3 Rewarding Tips for Businesses to Optimize the Enormous Value of Digital Twin Technology
Usually, organizations struggle to develop new products or services. Watching and improving their performances can become slow and inefficient. However, the use of digital twin (DT) technology can hasten and transform this process.
DT is the most useful application in employing product and service asset management. This technology is helpful to numerous business and government models. Digital Twin’s real-time flowing of data, simulation abilities, and relationship detection make it a valuable tool for every business going forward in Industry 4.0. Here is how companies can optimize the value of digital twin.
How does digital twin derive its value?
There are many uses for the digital twin that bring about different results. With that said, four aspects work in concert from which the application creates value for an organization. These four aspects are:
DT provides a visual of a product before it’s being built. It also creates transparency between the customer and the manufacturer. The DT allows the design process to stay in-house instead of outsourcing it. Because of DT’s one-step production process, organizations can achieve mass customizations quicker.
DT design creates input fast. This fast creation of information speeds up generation and expansion of a digital ecosystem. The design input links onto production and machinery. DT’s storage and analysis capabilities of input streamline four areas of production:
With this kind of capability, organizations decrease production corrections.
Through simulation, DT allows interaction, examination, and confirmation of a product or asset. It permits testing to see how a change affects quality. Also, DT can simulate a machine’s run time at specific speeds. This simulation helps to identify places that will need maintenance.
4. Merging the Digital Ecosystem
The DT connects design, production, and maintenance schedules within a digital ecosystem. It can also connect external equipment suppliers that join this ecosystem. This kind of connection allows suppliers to act quickly when a breakdown happens. It can also prevent failures before they happen.
Ultimately, the four aspects mentioned above work together to solve the problem of data modelling. Solving organizations’ data modelling problems is the principal value that digital twin provides. The four aspects mentioned above allow DT to deliver that value.
There are three things businesses can do to maximize the value of digital twin. However, before using this technology, organizations need to make sure they have updated IT infrastructure. Also, they need to ensure they have enough resources committed to the project, and knowledge of the kind of information to gather and a plan to protect that input.
With that said, here are three tips to optimize the design, data, simulation, and integration of digital twin to maximize its value.
Knowledge graphs encode information positioned in a web of nodes and links instead of tables of rows and columns. They are best suited to discover relationships between data. Knowledge graphs can correlate varied inputs about products and assets and their surrounding settings. Each source of information becomes a node linked to other nodes containing input.
Understanding the relationship between varying sources of information produces the foundation of the insights a digital twin can deliver. It contextualizes input concerning other data to give smart conclusions about performance.
This relationship detection ability is why knowledge graphs allow organizations to get the most value out of a DT; relationships continually happen in surprising places. Knowledge graphs can adjust fast to new situations to seize upon them as fast as the speed of the Internet of Things (IoT).
2. Hybrid Simulation
Digital twin technology depends on various simulation platforms to enable 3D models. Hybrid simulation is essential in creating 3D models of information for IoT assets. Organizations should use simulation applications that support:
- Discrete Event Simulation
- Agent-Based Modeling
- Systems dynamics
Discrete Event Simulation refers to a model of the operation of a system as a distinct order of events in time. Each event happens at a precise moment in time and marks a change of condition in the system. Agent-based modelling refers to the different actions of people that affect models. System dynamics describes the delivery of extensive ranges of the impact of various factors on one another — for instance, the impact of different elements of a supply chain on inventory.
Employing big data frameworks like data lakes shrink the scale of input in a digital twin. Cloud-based Hadoop distributions contain various sources that foster the value digital twin gives organizations.
These origins include the following:
Semi structured or unstructured data released in the IoT
Structured internal information i.e. input about supply chain management.
There does exist low-cost alternatives to Hadoop for the actionable deployment of DT in production.
To maximize the full value of digital twin technology, organizations should use knowledge graphs, simulation tech that supports 3D modelling, and data lakes. From employing these three techniques in concert, the DT will be able to simulate entire enterprise settings, and accurately predict that organization’s operational needs.