If You Build Products, You Should Be Using Digital Twins

If You Build Products, You Should Be Using Digital Twins

Digital twin technology is one of the fastest growing concepts of Industry 4.0. In the simplest terms, a digital twin is a virtual replica of a real-world object that is run in a simulation environment to test its performance and efficacy.

According to Gartner, 13% of organizations implementing IoT projects already use digital twins, while 62% are either in the process of establishing digital twin use or plan to do so. The digital twin market is estimated to grow from $3.8 billion in 2019 to $35.8 billion by 2025, at a CAGR of 37.8%, according to the latest report from MarketsandMarkets.

Growth in IoT and cloud—and the goal to cut down costs and reduce time for product development—are the key factors driving growth in the digital twin market. IoT now enables engineers to test and communicate with sensors that are integrated within a company’s operating products, delivering real-time insights about the system’s functionality and ensuring timely maintenance.

While digital twin technology is already applied in various industries, it’s crucial for product manufacturers. Let’s take a look at the benefits of using a digital twin model, what you should consider before adopting one, and a real-world example of how GlobalLogic deployed digital twins for a leading warehouse automation company.

The Benefits of Using Digital Twins

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.

Things to Consider Before Implementing Digital Twins

1) Update your data security protocols

According to Gartner’s estimation, 75% of the digital twins for IoT-connected OEM products will utilize at least five different kinds of integration endpoints by 2023. The amount of data collected from these numerous endpoints is huge, and each of the endpoints represents a potential area of security vulnerability. Therefore, companies should assess and update their security protocols before adopting digital twin technology. The areas of highest security importance include:

  • Data encryption
  • Access privileges, including a clear definition of user roles
  • Least privilege principles
  • Addressing known device vulnerabilities
  • Routine security audits

 

2) Manage your data quality

Digital twin models depend on the data from thousands of remote sensors that communicate over unreliable networks. Companies that want to implement digital twin technology must be able to exclude bad data and manage gaps in the data streams.

3) Train your team

Users of digital twin technology must adopt new ways of working, which can potentially lead to problems in building new technical capabilities. Companies need to make sure that their staff has the required skills and tools to work with digital twin models.

GlobalLogic Case Study: Virtual Automated Warehouse Management Solution

The Challenge

Our client, a leading warehouse automation company, was experiencing challenges in testing new releases of different modules and microservices. The company had to stop its production warehouse operations for several hours to test new software releases, which led to losses in productivity. The client also found it difficult to model and test new warehouse structures to find the most effective and productive setups.

The Solution

GlobalLogic developed a new testing platform that used production deployment scripts, which enabled the client to test any combination of modules and microservices on a dynamic environment. We also designed a warehouse modeling solution that allowed the client to visually create a warehouse structure with all the required technical specifications. Finally, we created a documentation solution to review the client’s software stack landscape. To deliver the digital twin model, we implemented the following technologies:

  • Warehouse physical structure modelling (e.g., levels, shelves, inbound/outbound cells, lifts, etc.), including visualization
  • Hardware emulation on a messaging level
  • Fully automated system deployment on a virtual infrastructure, which is created on-the-fly and released after work is completed

These different components work together to emulate the whole warehouse operation, which in turn is used to evaluate performance levels and make informed decisions about how to implement real production warehouses.

The Outcomes

GlobalLogic’s digital twin solution enabled the client to iteratively test thousands of warehouse structure options automatically in order to find the most effective production model. In addition to now being able to implement a system efficiency check before actual installation, the client realized the following benefits:

  • 20% faster time-to-market due to the simplified and automated warehouse design tool (creating a warehouse structure decreased from 12 weeks to 2 hours)
  • 85% reduction in the labor force required for structure design processes
  • 10x increase in testing intensity due to digital twins
Conclusion

Digital twin technology — combined with the latest machine learning and artificial intelligence tools — is helping companies across many industries reduce operational costs, increase productivity, improve performance, and change the way predictive maintenance is done. For product manufacturers in particular, digital twin technology is crucial to achieving more efficient production lines and faster time-to-market.
Digital twin technology — combined with the latest machine learning and artificial intelligence tools — is helping companies across many industries reduce operational costs, increase productivity, improve performance, and change the way predictive maintenance is done. For product manufacturers in particular, digital twin technology is crucial to achieving more efficient production lines and faster time-to-market.

References:
  1. https://www.marketsandmarkets.com/Market-Reports/digital-twin-market-225269522.html
  2. https://www.gartner.com/en/newsroom/press-releases/2019-02-20-gartner-survey-reveals-digital-twins-are-entering-mai
  3. https://www.gartner.com/doc/3873175
Yana Arnautova

Author

Yana Arnautova

View all Articles