How Digital Twins Empower Smarter Business Strategies
Digital twins are revolutionizing business strategies by providing organizations with powerful tools to simulate real-world scenarios, optimize processes and enhance decision-making. The transformative impact of digital twins across industries—from automotive and manufacturing to energy and agriculture—highlights how they empower companies to predict outcomes, train effectively and protect their operations.
Throughout history, replicas—whether digital, physical or conceptual—have been invaluable tools for predicting outcomes, enhancing training and mitigating risks. For instance, medical anatomical models, which date back to as early as the 3rd century BCE, have provided critical insights into human anatomy and surgical techniques. In the 1940s, crash test dummies revolutionized automotive safety by allowing engineers to assess the impact of collisions on the human body without endangering lives. Similarly, NASA’s use of flight simulators was pivotal to the Apollo 13 mission, demonstrating the critical role of simulation in crisis management.
“Every makeshift procedure carried out in space was first tried out on earth and rejected if the simulators showed it to be dangerous or impractical.”
As portrayed in the 1995 film Apollo 13, the simulators were instrumental in developing a power-up sequence and determining the correct alignment of the spacecraft, which ultimately allowed the lunar module and the three astronauts onboard to return safely to Earth despite severely limited resources. The New York Times captured the significance of this approach, stating “The countless improvisations that nursed the crippled spacecraft along were in large measure the product of an extraordinarily elaborate assembly of simulators at the Manned Spacecraft Center in Houston and elsewhere. Every makeshift procedure carried out in space was first tried out on earth and rejected if the simulators showed it to be dangerous or impractical.”
Redefining How We Predict, Train and Protect
“McKinsey has projected that by 2026, investments in digital twins will exceed $48 billion.”
Today, this same principle of using detailed replicas to predict outcomes, enhance training and mitigate risks is evolving rapidly with the advent of artificial intelligence (AI)-driven technologies. Leading the charge are digital twins—virtual representations of physical objects, people, systems or processes embedded within digital simulations of their real-world environments. These advanced replicas are reshaping how businesses and industries can foresee challenges, optimize performance and drive decision-making with unprecedented precision.
In essence, a digital twin allows companies across various industries to rapidly simulate real-world scenarios and predict their outcomes, enabling more informed decision-making.
McKinsey has projected that by 2026, investments in digital twins will exceed $48 billion. Among these investments are product twins—digital replicas that represent products throughout their entire life cycle, from initial concept and design to full-scale operation. These twins provide live, real-time data as if the product were actively in use, allowing companies to enhance product development through improved prototyping, performance optimization, supply chain management and quality control.
Several companies are already integrating digital product twins into their research & development and operations:
1. Automotive:
BMW uses digital twin technology in its smart manufacturing plants to simulate production processes in real time, identifying bottlenecks, streamlining workflows and optimizing operations. By integrating data from machinery, BMW boosts efficiency, reduces waste and ensures high-quality output.
2. CPG:
Unilever uses digital twins to improve its operational efficiency, ensure that its supply chain is more resilient and to reduce waste, as well as in product development. By rethinking the supply chain, Unilever can proactively address issues before they impact production, ensuring consistent product quality and timely delivery.
3. Manufacturing:
Siemens integrates digital twins into its manufacturing processes, using them to simulate production lines, predict equipment failures and streamline operations.
These examples highlight how companies are leveraging digital product twins to drive efficiency, reduce costs and enhance overall performance, demonstrating the transformative potential of digital twins in modern industry.
Another example of digital twin technology is “twin data.” The same McKinsey article references Google Maps as an example, describing it as a digital twin of the Earth’s surface. By integrating real-time traffic data, Google Maps helps users navigate their commutes more efficiently.
Digital twins can take various forms, including:
Infrastructure Twins:
These twins represent physical infrastructure, such as highways, buildings or stadiums, providing insights into structural health, operational efficiency and maintenance needs.
System Twins:
These twins model the interactions between physical and digital processes. They are used in scenarios like manufacturing workflows, end-to-end supply chain management, store operations, and mapping customer journeys to optimize performance and outcomes.
Each type of digital twin serves a unique purpose, enhancing decision-making and operational efficiency across industries. Examples of these include:
1. Aerospace:
Rolls-Royce uses digital twins of its jet engines to collect data from sensors and satellite connectivity, providing real-time information on engine performance, component wear and operational efficiency. This data helps predict maintenance needs, extend the life of engine parts and reduce aircraft downtime.
2. Healthcare:
Philips uses digital twins to create virtual representations of hospital operations, simulating equipment, staffing, and patient flow. This data allows healthcare providers to test “what-if” scenarios, optimize care pathways, and improve operational efficiency without risk to patients or staff.
3. Agriculture:
John Deere collects digital twins data, including soil moisture levels, weather conditions, crop growth stages and machinery status. This data is used to optimize planting schedules, irrigation management and equipment usage, enhancing overall farm productivity.
The Impact of Digital Twins on Organizations
Digital twins have the potential to revolutionize how companies leverage data to predict, train and safeguard various aspects of their operations. By enhancing efficiency, they also provide businesses with the agility to learn rapidly and iterate toward better outcomes, ultimately driving continuous improvement and resilience.
I found this example particularly insightful when highlighting the impact of companies that invested in digital modeling versus those that did not: “Even before the global financial crisis hit in 2007, Goldman Sachs, a bank, built a system called Secdb, which, among other things, regularly calculated the different types of risk facing its different financial assets. When Lehman Brothers, another bank, went bankrupt in 2008, this system allowed Goldman quickly to understand its exposure to the failing firm.”
“A corporate twin will not just help managers make better plans; it will also implement them, learn from the outcomes, and optimize itself to achieve certain corporate objectives—over and over again.”
The same story underscores the advantages of having a comprehensive, real-time view into the entire company lifecycle. “With access to information from all over the company’s operations, as well as from its customers and suppliers, a corporate twin will not just help managers make better plans; it will also implement them, learn from the outcomes, and optimize itself to achieve certain corporate objectives—over and over again.”
Using AI to Evolve Digital Twins
By layering on AI and machine learning, it will significantly influence the evolution of corporate digital twins. AI will simplify the modeling of internal processes by uncovering insights from vast datasets, making this discovery process more dynamic and requiring less manual input.
Similar to how large language models (LLMs), like those powering ChatGPT, extract patterns from text, AI-driven corporate models can analyze business data to reveal key operational insights, as noted here by Dario Gil, head of research at IBM. These models will also make digital twins more adaptable. Traditionally, enterprise software is rigid, often making custom adjustments difficult. However, LLMs trained on unstructured data, like customer complaints, could allow for greater flexibility, or even automate tasks. Microsoft’s Charles Lamanna suggests that enterprise software will shift towards being more dynamically generated and self-assembled, enhancing adaptability and responsiveness.
Digital twins are reshaping how organizations operate by offering unprecedented insights and agility. By integrating AI and machine learning, these virtual models can continuously adapt, optimize processes and enhance decision-making across the entire business lifecycle. Companies like BMW, Unilever and John Deere are already leveraging digital twins to drive efficiency, reduce costs and improve overall performance. As technology continues to advance, the potential of digital twins to revolutionize business strategies and foster resilience will only grow, positioning them as essential tools for navigating the complexities of the modern business landscape.
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