Adapting Quickly to Disrupted Operations

Are you facing challenges in planning and scheduling your manufacturing operations? If so, join us for this MFG eCommerce Success show to hear Celeste Lefebvre, Application Specialist at Optessa Inc., talk about how Optessa’s advanced planning and scheduling software can help manufacturers adapt quickly to disrupted operations.

Are you facing challenges in planning and scheduling your manufacturing operations?

If so, join us for this MFG eCommerce Success show to hear Celeste Lefebvre, Application Specialist at Optessa Inc., talk about how Optessa’s advanced planning and scheduling software can help manufacturers adapt quickly to disrupted operations.

Celeste has experience in research and engineering in various industries, including automotive and aerospace. She is passionate about helping customers solve complex problems and achieve operational excellence with Optessa’s solutions.Optessa is a leader in intelligent planning, sequencing, and scheduling optimization software with successful implementations among top-tier global manufacturers. Optessa’s software can model manufacturing requirements, optimize supply network costs, properly sequence assembly lines, schedule molding lines, optimize workforce schedules, and more.

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Damon and Nicole start the show with remarkable energy. Since Nicole has replaced Curt today, it leads Damon to say, “Curt is wearing a wig today,” injecting humor into the show’s atmosphere. Nicole, taking charge of the show, asks Celeste about her childhood hero.

Celeste, originally from France, regards her mom as her hero. She reveals that she pursued engineering studies there and later in the US for a master’s in Industrial Engineering. Her dream of coming to the United States became a reality, where she found academic success.
“Cool,” exclaims Nicole.

Celeste further reveals that she was drawn to combining positive aspects of French and American cultures to create something incredible. She was fascinated with the American mentality of “Go big or go home” and wanted to immerse in it.

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The guest recognizes that Americans excel in experimentation and are adept at putting ideas into action.
At Nicole’s request, Celeste elaborates on her position at Optessa. She is an Application Specialist. She’s also involved in project management. Her primary responsibility is to configure the company’s production optimization software for customers. She closely works with customers to understand their needs, integrating those requirements into the software and providing training on its usage. After customers go live with the software, Celeste remains a support resource for addressing issues, changes, and updates to the software model.

Damon raises the question of the complexity of the planning systems Celeste and her team implement.

Celeste discusses the dynamic nature of supply chain operations. In her view, constant updates and adjustments are necessary owing to the ever-changing situation of supply chain technologies and emerging trends. Adaptation to customers’ evolving landscapes is crucial, and their needs change over time.

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Damon, curious, inquires about the extent of integration in the supply chain optimization process.

Celeste explains that they integrate with the customer’s in-house systems and ERP to gather the necessary data for optimization. Creating a link between different entities in the supply chain could lead to improved quality in sequencing and planning.

Nicole advances the conversation by pointing out that Celeste mentioned three important terms: plan, sequence, and schedule. Nicole asks Celeste to define these terms for the audience and explain their distinctions.
In response, Celeste shares her screen and introduces Optessa’s event planning and scheduling solution, observing its applicability across various industries. She explains the distinction between planning, sequencing, and scheduling, outlining how they allocate orders, determine production sequences, and set specific start and end times. These processes, to Celeste, are influenced by business requirements and constraints.

Celeste introduces Optessa’s “virtual flow line” (VFL) mode, a powerful feature within their software. This mode calculates sequences for multiple centers or zones simultaneously, streamlining the optimization process. Unlike traditional methods, which calculate sequences individually, the VFL mode handles complex topologies and diverse routing scenarios.

Nicole talks about supply chain disruptions and asks Celeste how Optessa’s software handles these challenges.
Celeste brings to light the role of Optessa’s VFL mode. It models complex scenarios between production areas and centers. VFL handles processes like reentrant flows, partial routes, and buffers. These capabilities enhance accuracy, throughput, and downtime reduction. By taking into account diverse complexities, VFL improves plans, sequences, and schedules and allows companies to anticipate and mitigate disruptions effectively.

Moreover, Celeste says that Optessa employs AI features to enhance their processes. They’re developing a feature that predicts KPIs before generating a schedule or sequence. This predictive capability helps customers anticipate outcomes based on constraints and historical data.

Similarly, Optessa’s user interface is simple and user-friendly. She explained the concept of a “scenario” in Optessa, which encompasses orders, constraints, and capacity over a defined timeframe.
At Nicole’s request, Celeste reveals the hierarchy of her organization.

The framework comprises a plant, production area, and process. She discusses the concept of “base scenarios” that serve as templates containing unchanging data for reuse. When creating a new scenario, users copy from these base scenarios, importing essential information like sales orders, order features, capacity, and constraints.
Nicole asks the guest about the scheduling process in Optessa’s platform. Celeste responds with the planning, sequencing, and scheduling timeline in Optessa’s workflow. She explains that these processes might occur in the same week but are not synchronized simultaneously. Furthermore, planning is done in advance, often spanning several months.

In the same breath, Celeste uses the grid browser tool to demonstrate the value of Optessa’s outputs. Each square in the grid corresponds to a sales order arranged in their production sequence. Clicking on a square reveals order details and additional information if needed. Celeste highlights the simplified example, showcasing a basic setup of a body shop, paint shop, and trim shop. She mentions that more complex setups are possible, tailored to various industries. She demonstrates the software’s good enough ability to optimize paint batches, using a demo with two-tone vehicles that go through the paint shop twice, ensuring efficient color coordination for the two passes.

Presenting another aspect of the software, Celeste displays the paint and trim shop. She demonstrates how the model of the vehicles follows a pattern: three 300k types followed by two 400k types, cycling across the sequence. To the hosts’ surprise, Optessa can handle 23 constraints to model various business requirements without limitations.

Damon reflects on the complexity and difficulty of the tasks, comparing manual efforts to the software’s automated process.

Answering Nicole’s query, Celeste maintains that Optessa can cater to companies of any size and maturity level, with the main challenge being to obtain accurate data from the shop floor.

At Damon’s request, Celeste showcases another constraint related to vehicle types and their sequence in the body shop. She demonstrates how Optessa lets the application of constraints match specific business requirements. By utilizing filters and constraints, Tessa can ensure that within a sequence of 300k models, no consecutive “h” type vehicles align with the business requirement.

Nicole asks Celeste about the features that distinguish Optessa from its competitors. The guest explains that some competitors rely on heuristics instead of advanced AI-driven techniques like Tessa. Additionally, some competitors might lack the ability to compute all zone sequences together, which sets Tessa apart by providing the feature to generate sequences for multiple zones in a single run.

Moreover, Celeste discusses upcoming enhancements to their software, including integrating more AI functionalities, improved input data analysis tools, and better outputs. They aim to leverage advancements like ChatGPT to enhance user interaction and meet user expectations more effectively, using machine learning and AI to simplify model setup and user workflows.

On the fun side, Celeste’s spirit animal is a cat.

The conversation ends with Damon and Nicole thanking Celeste for her time.

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sequence, orders, constraints, customers, scheduling, tessa, disruptions, good, shop, account, paint, data, model, sales orders, week, ai, cool, shop floor, people, work
Celeste Lefebvre, Damon Pistulka, Nicole Donnelly

Damon Pistulka 00:03
All right, everyone, welcome once again, it is Friday. And you know what that means? It is time for manufacturing ecommerce success. And yes, Kurt is wearing a wig today.

Nicole Donnelly 00:15
Yeah. What do you think guys? Right

Damon Pistulka 00:19
now the today we’ve got, we’ve got Nicole Donnelly filling in for Curt Anderson today he’s off on adventures. And we’re going to be talking with Celeste here today, from OB Tessa, we’re going to have some we’re going to talk about adapting quickly to disrupting disruptions in your operations and scheduling. That’s just let’s just get going, Nicole.

Nicole Donnelly 00:43
I’m so excited for this conversation. Celeste comes from a very, very special place in my heart. Lovely, lovely place, France. And so I’m so excited to talk a little bit about that. But first, we can’t start the show, Damon without asking the question that you guys always ask every single week. It’s this question, Celeste, are you ready for

Celeste Lefebvre 01:08
it? Yes.

Nicole Donnelly 01:10
When you are a little girl growing up? Who was your hero?

Celeste Lefebvre 01:16
Oh, that is an interesting question. I guess I mean, my mom. I mean, I know it’s very common. I’m sure a lot of people will say this. But I always looked up to my mom. And to me, she was this amazing woman who was really good in school, made it through life. And I saw her and I was like, Okay, I’m gonna be at least as good as she is. And I’ll try to be even better.

Nicole Donnelly 01:51
Oh, that’s a very, I’m sure you more than lived up to that.

Celeste Lefebvre 01:58
I’ll have to ask her. Yes, very

Nicole Donnelly 02:00
cool. Now tell me a little bit about where your family lives and your where you’re from. Tell us a little bit about you heard from.

Celeste Lefebvre 02:07
So I am from France. I grew up there. My family’s from Normandy, strong American history there. So a little piece of the American history in France. And I, after high school, I studied engineering and in the south of France. So I’ve been a little bit everywhere in France. And I always wanted to come to the United States. So I did everything I could to come to the US. And I studied a master’s degree in Industrial Engineering. And then I graduated. And I had the amazing importante to stay here. Because I found up Tessa, and

Nicole Donnelly 02:55
very cool. Yes. And it’s so nice for working for such an amazing company. What made it what was it that you wanted? Why did you want to come to the US so much?

Damon Pistulka 03:03
Yeah, that’s my question to,

Celeste Lefebvre 03:05
um, it was, I felt like the French culture and the American culture together, if you take the good in both can result in something that is amazing. And I wanted to learn from Americans I am, I’ve always been fascinated by this culture of Go big or go home. I wanted to be in and it was my American dream. It was it was really what I was looking for.

Nicole Donnelly 03:38
Oh, that’s amazing. So I love how you said that you love the good of the American culture and French culture. So America, you love to go bigger go home. What do you love about French culture that you think Americans can kind of learn from or that you love?

Celeste Lefebvre 03:50
Um, I think French people may feel slightly more detailed oriented. They have, they’re more theoretical. So I’m always here. The way we are in school, the way the classes are, make it that we can very easily visualized very theoretical concepts. And a bit were terrible and experimentation, which Americans are very good at. Oh, we

Nicole Donnelly 04:22
love it. We are We don’t mind looking the fool right Damon.

Celeste Lefebvre 04:28
You thought you had labs in schools and you don’t mind trying trial and error is something that works very well here. So yes, the two together I think work pretty well. Because the French people sometimes get a little too, too much in the details too invested in it, and they get stuck in it. And that’s where I feel you need the American Vision to come in and say just let’s try. Let’s see what comes out of this. And we can make it better. Yeah.

Nicole Donnelly 04:59
Very well. Very cool. I love it.

Damon Pistulka 05:01
Got some comments we got of course we got John here. And we got Yasser. Thanks so much for being here today got David. David can see. So, thanks for being here. Everyone dropped, dropped, let us know where you’re you’re viewing from. And we get going here, you’re gonna ask the question.

Nicole Donnelly 05:27
So tell us I’d love to hear a little bit more Celeste about what is it that what is your role at up Tessa? What is it you’re doing their day to day and maybe walk us through like, what’s a day in the life for you, and how you’re serving up Tessa customers,

Celeste Lefebvre 05:40
of course. So I am an application specialist at a PESA. I also have some project management responsibilities. And essentially, what I do is setup the software. So we provide a software for production optimization, production planning, optimization. And what I do is set it up for customers. So I work a lot with our customers to understand exactly what their requirements are, and I model it into our software, then I train our customers to use it. And then once they go live, and they are on their own to use it. I am there for support. So if anything comes up, or if there’s a change request, you know, they need to update the model. If they need to change something, then they can reach out to me and I help them through doing whatever needs to be done.

Nicole Donnelly 06:41
Awesome. I want you to be my implementation specialist.

Damon Pistulka 06:47
Often I mean, because we need you guys are putting in some pretty complex planning systems. I mean, I have to imagine that isn’t there a kind of, okay, we’re setting everything up. And then there’s a period of changes, changes changes, but they go down over time? I mean, do you ever get to a point to where these things? Are they just running? They’re running well, and we don’t have to do a lot of optimization, or is it always always changing? Yeah,

Celeste Lefebvre 07:13
there’s always updates. There’s always it’s also what is magic about supply chain supply chain is always changing. It’s in the new technologies that are coming in. There are always new things coming up, always always. So we always have to adapt to what is happening to our customers and make sure that it is working for them in the current situation they are in that current situation evolves with time. Yeah. So yes, no, it’s never it’s never done. There’s always something else that can be improved. There’s always something else that comes in. And we have to take that into account. And yes, it’s very interesting.

Damon Pistulka 07:55
Yeah, yeah. So when when you’re doing this, so say I’m the company that’s using up Tessa, are you also going to be integrating with some of my suppliers as well? So I can actually see, or is it just the company you’re working with? I’m just curious, because I’ve that I can imagine things, but I don’t know what’s actually being done?

Celeste Lefebvre 08:17
Sure. So we could, if there was the need, we could do some kind of link between our customer and another supplier of our customer. Yeah. Right now, most of the time, we really work with our customers, directly, we kind of connect to their in house system and house ERP, and get all the data information that we need from there and optimize their plan or sequence or schedule for them and try to come up with something that has good quality. But we could we could totally link kind of create a link between everyone. And we actually love to do that because it improves the overall quality of the sequence. We want information from everywhere. So yeah, it’s a win win for everyone to do this type of setup.

Damon Pistulka 09:18
Yeah, I was I was just thinking about that getting prepared for this. And in past experiences, you know, there are some things where, you know, when you’re doing things with different suppliers around the globe, multiple facilities, making multiple things and then suppliers making sub assemblies and some of the kind of places that you guys get into it’s, it is there is a lot of things come from a lot of places that you have to consider. And I can see from your standpoint and your customer standpoint, if I can get my data in order, that’s a huge thing. And and then and in addition to that, though, I can also see a ton of value if I could go my major supplier So I’ve got my 27 major suppliers here that I know what’s coming, you know how their their supply chain is doing but Right, yeah, cool stuff. I digress. But I think what what you guys are doing it is terribly interesting because when you look at this and you think about just imagine an automotive a car being put together the final assembly line I have I have tires and wheels and brakes assemblies and I have engine transmission and I have all these different things and and then the building of those in the other factories that comes to you and you go, Okay, now how do I know that if I’ve got I’ve got a shortage of a certain piston in my cylinder, that’s, that’s affected me this, you know, and that’s all gonna go all the way and trickle down. I mean, the kind of stuff that that you can do now, with these systems is crazy, cool.

Celeste Lefebvre 11:00
Supply chain is so big, there’s so much so many people, so many things happening. And as a customer of an automotive industry, or if an automotive company, we know when we buy our car, we might not realize everything that has been happening behind for months, usually, you know, and it’s just in this this unseen ecosystem working together to boom delivering that one thing that the people want, you know, yeah, it is. It is very interesting. Yeah.

Damon Pistulka 11:34
Cool. Cool. Well, Nicole, you got questions, you guys got things to do. So

Nicole Donnelly 11:39
I got things to do. People to see places to go to make, you know, plans to optimize. So Celeste, you mentioned three words, I think I’d love to hear you. You said that you help them plan, sequence and schedule. Yeah. So maybe you can define for our audience here today. Like, what is the difference between planning, sequencing and scheduling? And how does I’ve tested to find those maybe versus some other people in the industry?

Celeste Lefebvre 12:08
Of course, do you mind if I share my screen? Yeah, please doo.

Nicole Donnelly 12:20
Doo doo doo doo. Right there. Now you guys don’t want me to sing anymore. That’s

Celeste Lefebvre 12:30
you. Can you see my screen? Got it up. Okay. Yeah. Yeah, this is just my introductory slide to say, we provide a solution for events planning and scheduling. And well, I’m just gonna say this really quickly about Tessa we have clients in every industry. And this is something I really want to highlight is that everything I’m going to talk about what we do, we can do in any industry. We can model any type of industry, we have a lot of customers in the automotive industry. But we can also do white goods, oil and gas processes and everything, food and beverage, everything that comes up to me we can do. So going into what you were asking me if Tessa has different solutions. And we can combine any of those solutions together, of course, the more solutions, we combined together the back better the overall quality of what we will produce will be. But so we have usually at the enterprise level, something that we call bill to order, that’s a little bit of extra solution that can help our customers customer, place an order and reoptimize in the back end, the plan the sequence or the schedule for our customers, our customers, then there’s a plan. So what we call planning is really allocating orders to a bin, bin can be months can be weeks can be days can be hours, whatever the customer needs. Then there’s sequencing, sequencing and scheduling, I will group them together. One is usually zooming a little bit into the other scheduling, zooming a little bit into sequencing. And the idea is to get the order in which the sales orders are going to be produced. That sequencing scheduling zooms in that a little bit in the sense that it not only gives you the order, but it also gives you the start and end time on the supply chain for each of your orders and it takes into account resources that are available or not available. And then we also have a solution of real time optimization and that you uses real time data from the shop floor to work on the optimization of the schedule to to take into account everything that is happening on the shop floor. To give this maybe this will be easier to understand with this graph. So let’s say you have a set of orders, sales orders, so sales orders, so for sales orders from one to 16. And then the first step would be to allocate those orders to week bins. Again, it could be months, it could be days, it could be hours. And the horizon can be as long as it needs it to be. So it could be over the next three months, the next five weeks, the next year, the next five years, you know, any horizon is fine. And then once you have your orders allocated to bin, that is when sequencing comes in, and you give that order of your sales orders within each of the bins, taking into account the business requirements. So there are business requirements actually, at every level at the planning level sequencing level, and that scheduling level that I mentioned right after this, but all of this is generated is I will say even more optimized, based on the business requirements, the constraints on the shop floor. And then So zooming into scheduling, you see for week two, we have SOA one, six and seven. And it gives you the order, but it gives you a start time and time. And then you can see between SOA and SR one, the change over time is much shorter than between so six and so seven, right. And then if you add into this real time optimization, you can take into account new requirements that are coming in, for example, in this week to let’s say we need to prioritize S oh seven. So Tessa can generate a new schedule, taking into account that new requirement, while using bias to keep what was already planned scheduled, I should say before

Damon Pistulka 17:29
you re optimizing within your RE optimizing within the time buckets. Exactly. Yeah,

Nicole Donnelly 17:37
that’s amazing. So maybe walk us through like, I’m an automotive company. Okay, as an example. I’m an automotive company, what would it be said that, like walk us through the planning, sequencing scheduling, just like one example of what some of the constraints are, for example, that you might have to consider as you’re doing the planning, sequencing and scheduling.

Celeste Lefebvre 17:56
Um, so examples could be know back to back orders, or that would be for sequencing no back to back red and white vehicles in the paint shop. At the planning level, it could be that you can’t do more than x number of hybrid vehicles per week, are limited. The scheduling level, it can be this operator is able to work on this machine and this machine and that other operator can work on that machine in that other machine, but they can’t work at the same time. Got it, all those business requirements. And then you can take that into account. So every part of the plant will have their own set of constraints, right? So they’re always slightly different. And you have to take all of that into account.

Damon Pistulka 19:13
So does the AP test a solution go down like to a work area level? So that’s that’s how far it goes with it. I know you there’s there’s several different levels in the scheduling. Right. So we’re going into a week but there can be 100 different work centers in that week that are the in this assembly process.

Celeste Lefebvre 19:34
Right. So yes, yeah. So she have the slides. Okay, there we go. There’s this really, I mean, I really liked this functionality that we have in there. This motor will call it mode in our software, and it’s it’s one of the many modes we have available. So I’m really giving you the tip of the iceberg here. Yeah. But a lot of the times, what we used to do and a lot of what our competitors do, and even in house solutions, when you know, when we have a new customers, we usually take a first look at what they already have. And what we see is a lot of the time only calculate that plan sequence or schedule for only one of those centers, like you said, Yeah. Enough tests, I would call those centers zone. So at any point in on your line, that would need its own plan, sequence or schedule, because it has its own set of requirements. And so we used to do that one at a time. And then we would use that sequence that it’s, it’s usually also what we see, in a lot of places, you know, you have sequence for one zone, one center, and then from that sequence, you calculate the other one. And then from the other one, you calculate another one and you go kind of like, from your upstream system to your downstream systems like this, you know, you kind of do that. But what we did is we created this mode, virtual flow line VFL, that allows you to calculate all the sequences in one run in one. Yeah, it takes into sorry, it takes into account very complex topology. So you can have merging diverging lines, or you can have very, like a simple streamline. Because all of the plants, you know, it’s it’s zone, it’s a combination of zones that are serial parallel. And the products are routed through those zones. So we model those routings through each of those zones, you can have as many zones as you want in the model, you can have as many routes as you want in the model. You know, every anything that is complex we can do with a Tessa, and it takes into account the local and the global constraints with that mode. So you generate everything in one go. Yeah, taking everything into account. Yeah.

Nicole Donnelly 22:22
The complexity there is incredible to think about how you can create a system that can handle that level of complexity and still be able to be flexible and nimble enough to adjust to changes with that real time optimization. I mean, that is really remarkable. Very cool. Well, I think

Damon Pistulka 22:42
I’ll go ahead. But I’ll tell you what this does in a very complex company, you can simplify this down into each level where people need to see it all the way to the I’m Damon, and I’m in the in the engine department where we’re making. We’re making engines what I have to build next. Yes. And then you go behind it, and we and it’s checking to to make sure that our inventory is there and sub seven of these and all that kind of stuff. This is just crazy. Cool. It’s doing Yes,

Celeste Lefebvre 23:13
it does. It does everything for you. And you just have to click one button, and the engine optimizes everything. Yeah, that’s it. That’s that. Just it’s beautiful.

Damon Pistulka 23:27
Yeah, it really is. This, this is the heart of most manufacturing scheduling problems is because if you get more than a handful, right, you try to do legacy systems, you try to use some sort of Excel sales guy, I mean, you just run, you just run right over the top of the capabilities so fast, you put four or five work centers, and you go into a complex manufacturing plant, which may have 2030 different work centers and a bunch of just overloads. Most systems.

Celeste Lefebvre 24:00
Yeah, and I mean, it’s, it’s also all the using Excel is, you know, it’s, it’s horrible. It’s a mess, and it’s Excel is great, but it has a limit. And I think that’s where Tesla comes in is, you know, we we make it simple. We make it really simple.

Damon Pistulka 24:18
Super cool.

Nicole Donnelly 24:20
Let’s talk a little bit about that disruption piece of it like how, you know, I’m, you know, I’m a manufacturer, there’s been a disruption in my supply chain, you know, my widget x from that supplier is going to be delayed or whatever. How does the software get ahead of that disruption? Like, how do you bake into it? contingencies if you will, right? To prevent that from happening or to allow for that with if it’s like something that you don’t even know about in the future, you know what I mean, that could happen.

Celeste Lefebvre 24:51
So the cool thing about virtual flow lane is that because It takes into account all of those centers, zones. areas, we can model things that are happening in between those areas. So there are different functionalities that are linked to it. The first one is something that we call extended lead time. Essentially, the idea is that on your line, on your production line, some orders might exit and reenter the line. Or they might go twice through the same shop. So typically, orders that will need an inspection, right, they will leave the line and then they reenter it, because they’re not, you know, there’s so many to get produced. But like on the on the shop floor, they need to make sure that the specification are satisfied, or it needs to go twice through a same shop. So that’s a lot of what is happening in the automotive industry, to towing vehicles, vehicles that have two colors, they need to go twice through the paint shop. Or also for semiconductors, the chips, they it’s just reentrant flows, they go through shot, they go back over and go to a second one, go back to the first one and then go back to the second one. And it’s just reentrant flows and going back going back going back. So that’s one thing that VFL allows us to model. The second thing, it allowed us to model our partial routes, partial routes are essentially orders that will not go through the entire plant, they will leave the production line at some point or enter the production line at another point. And sometimes in the aerospace industry, once the plane is not fully assembled, but it’s to certain point of assembly, some of the orders will stay on site to get painted, some orders will leave to go in another site to get painted there and delivered there. So some of your orders exit the plant. You could also have, if you look at it from the point of view of the other plant, you will have orders entering the plant. So you know you can model of this taking into account the shift flags, everything that you can think of all the crazy cities did want to pour into this. The last thing that FIFA allows us to do is to model buffers of buffers or areas on the shop floor. And they can use for can be used for recovery. But it can also be used for Reconstruction and remodel it for reconstruction. And essentially, it allows to reorder your sales orders in between two shops. And it’s very powerful because it gives more flexibility between those shops that have different constraints. And yeah, and putting that in there really gives you more flexibility. And it’s essentially it tells you if an order comes out of a shop, it can actually enter the next one, let’s say five position behind. That way you push something else in front of it that will satisfy what needs to happen in the following shop. So going back to really answer your question is we get ahead of disruptions because if you fell allow us to model a more realistic situation of what is happening because it takes into account all the complexities, the reentrant flows, the partial res DLT buffer construction, it takes into account all of this. So it really allows to increase throughput, reduce downtime, increase any KPI we’ve seen that using vflw helped a lot with average batch size for our customers. And using all the new technologies, you know, using all everything, all the information that can come in leveraging what vflw can provide, you get ahead of any disruptions because the quality of what of the plan or the sequence or the schedule you’re purchasing is so much better, because it takes into account everything that the impact of disruptions later on are much easier to mitigate.

Nicole Donnelly 29:39
Yes, and also I imagine it’s looking at historical data right that way that this is I would assume so that it gets better over time.

Celeste Lefebvre 29:48
Right. So we are we are actually so we are using some AI features to help with that. So we are good. We were working Going on introducing a features to, before we even run the schedule the plan or the sequence to be able to tell customers, this based on your constraints, what we’ve seen in the past and your current set of sales orders. This is what you can expect to get for your KPIs. So let’s say car manufacturer is going to come in, and the system is going to be able to tell the user your average paint batch size on the paint shop is going to be around 5.6. Something is very powerful.

Damon Pistulka 30:44
Oh, yeah. Because you start to predict, and it’s based on the data from behind it, that that’s really cool.

Nicole Donnelly 30:55
That’s all only going to improve and get better and better and better.

Damon Pistulka 30:59
So we got some good comments in here from John, he’s helping me out. Let’s get back here a little bit. You know, he’s talking about doing it Senator by Senator doing the scheduling Senator by senator. And he’s talking about that before on some of the competitors that are doing it. And he talks about this is what I think really is, is pretty special about the AP test, as you’re you’re you’re doing as you talked about, Celeste, the zone production, like the work center kind of level, you’re doing that while take into account that everything and that’s that’s very rare to see that. So as the last thing John says, Here’s, you know, the plan is feasible before Yes. Running it.

Celeste Lefebvre 31:47
Yes. So yeah, there’s input data analysis tools that we’re developing with AI. Very powerful. Yeah, that’s even before doing anything if if it’s going to be good enough for the plant or not.

Nicole Donnelly 32:02
None of that American trial and error, right. Getting rid of all that risk.

Damon Pistulka 32:11
Just a different spot. We got a couple people jumping in here, too. from Morocco. I saw David up here from Turkey to go with David that want to get you get you right here from Turkey. Thanks so much. We got that going on. But thanks, everyone for the comments. The this is really interesting how you’re taking such a complex challenge. Because you know, what’s a what’s a car or worse an airplane? You know, what’s it what’s a Boeing 787 habit in for parts, I forget how many gazillion parts they got in them and they’re coming from all over the world. I see the fuselage, just go by my, the town where I live, you know, the, they, they come on the trains, and I see what you’re talking about where they come out of the factory and go to the paint factory and some some fly unpainted to other places to get painted before they go to the customers. And you know, and we’re talking about all this stuff. And you’re, you’re able to take all of that data on something like that, until Damon, what I have to do in the place where I’m building the wheels for the landing assembly. I mean, that’s and how that ties together with where that’s going to. I mean, yeah, this is cool.

Celeste Lefebvre 33:33
I can share you if you want a little bit of an example in the web app. If you Yes.

Nicole Donnelly 33:39
Let’s check it out. We’d love to see your life example.

Celeste Lefebvre 33:43
Okay, too.

Nicole Donnelly 33:47
This is how the sausage gets made. Let’s see. Yeah. My daughter loves sausage. She told me the other day Mom, I love sausage more than bacon. And I was like, I don’t know if you can still be my daughter because it’s like I guess it depends on the sausage. You know? Like if it’s really really good. I don’t know there’s so many good sausages. Oh,

Damon Pistulka 34:13
smoke kielbasa smoke kebab from the Rhine house in Leavenworth Washington.

Nicole Donnelly 34:22
Is it better than bacon? Must be? No,

Damon Pistulka 34:25
no. Bacon is number one.

Nicole Donnelly 34:31
So since bacon is king, let’s show us show us how the bacon gets made. Yeah.

Damon Pistulka 34:37
Someone said bacon. Went off the tracks.

Nicole Donnelly 34:40
There was nothing like waking up the more in the morning to the smell of bacon. All right. Oh

my goodness. Yeah.

Celeste Lefebvre 34:47
All right. Okay, this

Nicole Donnelly 34:49
is it. This is OB Tessa.

Celeste Lefebvre 34:51
Tessa, this is what you see when you want to log in. So I log in. I go straight to In a scenario, so we talk in terms of scenario in the Tessa scenario basically is a set of orders, constraints, capacity over a specific horizon. So, every day, if you do a sequence every day, every day, you’re going to have a new scenario.

Nicole Donnelly 35:24
And this is all what you’re building, that implementation phase is really factoring in all the constraints that they have. And building that into the platform, customizing it for the customer through implementation, you can just deploy it in one click, have it all go until they want to reoptimize again and make updates. Data. I mean, I’m just thinking about the constraints. I mean, there’s just so many constraints that you have to think about labor, warehouse space, you know, inventory, material

Celeste Lefebvre 35:56
availability, and,

Damon Pistulka 35:58
yes. So this is kind of a weird question, maybe. But if I did, if I’m sitting there now, and I’m using AP tests, and I wanted to do another scenario, are we talking this thing going to churn data for hours? Or is it what’s an update? Time take.

Celeste Lefebvre 36:17
Um, so what happened? So if your user and you need to create a new scenario, you’re going to so there’s an here I know,

Damon Pistulka 36:29
it’s an I noticed, I’ve kind of off topic, but I just got to think that the amount of data that you have to process is,

Celeste Lefebvre 36:36
so there’s a tree here, essentially, you’re within a plant within production area within a process. And then we do have based scenarios. So those are the scenarios you copy from. And they include all the data that will not change from scenario to scenario. And you will make a copy with that. import all the information you need. So sales orders, sales order features, capacity. Constraints, if you decide to import constraints, you can also maintain them in attesa, if you’d rather maintain them in atossa. That’s the four most important things that users would import. And that import. The time it takes depends on the amount of data you have. Because you can imagine, especially with the sales order features, let’s say, if you have, I don’t know 1000 features, and 100 orders if you multiply all this. So you have 1000 times 100 lines of data to invoice. So it can be very fast some of our customers for some of our customers, it takes a couple of minutes, you know to import because there isn’t that many orders for some of our customers, especially when you do planning for several months, or more orders more features coming in. That can take longer.

Damon Pistulka 38:07
Yeah. I just wondering in practical, in practicality, so your users are typically regenerating this once a day.

Celeste Lefebvre 38:16
It depends on the business processes. Sequencing usually is once a day, scheduling can be once or twice a day. Planning will be once a week, maybe more.

Damon Pistulka 38:29
Okay. Yeah.

Celeste Lefebvre 38:32
Yeah, that’s, that’s what I’ve seen. But

Nicole Donnelly 38:36
like you run planning on this day of the week, and then you’re going to run sequencing every like is is that all planned in sequence to in terms of when you’re running the schedule?

Celeste Lefebvre 38:45
Yes. So usually the plan is done way ahead of time. So it’s actually the, if you take the same week, the moments when planning, sequencing and scheduling happened are not necessarily linked together. So let’s say because planning, you’re going to prepare the next three months of planning. But for sequencing, you’re going to do, I don’t know the sequence for the day in 10 days, or nine days. So even if you act technically do it the same week, you’re not looking at the scene moment of time later on.

Damon Pistulka 39:23
Yeah, totally. Very cool.

Celeste Lefebvre 39:27
Um, okay, so I’m gonna jump straight to the outputs to show you the value in this. And so this is the grid browser. It’s one of our output analysis tool. It’s very nice. So each square represents a sales order. And they’re all in the order that they should be produced. And here we have the David. So this is for every eighth February 9, returned, you have exactly what order you’re supposed to produce. And if you click on on it, you have the order number, you can add different information, depending on what the users need to see could add the caller ID, the model of this demo is based on in automotive industry. But again, if Tesla can do any types of industries, I have to pick one, I pick the industry. And so this is what this demo is for. But we can do a lot more than that. And it’s a fairly simple setup. It’s simplified because it’s a demo. But we can do as I’ve shown before, we can do much more complex topologies. And we do more complex topologies for our customers. So it’s just a body shop, going into a paint shop going into a trim shop. So very streamlined, simple. And what I can show you is let’s see, first the paint shop, I’m going to put the bill into shifts because it’s easier to see. And I’m going to display the color because usually that’s why you try to optimize in the paint shop. So you want to prove. So this is what this is the output of the software. The interesting part is, in this demo, there are two tone vehicles. So they go twice through the paint shop with a 25 slot lag in between the two passes. So if you look here, for example, we have lot 145 Dash 25 Because the second pass in the shop, is Operation 25. And if I go 25 slots before, exactly 25 slots before, I have the first pass here, which is a red path. So we can see the first pass red than the second pass black in the system, was able to optimize the paint batches, using the fact that those orders are going through the paint shop twice. So the red pass is surrounded by red vehicles. The black pass is surrounded by other black vehicles. Yeah. Yeah. At all. Yeah.

Damon Pistulka 42:31
Because you’re optimizing setup times you’re Yeah, change over.

Celeste Lefebvre 42:36
This is sequencing. Yeah, I forgot to mention this the sequencing? Um

Damon Pistulka 42:48
Well, it’s it’s, this is, this is what scheduling should be like? Yeah, it really is. It really is. Because you you, most companies don’t care big, small, whatever, they kind of bandaid something together. And at this level, the people that run the area are often doing that themselves.

Nicole Donnelly 43:07
Yep. And think about your time that it’s taking them to figure out what they’re missing. And all of that, like this just streamlines that whole process. Yes, yes,

Damon Pistulka 43:17
they save time. Yes, exactly. And if you have an inventory problem with a, that you don’t realize, because this is looking at that, too, you know, you’re you’re you’re really optimizing based on that as well. Yes, and you just go, then we can then then those people and those work areas can concentrate on, this is what I need to do next, rather than how the heck you know, we just got all ready to go for this and it’s not ready to go. And you know, those kinds of things cost, downtime. 1000s of dollars. Yes,

Nicole Donnelly 43:50
downtime is the worst, right? You’re losing dollars on that downtime.

Celeste Lefebvre 43:55
Yeah, and there’s one, there’s another thing I can show you. So if I display the paint in the trim shop, and this time we look at the model of the vehicles. This is what is going to come out. So there is a pattern for the trim shop for the model. So it’s three, I believe, three 300k. So there are types 300k and 400k. So, you have three 300k followed by to 400k. And we go like this and cycle across the entire sequence. What we can see is that the pattern is satisfied at the trim shop because this is where the constraint is. But it is not satisfied in the paint shop because that is not where the constraint is. And instead, as I showed you before, it focuses on satisfying the constraints that are in the paint shop in this house I’m not even having buffer construction between those two lines. So you can imagine if we had buffer construction in between those two zones, we would have an even better quality sequence for those two zones, well, the pattern is satisfied. So it’s hard to do better than that the job, we would have an even better quality of the sequence, because it would be even more decoupled from the trim pattern. So all of this falls together, and it brings the best that it can do.

Nicole Donnelly 45:37
For drop the mic.

Damon Pistulka 45:42
Yeah, the complexity and what you’re doing is, it’s it’s hard to imagine if you haven’t seen it tried seeing it being done in application in real life. This right here is crazy hard to do. If you think manually, you think it’s crazy hard to do. And usually, between a paint and something like this, there’s going to be a huge, huge buffer inventory, and a fair amount of resources that are just trying to meet the constraint of a trim shop like this, by sequencing all that manually. Exactly. You’re there and then trying to figure out does does that really end up meeting our customer demands? And heavily you’re gonna miss something?

Celeste Lefebvre 46:24
Yeah. And Tessa has 23 types of constraints to model any business requirements. And we can do model as many as needs to be need to be modeled everything. We don’t have any limits, and anything, the number of zones, the number of routings, the number of orders, the number of requirements, we can do it all.

Damon Pistulka 46:49
Yeah, John, John says something this is this is more than likely, when you’re doing it manually. This is what happens, things I do the next day, it really is. It’s tough like that?

Nicole Donnelly 47:04
Well, what I mean, what company what maturity? Do you need to be as a manufacturer to be ready for this type of solution? You know, like, if I’m thinking if I’m a manufacturing shop, you know, what would you recommend in terms of like, Yep, this is your is the right fit for you, you’re a good candidate for the solution, what, what maturity do you need to be in order to rock tested, it makes sense for you as an org.

Celeste Lefebvre 47:31
And we can work with any size, any maturity of any company, that is not really the biggest challenge for us is to get good data. And that’s usually what we advise our customers on is, we can do as good as we can, if we have good data. So that would be the biggest advice I would give is to have a setup already there that can provide the necessary data, most of the manufacturers already do, because they will have an ERP system, something. So it most of the time we can get the good data. It is specifically through the for scheduling, because scheduling takes into time, the start and end time of everything. So sometimes there might be some information data missing for scheduling, like the maintenance, how long it takes to take the maintenance for the machine or things like this. In the schedule we we produce we generate can be as good as what data we get. Yeah, so yes, that’s what I would mostly focus on is for planning and sequencing, it’s easier. And usually manufacturers already have the data available. For scheduling, I would say the first thing is to focus on being able to provide the right data to get the level of information you need. If you don’t need to, to be too specific, then it’s okay. If it’s if you really want something that is very, very specific, then we need very, very specific input data.

Damon Pistulka 49:23
Super cool. Super cool.

Nicole Donnelly 49:26

Damon Pistulka 49:27
Yeah. Well, is there anything more you gonna share with a screen wise because I’ll drop that and we’re getting about to time we can we can start to wind down here. Or you can show us a couple more things.

Celeste Lefebvre 49:39
I have, I can show you one more constraints if you want to where we can. Cool let’s, let’s do it. Okay. Um, okay, so this constraint is for the body shop this time, and it’s for the vehicle type and the age specifically vehicle type. So what we see here is that in some location, right, we have back to back ah vehicles, the requirement, the business requirement is within the 300k model, no back to back ah type. So if I change the way we look at this, and I look for model equal to 300k and type equal to h, and I show this, then, voila, no more back to back. So, we are able to add a filter and the constraints to model the business requirement. So, within the sequence of the 300k models, there are no back to back ah type vehicles,

Damon Pistulka 51:04
which following the rules and you’ve filtered it to show that.

Celeste Lefebvre 51:10
And that is in the body shop. Yeah, so here I’m satisfying the constraint here maximizing while minimizing changeover in the paint shop, and satisfying a pattern in the trim shop.

Damon Pistulka 51:25
Yeah, that’s what you because you’re it’s yeah, you’re not just doing one here. This is the whole schedule working together. So you’re doing the paint shop, The Body Shop, the trim shop, it’s all all being sequence per each area, and all together to get the product out the door on time. Yes, yeah.

Nicole Donnelly 51:46
How does this differ Celeste from some of your competitors, or any other solutions that a manufacturer might be considering?

Celeste Lefebvre 51:54
So some of our competitors might actually not? So a lot of them? Well, not a lot of them. Some of them might use heuristics, instead of using an actual machine learning slash AI way of calculating our sequence quality. So we do we have we use simulated annealing. Some of our competitors also might not actually have the possibility to compute all of the zone sequences in one run. And they do one after the other. Like I’ve mentioned at the beginning, you know? What else? Yeah, that’s pretty much they use heuristics, or they would not actually not have this mode available at all.

Nicole Donnelly 52:46
Man, so yeah, but I can see why you get so much more. So. Detail.

Damon Pistulka 52:53
Yeah. So what’s exciting? What new things are you seeing on the horizon? That you go, wow, this is gonna be super cool. Now, you don’t have to tell us secrets? Right? Because I know you.

Nicole Donnelly 53:07
mean, what? You signed something before you came on today, huh?

Damon Pistulka 53:11
I asked it right. We got to ask that because you know, AI is infiltrating everything? I mean, are there going to be simple tools that AI is gonna help to point out more things to the people using it? Or is it going to be some deep thing that that you know, what, in your your viewpoint are exciting, things are exciting right now.

Celeste Lefebvre 53:29
So there are a lot of exciting things coming up in for the future versions of our software. We are integrating more and more AI functionalities and improving the input data analysis tools, with AI. And we’re also working on improving the outputs. We’ve all seen chat GPT coming out, and just everyone is using it. So we also know the way our users are going to interact with our software is going to evolve. Oh, yeah, yeah, we would like to use that to make our software evil. So it satisfies better what our users expect. So there are a lot of new things coming up really lots of machine learning and AI coming in to simplify even more the setup of the model and also the way users work with it.

Nicole Donnelly 54:31
Yeah, I mean, I really like if you look ahead, 510 years from now, point click, I think is going to be completely like the whole idea of okay, I’m going to click here and I’m going to select this from this drop down, it’s all going to just be used speaking to the system saying I need you to create a sequencing plan for the factors in this constraint for this, you know, you know, and it’s just will spit it out for you. I can’t wait for that day. I’m seeing it happening. Like HubSpot hat is has built out the JotSpot AI in their platform, where you can basically do some at a very basic level, you know, create a XYZ from this, you know, yeah,

Celeste Lefebvre 55:10
every customer has its own set of requirements, they have all their own complexities. There are no two customers that are the same. So it will allow to really be even better for each of them, you know, with their own set of requirements.

Nicole Donnelly 55:30
Very cool. So, bravo.

Damon Pistulka 55:34
Thanks. Thank you so much the last this is this has been an incredible learning experience and so glad we were able to let you share under the hood some of up Tessa and what you guys are doing, Nicole, what what other questions you have for Celeste today?

Nicole Donnelly 55:53
My last question you for you, Celeste, its last question of the day. If you are an animal, what would your spirit animal be? A cat? Oh, yeah. Why a cat? Tell me why

Celeste Lefebvre 56:09
independent? Uh huh. To get pet when you want to be pet, you get food?

Nicole Donnelly 56:18
Sounds like your husband takes very good care of you.

Celeste Lefebvre 56:20
He feels? I am very lucky. He does.

Nicole Donnelly 56:24
And he kind of let you have your own space. And he you know, you’re very nice. You knew the answer to that one right away. A lot of times when I ask people that question, they have to think about it for a bit. But you knew

Celeste Lefebvre 56:33
that was fun. Yeah, it was new. If I want to, I want to be a cat in a nice house, I will say you know.

Damon Pistulka 56:42
Outdoors, you don’t be a junkyard cat or something like that.

Celeste Lefebvre 56:45
Just not as fancy as the cat I have in mind.

Nicole Donnelly 56:51
So much for for coming on today. It’s actually gonna get under the hood and see the demo, specifically how the software works in you know, in real life and how you guys help people plan sequence and schedule for their facilities. And it’s and it’s just remarkable the level of complexity that Opteka can handle. And you know, how much time just thinking about the time savings, resources, all of that. So thank you for sharing that. And it’s been a great day. I hope everyone out there. Thank you all for joining us today. I don’t know Damon, what you got planned this weekend. But I hope you guys all have a great, wonderful weekend. And yeah, and we’ll we’ll see you on Monday, Kurt will be Monday. I’ll be back with bells on.

Damon Pistulka 57:39
We’ll be back again on Monday. So good. Thank you.

Celeste Lefebvre 57:44
Thank you for being here.

Damon Pistulka 57:45
Thanks, Nicole. awesome as always to be able to co host with you today. Thanks so much for everyone showing up to me. Yeah, we got a lot of people in here. That Luis and John and unique in MD and there’s a whole bunch of people in here today. Thanks so much for stopping by David. And thanks for dropping the comments. And yes, sir. Thanks. Just thanks so much. We appreciate you stopping by every week and listening and supporting our guests. But we will be back again next week. So everyone safe and happy weekend Celeste hang out with us for just a moment and we’re wrapping up. Have a great night everyone.

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