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THE DIGITAL TWIN With the rise of digital, manufacturers are finding themselves rich in data. Meanwhile, computing has emerged as the cheapest, most abundant resource that we can deploy against any problem. The problem in manufacturing is not the lack of new ideas and products, but the ability to design and build new products efficiently. An IDC Big Data user study found that operations-related processes were the top priority for analytics investments. This next wave of IT innovation, the rise of digital, is providing manufacturing with the engine to improve efficiency. IT has become an integral part of a product. This is because of cheap sensors and processors, cheap storage, purpose-built software, purpose-built clouds enabling data storage and ubiquitous connectivity. Simulating new innovations is the idea behind the digital twin in manufacturing. We can use stochastic simulation to generate future “what-if” scenarios and use those scenarios to avoid costly product quality issues, speed time to market, and increase throughput. This may sound exotic, but it is really just a modern twist on a very old idea — the scientific method. Build stochastic simulations, generate experiments, and use those experiments to minimize risk and innovate in the process. Tesla is an excellent example of this concept. Tesla has a digital twin of every VIN they manufacture. Data is constantly being transmitted back and forth from the car to the factory. If a driver has a rattle in a door, it can be fixed by downloading software to adjust the hydraulics of that particular door. Tesla regularly downloads software updates to their customers’ cars based on the data they are constantly receiving from each VIN. Future What-If Scenarios Real Manufacturing Innovation New Materials Product Design Innovations Process Innovations Manufacturing Process 101 1010101 Manufacturing Simulation f (x) Instrument the manufacturing equipment and feed our simulations streaming data Flaws Cost Performance Figure 1. Using the digital twin as a source of manufacturing insight 10 <strong>FORWARD</strong>: A CSC MAGAZINE
Prescriptive data, and pipelines Creating a digital twin starts with establishing new pipelines of manufacturing data. We can automate the collection, for example, of materials and design data. When integrated with historical operations performance data, we have the raw data required to support the creation of a digital twin. The next step is to take the manufacturing process and model it using rules. But instead of using the more common retrospective models, digital twin simulation uses prescriptive models. Retrospective models, like those commonly used in predictive modeling, try to calculate the future based on past trends. Models like that have been successful in some areas of manufacturing prediction, but they take us away from breakthrough innovation and keep us stuck in optimizing. Instead, we need to build stochastic simulations, or prescriptive models. We create rules for mapping from design to performance and add randomness to simulate risk. The prescriptive data from the simulations tells us how new products will work. We can detect design flaws early. We can predict and minimize cost. Because randomness is inherent in our models, we can simulate the kinds of uncertainty we encounter in the real world. Computer power is cheap — we can afford to run millions of scenarios. We can anticipate an entire spectrum of possible outcomes rather than just a single expected result. Continuous insights through IoT and industrial machine learning We can learn as much from the digital twin as we can from the real-world original. Internet of Things (IoT) technology allows us to augment the manufacturing process with sensors and automatically generate data about operations, performance and maintenance. If we use industrial machine learning to build and deploy, we can turn the streaming variant of the digital twin into a continuous source of manufacturing insight. 1 1 Access and Collect Data Ingest and Clean Agile Experimentation 10110 Scaled globally across the entire manufactuing process 2 3 Map to standard concepts and make insights repeatable Use experiments to produce reliable, measurable results Transform Manufacturing New Materials Product Design Innovations Process Innovations Generate Insights 4 Produce insights that can be distributed and used throughout the entire manufacturing process Figure 2. When deployed according to CSC’s Industrial Machine Learning, the digital twin becomes a continuous source of manufacturing insight Digital twin really sits in the continuum of the IoT. If we agree that the foundation of IoT is connectivity, sensors and analytics, predictive maintenance is an established IoT application. Predictive maintenance is case-based reasoning enabled by data for mitigation and repair. Digital twin incorporates product data from design to operation and beyond, including maintenance history. Harnessing all the data to enable a complete digital twin isn’t there yet. But examples and pilots showing the steps along the way are certainly relevant. <strong>FORWARD</strong>: A CSC MAGAZINE 11
Page 1 and 2: FORWARD Next-Generation Manufacturi
Page 3 and 4: Table of Contents 2 PUTTING IOT WIT
Page 5 and 6: “This is not just automating the
Page 7 and 8: 3. VR/AR With Facebook’s Mark Zuc
Page 9 and 10: Autonomous driving and electric car
Page 11: Q&A Under the Hood with Ford Roopak
Page 15 and 16: CSC at a Glance INDUSTRY EXPERTISE

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