Smart manufacturing: How digital twins optimizes manufacturing efficiency
In the next few years, the digital twin solution for manufacturing is expected to grow rapidly, driven by the need for improved productivity and lower maintenance costs.
From 2022 to 2027, the smart manufacturing market by enabling technologies for digital twins is expected to grow at a CAGR of 68.9%, from USD 2,124.7 million in 2021 to USD 43,614.8 million in 2027.
Smart manufacturing: Digital twin
Digital twins were first coined in 2002 by Michael Grieves to describe a new way to coordinate product lifecycle management. As a result of limitations in integrating processes and data across engineering, manufacturing, and quality teams, the concept stumbled along for many years.
Digital twins enable linkage between product production and its performance. Manufacturing companies are using digital twin technologies to accelerate digital transformation and smart manufacturing initiatives. As data integration, artificial intelligence, and the internet of things improve, digital transformation efforts are becoming more tangible, resulting in greater benefits.
Improve collaboration between teams by using digital twins for manufacturing
In addition to improving collaboration and workflow across different types of manufacturing teams, digital twins can improve engineering disciplines and product design teams as well. A well-executed strategy can yield fantastic results.
A great example is the Aerospace digital transformation. The U.S. Airforce has made extensive use of digital twins to design and build a new aircraft prototype in a little over a year, when this process would traditionally last decades.
Improving manufacturing efficiency by integrating different data sources into one database-Digital twin
In other industries, digital twins for manufacturing improves efficiency, lowering manufacturing costs saving millions of dollars.
The digital twin allows manufacturing teams to analyze different types of data sources, minimizing faulty units. Industries are able to reduce manufacturing downtime and increase productivity. The concept is also used to predict maintenance problems more quickly.
Data Collection – Gathering different types of data to a unified database.
Despite all the gains offered by the digital twin concept, many of these successes have been within a limited domain constrained by the technology platforms or systems integrators.
QualityLine’s AI manufacturing Analytics and automated data integration maximizes manufacturing efficiency and product quality as it continuously collects the entire manufacturing data into a unified digital twin database. Data sources from multiple global locations are then harmonized and analyzed in real time.
By ensuring that you incorporate all of your manufacturing data into the formula, you will be able to achieve a correlation between the data that will provide the most accurate insights for identifying and resolving issues.