Name: Digital Manufacturing Strategies
Uploaded: 2026-03-31T16:16:12.669Z
Duration: 1 h 8 s
Description: Digital Manufacturing Strategies

Transcript for "Digital Manufacturing Strategies": Before Amazon Business, buying for work was chaotic. Now it's easy to find products from thousands of suppliers in one place. Save on every type of purchase from individual items to bulk orders. At any time, you can view your spending on prebuilt, easy to use dashboards. Plus, you can free up cash flow if you choose to extend payment deadlines and view and approve your team's purchases easily. With Amazon Business, things just got a lot more manageable. Hello, everyone, and welcome to today's webinar. Webinar section on the Manufacturing Digital website for more insights. Also, our procurement and supply chain live series is heading to Chicago in just a few weeks for the US Summit. I hope to see many of you there. I'm Ella Wilkinson from Manufacturing Digital, and our topic today is digital manufacturing strategies. We aren't just talking about going paperless anymore. We're talking about the leap towards fully autonomous, resilient and sustainable value chains. Joining me to break this down are three incredible experts. First up, we have Rick MacDonald, a strategic advisor who helps leadership teams navigate the high stakes world of supply chain evolution. Mghali Emil, a global industry lead for manufacturing at CGI. Mghali brings a wealth of experience in digital transformation on how to actually scale these complex systems globally. And finally, Darryl Culpepper, a visionary leader at Integra who specializes in bridging the gap between legacy operations and the future of the factory floor. So welcome all three of you. Before we get started, I like to throw over to each of you to tell me a little bit more about who you are, what you do, your background and why you're an expert on this topic. So Rick, you're first in line on my screen. Let's start with you. Great. Hello, Ella. How are you? And so happy to be with you all today. I'm Rick McDonald, a retired Chief Supply Chain Officer of the Clorox Company. And I spent thirty two years at the company leading around the globe, 6,000 of the 9,000 company employees reported into the supply chain. And I began the digital transformation of the Clorox supply chain in about 2018. And, we've gone maybe a decade or so without, maybe a little bit more without a lot of digital transformation. So it was a massive undertaking and, lots of lessons learned as we, as we went through that process. Well, we're so excited for you to understand. You're gonna bring a wealth of experience, I'm sure. Okay. Well, good morning to everybody. My name is Daryl Paulpepper. I'm the vice president of supply chain at Integra. I started off my career in supply chain almost twenty five years ago in the military. Since then, I've worked with several different companies. Most recently, I've been in charge of building the economy ecosystem of supply chain within Integra, looking at ways that I can bridge those gaps from how we communicate to our clients back to how we integrate that into the manufacturing, and also looking at how we can move forward in advanced technology. So when you think about AI platforms and looking at how it drives the business and supply chain method, my role is to really integrate that information technology, I guess you can say, highway to advance the supply chain innovation. Well, we're very excited for you to be with us today, and a fantastic surname I was thinking this earlier. Magali, let's see if your mic is working. Hi, Ella, hi everyone, really pleased to be with you today. So my name is Magali Amiel. I come from France, but in Montreal for twenty two years and I arrived in Montreal for doing my PhD on supply chain. Supply chain is really part of my DNA. We like working on goods movement and how you work with the global ecosystem to be sure that goods are moving from A to B on a really seamless flow. During my career, worked on consultation across Europe and North America. And I also operate during the COVID transport and logistics for a retail organization, it was quite, you know, the training, and also a lot of knowledge about supply chain. Today with CGI, I'm working with the organization on manufacturing and also supply chain, with the integration of technology and AI to the shift that we are living in the industry. So really exciting time for the supply chain. Brilliant. Well, I'm so happy that all three of you are here. We've got years of experience between us, so I think we're in for a very exciting discussion. So we'll jump straight into our first discussion point, which is building an integrated digital manufacturing ecosystem. So, Mugali, I'm going to stay with you here. We talk about connected ecosystems, but in reality, many factories probably feel like a collection of islands instead. From your perspective at CGI, what is the glue that finally breaks the silos down between procurement and production? Is it like a specific technology or a change in data architecture? Well, that's a really good question to start. For us, the way we see that with our client and also the project we have, you know, breaking down silos, it's not just technology challenge, it's really strategic and a cultural one. It's really how you involve human and people on it, because the goal is to create seamless digital thread that connects every function from design to delivery. So what we're seeing is that a lot of manufacturers still have procurement, supply chain and production working off different data, different priorities, and often different timelines. The real objective is to create a digital thread across the value chain from planning to execution, as I mentioned before. So, you know, manufacturers can no longer simply manage disruption, they must design for it. So the core challenge lies on overcoming legacy workloads and organizational service cycles. So the way we work with them is really how you put a unified manufacturing approach. So first, the first step is how you unify the foundation. So it's about more than connected system. It's about creating a single, resilient operating model. So with our clients, we integrate their core IT and OT landscapes, we've done the MES, ERP, PLM to enable a seamless flow of data and this breakdown the world between departments, so a change in production plan is instantly visible to procurement and supply chain. This is critical, as our research shows 67% of metal and mining manufacturers see legacy systems as a primary barrier. So first we unify the foundation, then it's really how you align on outcomes and not function. So it's really important that you align your teams around shared outcome. If each function of your supply chain is only optimizing its own metrics, you reinforce the silo. So the manufacturers getting this right at the moment, focusing on resilience, agility, service and performance across the value chain. So the true integration is achieved when departments are aligned and share outcomes like resilience and performance excellence, instead of measuring function in isolation. So that's the way we work with them. And for that, one of the first thing that we do is how you establish a trusted data fabric. So it's a unified ecosystem, run on trusted data. This means that with this single source of through people, you connect sorry, plants, people and the entire supply chain. And that's a lower real time visibility and proactive decision making. So that's the way that we try to give a client to build three d, break the silos, implementing this culture also across the organization and working together. Brilliant. So, Darryl, then, where do you think the biggest friction points are when you're trying to get production data to talk to procurement? Is it technical or is it indeed cultural? No, I think it's more so cultural. I think it's technical because the system does what you ask the system to do. I think to elaborate a little bit, the way we connect from our sales to our design to our delivery is through a business process that we actually call SightOp. And so a lot of that means that we take the sales data, we turn that into a natural forecasting, and it actually goes into the ERP system. What happens is that a lot of times we find that the groups have a tendency to work in silos. Sales may have information that they're keeping to themselves, whereas we're on the manufacturing side, we're trying to figure out how do we build what the actual customer needs to be able to deliver on time. And so you find that the friction becomes, well, we didn't build this on time, and we didn't ship it on time, and manufacturing is telling sales, Well, you should have told us exactly when did you need this actual product to the customer, or how many you're trying to actually get to the customer. Because we may get one number, but then sales may be telling the customer, We can bill you five, when in reality, the facility is only built to bill three from a And capacity so what happens is that you get that friction, but there's sales in here trying to sell, and manufacturing is trying to help what's produced, but there's a gap in what the true expectation is. And so we utilize what we call a SIOP, and we use the system. So we put information from a sale. We turn that into an actual demand forecast. That then goes over to our procurement team because our procurement team sees that forecasting system. Now they can start reaching out to suppliers or placing POs, and then we have a production planning team that sees all that information in the system, and now they're setting the facilities up to say, Hey, this is when we need to start building based off of engineering lead time, based off procurement lead time, and based off the actual demand. And so, then that production plan goes to the actual facility. And so, now everybody is on the same page, and we utilize the system to do that, but at the same time, we have to break down those silos within those groups. So, we have a consolidated SIOP meeting that we come together and we shake hands to say, Okay, this is the actual plan. This is what we're going to deliver this month. This is the customer's expectation from a lead perspective. Do we all agree that, from a financial standpoint, we can get this? Do we all agree, from a capacity standpoint in the facility, that we can build this particular product? So that's how we run plans. But to go back to your original question, the friction becomes when the expectations are not met or they're unrealistic. And so, we utilize that process to break that down. Yeah, I think the silo departments is an issue everywhere, isn't it? So using using tech and data actually to to bring that together is a is a great idea. So, Rick, from a from more of an advisory standpoint, then how do you convince a C suite that integration is worth the upfront pain? And what is the risk of still staying siloed in 2026? Well, you know, the the driver here is the competitiveness of the firm, short term, medium term, and long term. And if we look at the big picture here, we're all trying to operate, or we should be trying to operate at the speed of the consumer. And in many industries, especially fast moving consumer goods, that cycle is accelerating. It's not slowing down. And so we're trying to go from reactive to proactive to autonomous. Autonomous is a bit down the road, but certainly we could be a lot more proactive using some of these tools. And the conversation with, executive leaders is just about this competitiveness of the organization as they work to operate at the speed of the consumer. And it multiple elements. And the risk of not doing that is you're going to fall behind, your costs are going to balloon, quality may suffer, and you're not going to have the ability to orchestrate your supply chain end to end as you might if you had a more unified set of systems that were talking with each other and providing visibility and data real time. So from your perspective then, what are the most effective ways to move from basic visibility across operations to that proactive data driven decision making that you're talking about? Well, I think one of the first things that we haven't talked about it to any great extent yet, but it's really getting the individuals in the process and the workflows involved. One of the biggest things that I see in organizations I work with is a lack of governance and lack of change management. And that is a huge challenge in terms of getting everybody on the same page relative to what the objective is, how we're going to move forward with it. Organizations by nature and individuals in them are not necessarily keen to change. But if you can identify the cohorts in your organization, the early adopters, the very slow movers and everybody else in between, and start to tailor messages to them, you have a chance of moving forward with a more optimized and orchestrated solution with a number of key components of systems. So let's move on then to implementing advanced technologies at scale. So what happens when we move beyond the pilot stage is really where we hit the issues. So Rick, I'm sure you've seen many, many strategies hit a wall when a company rolls out AI or automation across global sites. Where would you say that usually goes wrong? You know, Ella, I see it occurring primarily due to three causes. The first cause is really the digital fluency of the leaders. And what I mean by that is not that they are experts in coding, but they understand these digital capabilities and what problems each of them are best capable of solving and pairing those problems, up with solutions that work. You know, a lot of times when the digital fluency isn't there, Unfortunately, executives get sold a solution that doesn't really solve the problem they have. And one of the things we did at Clorox in the early days of our digital transformation is we went through and we basically cataloged, we inventoried all the problems we needed to solve within our supply chain. Then we pushed them through for risk filters, financial, regulatory, consumer and reputational. And that allowed us to kind of order the roadmap of what problems we needed to solve first. Then we went out and we explored the technologies and we got very, very granular on that. I formed a group led by a director that was responsible for nothing but assessing digital capabilities to solve these problems. So that's the first problem. The second one is the the the whole, identification of, change management. And, you know, the the the the the key learning for me during our transformation at Clorox was around us moving to a top right Gartner top right box planning tool. And we're very excited on go live day. We were raising the trophy and pouring the champagne. And, then we had our regularly scheduled S and OP meeting the very next week. And the planners came into the room with the world's most famous planning tool, Excel spreadsheets, not the outputs from the system. We just spent millions of dollars installing. And when we asked them what was going on, they said they didn't believe the outputs. And, they didn't believe the outputs because they would irregularly go in and change the cycle times of manufacturing processes while the system was constantly updating based on the latest data. And so we had to go back and do a lot of change management. And in every organization, you find there are three groups of people. You have the early adopters. You almost can't do anything to knock them off their path. They're very excited about the change. They want to lead. And so you celebrate their success and highlight what they're doing. On the other end, you have the 10% or so that, are just never going to adopt to the new system. They have no interest in doing it. They want things to stay the way they are. You either have to coach them up or coach them out. And then there's everybody else in the middle. And there are messages that you have to apply from a change management standpoint to all three of those groups. But the objective is to get that group in the middle moving more towards an optimistic wait and see point of view than not. And that was one of the things we did in subsequent implementations, and that helped us a lot with other elements of our digital transformation. So it's more of a big cultural shift then. How did you initiate that cultural shift and get that big bulk in the middle to move with you? Yeah, know, we thought we were doing a really great job of change management because we told everybody what was coming. We trained them. We had a day in the life. We were enthusiastic and we knew a lot about the technology that we were installing. What we failed to realize was how ingrained the behaviors were in those individuals who had to now adapt to this new tool. And so what we were able to do is really get them to understand what was happening, how it was happening, and learn about the capability, but also learn about what was going to change. And getting into a more granular level of conversation about exactly what was going to be different, what they should expect. And we did that quite well on subsequent implementations. Brilliant. So, Daryl, then, do you come across a lot of pilot issues where the tech works in one plant but fails in all 10 of the others? And what is the one operational must have that needs to be in place before a company even thinks about scaling AI? Well, I think so, yes, I have. And one of the things that I always say before you can actually scale with AI, in any type of innovation or technology, you've got to make sure the data is correct. That is the absolute must. You can implement all new types of systems, but if your data is skewed, then that's where the issue comes at. Then early on, I was part of a new ERP system implementation. And what happened is that it failed miserably because we didn't have the correct data. So, we didn't have the right financial tools that were going into it. We didn't have the right planning data. Our lead times and everything were incorrect. And so, you asked the question, Well, how does this happen? Well, it goes back to kind of what I talked about earlier, that a lot of these groups were used to working in silos, and now you're trying to now break these silos, but you're also trying to improve the data integrity. And so there's this massive data cleanup that had to go on. But in the early parts of this implementation, a lot of the data skewed. And we didn't spend the time to make sure that we scrubbed the data. We did spend the time to make sure that everything was correct on our end. And so that I'm not going to say it just completely failed. It took longer from an implementation standpoint because we had to go back and clean up the data. And so that becomes a point of failure with any system integration is that if you don't have the data correct, then you can it really becomes more of a disruption. The other thing that you have to look at is that you have to have the buy in of the stakeholders. You have to make sure that as you're implementing new systems or you're upgrading technology, how does that work for each department? Because on the procurement and the planning side, it may work well for me, but what happens if I now can't match a PO or something? Or, you know, the receipt that comes in the system doesn't line up with what we need to actually pay a supplier from a financial standpoint. So it's all those different little things that you have to look at when you're implementing a system. Like, I can upgrade the system, or I can bring in AI to do several different functions. But if it only works for one function, but it doesn't add value to the other, then the actual implementation itself is a bit flawed. So you have to look at the overall package of what bringing into the business, and then how it interacts with all the other different functions. So is that Daryl, could I comment on that? Know, Daryl, that data thing is such a huge element. And I find two or three things happen. First of all, organizations are paralyzed because they don't have good data. They don't know what to do, and so they don't do anything. In the second case, they clean up their data, but then they don't change anything around how humans and workflows interact with that data. They limit read write access. And so the data gets polluted very, very quickly. And the third case, and it sounds like maybe yours, you clean up the data, you change your process. And so now you've got clean data going forward, which allows the tools to work at their optimum capability. Absolutely. I was going to say essentially, there's a massive cultural piece there that needs to be done before you implement digital technology. But then there's also a huge technical piece just to make sure everything is seamless there. Mugali, from a CGI perspective, you obviously work with massive industrial players. For an organization still at their initial stage in their digital journey, what is the key metric that proves the value of AI or automation within the first six months? Well, that's a good question, but maybe first I think what Rick and Darrell just shared before, it's all about, you know, people and data, right? Because if you put AI and you don't have the people embedded in this project, and you and if you don't have a right vision of what you want to achieve, AI is not going to solve any problem. That's the way that we work with our clients because they say, well, with AI, I'm going to solve all my issue. No, absolutely not. First, it's really people and data and then you can have some KPIs and I think one of the first KPI and already talked about it, you embed your people because in the end, in a shop floor, in your supply chain, that's people first, right? They have the knowledge, they also operate, sometimes they know how to bypass technology and come back with the Excel sheet. So that's really this change management is really the way to start. First is really as an executive level, what is your vision, where you are today, where you want to go and then how you involve your people, your data and what you want to achieve with AI to have this KPI. So really for us, it's really outcome first, what when we talk with the client, and it's because some of projects we have done on AI or with clients, we want to go really fast, because oh, AI is really sorry, the best word, so we have to embed on it. But we didn't do the homework first. So I know it's not sexy to say back to basics, but if you want to have, you know, KPI and if you want to have success with your AI project, you really have to start with the basics. So what you want to achieve, how will embed your people on it and then also your data and it's not easy because what also we are living into the manufacturing side and also on the supply chain industry right now, that's the workforce, right? Interesting to see, we are six years after COVID and some of the organizations while manufacturing, we're seeing this acceleration of digitization, it's also because the workforce, it's more and more difficult to attract workforce and also to retain people inside the organisation with the knowledge. So we have to think, we have to keep the knowledge of people that work for years and years and knowing really well the processes and the other way you have to onboard a new generation, while working with the technology, but they don't have this knowledge of the business, right? So that's where we are before, so, you know, when you are C suite and you have to embed AI into your organization, you have all these different, you know, parts that you have to orchestrate to be sure in the end, can implement AI and having some KPIs. Of course, if you look at the machine and you say, okay, well, I want to review my automation, I want this result, it's easy. But before having that, you have all your operating model and your vision to work on. So that's the way we work with clients. For example, with some of the clients that we have a long term relationship, we have some clients, work with them for forty years, for example, we had the time to prepare them for AI. So, we know that they have the governance for the data, have the people trained, they also have this group with the ambassador of technology, and we like when we talk about the early adopter and the people in between and the ones that want to change, doesn't want to change, sorry. So we work with them and we have this group where we have the early adopters really talk and embed with them the other people from the organisation on the digital journey and that's why in the end we can have really interesting projects, for example, right now on AI with one of the big manufacturers, we are able to predict fifteen days in advance the disruption of the stock. So fifteen days in advance, we are able to push to the plant, you have to produce because we don't want any disruption for our clients. So when you have the right governance data and the right way of onboarding people on the project, you have really interesting results. But for that, meaning that if you start, if you come back to your question, if you are at the early stage and you start, so first people, data and vision. So I guess this is an open question then to all of you, because we've all mentioned the people piece and then the tech piece. What percentage do you reckon is human centric when implementing digital transformation? And what percentage is actually the tech? Oh, I think the percentage from a people perspective is huge. I don't know if I could put an actual number to it, but you got to have the buy in of the people to make sure that when you're doing these systems or when you're looking to do a more advanced technology standpoint, you need the people. You need people to buy in. You need people to actually utilize the system or it's all for nothing. So, there's a huge portion of, you know, we talk about change management that you can have people that have been in the company for twenty plus years and, you know, their way has always worked for them. So now they don't want to really upgrade to any new technology. So you find that you have a lot of that, so that goes back to the whole entire change management conversation. But the people element is really huge. I live off three principles: people, process, and technology. I think if you can get those three right, then you have a really good chance of moving your business up work or scaling it. So the people aspect, I think, is really huge. I think that's a higher percentage versus the technology. Yeah, I think I think Daryl is exactly right. And, you know, I would I would put I'll put a little different spin on this and words that I use when I'm on stage giving keynotes. The content I talk about is the mindset, the skill set, and the tool set of digital transformations. And invariably, when I ask audiences what the most important part is, and what do they think about when they think about digital transformations, people will yell out the names of various and platforms and tools and capabilities. Almost nobody says leadership mindset or the skill set. And I think it really has to start with, and my colleague mentioned it a minute ago, it's got to start with the vision and the leadership mindset, that digital fluency. What are the problems you're trying to solve? What are the best tools to solve them and having leaders really understand the ecosphere that they're operating in with respect to digital tools? The second is the upscaling and the rescaling of your cohort. You're not and change. You're not going to change out everybody. You've got the crew you've got. And so what are you going to do to change, manage them through to the next stage? And then the third and least most important part is the technology. It captivates everybody's attention because it's a shiny object. You just spend a lot of money on it. Everybody wants to talk about it, but it's really the mindset and the skill set that are two most important things as you go through these transformations. Absolutely. So let's move into our final discussion here, which is how data AI and automation are driving efficiency and performance. So efficiency used to mean cheapest, now it means resilient and sustainable cause we're seeing a massive push towards ESG transparency. So how can manufacturers use the same data they use for efficiency to prove their sustainability credentials to regulators? This is an open question. So I guess I can take a stab at that a little bit. And I look at it from a manufacturing standpoint on my side, it really helps with understanding disruptions. And so, what we do is we do calibrated scenario based planning, and we do have some AI embedded in that. And so, we look at multiple different ways that our supply chain can be disrupted, which would cause a disruption into the manufacturing. So, we look at our key partners from a supply chain standpoint. Have they had layoffs? Or is there a situation that may be going on from them from a financial standpoint? How many times have we had stock outs or late deliveries from them? Which means do we need to go get a third or fourth source? And then also, look at, you know, are there port disruptions? Because logistics plays a big part in what we do from a manufacturing standpoint, because we do import a lot of product from overseas. And so, you know, are there disruptions with port? Are there geopolitical risks? You know, we use AI to help us work a lot of those different scenarios. And so what we do, we base that into a bit of a risk analysis within our supply chain because the overall goal is to make sure that we have product in place on time to be able to start our manufacturing sequence. So we use AI to help us look at those different risk mitigations and make sure that if there is a potential for disruption, if flagged with a high risk disruption, then now we're putting plans in place to really mitigate that, to really make sure that we have components moving through our supply chain system to actually do from a manufacturing standpoint. That's how we utilize it. And again, it goes back to a lot of the scenario planning in itself within supply chain. So obviously, you mentioned there the volatility, the geopolitical volatility that we're living through. How are clients using that predictive analytics to move from just anticipating supply chain shocks to predicting them? Well, it it is a bit tricky. I mean, you because you look at things like tariffs and how that impacts all of us that work in the supply chain. And, you know, so we look at those things and then we, you know, obviously, the countries that we do a lot of business with, we try and kind of look at, you know, what is going on with that particular country from a stabilization standpoint. So, you know, things, you know, products that may be coming out of India, Taiwan, China, obviously, those are big, really, players in the ecosystem for us from a supply chain standpoint. So, you look at you have the system look at their potential risk or potential things that can fall out. And so, do you plans in place, say, if and I hate to say this if China goes invade Taiwan? And what happens from a supply chain standpoint? Or if they block a canal that delivers product for us, which we've seen happen. So, it's a lot of those different things that you try to kind of have that data that helps you kind of model out, Okay, what is going to be our plan for that? And so that's how we look at it from a geopolitical standpoint. Not that we can actually stop any of that, but can we plan for any of those particular disruptions? Yeah, Darryl's got that right. And if I could, I'll put a couple of words to this to get bandied around, and I'll give you my definition of resilience and agility. The resilience play is what you do before there's a disruption. You know, for most of us that were importing something from China, we started looking at this three, four years ago around what do we do to not not have that risk in the organization? And so it's the resiliency. Called an insurance policy. You can't insure everything, but you can look at multiple suppliers, different formulas, different geographies, near shoring, reshoring. Those are all part of the, what do you do before the disruption occurs? And then on the agility side, how fast can you come back from that shock? Because it's not a question of, are you going to have disruptions? Are you going to have shocks? You are. So what is your capability to respond to those shocks? And what are the workflows and tools that you need to have in place to do that? Yeah, agility is That was okay, Farrah, Darrell. No, I was going to add agility is one. Resilience and agility is really, you know, two words that go hand in hand in supply chain. To give you a perfect example, a previous company, I was overseeing a manufacturing site in Shanghai, China, and it got shut down due to COVID. And that particular plant could have easily crippled our business. But we had a general idea there was going to probably be some issues with COVID, that it could probably cause a bit of disruption. So, we started to near shore some of that, particularly that bill, to build it in The U. S. And we also increased our inventory. Now, that was an increase in capital, but at the same time, it allowed us to operate three or four it actually allowed us to operate three or four months without that plant because we have bought a lot more inventory over in over into The U. S. To be able to make sure that we could keep selling to our particular clients. That agility is really key in supply chain resilience, but also how can you come back from a disruption is also very key in our world. It's usually the proof in the pudding, isn't it? That it's actually not how well you prepare, it's how well you react in the supply chain instance. So my final question in this section then is: how is the Amazonification of the supply chain, so the expectation for instant data and one click simplicity, changing the way that industrial leaders think about their own performances? This goes back to something I said towards the beginning, and that is we're all trying to operate at the speed of the consumer, and those cycles from order to delivery are speeding up virtually every single day. At least here in The US, Amazon and Walmart have announced initiatives to quicken the delivery cycle. Right now, today, I can order something at 05:00 this afternoon and have it delivered between 4AM and 7AM the following morning. They want to speed that up. Walmart has a huge drone program here in The US where they've made over 250,000 deliveries via drone. Now the capacity isn't that great. It's only five pounds per vehicle, but the reality is that certainly speeds up things in the supply chain. I think it forces leaders to really look at the ecosystem that we're operating in. We've got to have as much visibility and orchestration across the supply chain. And, you know, we talked about the siloed nature of supply chains. That just isn't going to work going forward. The competitiveness of the enterprise will be impacted significantly if we don't piece together, a very orchestrated supply chain, have real time metrics from end to end, and then know what to do with that data once we get it. And that is a huge thing. A lot of organizations have tons of data, but they really don't know how to use it. They don't have the critical thinking and the ability to carve out those insights that allow them to make decisions faster and therefore utilize the data significantly. So I will move us on to our Q and A because we are running out of time. And my first question here, we've actually covered in quite a lot of detail, but it's how do you handle that talent gap when implementing digital strategies? And how do you use data and automation to augment the workforce and not give them the fear that you're going to replace them? That really goes back to the culture. I mean, you want to really work to try and change the culture and the mindset. And you have to understand your overall talent. You have to understand who is really a systems person and who's not. And so when you are able to identify the ones that you think are not, then you spend you try to spend a little bit more time with training, if possible, because everybody I think everybody wants to work better or even, you know, work more efficient at their job. So, I think it becomes a value analysis type thing, where you're trying to show the value of, Hey, if we do this upgrade, this is how it really affects your particular work, and this is where we see you can gain some efficiency. And you start to you start to understand who where the resistance may be and who's gonna be, you know, all aboard, as we talked about earlier, you know, those runners that are just gonna actually go for it. And I think I I think that's when you start to identify your talent and if there's particular gaps. But I think it really starts with trying to elevate the culture and getting that culture into the mindset as we talked earlier about leadership and vision. So I think it really starts there. This is an exercise and understanding that there are fewer people in Gen X than there are in boomers. So we've got a population challenge, and this isn't a US issue. It's a global issue. You have a much more expensive group of human beings who do the kind of work that we do in the supply chain. And then you have lower skills. It is very difficult in some roles to be able to find competent and capable talent. And that is not just here, it's in Europe as well. And so that's the talent side of it. And then on the change management side of it, as a leader, you're not talking credibly about what the change is going to be. What is it? What isn't it? Telling the story. If you don't tell it, your organization will tell it to themselves. It'll be way worse than probably reality. And then you've got to make sure you're continuing to keep them up to speed with what's happening, gaining their feedback, gaining their information. We did small groups and we had very small groups of people. We went in and had a scripted set of questions to try and understand what were they fearful of and how could we allay those fears. At the same time, if you know there's going to be an impact to some humans because of technology, you owe it to them to talk about it as reliably as you can right up front. So there's no kind of hiding the ball here. This is really about very transparent communications about what are you doing, what are you not doing, and how will it impact the individuals involved. So you spoke there obviously about boomers versus Gen Z. Are we potentially looking at Gen Z the wrong way when we're going, yeah, they haven't done the dirty work that we all did when we started, but they are very tech savvy, And is that going to help us? You know, think they're tech savvy in certain ways, but we're all learning about the AI capabilities and how those solve the problems that we have in the supply chain. And that's the part where I think there's going to be more experience required, more seat time required. The digital nature and we did an exercise in the organization asking people about their digital fluency. And in spite of those who grew up with a mobile phone in their hands, it wasn't necessarily always a correct assumption that they could connect the dots between I need to solve this problem and this technology is best designed to solve this problem. So we're all learning together here, I think it's just gonna be a matter of bringing ourselves along to make sure we get these supply chain problems solved. So we are rapidly running out of time, I'm going to ask one more question, and that is always what is one piece of advice you'd give to a leader now who's at the start of implementing a difficult digital strategy? If I start with you, Mugali. Yeah, well, it's a good question. Right now, first advice will be, you know, have a good team around you and a team with different aspects, right? You have your technical people, your change management people, your HR, you know, it's really important being a team on that because it's not one single person, it's not we want to break silos, right? So and it's really also to understand with your team, understand in which ecosystem you are evolving because a manufacturing and a supply chain, yes, can be one entity but you are part of an ecosystem with different factors and influence. So if you want to start and first also to be humble about the digital currency, about what is happening with the technology, what is happening with the business itself and a team and step by step. Having a vision, a bold vision is important but also knowing the steps that you have to reach to be sure that you have the right basis and the right bottom line to start and you can increase really quickly to your digital transformation. So, bold vision and step by step. Fantastic. Daryl? I think for me, I will echo that one surrounding, you know, making sure you have really good people in place, but also making sure that you have transparency in place as well. I think a lot of times that when we start to talk about, you know, systems and, you know, what it can do, sometimes we're not transparent about what it can't do and, you know, issues that it may cause. So I think, one, you need to have good people. Two, you need to have transparency. And three, you got to have a plan. You got to have a true plan, and you got to have failure mode built into that plan, because if not, then you're trying to react if something goes wrong within that. So I think you have to have a really good plan, good people, and then transparency of how the system is actually going to work. Thank you so much, Daryl. And I'll give you the last slide to you, Rick, because you were nodding away there. Yeah, thank you, Ella. And for me, this is assuming that there is alignment with the executives in the company that a transformation is going to take place. The number one thing is getting that inventory of the problems to solve. Without that, you're going to be running around chasing whatever hits your inbox, what the CEO yelled at you about, what the board said you ought to do. And, having that list of problems that you need to solve in your supply chain and the business case, I mean, that's my one metric. The business case, the ROI of these initiatives is the real driver. And so having that inventory of problems to solve the business case that supports it, that allows you to move forward. By the way, you're going to learn a lot about your digital fluency. You're going to learn a lot about the tools, and you're going to hopefully move into some level of change management that helps your organization move to the point where you get exactly what you want to get out of the tools that you bought. Brilliant. Thank you all so much. That unfortunately brings us to the end of today's session. My thanks to our partners at Amazon Business and a huge thank you to Rick, Darryl and Mugali for sharing your expertise. So, so much to unpack from this session, but it's clear that leveraging data driven insights and the efficiency of platforms, manufacturers can move beyond visibility and achieve resilient, scalable and sustainable growth. You can also catch more webinars over on the Manufacturing Digital website. So just head to manufacturingdigital.com where you'll also find the other recordings in this series. This recording will be available online shortly so you can come back and you can watch it again and again and share it with your friends, your family, whoever. We'll see you at the next one. So bye bye for now. Before Amazon Business, buying for work was chaotic. Now it's easy to find products from thousands of suppliers in one place. Save on every type of purchase from individual items to bulk orders. At any time, you can view your spending on prebuilt, easy to use dashboards. Plus, you can free up cash flow if you choose to extend payment deadlines and view and approve your team's purchases easily. With Amazon Business, things just got a lot more manageable.