【Industry 4.0 Series】How to create a digital twin?

The digital twin may drive tangible value for companies, create new revenue streams, and help them answer key strategic questions. With new technology capabilities, flexibility, agility, and lower cost, companies may be able to start their journeys to create a digital twin with lower capital investment and shorter time to value than ever before. A digital twin has many applications across the life cycle of a product and may answer questions in real time that couldn’t be answered before, providing kinds of value considered nearly inconceivable just a few years ago. Perhaps the question is not whether one should get started, but where one should start to get the biggest value in the shortest amount of time, and how one can stay ahead of the competition.

1. Digital twin process design and information requirements

The digital twin creation starts with process design. Standard process design techniques should be used to show how business processes, people enabling the processes, business applications, information, and physical assets interact. Diagrams are created that link the process flow to the applications, data needs, and the types of sensor information required to create the digital twin. The process design is augmented with attributes where cost, time, or asset efficiency could be improved. These typically form the base line assumptions from which the digital twin enhancements should begin.

2. Digital twin conceptual architecture

Secondly, the creation of the enabling technology to integrate the physical asset and its digital twin for real-time flow of sensor data and operational and transactional information from the company’s core systems. The digital twin conceptual architecture (figure 1) may be best understood as a sequence of six steps, as follows:

Figure 1. Digital twin conceptual architecture

1. Create: The create step encompasses outfitting the physical process with myriad sensors that measure critical inputs from the physical process and its surroundings. The measurements by the sensors can be transformed into secured digital messages using encoders and then transmitted to the digital twin. The signals from the sensors may be augmented with process-based information from systems such as the manufacturing execution systems, enterprise resource planning systems, CAD models, and supply chains systems. This would provide the digital twin with a wide range of continually updating data to be used as input for its analysis.

2. Communicate: The communicate step helps the seamless, real-time, bidirectional integration/ connectivity between the physical process and the digital platform. Network communication is one of the radical changes that have enabled the digital twin; it comprises three primary components:

1. Edge processing: The edge interface connects sensors and process historians, processes signals and data from them near the source, and passes data along to the platform. This serves to translate proprietary protocols to more easily understood data formats as well as reduce network communication.

   
   

2. Communication interfaces: Communication interfaces help transfer information from the sensor function to the integration function.

   
   

3. Edge security: The most common security approaches are to use firewalls, application keys, encryption, and device certificates. 

3. Aggregate: The aggregate step can support data ingestion into a data repository, processed and prepared for analytics. The data aggregation and processing may be done either on the premises or in the cloud.

4. Analyze: In the analyze step, data is analysed and visualized. Data scientists and analysts can utilize advanced analytics platforms and technologies to develop iterative models that generate insights and recommendations and guide decision making.

5. Insight: In the insight step, insights from the analytics are presented through dashboards with visualizations, highlighting unacceptable differences in the performance of the digital twin model and the physical world analogue in one or more dimensions, indicating areas that potentially need investigation and change.

6. Act: The act step is where actionable insights from the previous steps can be fed back to the physical asset and digital process to achieve the impact of the digital twin. Insights pass through decoders and are then fed into the actuators on the asset process, which are responsible for movement or control mechanisms, or are updated in back-end systems that control supply chains and ordering behavior—all subject to human intervention. This interaction completes closed loop connection between the physical world and the digital twin.

It is important to note that the above conceptual architecture should be designed for flexibility and scalability in terms of analytics, processing, the number of sensors and messages, etc. This can allow the architecture to evolve rapidly with the continual, and sometimes exponential, changes in the market.

A major challenge in undertaking a digital twin process can reside in determining the optimal level of detail in creating a digital twin model. While an overly simplistic model may not yield the value a digital twin promises, taking too fast and broad an approach can almost guarantee getting lost in the complexity of millions of sensors, hundreds of millions of signals the sensors produce, and the massive amount of technology to make sense of the model. Therefore, an approach that is either too simplistic or too complex could kill the momentum to move forward. Figure 2 offers a possible approach that falls somewhere in between.

Imagine the possibilities. The first step would be to imagine and shortlist a set of scenarios that could benefit from having a digital twin. The right scenario may be different for every organization and circumstance, but will likely have the following two key characteristics:

Figure 2. An overview of getting started with the digital twin

After the shortlist of scenarios is created, each scenario would be assessed to identify pieces of the process that can provide quick wins by using a digital twin. We encourage a focused ideation session with members of operational, business, and technical leadership for expediting the assessment.

Identify the process. The next step would be to identify the pilot digital twin configuration that is both of the highest possible value and has the best chance of being successful. Consider operational, business, and organizational change management factors in identifying which configurations could be best candidates for the pilot. Focus on areas that have potential to scale across equipment, sites, or technologies.

Pilot a program. Consider moving quickly into a pilot program using iterative and agile cycles to accelerate learning, manage risk proactively, and maximize return on initial investments. As you move through the pilot, the implementation team should support adaptability and an open mind-set—at any time of your journey, maintain an open and agnostic ecosystem that would allow adaptability and integration with new data (structured and unstructured) and leverage new technologies or partners.

Industrialize the process. Once success is shown in the field, you can industrialize the digital twin development and deployment process using established tools, techniques, and playbooks. This may include moving from a more siloed implementation to integration into the enterprise, implementation of a data lake, performance and throughput enhancements, improved governance and data standards, and implementation of organizational changes to support the digital twin.

Scale the twin. Once successful, it can be important to identify opportunities to scale the digital twin. Target adjacent processes and processes that have interconnections with the pilot. Use the lessons learned from the pilot and the tools, techniques, and playbooks developed during the pilot to scale expeditiously.

Monitor and measure. Solutions should be monitored to objectively measure the value delivered through the digital twin. Identify whether there were tangible benefits in cycle time, yield throughput, quality, utilization, incidents, and cost per item, among others. Make changes to digital twin processes iteratively, and observe results to identify the best possible configuration.

Most importantly, this is not a project that should typically end once a benefit is identified, implemented, and measured. To continually differentiate in the market place, companies should plan time to move through the cycle again in new areas of the business over time.

All in all, true success in achieving early milestones on a digital twin journey will likely rely on an ability to grow and sustain the digital twin initiative in a fashion that can demonstrate increasing value for the enterprise over time. To help ensure such an outcome, one may need to integrate digital technologies and the digital twin into the complete organizational structure—from R&D to sales—continuously leveraging digital twin insights to change how the company conducts business, makes decisions, and creates new revenue streams.

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For enquiries, please contact:

Nickie Wang

Senior Manager

Industrial Products & Services Program

Email: nickiwang@deloitte.com.cn

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