well, which is better. Clearly, with The US source, I could get away with keeping less inventory, but I'd have to pay more per unit. So what's really better in the whole picture in the long run? That that's her problem. So she's using an ERP. Right? Everybody has to. And in terms of sort of trying to solve this problem, she's got a couple different kinds of tools. One is, well, in an ERP, you could theoretically roll up costs with different situations, like, you know, what what would the cost impact be on all my products if I source from here or there or someplace else? But the problem is in the ERP, well, it it only allows one standard cost, which is usually some kind of average. So she can't really roll up alternate costs in a simulated environment. That's not something she can do there, and accounting won't really let her touch the costs anyway. So that's that's a big letdown. All those things kind of retie her hands. But in spite of that, she's kinda looking there. You know, a lot of ERPs these days say, well, well, you know, we have simulation. You can run simulations in it. But the simulation piece in the ERP is limited. It doesn't really have consideration for the holding cost, which is part of her her goal, right, to to lower the holding cost to improve returns. And it it basically you pretty much all these kinds of systems will let you have, say, a split strategy where you say, well, I'll order 50 from one supplier and 50% from another. But what she's looking for is a recommendation. Like, if I were to split between two suppliers, what should the ratios be? I don't wanna just tell it. I want it I want some advice, and it doesn't really do that. And then, the that simulation system doesn't really consider the lead time impact and inventory and delivery. It just doesn't have all the the pieces she would need to fully understand what our options are. So the ERP is not helping her. So she's like she does what a lot of people do. She's like, well, you know, I can just figure this out in Excel. I'll just create some rows and pages and sheets and kind of calculate it. But the problem there is if if you really wanna do a full, you know, a fully informed simulation that takes into account all the various impacts, she would need in her spreadsheet to to draw in, like, all the bills of material of all the affected products, figure out, like to figure out the the capacity implications, and the timing implications, you would need to represent her resources and do some level loading. That's really hard. It's 25 machines, 200 routings with a bunch of steps. At any given time, there's 400 customer orders and their prices and 2,500 supplier part costs. And this will be to try and, like, get the whole picture of the impact of just this one part, stock targets, holding costs, minimum order quantities. Like, after a while, she's just okay. Forget it. This this is too complicated. And to the extent that people do try to use Excel anyway, they usually leave out a lot of this information and go go with an overly simplistic model that's kinda half blind or three quarters blind. It just doesn't really see all the interrelationships of all these things that have an impact on, you know, whatever problem she's trying to solve or anyone like Ashley's trying to solve. And what's frustrating is you have all this information in an ERP or maybe in a PLM somewhere, but you just can't figure out how to use it. So, that brings us to Dassault systems. So as we mentioned, kind of the flagship products are SolidWorks and Antia shown on the left here. This all has a whole bunch of different brands indicated by all these squares surrounding a compass. We're not gonna talk about all those today. We're we're just gonna kinda focus on the ones that might, be of interest to manufacturers dealing with on the ground planning problems today. So we'll fade out a few of them and just focus on on these, four quadrants, the as I said, the CAD on the left. And even if you're in a business that doesn't use CAD, you might still be pretty interested in, SAW's Innovia product, which is a product life cycle management. It's designed to tie into the directly into the CAD, so there's there's a direct link between CAD drawings and and, engineering data in a database. We'll also be talking a little about what, this brand called Netvibes can do that gets that does some, business intelligence and artificial intelligence, machine learning, and things like that. And then the one we're gonna really focus on is this DELMIA square on the bottom, which is really all simulation and operations management. That's and the keyword is simulation. And just to point out, the common thread of all these things is this is all, to solve kind of a future thinking company. All of its products are dealing with the future. Right? What is what are we gonna do in the future? What are our products gonna look like? What are our plans gonna look like? So if we bring it together with what I mentioned earlier about the plan, do, check, act loop, which, by the way, this this was one of the contributions of Edwards Deming, the the fellow I mentioned earlier who's kind of a one of the pioneers. I won't say he's the only one. There were several. But one of the pioneers involved in kind of what we call today lean manufacturing, we have this idea that we the part of continuous improvement is, you know, planning carefully what you're going to do. Do do the thing. Go, you know, go build some things, check your work, and to the extent that there's something, that needs adjustment or fine tuning, act on that and then go back and replan again with your with your assumptions changed. So the question here is, well, how how can, the Soul Systems products, DELMIA in particular, help us with this. Right? So in the planning, we can take engineering data, from that's, you know, ultimately from your CAD systems or if you're using a PLM system, particularly DS's Innovia product, but any PLM could also feed DELMIA. We can take that data about the routings and the time it takes to build something, and all the material requirements and bring that over to do planning. We do our planning in DELMIA, using a full blown simulation, taking advantage of all that info. Then out on the shop floor, we also have some DELMIA products that can help with the manufacturing execution and gather information back about what happened on the floor. Alright? And then, so data collection. So we know, alright, what actually happened, that's the doing. Then we get to the checking. And the checking is, alright, we'll use some DELMIA dashboards powered by that Netvibes brand I mentioned earlier, that has the machine learning and whatnot that takes this huge amount of data, which, by the way, this is one of the big problems today with all these fancy databases we have is we get we get all kinds of data. It's just just we're over overwhelmed with data, and the hard thing is to figure out, like, what's interesting in there that we should be acting on. Right? I mean, we have you know, a lot of people have Power BI and Tableau and these kind of things today, and they're building they're building some kind of dashboard, but still you're already sort of when you're building dashboards, you're presuming you know what it is you're looking for. And one of the problems is trying to figure out, well, what should I be looking for? Like, what out of all that vast sea of data, what where might my problems be and kind of bring those to my attention? So that that finally, we can act on that. And acting would be, alright. Well, let's go back and update our our drawings and our and our, engineering assumptions if we need to so that we can get a better plan next time and better execution next time. So you get this virtuous feedback loop that this this, these products work together offer, which is, like, not some definitely, Excel sort of standing on its own wouldn't do this. And and ERPs aren't really tailored for this, because as I said, they're they're more interested in the accounting part. So the way I leverage Ashley can leverage her data now is by extracting from, any number of those systems we just mentioned, whether they're DELMIA systems or even other brands, the ERP, some PLM, the floor systems. We can bring input from those systems, into DELMIA planning and scheduling, do a simulation, come up with a plan, and then send the plan back. And here's what her world looks like. She's finally happy with this. So with the with the DELMIA, macro planner products, what we call it, this is from the S and OP planning suite, which I'll talk about in a second, she can now compare the outcomes of potential plans simulations for different scenarios. So the first three, she's saying, well, what if I just sourced? I continue to source from Vietnam. That's option one. Option two is I source exclusively from The US, and option three exclusively from Mexico. And option four, well, what if I just kinda blended a combination of those and figure out what the best combination is? And, really, I want this system to recommend the best mix. And as it turns out, yeah, she compares these side by side. It looks like a spreadsheet. Right? It's just sales, but, actually, there's all the simulation going on under the hood with all those bombs and routings and orders and things we talked about, driving all this. And, ultimately, she gets an idea of what the which of these options would be the most profitable, and we can tell right away which one it is because it's in green. Green means it's the best of the the on any given row of the four data points, the best one is green. So, Mexico was the worst. Mexico sole Solisource was the worst. Blended was the best. So that's the first takeaway. And just looking down a couple rows to sort of understand why, we see, well, you know, Mexico had the best sourcing cost. Right? It's the green on the sourcing, but it had the worst on the inventory holding cost. And USA had the worst sourcing cost if you just buy only from The US. But if we do the blended, we get both a pretty good sourcing cost, not the best, but a pretty good sourcing cost, but a much better holding cost. Right? And remember, turns inventory turns is one of our goals. So if she just sourced from The USA, she could get three and a half turns, little more than three and a half turns. With the blended approach, she can still get three and a half turns even though she's ordering some goods from Mexico. Sort of like best of both worlds. And, she can drill in and see the details of how this works because one of the benefits of having a full simulation that fully represents your whole supply chain is you can follow the flow map of what the planning suggestion is in detail. So looking here, on the far left, we see our three sources, Mexico, USA, and Vietnam. The plan here is recommending that we don't source anything for Vietnam. Now that's kind of obvious because that was ended up now being a more expensive option. But noticeable that it's suggesting a kind of a blended approach buying, about, 80% of these valves from Mexico so you benefit from the unit cost, but also buying some from The USA in order to help keep inventory low. Right? Because it's got a short lead time, she doesn't have to keep massive amounts of inventory. And then we see the flow as it continues on to the right. We get the deliveries, and then it fans out. That fan out on the right is where these valves go in a variety of different finished goods. So she's happy now that she has, you know, some plan results that are meaningful. They're they're speaking in in monetary terms that she can talk to her accountants about and her her c level execs and and kinda really justify why this plan makes sense and not just be guessing or using rules of thumb that might be terribly inaccurate. Alright. Let's dive a little deeper into the planning part because there's more to it. Right? Ashley was just one example. But the idea of planning is bringing the future in the present so you can do something about it now. Right? Because if there's gonna be a problem, I would like to act on it as soon as possible. And in fact, this is one of the problems, that we have, that we face is, I know one of, one of my problems when I was, you know, doing plan scheduling and and the supply chain management is I would have people spend days or weeks working on beautiful plans, and then something goes sideways in the plant or with a vendor or, some customer comes along with a problem, and then the plan is sort of thrown in the garbage. It doesn't work. So that's why I love this quote. Everyone has a plan until they get punched in the face. Yes. That's that's right. As soon as you get punched in the face, you know, like in the boxing analogy, well, you're just sort of reacting at that point. You're not really planning anymore. So what can we do to change that kind of thinking? Well, one important thing to think about is to kinda go back to what we were talking about earlier about the separation of concerns. So I mentioned ERP systems are really good at accounting. They're good at record keeping. Right? And the important thing that they need to be able to do is take a a history of what happened. It needs to be accurate. They need to control it because they're probably gonna be audited. And there's really only one history that's valid. Right? There can't be can't be two. There's one. And we wanna know what happened, and we want it to be accurate. There's only one. But thinking in the future, it's a totally different paradigm. And this is where Delmia is stronger. Because the future, you have to think in terms of simulation. Think of think in terms of other possibilities. Like, well, what how many different ways could we handle the future? And not to mention that there's competing goals. Right? Like, I need to keep inventory targets, but I at a certain level, but I need to ship things on time, and I need to keep costs low. And sometimes these things are working against one another, and it's not straightforward. There isn't just one solution to, like, how I should plan. There's, like, a lot of potential solutions. I could ways I could solve it. Should I do overtime or should I not do overtime? Right? Like, what what should I do? And this is where simulation helps because you can explore thousands or millions of different possible futures, and an optimizer can help you pick one. And even if you get punched in the face, then if you can run simulations quickly, you can adapt and maybe pick a different path forward because based on, you know, the new thing that just came up. And so what's really important about this is that optimizer technology matters. Like, it's really significant that and I've been saying this from the start that Dassault Systemes is a simulation first, kind of a future engineering thinking first company, versus worrying about the past. And and the reason that's important is because this optimization, this idea of simulation is a first class citizen in in their product suite. The DELMIA optimizers actually hold several world records. It's the best. Whereas, like, in a lot of ERP systems and and other kinds of systems, it's just an afterthought. Well, yeah, it does planning too, and it's just kind of a couple engineers are hired to maybe build that part of it. But for for us, it's it's job number one. Right? So the optimizer's KPI driven, built for enterprise networks. It has to reflect your reality exactly just like CAD systems have to reflect, accurate reality. If you're gonna design a plane or a car solely in the CAD system, which is how it works now, it needs to be accurate, and it's the same with supply chains. So in terms of, like, how these kind of systems work, if you're not familiar with it, there's, there's they're basically four different levels, and I'll just say there's two classes. The first class is is sales and operations planning, and and this isn't anything like we've invented. This is kind of industry standard. Sales and operation planning is usually planning, thinking years ahead, but this is Ashley's problem. Right? She's thinking for the rest of the year or years ahead. Like, who should I set up supply contracts with? What equipment should I buy? Or, you know, what how many staff should I hire or, like, go through attrition? Should I open a plant? Should I close a plant? These are long term things. Where you use a demand planning tool that takes historical information about your sales to help you project into the future where sales is probably headed. And then with supply planning tools, you simulate, like Ashley did, you know, running different scenarios with different situations, you know, different sourcing options, different machines available, and whatever. And you get these kind of flow diagrams, which we're seeing again here, and and and load predictions and so forth. So that's long term planning. Really useful for, like, especially today's environment where there's kind of a sourcing crisis. Then you have near term tools, just call just planning and scheduling or master production scheduling and detailed scheduling. That's the two arrows in the right. These are more concerned with the order book I actually have in my hands. I have orders on the books. They're here. I need to build them. We've already worked out what our equipment is and our staffing. What I really just need to figure out is what parts to order, if any. When an order comes in, when can I actually give a promise date to deliver on it based on what's actively on the floor right now? And then in terms of detailed scheduling, like figuring out how to order things. Like, in this this diagram or this screenshot we're seeing here, I'm not sure how familiar all our watchers are with Gantt. These kinds of Gantt charts, you might have seen them a hundred times. You might have never seen one. But in case you haven't, we're looking at a kind of a list on the left of, all the different machines, like a rotor injection, molding, assembly, transmission mounting, and so forth. These are resources. And then within going across the right is time, and the resources are scheduled a lot like an old time TV guide where we basically have programming. Right? We wanna make sure that we know what jobs we're running throughout the course of the day, over the course of time. And one of the really hard things to do when you get to this level of detail is making sure that everything's coordinated properly. So, for example, do you see there's a line in the lower right where there's a preassembly gearbox? When that's assembled, then it goes on to transmission mounting, and you can see exactly when that happens, and then it goes on to finishing the engine. So figuring out how to, like, organize these things in a way that, one, keeps all these machines occupied efficiently, but also make sure the material is coordinated in a a workable way and make on time deliveries and keep your inventory levels, this can be really hard to figure out by hand. But with, software tools like this, it's significantly, more streamlined. And just to kinda give an example of of what that looks like, we've got Dave here. Right? Dave Dave is a, a scheduler who works closer to the plant floor. So unlike Ashley, he's interested in not, you know, months away in suppliers. He's interested in keeping equipment running today. And I I kinda like this this image we got here of this fellow Dave. He reminds me of Obi Wan Kenobi. So we'll see if the force is with him. But, anyway, yeah, he's kinda happy at the beginning of the day. Delivery performance is projected to be 85%. Could be better, but not horrible. And then productivity is quite high at 87%. So he's happy with the schedule, but then, Murphy's Law strikes and he gets news that this rotor injector two is gonna be down for a couple shifts or down for a day or something like that. And so all the jobs that were planned to run on that thing now aren't gonna be able to run there. And, well, this kinda messes up his whole day because he immediately sees the impact. The way that's represented is the rotor injector two mold now is showing no availability. It's just grayed out. Those jobs are basically taken off, and that hits his, delivery performance. It's now dropped to 78%. Yikes. And Dave's trying to figure out how, you know, how can we mitigate this. And one thing he notices is that he can, there's an there's another road injection molder that was already down as, shown by the gray there, but that was preventative maintenance that was already planned. And he's wondering if the maintenance guys could get that up. It's it's supposed to be down for a couple shifts. He's wondering if they can bring it up a shift early. So he's gonna call them. And he says, talks to the maintenance guys, and they say, yeah. You know, we could we could bring that up a shift early so to help you with recovery on your plan. Now, I kinda mentioned this before. You know, when I've tried worked with people who do this stuff by hand in Excel, it takes, like, hours or days to, like, reorganize all the hundreds of jobs potentially impacted by something like that. It's maddening, and it's very disheartening. But Dave, he doesn't need the force. He can run the optimizer. So he changes to make the, to reflect that the machine four will be up earlier, starts the optimizer. It runs for about five minutes, and now he has a recovery plan. So we see the road injection mold comes online at ten now. It backfilled a bunch of jobs to run during that time. Probably had to shift out almost all the jobs in the floor as to some degree to accommodate this this change. His delivery performance is back up. It's not where it was. It couldn't be because we are, in fact, losing some floor time. But at least he has now, within minutes, the best recovery schedule possible to keep everybody happy. And to the extent that he couldn't quite get to the original delivery performance, he also has some concrete numbers he can share with management like, okay. You know, we rescheduled. We reorganized. This is the best we can do. We got some recovery, but there might still be some bigger mitigation to deal with, overall. So, so he got a pretty fast result, and, people get moving again. Alright. So, that's planning. Right? And then we talked about the next, plan do check act cycle thing is to do, do the right thing. And for that, we'll talk a little just a little bit about, how DELMIA helps on can help on that side. I mentioned earlier that MES systems, what they're helpful about is being super efficient at gathering information on the floor. Clicking through screens can be really, like, maybe people don't mind so much in the accounting department, but on the floor, everybody's sort of rated on efficiency, and you don't want a lot of clicking. You want the interfaces to be configured so people can quickly and easily and intuitively get their job done. Or if you can hook something to a machine, do that. So, dummy operations management can help with all the things that go on the floor. You can do receiving with it, right, which can include part inspection and put away integrated into the receiving process. Warehouse management, that can be tied into your just in time and kanban systems. Usually, something, accounting isn't as concerned about, but definitely man matters, matters to your floor. Production, you can have built in work instructions with videos, and the CAD drawings can pop up. We can do our whip tracking there. So have the the people on the floor give feedback as to what they're getting done or the machines give feedback to what they're getting done, which can also include quality metrics being fed back simultaneously, some often neglected and often separate. And sometimes, very often, I've seen quality being still done in physical paper notebooks. How about we get that all integrated so we have feedback from the quality, together with the WIP? Maintenance, you know, if you're gonna have almost everybody in manufacturing that has machines needs to maintain those. Sometimes they have to be brought down. We wanna coordinate that. Of course, shipping, we need to print shipping labels and do container tracking and all kinds of other difficult things. And finally, we want all that activity to be monitorable in real time. So this is, you know, from the floor's perspective, getting the details and doing it efficiently is everything. And we want it all kind of tied together. So, yeah, dummy can do all these things and bring it together, sort of what ERP promised to do. And and the ERP, in fact, would be updated with a lot of this information, but the point is it's just gathered in a more useful and effective way because it's meant for this purpose of working on the floor, not so much meant to, you know, handle accounting problems. So now that we've got data collected, we can get to the checking because what we wanna do is fix the problem, not the blame. It's an old Japanese saying. So, as I mentioned earlier, we are all sort of just overwhelmed with data today. It's just so much that nobody has time to, like, analyze it all or build dashboards for every possible thing that could be going on out there or making all kinds of correlations that are just might be hard to find. And that's where machine learning comes in, some of the new AI capabilities. And what machine learning can do is go through large volumes of data and not only identify problems, but even make predictions about what's likely to happen based on what we've seen happening before. And we all know that, like, if you've done any kind of scheduling on a plant for one of the big problems you have is a scheduler spend all this time building schedules, and then the floor gets it and kinda laughs and just tosses it aside and and does something else. Right? And and what you end up with is, bad scheduled adherence. And this is what Alan is interested in. He's a data analyst, and he's trying to see, like, how the shop floor is doing or how it's even predicted to do. And he notices that the predicted, adherence adherence means, like, how well do we actually think the floor will follow the schedule that was created. Even the software says it'll probably only get done 85% of the way we think it will. That's funny because we planned it on the software. What does it know that we don't know? Well, the planning was based on some assumptions about run times and things like that. You know, change over times, break times, whatnot. Clearly, there's something going on here that's means our assumptions are not quite right. So Alan's gonna dig into this a little bit, and he can get into some details here. And what he's seeing is a Gantt chart that's similar to the one we saw before. You've got time going across the top at with jobs scheduled. They're kinda like programming within the time blocks. But what's interesting now is on the left, we have the the machine list kinda like we did before, but there's two versions of each Gantt chart. There's the one of the actual schedule, and and then there's another one that says deviation. And then what the deviation does is, signal where the machine learning is detecting that it's not likely this is actually going to work out based on history. For example, Alan sees this one. Oops. Let me get the animation there. He sees this one that's red. Clearly, there's some kind of deviation. There's something wrong. When he clicks on it, he gets a lot of information about what it is. It's the work center GLAT. Item family is Frami. But the most notable thing is the note on the bottom that says it's going to be late for more than twenty four hours. Well, that's interesting. That means something's clearly wrong about our planning assumptions. Why is that happening? Why is it predicting this? So, a lot of times, we get asked to, hey. Can you come up with a system that just automatically learns and fixes scheduling? Take, you know, this learning and then update our assumptions. As from a kind of consultative standpoint, I would advise to be very, very careful about that. We're seeing that more and more in RFPs these days. We just wanted to kinda automatically figure this out. But here's the problem. Really, Alan needs to go talk to somebody on the floor and find out what's really going on here because it's one thing to just assume this takes twice as long as we thought, but shouldn't we find out why? So, you know, it's not all just software. Sometimes you have to interact with the real world. So we would advise, yeah, think before you act, then act decisively. And thinking means Alan's gonna go down to the floor and talk to folks and say, why why do we have a pattern here of these never getting done on time? What's the deal? And the people tell them, well, that's because the mold always sticks into that one. It's just poorly engineered, and we constantly have to spend time getting things out. Then stuff is stuck in the mold, and we have to clean it out, redo it all, redo work. It takes a lot of time. So then, what Alan can do here, you see, he can go talk to Rob at engineering and saying, hey. We're seeing a consistent pattern here that's affecting our planning. That's you know, we're just we can't finish these on time because of this problem we're finding on the floor with these sticking parts. And they say, alright. Well, let's they get together and, like, let's put in a corrective action. And the corrective action is what starts to link DELMIA back to the drawing. So if you're using SolidWorks or CATIA, you've got a drawing, already. And now you can kind of annotate it. So draw around. Yeah. Here's where it's sticking like we see here. And then engine it can go to engineering, and engineering can go back to, the, the Innovia product that we're he's using. In this case, the product life cycle management, which has all the drawings, integrated into it and make an adjustment. And the adjustment he's going to make is say, hey. In this setup, we're gonna change the work instructions that say, we need to add a lubricant to the mold to prevent the sticking. It's gonna add a little bit of time. So, yeah, that changes the the setup time, but we're expecting based on what we know now about how, the the the bad impact of things sticking, we're gonna make up for that a lot by reducing rework and production delays. So we've now gone through the full cycle. And and this is the key. Right? Like, it's the simulation. Having strong simulation tools is one thing. That's that's, like, an important prerequisite to having good planning. But another one is this kind of feedback loop. We are constantly checking your assumptions and making sure you're taking action on them so that, ultimately, your schedules get better and better and more reliable in the future, which gets back to our summary where we want to, yeah, we said before, improve visibility, act on what we see. So just to kind of, reiterate quickly through the the loop, we can use from the from the planning. We can take all the information about the parts involved and the run times and whatnot that we have and do a, like, a real virtual twin simulation, not just some, you know, rule of thumb spreadsheet, but do a full blown simulation, come up with a solid plan, execute on that, and to the extent that something goes wrong, and something always goes wrong. Hey. We we look for the outliers and and we we find them. We bring them to the surface with the checking, and then we, you know, do what's necessary to update our assumptions or, you know, chain make an engineering change or whatever would be helpful, so that in the future, our plans get better. And we have this kind of flywheel effect where things get better through continuous improvement, and we can do this quickly. Right? It doesn't take weeks and weeks of analysis, weeks and weeks to redo the planning. You know, the planning takes a few minutes. The analysis takes a few minutes. You can spend more time, you know, thinking through how to solve the problem rather than all this time, you know, just crunching data and stuff like that, which is not value add. So yeah. So, just to reiterate, our role here, The Logic Factory, we're especially focused on the planning solution piece of this, but we do work with GoEngineer who, is well versed in the, execution parts in Innovia. And just to, and and, yeah, we're the pit crew. Right? That's that was the analogy. We're the pit crew that, helps you win the race by by tuning your engine, your your your install engine. And it's this isn't all just, to to make people's lives easier, though we think it will make people's lives easier. If you wanna convince your management to adopt some of these ideas, you need to bring business value. Right? And so this is the kind of stuff that we see happen that, can help you make the case to to embark on the journey of implementing some of these tools. You increase your on time fulfillment, reduce your inventory, increase your revenue, and so forth. These are the kind of stats you wanna see so that you win the race in your business. Alright. And I think we're kinda getting near the end now. So, so, yeah, that's the the kind of the overview of the, the the how, dumb, sorry, just all systems tools that work within the planning and scheduling, space and how it ties, Dassault Systemes solution uniquely ties to its engineering solutions. We will go on a couple more deep dives. There's a follow-up webinar on this that goes more into the planning and scheduling. If anybody was hoping to see actual demos and not just screenshots, the next couple webinars will actually be going into the software and and and sort of showing how you interact with it and how things are solved, because we can spend more time just on narrower areas. So planning and scheduling will be the next one. That's the short term planning, which is often the biggest pain point day to day for most operations types of folks. And then on July 17, we'll do one on the sales and operation. That's the longer term stuff, like Ashley's problem. So, yeah, June 17, more like Dave's kind of problems. July 17, more like Ashley's kind of problems, and we'll see some demos. So, and that's that's it. Yeah. That was that that's fantastic. And, there's a couple of questions, and I guess if anybody else has questions, please, you know, you can drop them in the in the chat or the q and a window. But a couple that came in, John, is, you talked about a lot of different things, I guess. So do I need, do I need to have all of these solutions? So as kind of a vision, yeah, it would be a good idea to have all them as as a long term vision, but, of course, nobody nobody just implements all this stuff all at once overnight. It's not like ERP where you just implement it all at once, which is very painful. You you can implement things in pieces. So you could just implement the planning and scheduling as we're seeing here the the short term, or you could just implement the S and OP planning. Or if you don't even have, like if you're really lousy on floor collection and you feel like, yeah, yeah. We couldn't really schedule if we wanted to because we don't even know what's going on on the floor. Maybe it's best to start with the the manufacturing execution, get that, you know, in better shape, and then tackle the planning piece. So you can definitely handle it, in pieces, and that is in fact what we recommend, kind of an agile approach where maybe the the vision is the whole loop, but then we build a road map like how do we get there. Right? One piece at a time. And, of course, if you already have, like, a PLM solution or a floor solution, we can work with that. Right? You don't have to have all these dummy products. We of course, they work really nicely together if you have them all, but, we can work with what you have also. That's fine. Excellent. Another question. What what would a typical road map look like? Yeah. I kinda already kinda hinted at that. I think it depends on on where you are. If you have pretty strong data collection on the floor, we would you know? And a lot of cases, the biggest pain points we see these days is, what we've always seen is is in the planning and scheduling. But I think it really depends on, like, the context of what you're trying to solve. So I kinda open it up, pointing out all the different chaos going in the world, and and certainly the tariffs was one of the stories we highlighted. And if your big problem right now is figuring out how to do geographic sourcing, we might say, well, probably should start with sales and operations then. It really depends where your biggest, struggles are, and we would we would start there. So we probably wanna talk that through and develop a road map that fits your your situation. Excellent. You mentioned a lot of data sources that feed these analysis. Would these would these would this data have to be imported into the system, or can it connect to these other data sources to bring it automatically? Yeah. Right. I mentioned earlier, one of the sort of architectural pieces or brands in that Compass was the NetVibes piece. The NetVibes has the connector parts so that we can draw in your data. We can connect to other systems. It's kind of the thing that orchestrates the connections. So, so, yeah, I mean, we definitely have the tools to connect to any data source, SAP, Oracle, Agile, you know, Microsoft three sixty five or Sage or In for I mean, there's so many of them out there. We we can pretty much connect what we need to. Certainly, we have more experiences with the more common systems, so there probably goes a little faster and easier. But some people still have homegrown ERPs and MES systems. And if that's what you got, we'll work with that and we can connect to that. In the beginning, you had mentioned that people try to do this with ERP. I mean, are you testing that they don't need ERP, or is this complimentary? Yeah. I would say it's complimentary. I mean, as I mentioned, ERP is good at accounting, and everybody has to do accounting. Right? I mean, this is just something it's it's part of business, and you're not gonna throw away your accounting system because it's difficult to work with. So, you know, take advantage of what it's good at. Right? Get the most out of that. And then we just wanna sort of supplement it or complement it, with tools that are, you know, more, tailored to solve different kinds of problems. And and in the case we're talking about, futures, future seeking problems, not so much about, like, whatever happened in the past. The past is useful for doing the checking, right, the analysis, and we might even be able to get something out of your ERP that helps us with the, the analysis part of it, the checking. But, but, certainly, yeah, you would keep it for the for the things it's good at. Okay. Well, it looks like all the questions. So, again, thank you everybody for taking time out of your schedules. We hope that you found this informative. Again, if you wanna learn more, reach out to us. We're happy to to discuss what might be going on in your own environment, some of the problems that you guys are are facing, that we might be able to help with. So don't hesitate to do that, and mark your calendars for the next two, dates where we'll get into these topics, in a in a deeper fashion and and show you some of the software itself. So thank you again and, and have a a great rest of your day. We'll talk to you soon. Bye bye.