At Configit, we are often asked how Configuration Lifecycle Management (CLM) is used to support Digital Twins. Configuration Lifecycle Management can bring these same values and benefits, if not more, to Digital Twins in the manufacturing environment. But to understand how Configuration Lifecycle Management brings value, we need to understand what constitutes a Digital Twin, how it is implemented and the value it brings.
While some use the terms ‘virtual prototypes’ and ‘digital twin’ interchangeably, there are significant differences between the two.
A virtual prototype is a digital representation of a product, service, process or organization that has not yet been implemented in reality. While the virtual prototype can be extremely detailed and intelligent with the ability to model behavior under various conditions and stimuli, it is only when there is a physical instantiation of the virtual prototype, meaning a real-world representation, that it becomes a Digital Twin.
This may seem obvious to many, but the differences between the two are not simply the difference between virtual and physical. The value extracted from each also differs greatly. For a Digital Twin to provide value, it must provide an exact virtual representation of a specific physical entity where the evolution of both the physical and virtual representations occur simultaneously. In other words, as the physical entity ages, adapts or is influenced by external stimuli, this must be reflected in the Digital Twin.
Because the Digital Twin and its physical counterpart have a shared lifecycle and evolution, the Digital Twin can be used for analysis with the knowledge that it fully reflects the behavior and current status of the physical counterpart. Operations that in the past might have required a visit by technicians in the field, such as examining a physical product or system, can now be performed on the Digital Twin. Production lines can be analyzed using the Digital Twin without the need to take the physical production line out of operation.
Since Digital Twins can represent a wide variety of entities, there are multiple ways in which they can be implemented. From a manufacturing perspective, Product Lifecycle Management (PLM) vendors already provide support for building Digital Twins in their products. But Digital Twins can be implemented on any platform and need not necessarily be an exact replica of the physical counterpart.
The most important criterium for a Digital Twin implementation is that it reflects the behavior or status of how the physical counterpart is actually behaving. A Digital Twin can be as simple as a set of critical variables that are continuously monitored and analyzed. As long as the data and analysis perfectly reflect the behavior of the physical counterpart, then this is sufficient. Of course, what makes a Digital Twin “sufficient” depends on the objectives for building it in the first place. The more complex the behavior that needs to be monitored, the more complex the implementation of the Digital Twin.
In manufacturing, Digital Twins are most attractive for measuring the behavior of equipment in the factory and manufacturing processes as a whole. Each individual piece of equipment will have a discrete Digital Twin representation, while a composite Digital Twin (composed of all the discrete Digital Twins) can represent the behavior of the manufacturing line as a whole. Hardware and software sensors on each piece of equipment gather continuous data on what each piece of equipment is experiencing, which is used as input to the Digital Twin models. This allows analysis to be performed on how well the process is performing under different conditions, as well as a forecast of the Mean Time to Failure of each piece of equipment.
Today, most manufacturers need to build the Digital Twins themselves using enabling technology from vendors, including Product Lifecycle Management. There are also Digital Twin templates available from specialized vendors to provide a head-start. However, the next logical step in the evolution of Digital Twins is that each product manufactured is delivered together with a discrete Digital Twin representation.
Because Configuration Lifecycle Management provides a single source of truth on all valid, potential and available combinations of product components and options, it plays a key role in the design, manufacturing, sales and service of the product. When this information is shared with existing systems, including Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM), the entire organization operates from the same data, thus eliminating errors due to manual entries, data handover, multiple configuration data sources, and overlapping versions.
Manufacturers wanting to build a Digital Twin representation of each product delivered need access to the same, real-time configuration information. Since Configuration Lifecycle Management solutions are designed with open interfaces allowing integration with any platform, the Digital Twin can be hosted using any application, including a PLM system, a dedicated application, or a distributed model. The product configuration data remains maintained by the Configuration Lifecycle Management (CLM) platform, easily accessed by the Digital Twin.
Since a Digital Twin is a virtual replica of a physical entity with a shared lifecycle and evolution, the processes, solutions and systems needed to create the physical entity need to be mirrored in the development of the Digital Twin. In the physical world, Configuration Lifecycle Management provides value by capturing all of the potential combinations of options and components used to build a process, system, product or service. This enables manufacturers to manage specific variants, rules and conditions governing how options and components are combined to create that specific variant.
Configuration Lifecycle Management thus becomes the “single-source-of-truth” on all valid, potential and available combinations that can be used to define a specific variant. As a Digital Twin needs to be unique to a specific physical counterpart, the configuration information that was used to design and manufacture the physical counterpart is essential in modeling the unique Digital Twin.
The Configuration Lifecycle Management (CLM) platform becomes a source of real-time information on the current status of valid replacements and alternatives should a physical counterpart experience a malfunction in the field. This same information can inform the Digital Twin so that analysis can be performed on the impact of choosing an alternative using the Digital Twin before it is implemented on the physical counterpart.