We live and work in a world, where Big Data management tools, affordable cloud computing and storage, and AI machine-learning algorithms, have converged to provide us with a constant stream of analytical information. The challenge we face is decoding the data’s messages and finding effective ways of putting the learnings to good use.
Electronics manufacturers face the constant pressure of needing to introduce new, innovative, and durable products, at a faster pace, and sooner than their competitors. For many who have seen the value, and embraced the digitalization brought on by the Fourth Industrial Revolution, the deluge of information on hand is often overwhelming. The key to success lies in making sense of the information, knowing which data streams to pay closer attention to, and then implementing the learnings, to achieve improved productivity, increased efficiencies, decreased costs, and streamlined processes.
A growth market
Over the past two years, digital twins have increasingly been used to consolidate all this information through the creation of “living”, digital, simulation models that continuously learn, and update, from the real-world data that streams from the embedded sensors in their real-world double.
The market, which is being led by the Electronics and Electrical/Machine Manufacturing industry, is forecast to reach $15.66 billion by 2023 from $1.82 billion in 2016, at a CAGR of 37.87%, and is driven by the growing adoption of Internet of Things (IoT). The key players operating in the digital twin market include General Electric (US), IBM Corporation (US), Microsoft Corporation (US), Dassault Systèmes (France), and Siemens AG (Germany).
A valuable predictive tool
The IoT has made it easier, and less expensive, to create digital twins. Today, information is constantly streamed to the network-connected digital twins, from sensors embedded in the material twins, and design, simulation, and manufacturing and analytics software, help users to not only create, but also validate model-based digital twins of their products, and production operations.
The twins enable the analysis of changes in the performance, and or condition of a machine, possibly resulting from the temperature or moisture in the environment or failure of one of its components, all while in operation. It allows for real-time monitoring and troubleshooting enabling predictive maintenance of the product line.
Knowledge gained from simulations, which can now be run more frequently than in the past, where they often resulted in the costly destruction of the product prototype, allows for teams to better understand failure modes and results in design improvements. Using twins is valuable in setting the performance of the product for its lifecycle, and helps to optimize the process and reduce operational and manufacturing costs, and allows predictive methods to be used.
Applying best practice
A digital twin makes sense of what can, at times feel like, data sensory overload, and allows for insight-backed action, resulting in better products, new product development, predictive maintenance, optimized production operations, and the development of new operational business models.
When it comes to best practice for developing the twins, Gartner recommends that digital twin investments should be made value-chain-driven, to enable product and asset stakeholders to govern and manage products, or assets across their supply chain, in much more structured and holistic ways. They also encourage the establishment of well-documented practices for constructing and modifying the models, as this increases transparency, and allows users to collaboratively construct and modify digital twins. As it is difficult to anticipate the nature of the simulation models, data types and data analysis of sensor data that might be necessary to support the design, introduction and service life of the digital twins’ physical counterparts, supports defining an architecture that allows access, and use of data from many different sources, by IT architects and digital twin owners.
Improving future performance
As digital transformation enables new smart products and smart manufacturing, digital twins provide the opportunity for teams across all disciplines to access detailed information that can help them to develop, and evaluate opportunities and predict the performance of products, and factories.
This can only be achieved through a consistent digital thread, fed by the stream of data from the latest IoT connected sensors, monitoring devices, and quality intelligence solutions that allow for the simultaneous harvest and analyses of the huge quantities of data available from each stage of the production environment, across different sites. Electrical manufacturers who embrace the digital revolution, and make use of technologies such as digital twins, will be able to speed up development, optimize manufacturing, reduce costs and wastage, and use the insights gained to improve future performance.
SYSPRO’s in-house industry experts keep a close eye on the developments in technology spurred on by Fourth Industrial Revolution. Our innovative, electronic manufacturing ERP solutions are carefully crafted to accommodate, anticipate and embrace this revolution’s technological advancements to ensure our customers stay ahead of their competitors.