What is a Digital Twin?

The classic definition of a digital twin is: “A digital twin is a virtual model designed to accurately reflect a physical object.” — IBM

The digital twin collects system and process data with the help of various IoT sensors (operating technology data (OT)) and enterprise data (information technology (IT)) in an intermittent or continuous production process. It then processes this data to create a virtual model that will be used to run simulations, study performance issues, and generate possible intuitions.

The concept of a digital twin is not new. In essence, it is reported that the first application was carried out more than twenty-five years ago, for the London Heathrow Express facilities, in order to monitor and predict base drilling injection, in the early stages of foundation and dam construction. Over the years after this initial implementation, advancements in edge computing, artificial intelligence (AI), data connectivity, 5G connectivity, and Internet of Things (IoT) have made digital twins cost-effective and are now a must in today's data-driven businesses.

Digital twins are now so entrenched in the manufacturing field that the global industry market is expected to reach $48 Billion by 2026. This figure was $3.1 Billion in 2020, with a 58% annual compound growth rate (CAGR) taking the wind back of Industry 4.0.

Today's manufacturing industries are expected to streamline and optimize all processes in value chains through product development and design, processes, and supply chain optimization, to respond quickly to rapidly increasing demands, from customer feedback. The category of digital twins is wide-ranging and offers solutions to many challenges in the fields of production, logistics and transportation.

Here are some of the challenges that the manufacturing industry faces the most and that digital twins can offer solutions:

• The complexity of product design processes, high cost and increasingly long development times,
• Lack of transparency of the supply chain
• Lack of optimization of production lines — performance changes, unknown defects and unpredictability of operating costs
• Poor quality management — relying too much on theory, management by separate departments,
• Long downtime or process interruptions due to high reactive maintenance costs
• Inappropriate collaborations between departments
• Unable to receive real-time feedback from the customer

Why is the Digital Twin important?

Industry 4.0 and Smart Supply Chain have made significant improvements in improving processes and establishing an agile supply chain — but without digital twin technology, these improvements could translate into very high cost processes.

What Are the Benefits of Digital Twins? (Manufacturing Sector)

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• Product design and development are completed at less cost and in less time because repeated simulations offer the best or optimized design using multiple constraints — all commercial aircraft are designed using digital twins.
• Digital twins provide us with an awareness of how long inventory will take, when to replenish, and how to minimize supply chain disruptions — fuel and gas industry supply chain-driven digital twin to reduce supply chain bottlenecks during storage and in the middle of delivery, plan tanker unloading operations, and model demand for externalities They use it.
• Consistent quality control of the materials produced with ML/AI feedback primarily improves product quality — the final paint check performed in the automotive industry is carried out with a computer image installed on top of digital twin technology.
• By finding the most effective point between when to replace a part and using the components in full before the process deteriorates or fails, the digital twin provides real-time feedback — digital twins are the backbone of setting up an asset performance management program.
• Digital twins provide the necessary instructions in modules to achieve the required work/product volume, creating the opportunity to synchronize multiple departments — digital twins are the backbone of recording events that optimize production process flow.
• Customer behavior in sales creates a closed loop that enables an advanced product design, from purchasing preferences or product performance to integration into the product development process.

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