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:
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.
Regresyon, istatistiksel modelleme ve veri analizi süreçlerinde bağımlı bir değişken (sonuç) ile bir veya daha fazla bağımsız değişken (girdi) arasındaki ilişkiyi inceleyen bir tekniktir.
It can be defined in the form of enterprise marketing technology that provides contextually relevant experiences, value and benefit at an appropriate moment in the customer's lifecycle through preferred customer touchpoints.
Algorithm is mathematical logic or a set of rules used to perform calculations.
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