Hello everyone. Thank you for joining us today for our webinar titled The Future of Manufacturing Cloud Based SPC. My name is David Peralta and I'm an Area Marketing Manager here at Minitab. Just a few housekeeping notes before we get started. If you're experiencing any issues with audio or slides, just refresh your page. If that doesn't work, close the window and reopen that that webinar link in a separate window. That usually solves that problem. For the web audio, please make sure you have your speaker system activated and your speakers turned up. We'll also answer some questions during the Q&A session after the presentation, and you could submit those questions in the Q&A box towards the bottom of that webinar console. For the folks who are newer to our webinars, I'm just going to walk through the webinar console quickly. So on the top left, you'll see our media player. That's where you'll see me and then soon you'll see our speaker, Moise Juzar. Below that is our talk to Minitab window, which I've highlighted on your console. Now, if you're interested in scheduling a demo about Real Time SPC or whatever you see on today, you can click on that, submit some of your information and a member of our team, we'll get in contact with you. You can learn more about training, consulting or some of the other solutions that we offer by submitting your info and getting in touch with us. Now. Below that is our resources window and we've included a few links to some helpful content around many times real time SPC solution. We also have a link there that takes you to a leaning page going through some of the the details about our SAP partnership. So if you're an SAP customer and want to learn a little bit more about what our partnership entails, click on that link and you can check out that leaning page. Now to the right of that is the QA box, which I've highlighted now, and that's where you can submit your questions. So if you have any questions during the presentation, submit those and we'll get to them at the end of this presentation. And then to the right of that is a banner promoting a previous real time SBC webinar that we had earlier this year. It's around mitigating risks, improving quality and reducing costs with real time SBC. So please check that out at the conclusion of this webinar. We don't want you missing out on on this presentation. But above that banner, we have our survey, which I've highlighted now. So submit your questions there, sorry, submit your your feedback there. We'd love to get your feedback. We do tailor these webinars based on that feedback, so would really appreciate it. You also get a chance to be entered to win a $50 Amazon gift card by submitting those that survey. So so please do so. All right now I have the pleasure of introducing our speaker, Moyes Juzar. Moyes is a technical solutions architect that Minitab. He helps Minitab customers through their data journey. He also brings 10 years of analytics and business intelligence experience to Minitab and has presented papers at numerous domestic and global conferences on strategies of how organizations achieve better results through efficient data analysis. So with that, I'm going to kick it off to noise. Thank you, David, and thank you everyone for joining today's webinar. Once again, my name is Moyes and I look forward to presenting this webinar to you. Like David mentioned, if you do have any questions, please feel free to ask them at any point in the Q&A section in the Q&A box and I will try to get to them at the end of this session. We'll kick off today's webinar with a simple poll. And the poll is, are you currently analyzing real time data for SPC D? We have three options. Yes, we are. No, we are not. No, but we would like to. So please take a quick second to go ahead and do a quick response on whether you are currently analyzing real time data for SPC. I'll just go ahead and give it a another couple of seconds to get some more responses. And we've reached roughly about 50%. So I'll go ahead and you know, switch over. But we have about 27% that are currently analyzing real time data for SBC, about 35% that are not and about 40% that would like to as well. So thank you for responding to that poll question. And let's start off with a quick agenda for today's webinar. First, we're going to talk about what is SBC, just to set the ground, you know, we have a wide range of attendees coming from a variety of different backgrounds. So we'll just cover a very high level on what is SPC and some of the tools like control charts and capability. If that is a repeat for any of you, I do apologize, But again, just since it is a larger large audience, we kind of want to start from the ground up and cover kind of what is SPC and what the value of that is. I'll talk briefly about the evolution and history of SPC and then why cloud SPC? Why did Minitab decide to go forward with the cloud SPC solution, which has, you know, the capability of providing real time integrations and scalability to your organization? After which we will jump into a use case and a live demo followed by a brief Q&A session. And once again, to ask any of your questions, please do submit your questions in the Q&A section of the webinar panel itself. So what is SPC? SPC, or Statistical process control? It is a method for achieving quality control in a manufacturing environment. There's a variety of reasons organizations implement an SPC initiative and some of them are primarily to improve your processes. So most of the users on this call are going to be manufacturers and SPC allows them to improve their manufacturing processes. It also allows them to monitor and control those ongoing processes. So again, in terms of process management, it does allow you to improve on that process and move away from being reactive to more of a prevention production model. So instead of reacting at the end of the month or at the end of the quarter as to what happened with the data, being more reactive in a prevention production model allows you to respond as quick as possible, which of course leads to reducing waste, lost labor hours, reduces rework as well as scrap at your organization. Now when it comes to SPC, there's our, there are a variety of tools, you know, there's like six or seven tools, process maps and some that I haven't identified here. When it comes to analyzing data, when it comes to looking at data, and when it comes to the tools required for real time data analysis, they're primarily going to be three set of tools. And those are going to be your control charts, your capability analysis, and your Pareto charts as well. So real quick, I'm going to jump through what these are, but before I jump through those, let's talk about why we implement SPC. And the main primary reason is to identify variation in your process itself. Now there's two types of variations in any given process. You have your common cause variation, which is variation that is, you know, at present it's always there. It's naturally inherent, such as the weather changes, the change in the temperatures, environmental changes. Those are some of your common cause variation. And then there's special cause variation, which represent assignable or unusual sources of variation that are typically not part of the process. These special causes can be either good or bad to your process, but those are the two main sources of variations that exist in any specific process itself. Now understanding we want to look at variation, there's a couple of tools that we should understand. And again, if this is a repeat for any of the attendees today, I do apologize, but again, there's a large amount of attendees. So to kind of standardize what we're going to be discussing, one of those tools is going to be the control charts. Control charts allow you to demonstrate that a process and stable and consistent overtime. So usually on the X axis, and we can see this control chart over here, usually on the X axis you're going to have either an observation count or a time series or a time axis and you're looking at how stable that process is. And a stable process is one that includes only common cause variation and does not have any out of control points itself. So again, anything that goes out of control, we'll kind of discuss what those control points are. But the idea is, you know, stable process and control charts allow us to identify whether that process is stable. A stable 1 is where there are no out of control data points identified. It also allows you to assess the effectiveness of a process change. So when you make any changes on that manufacturing floor, you can see how that is affected the data on a control chart and it is easy to see those shifts in the process mean and changes in that process variation. It also allows you to easily communicate the performance of your process during a specific period. It could be, you know, the last hundred data points or it could be a specific date range, but it allows you to communicate with your peers, your customers, what the performance of your process is doing. And then finally, it allows you to also verify that your process is stable. Primarily, once you identify that your process is stable, you would perform a capability analysis. And a capability analysis is only valid when performed on a stable process. When we look at statistical process control, one of the first things we would be doing is of course, implementing control charts on your process to identify whether the process is stable. Once we've identified that the process is stable, we would use our capability analysis. And the capability analysis allows you to determine whether the process is capable of producing a product that conforms to your customer specifications. With any capable capability analysis, there are two parameters that would be required. And those two parameters are going to be your customer specifications, a lower spec limit and an upper spec limit. And once you specify those lower spec limits and upper spec limits, the histogram allows you to see how much of that data actually fits in those specification limits that were identified. And these are typically, like I mentioned, performed on stable processes. So now we know that there's a stable process running. Now how capable is that process of meeting our customer specs is the idea of performing that capability analysis. The advantages include to identify whether the process is capable of consistently producing parts and focusing on certain metrics such as CPKPPK or percent out of specification. Now again, we're not deep diving into the CAPA analysis today, but the idea is understanding how capable your processes is a good metric to monitor overtime to rate your performance of your process itself. Now, once you've looked at your control charts as well as your capability analysis, the last tool is going to be a Pareto chart, which leads us to the Pareto principle of the 8020 rule. 80% of the problems are due to 20% of the causes. So when we identify that there is variation available or there's inherent variation in the process, but we're able to identify those special cause variations and associate A cause with them, we're saying This is why that actually happened. And adding that information in here allows us to understand where should we focus our energy in terms of solving these problems. So again, the Pareto chart is a combination bar chart of the percent frequency along with the curve of the cumulative frequency. So in this case, as you can see, it's a bar chart with two options. And the top option, of course, is the largest bar, which tells me that is the problem that most mostly we are seeing in the process itself, which allows me to prioritize the problems or causes of a problem and enables us to focus on what we should actually focus on to improve the process itself. One of the disadvantages is the Pareto chart. We'll provide a snapshot of the process at any given time, and this should be compared over time, which again leads back to that continuous improvement is how do we make the process better, how do we get more efficient, and how do we continually improve our process when it comes to manufacturing. So again, those are the three main tools that are used in statistical process control. Now, statistical process control isn't particularly new. It's been around for quite some time. And we'll start of understanding the history of statistical process controller SBC by looking at Walter Schubert, who is recognized as the father of modern quality control, integrated stats engineering, economics to revolutionize industrial practices with his invention of the control chart. So again, a very smart gentleman, he was part of the inspection engineering department at Western Electric in 1918 and quality control primarily just involved inspecting finished products for defects. So that's where we were in, you know, about 100 / 100 years ago. Sure. Basically expanded his statistical methods to central station switching systems and factory production, transforming the approach to industrial quality and looking at, you know, the input variables and how they affect the output variables itself. So in about 1924, Short introduced the control chart through a concise memorandum emphasizing the need to reduce variation in a manufacturing process and warning that reactive adjustments to non conformance could worsen quality. So it again comes back to that same idea of understanding where my variation is happening, reducing the amount of variation in the manufacturing process, which is again going to lead to the best quality products that are being produced at the at your organization itself. Now again, this was prior to some of the world wars that were happening and Edward Deming basically built on Schubert's concepts, integrating them into a broader management philosophy, which is still relevant in today's organizations itself. So before World War 2, American industrial management largely overlooked Deming's statistical techniques and open management style. However, he was, you know, lecturing in Japan and Japan embraced his ideas during their post war production efforts. There is a great documentary on YouTube that I watched to kind of prepare for this presentation, and it's called if. If Japan can, Why Can't we? And I would highly recommend if you want to learn about some of the history associated with American manufacturing and Edward Deming, you know, I would highly recommend watching it. If Japan can, why can't we? It was produced in the late 80s, early 90s, but his lectures in Japan during the early 1950s led to the implementation of his methodologies by several Japanese companies, which resulted in significant improvements in quality and productivity for those organizations. Deming emphasized that quality is a company wide responsibility advocating for process control and collaboration across departments, a philosophy that gained traction in the US. There have been, you know, largely a variety of different tools that do control charts and allow customers and organizations to understand the variation of their process. But this is kind of the history of statistical process control in terms of identifying who is responsible of kind of bringing this all together. So now let's talk about why cloud and why Minitab chose that cloud SPC method. So one is it provides that immediate real time detection of your process issues. You know, the immediate and the real time becomes important when you're trying to prevent the production of non conforming product. Time is critical. You know, we always say that we want bad news to travel fast. The last thing we want to do is, you know, have a lot of rework, have a lot of scrap and being able to identify the immediate detection allows us to makes it prohibitive to kind of identify, you know, and do that rework and scrap itself. It also reduces the risk of costly process disruptions and engineers can kind of monitor and control the variability in a manufacturing process so that ongoing monitoring of that process itself. It also improves the decision making at your organization. You're becoming more data-driven. So, you know, it's fostering a culture of data aggregation and collecting data on those actual measurements rather than, you know, those gut feeling. This is why I think it's happening and it leads to an early detection of issues. So again, that more immediate real time detection variation identification can indicate if there's other problems that exist in that process itself and then overall operational responsiveness. So fostering a culture of that continuous improvement SPC, you know, encourages the ongoing refinement of the process. If we make this change to the process, how is it going to affect our variation? These insights that are gained from statistical process control help organizations adapt their processes to changing market demands or production requirements and of course, informed decision making Making data-driven insights from SPC enable those quicker and more informed decision making. You can answer your customers as to why something happened in the process. You can answer to management as to why something happened in that process itself. Now folks have been kind of doing SBC for a while using Minitab. So you know, let's talk about how customers typically interact with their manufacturing data today. Minitab, you know, we've been around for over 50 years and one of the first steps in being able to kind of do this analysis, you know, and when we say analysis, create a control chart is we want to collect and prepare the data. So many times we'll have users that collect and prepare data using pen and paper today, or they automatically collect the data. They reach out to their IT folks and ask for the data itself. Somebody pulls the data, or they have this equipment, you know, they have a CNC machine, they'll walk up to it, they'll stick a USB thumb drive in there, download the data to that file, move back to their PC, and then they load that data into Minitab. And once they load that data into Minitab, they build out a control chart. There's a wide range of control charts. We're not gonna spend too much time discussing an IMR versus an Xbar versus AP chart, but they create a variety of different control charts. And then they would typically see how their process performed and then they would export that chart and maybe possibly have an improvement discussion at the organization based on the data that they've identified. This is the same process that we see customers are doing to analyze their data when it comes to the quality of their products. And there are limitations with this current model itself. One is it takes a long time to collect and prepare that data. Going and asking someone for data extends that time as well. There's a lot of delayed insights. If you're not really able to see what's happening now versus you might be looking at things, you know, maybe what happened in the past week or in the past month, which hinders your ability to react and respond to those as well, which leads to missed opportunities for improvement. So the question really is, is if you are currently doing any sort of control charts, how often are you running and evaluating those? So I have another poll question that I'm going to ask the audience to participate in. And that poll question is, are you using Minitab statistical software to make control charts after the fact? And again, by after the fact, we mean is after you have done your production, you get that data, you plug it into Minitab statistical software and you build some control charts. I'll give it just a couple of seconds to get some responses here. And once we do get some responses, I will switch to the results to show you how users have responded to this poll. Give it just another 10 seconds. Perfect. And as we can see, about 70% of attendees responded yes to the fact that they are using Minitab statistical software to build their control charts. Which leads me to our next poll. Now that we know that you are, you know, 70% again, a large amount of users that are running this analysis. How often are you running this analysis? Is it once a day, once a week, once a month on demand or again, never again. You know, 30% of attendees, I did say they don't use Minitab statistical software. Maybe they're using Excel to kind of build their control chart. But how often are you running this analysis at your organization? Again, we'll give it a couple more seconds for attendees to go ahead and submit their response here. And the question again is how often are you running your analysis in Minitab statistical software? And this will allow us to really get an understanding of what's going on in the marketplace as well as allow your peers to see how organizations are currently analyzing their data. I do have about 60% responded, so I'll go ahead and show it. And as you can see, the majority of the users responded that they are running this analysis on demand. So usually and typically after the manufacturing is done, they are going in or somebody is asking them to go ahead and run the analysis or they, you know, have chosen to do it on demand when they feel that there is some variation in the process. And I should go check if there is variation in the process. But again, we can see a widespread of, you know, ranges in regards to how often users are running their control chart analysis as well. I want to thank everyone for that participated in this poll as it shares some insight into our solutions itself. So kind of circling back again as to why cloud SPC. So 1 is what we at Mini Tab, we learnt that there were a variety of different tools being used in different plants at the same organization. Plant A might be using Mini Tab, Plant B might be using Excel. Plant C might be using Mini Tab but doing the analysis once every quarter, while Plant D is also doing Mini Tab but doing analysis once a week. So we noticed that across organization there's all these different archaic and out of date solutions that are being used. Some of them are desktop based, some of them have servers at one plan and there's no way to really aggregate all of that data into one solution. And the easiest way to do that would be creating a platform that can be standardized for all plants at an organization to get value and that is the Mini Tab real time SPC platform. 2nd, we wanted it extremely modern and easy to use and easy to enhance. We looked at some of the tools that are out there in the marketplace and they required a lot of subject matter expertise one and a lot of software knowledge knowing where to go to make any changes. If I need to change my spec limit because it's being produced for a different customer, it should only be a few clicks. Having that simple and intuitive user interface to make make it extremely easy for any company to get value is the primary reason of again, us going very cloud centric scalability. So again, effortlessly scale the software to accommodate the growing business needs, adding users and features as required. Today, you might do SPC on a very small subset of processes that you think are important at your organization. But imagine applying those same principles to a variety of different products and processes that you are manufacturing in your organization and being able to grow that very simply moving from plant to plant or in your data storage and acquisition itself. And then finally having those automatic updates. So being able to benefit from regular software updates and maintenance that would be handled by Minitab, ensuring the access to the latest features and security as well. So making it agnostic across the plants also allows us to aggregate all of that data, get a holistic view of how each plant is performing at an organization itself. So really summarizing it, it allows us to kind of provide a platform that modernizes and streamlines an old concept of statistical process control. Again, you know about over 100 years old in terms of the actual functionality, but providing it in providing it in a interface that makes it want to be used by organizations across the across their organization across all their different plants as well. So why real time integration? So now when we talk about cloud, we need to talk about how we're going to get access to that data. And getting access to that data is critical. Since we are looking at the data and plotting it on the control chart, we need access to that data. So now we can get seamless access to a variety of different data sources with a lot of those on Prem data, on Prem SPC tools or Minitab. You just don't have access to those sensors, to those equipment that you're running, to your MES systems, to your limb systems, to your ERP systems, all these different systems that you're already capturing a lot of data in. We're now able to seamlessly get that data from its source system in near real time, improving the accuracy and reliability of your quality metrics itself. It also allows you to make that faster decision making. So by integrating these systems, that data is readily available for analysis. So you don't have to wait to get the data. You don't have to go ask to get the data. It's already LinkedIn. You're enabling quicker identification of issues by giving access to the decision makers to make those decisions with that real time data coming from those systems as well. And then it increases the efficiency. Once we start automating all of this, you reduce any manual entry errors as well as you save a lot of time, which allows teams to focus on a variety of other things such as analysis of the data and improvement of the process rather than data collection and data wrangling. And while we do want to continue to reduce the manual entry errors, we still deal with organizations that are currently capturing data manually on pen and paper. I've heard stories of them scanning that and then having a data entry person manually enter the data. Minitab Real Time SBC allows automatic data integrations as well as a manual data entry as well. Today's topic isn't so much about the manual entry, but if that is something of importance to you, please don't hesitate to reach out to us and we can absolutely discuss on how we can simplify that process for you. And then finally, enhanced responsiveness, so that real time integrations really allow for that immediate respond response to your quality issue. Again, that same phrase that I used earlier, we want bad news to travel fast. The idea of bad news traveling fast allows us to, you know, help us from having that additional rework, that large amount of scrap, and of course, facilitating proactive adjustments in the process to maintain those optimal performance and minimize your disruptions of your process itself. At the end of the day, you want to keep producing product because producing product make sure that you're able to meet your customers requirements and having access to that data allows you to manufacture the best quality products for your customers itself. I do want to, you know, coming to our slide from integrations, I do want to point out our SAP partnership and again, we have integrations with a variety of different data sources, you know SQL databases, AWS, Snowflake, but we also have a partnership with SAP on their digital manufacturing platform where it allows that data being collected by an operator in their MES system. So again, just another way for us to get that data directly into our real time SPC, allowing them to then look at the data on control charts and react to that data as they are manufacturing, whether you're making drinks or your packaging or you're producing batteries. It allows you to really standardize how you're getting your data from your MES, from your ERP, from your limb system and bringing it into our real time SPC application. Why scalability? So scalability is important to organizations as well for primarily adaption to that growth. So as your business expand, you want to look at a scalable SPC solution to accommodate that increased data volume or the user demands without requiring a complete system overhaul. So imagine licensing a solution, just that one specific plan, and then as you add a new plan, you got to do a complete RE implementation. So going with the cloud SPC vendor gives you that adaption to growth. You can start off at one plant, and then as you get value, you could go ahead and roll that out to multiple plants accordingly. It gives you a lot of flexibility for change. So scalable systems allow your organization to implement new enhancements. I'm making a new product with new specifications. Let's track the process. I want to make a change to how we are manufacturing this process. Let's make a change to it. Let's change our spec. Let's change your mean. Let's change our standard deviation. You can response, respond and be flexible with a cloud based solution to make those changes immediately at your organization. It is cost effective. So investing in a scalable solution helps avoid those significant future costs associated with upgrading or replacing systems as the organization grows. In this case, you know, the amount of data that you are storing is the primary driver, but again, it is cost effective in terms of allowing your entire organization to get value from the solution. And then of course, enhanced collaboration. Corporate can look at the data, plant managers can look at the data and it fosters collaboration. Being able to discuss, you know, why is why are we spending all our time fixing this issue? Let's actually fix this in our process is the value of that scalability and of course maintaining real time access to critical quality data as well. So now I want to ask you, you know, we see organizations and a variety of different buckets, which I've, you know, identified as stages over here. And there are a variety of these different stages of the journey of statistical process control. You know, stage 4 is manufacturing excellence and that is, you know, we are producing the best quality product and what does that journey look like? And we see a lot of users in that stage 1, about 65% of those organizations, they currently collect that quality data and conduct the capability studies. These are going to be folks doing it in Minitab statistical software. And as we saw from the attendees responding to the poll, you know, they're done ad hoc or on demand to show that they are meeting, you know, customer specifications itself. So again, that's going to be your stage 1, all of your SPC. And then you want to kind of promote yourself to Stage 2 where about 20% today of organizations strategically use data for real time quality monitoring, you know, and they basically aim to achieve process stability. So again, when looking at the control chart, they want to make sure that the process is stable and to enhance the effectiveness of their SPC. Your stage 3, which is a very small subset of organizations, about 11% and they have systems to define statistical control limits. They have also created a culture that supports stable and capable manufacturing in response to SPC signals. And then finally, that's stage 4, about 4% of have achieved manufacturing excellence. And manufacturing excellence is understanding the relationships between your parameters, between your measures and monitoring these measures to be able to predict a controllable output itself. So again, Stage 1 being more of that ad hoc versus stage 4 being that manufacturing excellence. Now I want to ask you which stage do you think your company is at when it comes to statistical process control? Is it going to be that stage 1, which is the mini tab way? You know, you gather the data manually, you take it into mini tab statistical software and you build a control chart, typically on demand or ad hoc. Stage 2 is gonna allow you to again kind of build some sort of statistical process control and monitoring in place. Stage 3 is fostering a culture of the feedback loop. And then Stage 4 is manufacturing excellence where you're monitoring your input and output variables to kind of understand the quality of your products. Again, I'll just give it a few more seconds for our attendees to go ahead and respond to this poll. Again, Stage 1 being the ad hoc on demand I can produce a control chart, and Stage 4 being that manufacturing excellence where we monitor our input variables to try to identify how that effects our output variables itself. I'll give it just a few seconds and show everyone the responses for the poll. Perfect. I'll go on next. And as you can see, there are a couple of organizations that believe there are Stage 4, which is manufacturing excellence. Absolutely would love to hear your journey on how you're achieving this. But a majority of those users are going to be again in that Stage 1 and Stage 2. And you know, moving to Stage 3 and Stage 4 does require and does add a lot of value to an organization's manufacturing promises practices. Excuse me now coming to the second-half of this presentation is we'll take a look at our real time SPC solution. And this is a use case of a surgical light handle cover. This is exactly what it looks like. And if you look at this image below in the operating room, you'll see the yellow arrow pointed to that, you know, green surgical light handle cover. And this manufacturer has it's a, you know, the surgical kits received a lot of customer complaints because they would become loose and they would fall off the light, which of course breaks the sterile barrier. And you don't want it falling off of the light, you know, while you're operating on someone and, you know, the quality had to put all outgoing product on hold. This can really affect an organization. They had to send representatives out to customers to replace those handles in those customer kits with good stock and of course identify the root cause of this problem and why that it's happening. So a little bit about these surgical lights, you know, these these these must be maintained in a sterile condition to prevent any infection. You know, their cleaning sweets to a sterile condition is costly and time consuming. So these covers have become very popular since they easily provide a sterile barrier without the need for a thorough cleaning process during surgical prep. And they're manufactured via a plastic vacuum forming process. So the process that we're looking at is a plastic vacuum forming process. Again, the product is a surgical light handle cover, but the process is a vacuum forming process itself. So taking that data, there's a couple of different variables that are collected as part of the analysis and those are gonna be something like the cycle time, the ejection pressure, the film temperature, the plug depth, the tool temperature, vacuum timing, and the vacuum pressure. These are gonna be some of my input variables to my process itself. It was really unknown which of these process variables have the most leverage for that thinning of a handle cover. And the process engineering team and the quality team must produce a permanent action to mitigate thinning surgical handle covers. You know, keep in mind you're sending representatives to go and take stock back that you've already shipped out. So we, the organization wants to solve this problem and not only real time SPC, but pushing that data to real time SPC allows you to catch the issue, but also allows you to identify why that issue is happening in the 1st place itself. So let's take a look at this example in a live demo. I'll just go ahead and start by sharing my screen. And this here is mini tabs real time SPC application. It is a web-based application as you are seeing over here. The tool is again, modern and very easy to use and is designed to be intuitive for both the operator and the engineer. So there are two types of users that would primarily be using the solution and of course supervisors. But let's focus on, you know, the data entry portion. You have the operators who will basically monitor the process as it is happening and then you have engineers who are responsible for the operators and the overall process as well. The tool Real Time SPC is broken down into a couple of different portals and imagine I am an operator. I log into this application. I am we are making the surgical handle product as you are seeing over here and I am working on line A, which is the line that I am responsible for. I can simply navigate to the dashboard and see my control chart as you are, as you will see here in just a short second. Now with any of these, whenever I see a red dot, I know that that is either out of control or out of specification. I, as an operator can simply click on that dot and of course, add an assignable cause or a corrective action. As you are seeing over here. Adding that assignable cause and that corrective action leads to that Pareto analysis that allows you to identify where your organization should focus to solve these problems. That operator that are part inherently part of the process itself. So I as an operator can easily interact with the solution and that data keeps coming in. As you're seeing my chart refresh, new data just keeps on coming in. And as we can see, the last data was imported about a minute ago as we are seeing over here now. Of course, these are a couple of different measures. This is one of my input variable cycle times. This is one of my output variables is the measurement, you know that quality measurement of the minimum thickness as you are seeing over here. Now an engineer wanted to understand, you know, we're looking at this data, we can understand any variation in the process. But how do I overall look at this data in a holistic view and kind of identify what's going on is I can navigate into my engineering portal and I can see in my last 24 hours for my surgical handle product and that vacuum forming process I can see based on all the different lines that we have running. So we only have two lines running. Out of the three, I can see which one is largely out of control and which one is largely out of specification as you are seeing over here. If I wanted to see the details for my minimum thickness, which is my output measure, I could simply click on the details section to kind of drill through and see the details and the control chart associated over the last 24 hours as we will see here in just a short second. Should not take that long. Perfect. And here it comes. And we can now see that minimum thickness plotted on my control chart as you are seeing over here. And this is primarily for that line A station as we are seeing here. I, as an engineer can click on any of these data points. Of course, all these data points are in control. I can add any commentary, I can change any of the information as we are currently seeing over here. And then I can easily flip between my three main tools, my control charts, my capability analysis. If I want to see how capable that process is, I can simply go ahead and run my capability analysis as you can see over here, which then shows me some of my metrics, like my CPK as you are seeing over here, and my PPK as you are seeing over here. And then of course, those assignable causes and corrective actions. I don't think I have any over the last 24 hours, but as I do, they will show up directly here in this section itself. If I wanted to increase the date range, I could simply do that here, which would then of course allow that data get it to show up as we are seeing over here now, this is the ongoing monitoring. This is letting me know that there is variation in the process. But if I want to identify, you know, taking that surgical handle example, if I want to identify what's actually going on, I need to be able to download this data directly into Minitab to be able to do that statistical analysis on my data to identify my root cause. So even though we're only looking, bear with me, even though we are only looking at the data for our minimum thickness, we can identify a variety of other variables to kind of identify that information as you are currently seeing over here itself. So to do that root cause analysis, we can easily download the data directly into Minitab to do any further root cause analysis itself. Now jumping back into the presentation, that was a brief overview of our real time SPC application. Just a quick you know what is real Time SPC delivers the exceptional quality and performance tracking you need from the brand you trust. You can dynamic control charts and dashboards update automatically in real time. So real time visual process monitoring and customizable immediate alerts. If you want a text message notification or you want an e-mail notification, you can build and design those directly within our solution, which will alert your operators and engineers to react within our with any change to your process measures itself. My last slide is just a high level overview of how it all works. You can bring in your data directly from a variety of your different systems, clean up that data, build that data, analyze that data using SBC. If the process is in control, you're good to go. If the process is out of control, it notifies you and allows you to have that conversation as to what is going on at your organization with that specific change in the process itself. My last poll for today before we jump into Q&A is, is SPC an important priority for your organizations? And again, the options for this poll are yes in the next few months, yes, in late in 2025, yes, in 2026 or beyond, or D, not in the foreseeable future. Once you're done with the poll, I'm just gonna leave this poll on for a little bit longer, but there is a QA section in the Elite Studio webinar portal. Please feel free to type in any of your questions. I'm going to toggle through the slide as me and David start going through some of these questions and responding to them. Again, we won't be able to get to all the questions today, but if you do have any questions, please don't hesitate to ask them. We will reach back out and set up some time to answer those questions for you. So again, I'm just going to leave this poll on for just a little bit. And then David, let's kick off some Q&A as we get more responses. Sounds good, so here's a question for you Moise. Where is our data stored? Does it matter where this data lives? That's a great question. So this is again a real time Minitab. Real time SPC is a cloud based solution, so all your data is currently being stored once you start using our solution in Microsoft Azure. So again, it's in the cloud. You don't need to set anything up. You set up the solution, you license this product, your data automatically gets stored in the cloud and you can download that data whenever you would like as well. Awesome. Thanks for answering that question. Moise, here's another question. Do you support IMRRS charts? A real time SPC solution does provide support for a variety of different control charts including the IMRRS charts, the within between charts. We also provide support for you know, IMR X bar, RX Bar S charts, P chart, C chart. So a variety of different control charts are supported in the Minitab real time SPC application itself. Another question here, can you support manual data entry? Yes, we can. And we do know a lot of organizations do capture a lot of data today on pen and paper or in Excel. We can absolutely support the ability to manually enter data directly into our real time SPC application itself. I'm just going to quickly flip to the next slide to kind of show you the poll data for all the attendees on the call. And I wanna thank all the attendees for answering this poll as well. And I'm gonna continue on with some questions. David, you're gonna pick a couple more questions that we can ask. Yeah, here's one that came up a few times. Is there a list of data sources that are supported by Minitab for the integration? Yes, we do have a list of data sources. I can't name them all, but we support a wide range of data sources from databases to cloud applications to REST APIs. If you do have any specific and you can find more information on our website. If you do have a specific use case or data source that you have in mind, please don't hesitate to reach out to us. We'd love to discuss with you what your data sources are and how we can get them into our system itself. There are a lot of questions on will this PowerPoint be available? This webinar and the PowerPoint will be available after this session. And again, feel free to reach out to Minitab if you do want access to it if you cannot locate it as well. Yes, we will be sending the recording of the webinar through via e-mail, so just keep an eye out on your inbox for that to come out in the next few hours. Here's another good question Moise. Is there still a need for Minitab statistical software after implementing real time SPC? Yeah, great question. So real time SPC allows you to monitor your ongoing performance of your process. Minitab statistical software adds value in being able to do your root cause analysis. So if you want to do any statistical analysis on the ongoing monitoring, that's what Minitab statistical software would provide. So they absolutely work hand in hand. And matter of fact, you know, customers who license our real time SBC application get the most value by taking that data and analyzing the data that they've collected in our real time SBC in Minitab statistical software to again identify those root causes, identify what's going on with their process itself. Great. I think we have time for one more question. Here's one from Diego. He's asking do you need to prep the data before using it in real time SPC or do you or can you data prep within the platform? Yeah. So data prep is an important piece of being able to get your data in the format so that it can be plotted correctly on the control chart itself. And real time SPC does come with the functionality to allow you to prepare your data as a one time effort. So you basically take the steps that you need to do to prepare your data and you save it. So as new data comes in, you don't have to repeat those steps over and over again, but it does provide the ability to prep your data before it gets plotted on a control chart itself. Great. Thank you, Moise. And I know we're getting a lot of questions here. We'll try to get back to some of these questions via e-mail. But again, really appreciate those questions. And also just a reminder, we do have that survey box where we'd love for you to share your feedback with us. Again, we tailor these webinars based off that feedback. And now I'll just close this webinar. So I'd like to share some information for those who may not be as familiar with Minitab. At Minitab, we help customers around the world leverage the power of data analysis to gain insights and make a significant impact on their organizations. By unlocking the value of data, Minitab enables organizations to improve performance, develop life changing innovations and meet their commitments, delivering high quality products and services and outstanding customer satisfaction. Thank you again for for attending today's webinar and we hope to see you next month. Take care everybody. Everyone, goodbye. _1734213639177