Digital Twin has gained significant popularity and awareness in almost all digital transformation discussions due it its impact and potential across many industrial segments. Digital twins are deployed across a range of industries, from healthcare to aviation to automotive. In manufacturing, digital twins are increasingly being used to optimize the design and production of complex products and systems, such as aircraft engines, wind turbines, and electrical or mechanical devices. The digital twin of a product, facility, or process is a virtual replica of its physical peer. It allows monitoring of real-world future state, hence offering greater insight and enabling companies to respond quickly to any abnormalities such as unplanned outages or supply chain interruptions. Across many industrial segments, we have witnessed companies embarking on a holistic Digital Twin strategy spanning the entire product lifecycle. Digital Twin offers some key benefits:

• Reduce operational costs: digital twins can optimize operations and predict potential failures before they occur therefore eliminating reactive maintenance costs which can be in the range 40% above scheduled maintenance according to Dept Of Energy (DOE) study. Improving asset utilization and maximizing output while reducing energy consumption.
• Faster time to market: Digital twin allows rapid prototyping and testing, enabling designers and engineers to experiment with different designs and configurations without the need for physical prototypes which may not have been possible with traditional methods.
• Improve customer experience: explore new product designs or functionalities that are aligned with current or new customers’ expectations and define the needed steps for production adjustment and modification to retain or capture new markets.
• Enhance product attributes: Conduct more accurate simulation on the digital twin to enhance product qualities, and functionalities to meet regulatory requirements and establish rules for intelligent operation decision-making based on insights gained from simulation.

However, creating and deploying a Digital Twin(s) across manufacturing with the expectation of significant value without detailed investigation is a recipe for failure. Companies must have a realistic view of any expected benefit, carefully examine the contribution to business and thoroughly rationalizing the costs involved. A strict discipline must be followed in selecting a “fit for purpose” digital twin, additionally address the following:

1. Real-time Data Ingestion: to ensure the benefit continuity, a digital twin is to be updated regularly with real-time data from sensors to reflect the current state of the physical object or system accurately
2. Interoperability: Select a flexible, robust digital twin architecture that can deal with data heterogeneity, redundancy, and interoperability. The digital twin should be designed to integrate with other systems for seamless data sharing and analysis.
3. Scalability: The digital twin should be scalable to accommodate future growth and changes. Determine its feasibility and data sets evaluation, resources availability, and industry domain knowledge within your organization
4. Data governance: establish a clear data governance structure and policy for the proposed digital twin to ensure data quality, consistency, and compliance with relevant regulations.
5. Collaboration: Promote robust collaborations amongst all internal stakeholders such as designers, operators, and maintenance personnel.

Future of Digital Twin The digital twin as an “operating model” will continue to proliferate and is expected to become a critical and mainstream part of manufacturing technologies. According to IDC’s “worldwide semiannual digital spending guide “, the digital twin market is projected to reach $35.8 Billion in 2025, with a compound annual growth rate (CAGR) of 30.8%. Overall, IDC anticipates a bright future for the digital twin market and is likely to become an increasingly key component of any disruptive technologies in many industries. Here is what we can expect:

1. Integration with Internet of Things (IoT) technology: by combining IoT and digital twin technology, organizations will ascertain a comprehensive understanding of the performance of their systems and make more informed decisions, predicting production bottlenecks or potential systems failure.
2. Merging artificial intelligence: we can expect a firmer integration of AI algorithms and digital twins. Composing AI models to analyze data collected from sensors and other sources, to create accurate predictions that can be validated on the digital twin platforms for optimization of physical systems, further improving their performance.
3. Combined use of blockchain technology: distributed ledger technology will bridge IoT and digital twins and offer a “single source of truth” of data history and transparency. Blockchain encryption will improve the security and privacy of digital twin data and production systems/processes by creating a shield against hacking or potential data breach. This can be particularly important in industries such as defense or even healthcare where patients’ data must be kept secure.
4. Expansion into new industries: In addition to manufacturing, we should anticipate a proliferation in other industries such as construction, transportation, and municipalities. These industries will reap benefits in design optimization, prediction and prevention of unplanned stoppages and improve overall efficiency.
5. Sustainability: digital twin and its capability to simulate physical systems can discover efficient operational scenarios resulting in reduced waste and energy conservation, hence lowering emissions, and minimizing other environmental impacts. Designers and engineers can simulate the use of different materials or equipment to discover their impacts on the environment and consider optimum design scenarios with less carbon footprint.

In conclusion, digital twin technology is here to stay, with many potential applications and advancements on the horizon. As the technology continues to evolve, we can expect to see more industries adopting digital twins to deconstruct and disrupt traditional manufacturing operations and allow businesses to optimize operations, enhance performance and improve business outcomes. About the Author Sam is a Practice Leader at Capgemini with a focus on engineering solutions for Energy, Chemicals and National Resources. He has 25+ years of executive experience in the energy industry and was one of the early pioneers in advancing the roles of Digital Twins, Advance Analytics in multiple energy segments.

By Sam Hemeda | 2024-07-12