Enhancing Business Performance with a Digital Twin
Enterprises today are at various stages of their digital transformation journeys and has reached a stagnant maturity level, with many already employing some form of a digital model for their operations. Most have completed the first three steps on this journey, visualization, integration and analysis. The last three steps include prediction, prescription and symbiosis. This is where digital twin comes in. A digital twin is avirtual replica of a physical system that can model, simulate, monitor, analyze and constantly optimize the physical world.
Digital Twin brings in the ability to blend various powerful technologies and provide enormous synergies Digital Twin holds the potential to process large volume of data and blend with Machine learning models and simulation tools to move us closer to the physical world and achieve high level of optimization.The global digital twin market size was valued at USD 11.12 billion in 2022 and is projected to exhibit a compound annual growth rate of 37.5 per cent from 2023 to 2030.
The current industry practice involves creating a digital model for new physical devices or manufacturing plants before their launch. However, these digital models are usually left in isolation and not connected to an IoT platform for real-time monitoring and optimisation.
In the future, enterprises will convert these digital models into digital twins and leveraging them for continuous improvement. Some are already doing it. Digital twins enable companies to realise the next level of benefits from their IT investments and Industry 4.0. With a digital twin, enterprises can extract greater value from the prediction and prescription abilities that are enabled by digitisation efforts.
Deriving value through a digital twin
Let’s take the exampleof a coal manufacturing company that wanted to ship coal to each client in line with theircontract terms. Thisis crucial as shipping lower-quality coal can result in penalties while higher-quality coal leads to losses. The company had streamlined their planning process across their value chain but was experiencing quality deviations during shipping. The company was struggling to identify the root causes of the deviations as it could originate from any part of their value chain.
Sivakumar Viswanathan, Manager, Strategy & Operations, Hitachi Vantara
This company implemented a digital twin by virtualizing their entire value chain and developed a mechanism to control the quality before any deviation occurs. The solution orchestrates all possible material movements, virtualizing stockpiles, and production optimizer models to advanced intelligence to the operations. This has made a positive impact on their client shipments.
As members of the WEF Global Lighthouse Network, we have seen the benefits of implementing a digital twin in factories. We have seen how factories reduced customer complaints by 80 per cent and downtime by 25 per cent and improved productivity by 20 per cent. Also, these factories reduced safety incidents by 50 per cent, energy costs by 38 per cent and maintenance costs by 10 per cent. Digital twins can revolutionise manufacturing processes and drive operational excellence.
"Digital twinning is a complex process that involves developing a physics-based model and it can be a technology overkill insome cases. Integration is another major challenge since relevant data typically exists in silos, which may not be standardised."
Identifying where to apply digital twin is key
Enterprises can reap significant benefits by realising the potential use cases of digital twin technology and acting quickly to implement it. However, companies must evaluate where a digital twin is applicable across various categories, such as product, process, people, places and assets. This evaluation should be based on the specific processes they represent.
Below is a visualization to understand the types of digital twins with examples:
A virtual replica of a physical object is required, with data connectivity between the twoto create a digital twin. Each physical object must have a unique identifier for effective tracking, especially in cases where there are multiple similar objects. A digital twin cannot be generic and must be specific to a particular physical object. The creation of a digital twin enables enterprises to better monitor, simulate, analyse, control and optimise each physical object.
Dealing withchallengesin digital twinning
Digital twinning is a complex process that involves developing a physics-based model and it can be a technology overkill insome cases. Integration is another major challenge since relevant data typically exists in silos, which may not be standardised. Creating a comprehensive digital twin model, along with implementation and training is a high investment. Privacy and security are also major concerns since with increased control over the physical object, cyber threats become critical. There are also issues with competency and perspectives as different stakeholders can have varying objectives for a digital twin, leading to a lack of alignment and not getting the right digital twin in place. These challenges highlight the need for alignment, standardisation, and security.
By bringing together disparate data sources and using the power of digital technology, digital twins can help organisations prescribe solutions to deal with day-to-day variability in operations. Digital twins can help organisations deal with these variables in the most optimal way. Organisations must bring together all relevant data sources and embrace the power of digital technologyto fully leverage the benefits of a digital twin.
Digital Twin brings in the ability to blend various powerful technologies and provide enormous synergies Digital Twin holds the potential to process large volume of data and blend with Machine learning models and simulation tools to move us closer to the physical world and achieve high level of optimization.The global digital twin market size was valued at USD 11.12 billion in 2022 and is projected to exhibit a compound annual growth rate of 37.5 per cent from 2023 to 2030.
The current industry practice involves creating a digital model for new physical devices or manufacturing plants before their launch. However, these digital models are usually left in isolation and not connected to an IoT platform for real-time monitoring and optimisation.
In the future, enterprises will convert these digital models into digital twins and leveraging them for continuous improvement. Some are already doing it. Digital twins enable companies to realise the next level of benefits from their IT investments and Industry 4.0. With a digital twin, enterprises can extract greater value from the prediction and prescription abilities that are enabled by digitisation efforts.
With a digital twin, enterprises can extract greater value from the prediction and prescription abilities that are enabled by digitisation efforts.
Deriving value through a digital twin
Let’s take the exampleof a coal manufacturing company that wanted to ship coal to each client in line with theircontract terms. Thisis crucial as shipping lower-quality coal can result in penalties while higher-quality coal leads to losses. The company had streamlined their planning process across their value chain but was experiencing quality deviations during shipping. The company was struggling to identify the root causes of the deviations as it could originate from any part of their value chain.
Sivakumar Viswanathan, Manager, Strategy & Operations, Hitachi Vantara
This company implemented a digital twin by virtualizing their entire value chain and developed a mechanism to control the quality before any deviation occurs. The solution orchestrates all possible material movements, virtualizing stockpiles, and production optimizer models to advanced intelligence to the operations. This has made a positive impact on their client shipments.
As members of the WEF Global Lighthouse Network, we have seen the benefits of implementing a digital twin in factories. We have seen how factories reduced customer complaints by 80 per cent and downtime by 25 per cent and improved productivity by 20 per cent. Also, these factories reduced safety incidents by 50 per cent, energy costs by 38 per cent and maintenance costs by 10 per cent. Digital twins can revolutionise manufacturing processes and drive operational excellence.
"Digital twinning is a complex process that involves developing a physics-based model and it can be a technology overkill insome cases. Integration is another major challenge since relevant data typically exists in silos, which may not be standardised."
Identifying where to apply digital twin is key
Enterprises can reap significant benefits by realising the potential use cases of digital twin technology and acting quickly to implement it. However, companies must evaluate where a digital twin is applicable across various categories, such as product, process, people, places and assets. This evaluation should be based on the specific processes they represent.
Below is a visualization to understand the types of digital twins with examples:
A virtual replica of a physical object is required, with data connectivity between the twoto create a digital twin. Each physical object must have a unique identifier for effective tracking, especially in cases where there are multiple similar objects. A digital twin cannot be generic and must be specific to a particular physical object. The creation of a digital twin enables enterprises to better monitor, simulate, analyse, control and optimise each physical object.
Dealing withchallengesin digital twinning
Digital twinning is a complex process that involves developing a physics-based model and it can be a technology overkill insome cases. Integration is another major challenge since relevant data typically exists in silos, which may not be standardised. Creating a comprehensive digital twin model, along with implementation and training is a high investment. Privacy and security are also major concerns since with increased control over the physical object, cyber threats become critical. There are also issues with competency and perspectives as different stakeholders can have varying objectives for a digital twin, leading to a lack of alignment and not getting the right digital twin in place. These challenges highlight the need for alignment, standardisation, and security.
By bringing together disparate data sources and using the power of digital technology, digital twins can help organisations prescribe solutions to deal with day-to-day variability in operations. Digital twins can help organisations deal with these variables in the most optimal way. Organisations must bring together all relevant data sources and embrace the power of digital technologyto fully leverage the benefits of a digital twin.
