The concept of digital twins has been around for a long time in terms of modeling and simulation. However, the two are vastly different. The digital twin is a living-learning model, as described by Colin Parris, CTO of GE Digital. Thus, it is continuously running and updating itself based on data received from the subject of the twin.
This implies that the digital twin must always be connected to real-world entities and processes, which is the factor that differentiates it from the more traditional systems. It makes a digital twin a potent tool in the measurement and execution of processes and products in the real world.
Looking at digital twins only as a representation of physical assets has been complemented by using the concept of digital twins for highly complex systems like a supply chain. To represent a supply chain, one needs an ontology that allows for the virtual representation of the relevant business objects, their properties, and relations.
Digital Twins and their relevance
As mentioned, a digital twin can be a powerful ally in managing a product, process, service, or whole system. It helps in the following ways:
Helps define and identify data and pre-contained information
A digital twin must receive data about all relevant properties constituting an accurate digital representation of the real-world entity or system. Moreover, the data for a digital twin can come from sources, many of which are already existing and are only waiting to be tapped. Therefore, leveraging this data with a digital twin can make predictions and optimizations much more precise and accurate.
Can be a product, inventory, a supplier, or a combination of the these
We can create digital twins for nearly any and every aspect. According to Siemens, there can be three types of digital twins relating to a product: product, production, and performance.
However, smart supply chains generate massive amounts of data. Together with the existing enterprise systems, they offer an incredible source for performance improvements. Gaining actionable insight by analyzing these data allows better decision-making and continuous follow-up on the impact of all decisions and constantly getting feedback and learning from it.
Easy to follow up
The digital twin is ideally able to simulate the circumstances of the physical asset or process accurately, as long as it gets data for all possible variables.
When a digital twin precisely represents the actual object, one does not need to expend resources, effort, or time to perform elaborate testing and assessment of the object to follow up on any effects. Instead, they can check the digital twin to evaluate the object's state.
Connected to real objects in business
A model or simulation often represents the ideal situation, with the actual object digressing further and further from this ideal representation as time goes on. But digital twins are always connected live to the actual objects or system of objects which makes them more real and accurate than traditional models and hence, more relevant.
So, how can a 'Digital Twin' help improve supply chains?
A digital twin can be a valuable asset in your execution system. It makes a closed-loop approach towards performance management, focusing on analytics, ML recommendations, and execution of measures.
- 100% transparency by creating a true end-to-end view on supply chains – Building a digital twin will firstly create E2E visibility across the supply chain within the company and for selected products & services across all tiers of a supply chain. A digital twin provides the level of transparency required to make informed decisions.
- A clearer idea of what is working and what's troubling - A digital twin provides real-time visibility of your system's going on. It helps you understand clearly which aspects of your process are working and which are not, allowing you to take action easily and quickly.
- Getting from insights to actionable recommendations - based on the real-time visibility it's now you have the actuals, historical data and can mix it up with additional data from external resources to create forecasts, plan or evaluate critical events to come forward with the best recommendation using ML algorithms and scenario planning tools.
- Top-down and bottom-up execution management - A digital twin considers every relevant property and gives 360-degree visibility of a system down to each business object. Therefore, whether you employ a top-down approach or a bottom-up one in execution management, the digital twin will easily help understand and resolve internal complexity, one of the root causes of performance gaps in the supply chain, in execution management.
- Digitizing tactical decision making - As Alastair Orchard, VP of digital enterprise at Siemens Digital Industries Software, puts it: the true power of a digital twin is revealed when it is utilized in cross-domain optimization, allowing tactical decision-making with greater chances of success. And that's what we are doing in Supply chain management.
The world is getting more and more connected every day, and so are supply chains and their objects. This paves the way for depicting more complex systems as virtual entities at a reasonable cost.
A digital twin can bridge the performance gap between current and state-of-the-art in every aspect of an industry, but most notably in the supply chain. In brief, the digital twin expands your vision and capabilities to improve a supply chain with execution management.