How can a Digital Twin Help Current Production Performance?

This article will discuss:

• The importance of digital twins in achieving the digital transformation needed to implement Industrie 4.0 business models
• How the digital twin supports data analytics and Industrie 4.0 growth across the industrial sector
• How the digital twin enables optimized performances within industrial facilities

Gartner 2020 Technology Trends list it as one of the pivotal tools that enterprises will employ to increase productivity. The World Economic Forum expects it to drive Industrie 4.0 growth in complex systems. PWC says it’s a game changer for real-time analysis. This is the digital twin, a best-in-class, intelligent, object-based, digital environment that supports real-time industrial processes.

The digital twin in the context of Industrie 4.0

Industrie 4.0 encompasses the diverse technologies and solutions required to achieve the proverbial “lights out” factory in which human presence is drastically reduced and safety in the workplace is optimized. The process of implementing Industrie 4.0 business models starts with data collection, analyzing captured data, and gaining insight or taking specific actions. Data capturing and analytical tools such as edge computing hardware and IoT solutions are constantly deployed within facilities to implement diverse data-driven policies. The digital twin brings all the data captured by these technologies under one virtual environment to mirror the exact and current operational status of an industrial facility.

With a digital twin, an enterprise gets a virtual mirror of its factory operations in real-time. Data-capturing tools feed the digital twin with the real-time status of shop-floor operations. The digital twin can then be leveraged in diverse ways to improve current production performances and drive Industrie 4.0 growth. The digital twin also supports the interconnectivity of systems, which is a major feature of Industrie 4.0. It enables the transfer of data from industrial data-capturing tools, equipment, and systems, as well as supporting the sending of data packets to hardware with receiving capabilities which drive industrial automation.

How digital twins support data analytics and Industrie 4.0 growth

The digital twin also serves as a data-analysis tool as it provides a platform for executing simulation and scheduling evaluations to optimize factory operations. It is important to note that digital twins of any facility process can be created to analyze the functions of that specific process or system. According to Gartner, enterprises will run multiple digital twins concurrently to optimize specific operational systems. One example is the development of a digital twin for an automated material handling system within an industrial facility.

A digital twin of a material handling system collects data relating to the movement pattern, distance, and activity time frames of individual robots or carts that make up the system. Using this data, a digital model of both individual cart and facility-wide operations can be created. Thus, enterprises can understand and evaluate the performance of individual agents, as well as that of the entire material handling system.

A digital twin of a material handling system can then run with this data to develop a virtual environment. The virtual environment will then remotely monitor these assets or create optimized schedules to ensure materials are delivered on time, which is a subset of the data-driven plant performance optimization business model that leads to Industrie 4.0 growth.

How the digital twin helps current production performances

The example of optimizing schedules is just one aspect showcasing the application of the digital twin in improving production performances. Advanced simulation and scheduling software can evaluate specific operations and develop optimized schedules – activity which leads to popular misconceptions concerning the digital twin's similarity to simulation modeling. Unlike simulation software, the digital twin boasts two very important factors that distinguish it:

1. Its ability to map out and improve the current status of a production process, and
2. The interconnectivity it supports.

The current status of a factory refers to its equipment’s working cycles and operational timelines. Unlike simulation software, which relies on historic or already captured data, data-capturing solutions feed the digital twin with information concerning the current status of the factory. Near real-time optimization decisions can be taken as long as accurate data is constantly fed to the digital twin. Two important ways that the digital twin both improves production performance and Industrie 4.0 growth include:

• Democratizing factory-floor data – One of the major challenges that factory owners face with implementing Industrie 4.0 business models is deciding how to put the data they collect to work. The big data sets coming from the average factory floor can overwhelm anyone who isn’t a trained data analyst. The digital twin takes this data and creates a virtual model of physical processes, thus linking specific data sets to equipment and processes so that everyone involved with the production process can understand.
  
  The enhanced virtualization of factory-floor data simplifies Industrie 4.0 strategy sessions for both technical and non-technical operators. The ability to transmit results from the digital twin to smart devices, web-based HMIs, and smartphones also eases access to factory-floor data while supporting remote monitoring initiatives.

• Improves virtual testing and validation processes – Virtual testing for process validation is an important Industrie 4.0 concept as it reduces waste, eliminates downtime, and educates factory-floor operators about new initiatives. The digital twin is being positioned as the ultimate validation tool due to its ability to map out factory operations in real-time. Industrial enterprises can apply the digital twin to evaluate strategies before implementation, train new operators, or troubleshoot issues related to decreased throughput.

The virtualized environment that the digital twin provides and the high accuracy at which it mirrors physical operations makes it a best-in-class validation tool because the effects of constraints can be viewed and properly analyzed without the limitations that only numbers provide.

Conclusion

The ability of the digital twin to advance Industrie 4.0 growth is why 62% of enterprises that have implemented IoT strategies and other digital transformation technologies intend to leverage the digital twin to optimize operational processes by 2022. Leveraging the digital twin to support Industrie 4.0 business models is also expected to lead to a 10% increase in operational efficiency. In light of these figures, factory owners must consider leveraging the digital twin to enhance specific Industrie 4.0 applications and to remain competitive.