The Digital Twin with Engineering
“We are conscious that the adoption of new digital technologies is transforming processes and business models in every industry, evolving the existing competitive scenarios or creating new ones. The best way to study the impact of new technologies is to employ simulation models that enable you to study their effects in complex contexts and monitor decisions over time. We are ready to face this challenge, and, thanks to the power of the Digital Twin, we are ready to offer our customers a cost-effective virtual training environment where they can study and make decisions without putting their operations or business at risk.” - Alfredo Belsito, General Director Industry, Services and Infrastructures, Engineering Group
A Few Words
Emerging technologies such as IoT, AI and advanced modeling are enabling an intelligent, connected and digitally empowered mesh of people, things and services which outline a definitive concept, the Digital Twin, for today’s business. The Digital Twin has moved beyond the manufacturing sector, its traditional ecosystem, into all sorts of service and goods-based businesses ranging from automotive to healthcare. And, it is so imperative to business today, that it has continuously been named within Gartner’s Top 10 Strategic Technology Trends for the past couple of years. In this whitepaper, we dispel some of the myths surrounding the Digital Twin and answer your biggest questions: What is it? Why does it matter? What are the benefits and the challenges? What are its building blocks? After reading this white paper, you will have a clearer and more transparent understanding of the Digital Twin and its applications, and will be able to better define your own strategy for the Digital Twin.
What Is It?
Enterprises are becoming increasingly digital. Even though this process holds great promise for delivering value, many companies and organizations struggle to realize this potential at operational and strategic levels. The concept of “Digital Twin” was originally formed in 2002 at the University of Michigan in the context of Product Lifecycle Management (PLM). Though the terminology has changed over time (e.g. “3S” standing for Sensors, Software and Services), the concept has remained fairly stable. It is based on the idea that a digital information construct of a physical system could be created as an entity of its own. This digital information would be a “twin” of the information that was embedded within the physical system. The two remain tightly coupled through the entire lifecycle and evolve together. Therefore, the definition of Digital Twin can be given as a set of virtual information constructs that fully describes a potential or actual physical product, system or process from the micro to the macro level. At its optimum, any information that could be obtained from the study of a physical asset should be able to be obtained from its Digital Twin. Digital Twins can be implemented at different scales:
- Micro/Machine Level: CNC machine, gas turbine, engine, etc.
- Meso/Factory Level: assembly line, power plant, tanker, etc.
- Macro/Organizational Level: supply chain, electricity delivery network, transport fleet, etc.
Though different implementation scales of the Digital Twin indicate various levels of complexity, the main concepts, features and challenges remain almost unchanged. In a nutshell, the Digital Twin can be described as a near-real-time digital image of a physical system that helps facilitate the monitoring and optimization of business performance.
Why Does It Matter?
The future development of industry and society is exhibiting significantly increasing complexity and, at the same time, ever-shorter innovation cycles. Managing uncertainty is also becoming a daily struggle in many industries. Given this context, companies are starting to rely on gathering as much information as possible about their business to preserve a competitive advantage. The recent spread of digitalization has highly enhanced data collection capabilities at an organizational level. On the one hand, this breakthrough has helped make up the deficit of information necessary to face these issues. But on the other hand, it has posed new challenges for mining data’s potential value. Industry 4.0 embraces this digitalization and provides a vision of an interconnected factory where equipment is online, intelligent and capable of collaborating in a vertically integrated fashion. Industry 4.0 describes a digital environment that collects and combines data from different sources and business functions to create an orchestrated enterprise that communicates, analyzes and uses this information to drive intelligent action back into the physical world. The Digital Twin is the embodiment of this concept, impacting the business ecosystem on different fronts: technical, information, business processes and business competitiveness.
