Digital Twin

The Digital Twin for Industry 4.0

The concept of the “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 as it has expanded to relevance in other industries, the concept has remained unchanged and focused around simulation. The Digital Twin 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 real-world physical system, and the two would remain tightly coupled throughout the lifecycle and evolve together. Therefore, the definition of the 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. Though different implementation scales of the Digital Twin indicate various levels of complexity, the main concepts, features and challenges remain the same. In a nutshell, the Digital Twin acts as a near-real-time digital image of a physical system that helps experts and organizations understand how that system works and how it responds to certain inputs or constraints, thus facilitating the monitoring and optimization of that system in the real world.

Our Simulation & Decision Science Practice

Simulation modeling, the means by which we are able to create a Digital Twin, is ubiquitous in most engineering and industrial organizations, and has been for many years. Utilizing technology to imitate a real-world process or system enables manufacturers to study that process or system within a risk-free, controlled and repeatable digital environment. For example, an aircraft manufacturer will perform simulations on the air flow and drag around their aircraft during the design process. The results will be used to either validate the design or identify design changes needed to reduce drag. But while simulation is well-established during product design and verification, industrial organizations typically do not employ this powerful methodology in the next stages of their value chain: the manufacturing and supply chain of the product. As a result, these organizations lose opportunities to study the behavior of their manufacturing processes and supply chain systems before they are deployed. Since commissioning of new manufacturing facilities, production lines and processes is often costly and capital-intensive, applying simulation methods to manufacturing and supply chain can yield enormous benefits.

Engineering Industries eXcellence is proud to boast one of the few global technology practices dedicated to industrial simulation and decision science. Our team of simulation experts and data scientists specialize in applying simulation methods to product design, manufacturing and supply chain processes. Our consultants help organizations identify opportunities in order to strategically employ simulation to study their processes and systems, giving teams the information and tools needed to drive real improvements.

Applying Simulation Modeling for Industry 4.0

1. Manufacturing Simulation Modeling

How can we find improvements and gain efficiencies in our shop floor processes?
How can we demonstrate to management that new equipment is worth the investment?
Corporate is telling us to increase production line speeds. How can we be sure that this will ultimately help increase throughput and yield?

Before making any changes on the actual production floor, we can leverage simulation to model our manufacturing plant and layout. There are two main reasons why we would want to build a virtual twin of a facility and run simulations against it: First, to better understand how a production plant is currently performing. A facility may be running, but due to the complexities of the processes, number of variables and constraints in the system, it is very difficult to comprehend all the moving parts. How can you determine true capacity, identify bottlenecks and generate reliable Key Performance Indicators (KPIs)? Running a simulation of a plant allows you to discover these crucial factors and identify existing issues as well opportunities to increase throughput.

Second, to run “what-if” scenarios. These scenarios allow us to make virtual changes to a plant in terms of layout and workflows, then evaluate those changes. Due to the time, effort, and costs involved in making real-world changes, it is critical that the changes being made are correct for the facility and will meet the desired outcome. This is much faster, cheaper and easier to do virtually using simulation.

2. Robotic & Human Ergonomics Simulation Modeling

How can we minimize the ramp-up time for operators when introducing a new line of products in the plant?
How will this new modification of the process impact safety?
Management wants to minimize the multisite rollout costs of new robotic operations. How can we anticipate where we will find most of the issues?

Simulation of manufacturing processes and operations can help organizations understand and prevent any design, safety and/or ergonomic issues during the commissioning of new products, plants and processes. Process simulation models provide an advanced 3D environment capable of emulating realistic behavior of manufacturing processes to drive the optimization of cycle times and process sequence. They also enable the simulation of assembly processes, human operations and mechanical procedures and behavior of tools, devices and robots.

Can we feasibly build the product based on our design? Will it meet cycle time requirements that we are expecting? All of these questions can be answered by running simulations of the process within a simulation tool. The process simulation can take many factors into consideration, covering both the manual and automated constraints and participants. Modeling humans, robots, conveyors, tools and other typical workflow elements can help manufacturers to truly understand their process and identify the best ways to make real improvements.

3. Supply Chain Simulation Modeling

What are the best locations for warehouses, distribution centers and production sites?
What are the best policies for replenishment, sourcing and transportation?
How stable and reliable is our supply chain?
What if we change inventory policy?
What if we increase the capacity of a distribution center?
We took over a competitor. What is the most cost-effective way to join our supply chains?

When making decisions, global manufacturers have a number of options, and simulation modeling can enable them to study and accurately understand the risks and benefits that each option would result in for both their operations, customers and business performance. System dynamics modeling, discrete event simulation and agent-based simulation methodologies can all be used to help enterprise organizations study and optimize their regional and global supply chain and logistical networks. The right simulation technologies can reliably support management in calculating, identifying and making less risky, more profitable supply chain design and management choices.

Simulation models are very useful in determining the impact of supply and demand variability, network constraints and bottlenecks on the efficient operation of a supply chain. While analytical tools allow you to better understand and handle large-scale problems, simulation tools enable you to isolate and manipulate specific factors and aspects of a supply chain to understand how they can impact downstream processes or the dynamics of the network as a whole. The best approach is to leverage both these techniques together. In this way, it is possible to identify a solution, then check its quality and validity by simulating the resulting scenario and seeing how it would impact other nodes along the supply chain network.