Digital twins in the supply chain

What is the impact of Digital Twin in Supply Chain Management?

“A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.”  

A digital twin is a digital representation of a physical object, process or service. It can be a digital replica of an object in the physical world, such as a jet engine or wind farms, or even larger items such as buildings or even whole cities.

 As well as physical assets, the digital twin technology can be used to replicate processes in order to collect data to predict how they will perform.

A digital twin is, in essence, a computer program that uses real world data to create simulations that can predict how a product or process will perform. These programs can integrate the internet of things (Industry 4.0), artificial intelligence and software analytics to enhance the output.

Digital twins work closely alongside other high-level technology such as artificial intelligence, machine learning and IoT (Internet of Things). As everything is digital, IoT plays an important role in keeping all systems connected with data sensors that fuel the digital twins with information in order to run the simulation.

Benefits of Digital Twin

1) Accelerated risk assessment and production time

With the help of a digital twin, companies can test and validate a product before it even exists in the real world. By creating a replica of the planned production process, a digital twin enables engineers to identify any process failures before the product goes into production. Engineers can disrupt the system to synthesize unexpected scenarios, examine the system’s reaction, and identify corresponding mitigation strategies. This new capability improves risk assessment, accelerates the development of new products, and enhances the production line’s reliability.

2) Predictive maintenance

Since a digital twin system’s IoT sensors generate big data in real-time, businesses can analyze their data to proactively identify any problems within the system. This ability enables businesses to more accurately schedule predictive maintenance, thus improving production line efficiency and lowering maintenance costs.

3) Real-time remote monitoring

It is often very difficult or even impossible to get a real-time, in-depth view of a large physical system. However, a digital twin can be accessed anywhere, enabling users to monitor and control the system performance remotely.

4) Better team collaboration

Process automation and 24×7 access to system information allows technicians to focus more on inter-team collaboration, which leads to improved productivity and operational efficiency.

5) Better financial decision-making

A virtual representation of a physical object has the ability to integrate financial data, such as the cost of materials and labor. The availability of a large amount of real-time data and advanced analytics enables businesses to make better and faster decisions about whether or not adjustments to a manufacturing value chain are financially sound.

Industries used Digital Twin

• Manufacturing: Real-time analysis of the functioning of machinery using their digital twin helps reduce maintenance costs and optimize production output. General Electric Co. had over 1.2 million digital twins as of September 2018.
• Automobile: Virtual replicas of vehicles collect behavioral and operational data of their physical counterparts to help analyze and improve vehicle performance. They also assist in developing and testing new product models, including hybrid and electric vehicles.
• Construction: Digital twin models of buildings collect real-time data about the structure via sensors and other wireless technologies. They help improve the design and quality of construction projects as well as reduce maintenance costs.
• Aerospace and defense: Digital twins are used for improving product designs and performance. The National Aeronautics and Space Administration (NASA) uses digital twins to operate, maintain, and repair systems located in outer space. The U.S. military uses digital twins to validate the integrity of the chips and semiconductors used in its weaponry.
• Sports: Sensors and analytics help create virtual representations of athletes and simulate game situations to identify most likely injuries and improve performance. Formula One racing teams use digital twins to analyze adjustments that can improve the performance of their cars.
• Healthcare: The digital twin of a patient or an organ allows doctors to practice procedures in a simulated environment. Digital twins are also used to conduct virtual clinical trials before rolling out new drugs or vaccines.

 Digital twins in the supply chain

 Digital twins are implemented as a new infrastructure, frequently in the cloud, existing adjacent to the company’s operational infrastructure and attempting to be a near-real-time digital representation of the state of the operation. The digital twin stays in synchronization by continually obtaining “sensor” readings at various points along the operating topology and enriching these sensor readings with other data sources. Digital twin systems help create models of physical environments and commonly use spatial graphs to model and visualize relationships and interactions between people, locations, and sensors. They also come pre-packed with common machine learning techniques for analysis, and the ability to accept business logic code. In other words, digital twins are a convergence of the Internet of things (IoT), artificial intelligence (AI), and business analytics.