Digital Twin Implementation for Industrial Automation
As industries are undergoing a digital revolution, digital twin technologies are introducing new and innovative ways for product designing, operations, and post-sale services. Digital twins combine IoT, artificial intelligence, machine learning, and deep learning to provide a rich and real-time view of an organization’s processes and assets. Digital twin deployments are mainly driven by safety, sustainability, and reliability. A recent study found that organizations working on digital twins have experienced a 15% improvement in sales, turnaround time, and operational efficiency. Energy and utilities and the consumer product industries are leading the way for the use case of digital twins to accelerate their journey towards intelligent operations.
GE’s Digital Farm solution collects and analyses data on the wind resources at the unit and site level to determine the best turbine configurations, which enable predictive maintenance and operations optimization. AspenTech leverages digital twin technology for supply chain planning, effective maintenance scheduling, and solutions. According to a recent survey, 13% of organizations implementing IoT projects are already utilizing digital twins, while 62% are planning to establish digital twins.
Here are three ways digital twin are accelerating industrial automation.
Faster Product Development
To stay ahead of the competition, OEMs are in a perennial race to devise and launching new products. The speed of product development is only increasing with the arrival of industrial 4.0. Digital twins are enabling industry manufacturers to deconstruct and optimize manufacturing processes, adopting virtual prototyping. Implementation of digital twin has helped to reduce their time to market as digital models are easier to conceive than creating a physical prototype.
Enabling Predictive Maintenance
Predictive maintenance is expected to account for 24.78% of the global digital twin market by 2027, owing to end-user industries’ greater adoption of digital twin. Predictive maintenance in the industrial environment can be a game-changer. Digital twin technology enables collecting data in real-time through sensors for conditional monitoring machines against the historical data. Predictive digital twin models generate a pattern to help predict failure and make data-driven decisions. Leveraging digital twins, operators can analyze various components, detect issues, and identify the quality of the product being developed.
Strengthening Supply Chain
Utilizing digital twins, the organization can recreate strategies to meet customer expectations at minimal cost, everything from shipping to trucking, inventory within warehouses and distribution centers, and last-mile delivery of products. Supply chain twins can also facilitate planning, scheduling, operations, and distribution, bringing all the data together for strategic planning.
Better Process Optimization
Processes are becoming more complex and, therefore, less efficient and more expensive. A digital twin can help identify inefficiencies, understand bottlenecks, and model the outcome of the specific targeted improvement interventions. Eliminating certain steps in the manufacturing production line, improving the utility of a product, and minimizing packaging handling can result in greater efficiency, productivity, and capital utilization.
According to TechSci Research report on “Digital Twin Market – Global Industry Size, Share, Trends, Competition, Opportunity and Forecast, 2017-2027 Segmented By Type (Process, Product, System), By Technology (Internet of Things, Artificial Intelligence & Machine Learning, Extended Reality, Blockchain, Big Data Analytics, 5G), By Application (Manufacturing Process Planning, Product Design, Predictive Maintenance, Others), By End User (Manufacturing, Automobile and Transportation, Healthcare and Lifesciences, Aerospace and Defence, Energy & Utilities, Others), By Region”, the global digital twin market is expected to grow at a CAGR of 32.34% by 2027. The market growth can be attributed to the rising penetration of smart technologies coupled with increasing adoption of 5G technologies.
