Hello everyone. My name is Shaka and I am a supply chain professional. I currently work at Meta Platforms as a supply chain solutions program manager. Today we are going to dive deep into a topic that is at the forefront of modern logistics, transforming supply chain performance through digital twin technology. This isn't just about buzzwords, it's about creating a virtual real time replica of our entire physical supply chain. This digital twin allows us to stimulate, predict, and optimize every aspect of our operations with incredible accuracy and speed. To achieve this, our approach is built on two key pillars. First, we'll explore advanced route optimization, which goes far beyond simple GPS, adapting in real time to traffic, weather, and other unforeseen events. And second, we'll discuss the power of a rust powered infrastructure. A choice we made for its superior performance, security, and reliability, which is essential for handling the immense data generated by a digital twin. Our ultimate goal is to present a comprehensive cutting edge strategy for truly revolutionizing global supply chain networks. Okay, great. Let's walk through our roadmap for today. Our agenda is designed to guide you through a complex digital transformation journey. We'll start with the fundamentals and build up to practical real world application. First, we'll lay the groundwork by exploring the digital twin foundations. We'll define exactly what this technology is, how it functions, and why it's becoming the cornerstone for every supply chain management and delivering unprecedented operational improvements. And next, we'll dive into key components of the system, variance based route optimization. This is a critical departure from traditional methods. Instead of just optimizing for the fastest route, we'll look at strategies that prioritize predictability and consistency. This approach dramatically improves shipment tracking accuracy and reduces the frustrating surprises that come with unpredictable transit times. Then we'll talk about the underlying engine that makes all of this possible a rust powered infrastructure. And I'll explain why we choose this specific language for its memory, safety, and speed, which allows our system to process massive volumes of high velocity data with micro speed precision a non-negotiable requirement for real time digital twins. And finally, we'll bring it all together with a look at implementation and ROI. This is where we get practical. We'll cover the data integration requirements, the importance of change management, and most importantly. We'll show you how to measure a substantial and documented return on your investment. And by the end of this session, you'll have a clear understanding of not just the technology, but how also leading organizations are using this to achieve transformative results with real world case studies and documented metrics. Alright? All right. Now let's talk about how this technology is fundamentally changing the way we manage our operations. Yes, we are moving beyond the traditional supply chain control tower, which often just provides a dashboard of historical data and into a new era powered by digital twin technology. Think of a digital twin as living, breathing, virtual replica of your entire physical supply chain. It's not just a static model. It's a dynamic, real-time representation that enables unprecedented visibility and control and the results for organizing. Organizations implementing this are tangible and measurable. First, let's look at time to market reduction by building a digital twin, companies can now stimulate new product launches and test different supply chain configurations in a virtual environment before ever committing to a physical implementation. This allows them to identify and resolve bottlenecks in a matter of hours, not months leading to product launches. That are an incredible 30 to 45% faster. This leads directly to significant design and development savings by identifying and mitigating supply chain constraints virtually things like capacity limitations or sourcing risks we can avoid. Costly physical companies are also seeing average cost reductions of about two to five millions on new product. In introductions alone, it's like having a crystal ball for your supply chain. And finally, the impact on operational efficiency is profound. The digital twin powered by AI allows for continuous automated scenario planning. It can dynamically reallocate resources, optimize transportation lanes, and adjust inventory levels in real time, resulting in a 15 to 25% improvement in overall resource utilization. Ultimately, this is a paradigm shift. We are moving away from the reactive model. Where we spend our time putting out fires to predictive one, where real time digital replicas enable continuous optimization and automated exception handling often before an issue even arises. Okay, let's move from a theory to concrete example. This case study focuses on a real world problem optimizing global maritime shipping routes for years. Traditional route optimization focused on just two things, finding the fastest path or the cheapest path, but our work with leading global shippers shows that this isn't the whole story we have discovered. What truly delivers superior outcomes is variance based route optimization, which prioritize consistency over sheer speed. This means we're not just looking for the route that gets there in the least amount of time. But the route that gets there in the most predictable amount of time every single time, and the results are powerful. First, this approach improved shipment tracking accuracy by 37%. When you can really predict when a shipment will arrive, your tracking data becomes far more accurate. This predictability enables precise inventory planning, and crucially allows you to significantly reduce the need for an expensive safety stock. Our advanced algorithms were able to identify entire shipping lanes with a 42% lower transit variance, even when their average transit times were similar to other routes. This means we found routes that were simply more reliable and the financial impact is substantial. A Fortune 500 manufacturer was able to achieve about 3.2 million in annual savings simply by being able to reduce it safe safety stock levels. A direct result of more predictable and consistent deliveries. This level of optimization is only possible with sophisticated digital twin models that can simulate thousands of route permutations while accounting for a historical variance not just average transit times, it's a game changer for building resilience and inefficiency. All right, moving on. Let's talk about the key capabilities of Rust. First, let's look at Rust Score strength. Starting with a memory safety, a major source of bugs in software comes from how it manages memory. Rusts unique ownership model virtually eliminates these bugs for supply chain monitoring applications, which are often left for run for months or even years. This is a huge deal. We can avoid the kind of gradual memory leaks that cause systems to slow down or crash, ensuring our applications are cons, consistently reliable. Next we have zero cost abstractions. This is one of rusts most impressive features. It means we can write code that is clean and high level, easy to read and manage, but compli, but compiles down to the same level of performance as a low level language like C. This lets us execute complex algorithms with minimum overhead, which is essential when you're trying to optimize logistics and route planning on the fly. Third. Rust offers fearless concurrency. The modern supply chain is a whirlwind of data, especially from iot sensors. We're talking about high velocity data streams from thousands of touch points. Rust makes this possible to write multi-threaded code that is completely free of data races. This is a game changer for processing massive amounts of data concurrently, allowing us to keep up with the pace of real time global supply chain. And finally, the cross platform development. And this is about versatility. Rust can compile to web assembly, which means a rust powered analytics and applications can run efficiently on everything from tiny, low powered, embedded shipping sensors to powerful cloud data centers. This allows us to have a consistent and efficient code base across the entire infrastructure from the smallest decide device to a largest server. So what does this all mean for the organization? These capabilities make rust uniquely suited for supply chain applications that demand both extreme reliability and performance. While Python is fantastic and popular language for data science and prototyping, organizations are increasingly MA making a strategic shift. They're moving to a hybrid architecture where Python is used for initial data analysis, but final mission critical production services where failure simply isn't an option, are built in rust. Okay, now let's get a bit more technical and look at how rusts architecture directly benefits the organization. Rusts ownership model is powered is a powerful concept for 24 by seven supply chain operations. It eliminates memory leaks by managing the lifespan of data. Let's look at a simple example. Imagine a function that processes data from a container in the code. When the process container data function is called, it takes ownership of the container object. This means that once the function is finished, rusts compiler guarantees that the container is automatically de allocated. This prevents a common class of bugs where memories accidentally used after it's been freed, which is a major cost of crashes in a long running system. It's a critical feature for all the always on monitoring applications. Next, let's talk about the trade. System. This feature enables to create a modular and interchangeable component for things like route optimization without any performance cost At runtime. Consider a scenario where we have multiple ways to optimize a shipping route. We can define a route optimizer, optimizer trade, which is like a blueprint for what? A route optimizer we must do. This rate simply states that any route optimizer must have an optimized method. Okay, we can then create different implementations of the straight, such as variance based optimizer and then swap them out as needed. The key here is that because of how rust compiles this, there is no or zero runtime overhead. The compiler knows exactly which version of the optimized function to use at compile time, so we get a flexibility of a high level object oriented design without any performance penalty. The end result of this approach is a new class of systems that can achieve microsecond level precision and maintain incredible reliability even under the most extreme op operational loads. This is the kind of technological foundation needed for model modern digital twin implementations and other next generation supply chain solutions. All right. Okay, now we'll quickly cover the three core pillars of a successful digital twin development. First data integration. A digital twins power comes from its data. We need to connect thousands of data sources from ERP systems to IOT sensors. Processing over 10,000 data points per second. Rust synchronous runtime makes this real time high volume integration possible, giving us a compile holistic view of the supply chain. Second. Algorithm optimization data is useless. Without smart algorithms. We have seen a 25, 20 7% improvement in predictive maintenance by using high performance pipelines. This is achieved through techniques like parallel processing and GPU acceleration for complex modeling, which allows us to act on insights with precision. Third, change management technology is only half the battle. A successful deployment requires an organization. Organizational transformation and all our data shows that focusing on this pillar leads to a 92% success rate. To achieve this through skills training, process redesign, starting with the phased implementation to build momentum and buy-in, and the most successful implementations blend technical excellence with organizational transformation. By excelling in all these three areas, one can achieve about 3.2 times greater ROI from the digital twin project. Yes, start with the small, highly visible process, high visibility process to demonstrate value and get the entire organization on board. All right, moving on. To illustrate the power of digital twins, let's look at a case study from a leading global supply global shipping company. The company needed to reduce operational costs and envi environmental impact. While improving delivery reliability across this fleet of 1 47 container vessels, their solution was a digital twin platform powered by rust based edge computing. This system provided real-time monitoring of over 1200 parameters per vessel, enabling, dynamic route optimization, predictive maintenance, and AI powered fuel efficiency. This architecture relied on rusts performance and reliability achieving. A five millisecond processing time for critical data and almost a hundred percent system uptime and a small eight MB memory footprint. Critically, its efficient data compression, reduced satellite transmission costs by 76 percents. What were the results? The implementation led to a significant measurable improvement, operational cost reduction, about 18%, fuel efficiency improvements about 12%. Reduced maintenance downtime above 32%. This case study demonstrates that a well-designed digital twin blending excellence with a clear business strategy can lead transformative results in a demanding environment. Alright, the digital twin market in the supply chain is growing rapidly at A-C-H-E-R of about 37.4%, driven by the need for greater efficiency and resilience. Organizations that daily implementation risk falling behind is early adopters need a powerful compounding advantage. From the data, the most forward thinking organizations are now combining digital twins with emerging technologies to unlock new capabilities. Quantum computing integration is leading to a 93% reduction in complex system calibration time with rust serving as the foundation of these hybrid algorithms, machine learning. Advanced machine learning models built on rusts ecosystem are achieving about 47% higher reception prediction accuracy than traditional methods. The early movers are developing capabilities that were impossible just 24 months ago. This integration of digital twins with these advanced technology technologies represent the next frontier in the supply chain optimization. The time to act is now. All right. Let's look at the measurable ROI of digital twin implementation highlighting key three areas. First one is real time optimization. By using real time data and predictive analytics, we can achieve a 21% reduction in inventory holding costs. This is a quick win with an implementation timeframe of four to six months and a payback period of just nine to 12 months. Second one is transportation and analytics. The rust implemented dynamic routing algorithms process over 50,000 permutations per second, resulting in a 17% reduction in transportation costs. The project has a fast payback of six to eight months for the long term. Advanced analytics deliver about a 42% improvement in forecast accuracy with the payback of about 10 to 14 months. Third one, the compounding effect. These benefits are not one-time gains. As the digital twins learn, we see an additional 70 12% performance and improvement year over year. The key to maximizing this ROI is a comprehensive measurement framework aligned with the business goals from day one. Alright, all right. Here's a proven 18 month roadmap for a successful digital twin implementation. Broken down into four key phases, phase number one, months one to three. The foundation. This phase is all about building a solid base. We'll select a technology stack, including rust. Define the project scope, identify a pilot process for initial development, phase two months, four to six, initial development. Here we execute on a limited scope, launching a first digital twin. We integrate core data from systems E-R-P-W-M-S, et cetera, develop basic simulations and start gathering user. Feedback. Third phase months, seven to 12, which is expansion. This phase is about scaling up. We'll deploy IOTs sensors for realtime data, improved advanced rust algorithms, and establish a central control tower. This is also when we'll perform our first major ROI measurement and last phase months, 13 to 18 optimization. In the final phase, we move towards a fully optimized system. And enhance capabilities with AI and machine learning. Integrating external partners for full visibility and work towards fully autonomous optimization. This structured approach balances quick wins with long-term goals, leading to about a 2.7 times higher success rate. And remember to maintain executive sponsorship and focus on robust change management throughout the journey. Alright. To conclude, let's review the key takeaways and outline a clear path forward for the organization. Digital technology. Digital twin technology is transformative. Digital twins are delivering documented improvement in time to market and operational efficiency, offering unprecedented visibility and control, variance based optimized optimization performance. Focusing on consistency over speed, deliver superior results with about a 37% improvement in tracking accuracy and significant cost reductions. RUS provides the essential technical foundation. Its unique features like memory, safety and fearless concurrency enabled next generation infrastructure that processes high velocity data. With microsecond level precision implementation requires a holistic approach. Success depends on excellence in data integration, algorithm optimization, and change management. Organizations that excel in all three achieve a 3.2 x greater ROI. Here are the five immediate, actionable next steps for any organization. Conduct a digital maturity assessment. Identify your organization's readiness. Identify high value pilot opportunities. Select a manageable project with clear KPIs and measurable ROI Potential. And develop a technical skills roadmap, acquired the necessary rest programming capabilities to support the new infrastructure. Establish data governance framework, ensure data quality, security, consistency for successful initiatives, and lastly, create an executive steering committee, establish leadership to guide and support the transformation efforts. Hopefully this session was helpful to Y all, and thank you so much.