Digital Twin is a new concept in supply chain management that has gained popularity over the past few years. Digital Twin technology creates a virtual model of a physical product by digitizing its design, manufacture, and distribution process. This allows businesses to collect data about their products and use it for predictive analytics to improve efficiency, reduce costs, and cut down on waste. Read this blog post to learn more about Digital Twin basics before you start Designing a Supply chain network!
What is a digital twin, and how does it work?
A digital twin is a virtual replica of an existing physical entity, such as a machine or product. It can be used to predict changes that will occur in the future and also help with decision-making processes by providing insights into how those changes will impact performance metrics like supply chain cost per unit shipped, customer satisfaction ratings, etc.
The digital twin works by taking measurements from the physical entity and generating a virtual model. The data is collected via sensors installed on or around the object being monitored to record changes over time. These sensors collect information like temperature fluctuations, system efficiency levels, amounts of wear-and-tear damage done throughout its lifespan, etc. This can be used to produce insights into how that change will affect performance metrics later on down the line. This process enables better decision making about product design and life cycle management for companies looking at long term results instead of short term gains.
Benefits of using a digital twin in supply chain management include:
- Improved decision making by using data from the digital twin to inform decisions about product design, life cycle management and service level agreements.
- Reduced time required for new product launch as a result of faster prototyping with the use of virtual models that can be tested without having actually to build physical prototypes first. This means more efficient workflows are freeing up resources for other projects instead of spending time building components only to find out they don't meet specifications. In addition, working off a virtual model reduces waste since prototype iterations are not needed before finalizing designs/products, which would have been scrapped otherwise due to being non-compliant or unusable in some way.
- Increased reliability via early fault detection resulting from integrated sensors on products connected through IoT devices, which in turn improves serviceability by allowing problems to be detected sooner.
- Reduced risk of disruption to operations caused by accident or system failure because the digital twin can provide real-time data about how changes will impact performance metrics like availability and reliability before they happen.
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