Digital Twin Technology Applications: Expert Guide for Supply Chain & Healthcare (2025)
Digital twin technology applications are transforming industries with their remarkable growth trajectory. According to a Deloitte study, the global market for digital twins is expected to grow with a 38% CAGR to reach $16 billion by 2023. Even more impressive, the market is projected to reach $259.32 billion by 2032—representing a compound annual growth rate of 39.8%.
What's driving this explosive growth? Digital twin technology combines advanced data modeling, machine learning, and Internet of Things (IoT) to create comprehensive virtual counterparts of physical systems. Furthermore, we're seeing digital twin examples across multiple sectors, with healthcare executives particularly enthusiastic—66% expect increasing investment in digital twins over the next three years. While only 13% of IoT projects used digital twins in 2019, that figure has now climbed to approximately 29%. By 2025, we'll witness digital twins evolve into dynamic, adaptive, and predictive models driven by advancements in AI and real-time data processing. In this article, we'll explore practical digital twin use cases in supply chain management and healthcare, examine how digital twin is revolutionizing these industries, and provide insights into the most effective implementation tools and strategies.
Understanding Digital Twin Technology in 2025
Image Source: Wiley Interdisciplinary Reviews
The essence of digital twin technology lies in its ability to create virtual replicas of physical objects, systems, or processes. In 2025, these dynamic virtual models represent a significant advancement beyond static simulations, allowing organizations to gain unprecedented insights through real-time data analysis and visualization.
What is a digital twin and how it works
At its core, a digital twin is a virtual representation of a physical asset that spans its entire lifecycle, continuously updated through real-time data, and employs simulation and machine learning to support decision-making. Unlike traditional digital models, digital twins maintain a unique connection to their physical counterparts, enabling them to mirror real-world conditions as they evolve.
Digital twins operate through a sophisticated process of data collection and analysis. Initially, sensors embedded in physical assets gather operational data that flows into the virtual model. Subsequently, this information updates the digital twin in real-time, creating an accurate reflection of the physical asset's current state. The virtual model then processes this data using advanced analytics to generate insights about performance, predict potential issues, and optimize operations.
Consider the example of a jet engine with sensors tracking temperature, pressure, and vibration. Its digital counterpart mirrors these conditions precisely, allowing engineers to simulate wear and tear, schedule maintenance, and anticipate failures without physically examining the engine. This real-time mirroring capability represents the fundamental value proposition of digital twins.
The concept has evolved significantly since its introduction by Dr. Michael Grieves in 2002. Initially developed as static models, digital twins progressed to "digital shadows" in the 2010s, where data flowed one-way from physical objects to digital models. The breakthrough came with fully interactive digital twins that established two-way communication between physical assets and their virtual replicas.
Types of digital twins: product, process, system
Digital twins exist in several distinct categories, each serving different purposes across the asset lifecycle:
* Component or Parts Twins: These represent individual components of a larger system, such as sensors, valves, or motors. They enable detailed monitoring of equipment parts and facilitate timely maintenance.
* Product or Asset Twins: Consisting of multiple component twins, these model complete products like engines, pumps, or buildings. They analyze how separate parts interact and perform together, providing insights for performance enhancement.
* System or Unit Twins: These demonstrate how different assets combine into functional units, offering a large-scale overview of operations. They enable testing of system configurations to achieve optimal effectiveness.
* Process Twins: The most comprehensive type, process twins connect system twins to explore collaboration between different systems. They provide maximum visibility into workflows, allowing for deep analysis and optimization.
Additionally, the digital twin maturity follows five developmental stages: mirroring, monitoring, modeling and simulation, federation, and ultimately autonomous operation. By 2025, many digital twin implementations will reach the federation and autonomous stages, where multiple twins connect to improve complex systems and solve problems with minimal human intervention.
Key components: IoT, AI, real-time data, simulation
The functionality of digital twins depends on several critical technologies working in concert. First among these is the Internet of Things (IoT), which forms the foundation by enabling real-time data flow from physical systems. Sensors collect and transmit operational data, establishing the vital connection between physical assets and their digital versions. Industry experts predict that 95% of IoT platforms will offer digital twinning by 2029.
Artificial intelligence and machine learning constitute the analytical engine that transforms raw observations into valuable business insights. These technologies process the massive amounts of sensor data, identify patterns, and enable predictive capabilities that make digital twins increasingly valuable over time.
Cloud computing provides the necessary storage capacity and computational power for simulating complex systems. This infrastructure supports the analytical requirements of digital twins and enables real-time collaboration across manufacturing spaces.
Simulations represent another essential component, allowing organizations to test scenarios and predict outcomes before implementation. Through simulation, operators can analyze the impact of equipment failures, test potential solutions, and validate production changes in a risk-free environment.
The integration of these components creates digital twins with remarkable capabilities, including accelerated problem identification, real-time collaboration regardless of physical location, and advanced scenario planning. As we progress through 2025, these capabilities will continue to expand, with digital twins becoming more sophisticated and accessible across industries. Indeed, the combination of more accessible reality capture data (from drones, sensors, and cameras) with advanced AI techniques is opening a new frontier of digital twin applications. This evolution enables businesses to simulate increasingly complex environments that were previously too difficult to model virtually.
Digital Twin Applications in Supply Chain Management
Image Source: Microsoft
Supply chain managers are harnessing digital twin technology applications to enhance visibility, resilience, and efficiency across their operations. These virtual replicas of physical systems have evolved from basic conceptual frameworks to sophisticated models that deliver measurable business value.
Warehouse layout optimization using digital twins
Digital twins excel at optimizing warehouse designs without disrupting ongoing operations. By creating virtual replicas of facilities, companies can test different configurations to identify the most efficient layouts for their specific needs. This optimization leads to reduced picking times, improved throughput, and minimized bottlenecks throughout warehousing operations.
The process begins with developing an accurate digital representation that captures the physical structure, digital assets, and operational data. As the digital twin matures, it moves from a basic representation to a model capable of simulation and eventually real-time data integration. Companies using warehouse digital twins have reported substantial gains, including:
* 40% increase in labor productivity through optimized pick-and-put away operations
* 15% improvement in space utilization through 3D visualization
For instance, CEVA Logistics implemented a warehouse digital twin near Berlin that operates with real-time data from multiple sources. Their system uses Monte Carlo simulation engines to process data, run scenarios based on customer forecasts, and optimize resource allocation—ultimately improving overall operating capacity.
Predictive logistics and route planning
Digital twin applications extend beyond warehousing to entire logistics networks. By creating virtual replicas of transportation systems, companies gain holistic visibility that ensures timely deliveries while reducing costs and carbon footprints.
Route optimization represents one of the most impactful applications in shipping and delivery. Digital twins incorporate real-time traffic data, weather conditions, road layouts, and construction information to design optimal distribution routes and inventory storage locations. Moreover, these systems continuously update as conditions change, providing:
* Real-time shipment tracking for proactive customer communication
* Dynamic capacity management through continuously updated warehouse utilization data
* Precise ETAs that update as conditions change rather than generic time windows
Companies leverage these capabilities to simulate disruption scenarios, like supplier delays or demand surges, allowing supply chains to shift from reactive to proactive management.
Packaging material testing and shipment protection
Digital twins enable virtual testing of packaging materials before physical implementation, helping logistics companies determine material feasibility without costly trial-and-error. Product packaging can be virtualized and tested for errors, essentially creating a risk-free environment to explore packaging innovations.
This application extends to analyzing how different packaging conditions affect product delivery throughout the supply chain journey. Consequently, companies can identify optimal packaging solutions that balance protection, sustainability, and cost considerations. Digital twin simulations help predict performance under various conditions, reducing damage rates and improving sustainability metrics.
Fleet performance monitoring and maintenance planning
Fleet operators utilize digital twins to maintain comprehensive overviews of their vehicles and transportation assets. By aggregating data from individual vehicles, fleet managers can monitor performance metrics in real-time, identify maintenance needs, and allocate resources effectively.
A key advantage is predictive maintenance capability. Digital twins create models that reflect highly complex characteristics, enabling forecasting of specific scenarios such as modified inspection intervals or wear-reducing operating modes. For fleet operations, this translates to significant benefits:
* 10-30% increase in asset availability
* 5-10% decrease in maintenance costs
Digital twins also help monitor fuel consumption patterns. By leveraging sensor-derived and historical data through machine-learning algorithms, these systems can detect the exact time, location, and quantity of fuel fillings—and potential theft. Fleet managers using such platforms report average improvements in fuel consumption between 5% and 10%.
As supply chains grow increasingly complex, digital twins provide the foundation for autonomous logistics operations by offering curated, real-time views of network states that enable automated decision-making systems.
Digital Twin Use Cases in Healthcare Systems
Image Source: Nature
Healthcare organizations are implementing digital twin technology to address critical challenges in patient care, resource management, and medical innovation. Throughout the healthcare ecosystem, digital replicas are providing unprecedented opportunities to test scenarios, optimize operations, and improve patient outcomes without risk to actual patients.
Digital twin of a hospital for resource optimization
Hospital digital twins mirror physical facilities, creating safe environments to test operational changes dynamically and risk-free. These virtual models help healthcare administrators optimize clinical operations by analyzing workflows, enhancing resource allocation, and improving operational efficiency.
The technology offers tangible benefits for hospital management:
* Capacity planning: Digital twins help project demand and identify constraints so hospital leaders can take preemptive action.
* Resource allocation: By analyzing patient data, historical trends, and real-time information, digital twins optimize staff, equipment, and facilities distribution.
* Workflow simulation: Administrators can test different scenarios before implementing changes, immediately visualizing impacts on hospital performance.
In practice, healthcare institutions are already seeing results. Children's Mercy Kansas City uses digital twin technology to prepare for patient surges, predicting when surges will hit, what diagnoses will predominate, and what resources to allocate—enabling them to prepare bed space in advance and efficiently assign valuable staff.
Modeling human organs for personalized treatment
Digital organ twins represent a substantial advancement in personalized medicine. At present, digital twins in healthcare dynamically mirror various data sources, including disease registries, "-omics" data, physical indicators, and lifestyle information to track an individual's progression over time.
One noteworthy initiative is the SIMULIA Living Heart project—a validated digital twin model of the human heart developed through collaboration between Dassault Systèmes and the FDA. This technology examines drug interactions with organs in a virtual environment, helping predict individual responses to therapies.
Digital twins facilitate continuous real-time monitoring of patient data, providing healthcare providers with up-to-date information on vital signs, biomarkers, and medication adherence. In addition, they can simulate disease progression based on real-time patient data, helping anticipate complications and estimate treatment outcomes.
At Stanford University, researchers are developing medical digital twins that predict health trajectories, simulate treatments, and offer personalized care recommendations. In the context of diabetes management, a recent study demonstrated how digital twins can simulate metabolic status, dietary intake, and lifestyle habits—resulting in significantly improved hemoglobin A1c levels and reduced medication requirements.
Simulating surgeries and treatment planning
Digital twin technology enables healthcare professionals, especially surgeons, to rehearse and refine their skills in simulated environments. By replicating procedures and simulating different scenarios, these virtual models allow practitioners to gain experience without risk to actual patients.
The term "Digital Twin-Assisted Surgery" (DTAS) describes the integration of digital twin technology in computer-assisted surgery to enhance perioperative processes. Prior to surgery, DTAS allows surgical teams to virtually attempt new techniques or explore different access routes. Following surgery, it facilitates patient-specific documentation and contributes to virtual surgery databases.
In colorectal surgeries, digital twins integrate imaging data from CT and MRI scans to create virtual models of the colon and surrounding tissues, mapping tumor locations and critical structures like blood vessels. Similarly, in partial nephrectomies, surgeons can simulate the optimal cutting plane to remove tumors while preserving healthy kidney tissue.
At Johns Hopkins University, the Trayanova Lab creates computer simulations of patients' hearts the week before ablation procedures for atrial fibrillation, providing blueprints to identify areas of the heart for targeted treatment. With current ablation success rates at 50-70%, researchers are studying whether these personalized plans improve outcomes and reduce complications.
Medical device testing in virtual environments
Digital twin technology offers significant advantages for medical device testing. Traditional usability evaluation methods face difficulties in creating sufficiently realistic simulation conditions representative of real-life scenarios. Virtual reality environments provide high-fidelity simulation at lower cost, though they aren't yet widely used.
Medical device simulation allows for:
* Design validation before manufacturing, saving time and money by identifying potential flaws early.
* Training medical professionals on device use, reducing potential errors during procedures.
* Regulatory compliance testing to ensure devices are safe and effective.
* Risk analysis to identify potential hazards and determine mitigation strategies.
Through simulation, developers can test different scenarios and predict outcomes before implementation in physical environments. This approach enables operators to analyze equipment failures, test potential solutions, and validate production changes without real-world risks.
As digital twin technology continues to advance, these healthcare applications demonstrate how virtual modeling is becoming an essential tool for improving patient care, optimizing hospital operations, and accelerating medical innovation across the healthcare spectrum.
Top Digital Twin Tools for Supply Chain and Healthcare
Leading software platforms enable organizations to implement digital twin capabilities across different industries. These specialized tools facilitate the creation and management of virtual replicas that mirror physical assets in real-time.
Ansys Twin Builder for medical device simulation
Ansys Twin Builder provides a comprehensive solution for creating simulation-based digital twins with Hybrid Analytics. This open platform enables engineers to develop accurate physics-based models that reflect real-world behavior of medical devices. The software potentially cuts model creation time in half while offering a 25% increase in product performance and maintenance cost savings up to 20% over the product's lifetime. Medical device manufacturers using Ansys' simulation solutions have shortened their time to regulatory approval by two years, saving $10 million in submission costs.
ThingWorx for logistics and asset tracking
ThingWorx, developed by PTC, excels at integrating with physical devices and consolidating data from multiple sources. The platform supports real-time data processing capabilities crucial for maintaining up-to-date digital twins of supply chain assets. Through its Mashup Builder, users create intuitive dashboards that provide actionable insights into asset performance. Coupled with MQTT protocol support, ThingWorx efficiently synchronizes data between physical machines and their digital counterparts.
Seebo for process optimization in healthcare
Despite limited information, Seebo appears to focus on process optimization in healthcare settings. At present, it likely offers capabilities similar to other digital twin platforms in the healthcare sector.
Oracle IoT Cloud for predictive
Oracle IoT Cloud Service implements digital twins through three pillars: Virtual Twin, Predictive Twin, and Twin Projections. Its predictive twin builds analytical models using machine learning techniques to detect future problems or states. The platform uses Apache Spark-based analytics for complex models and includes Oracle Stream Explorer for creating simpler models based on trends and patterns. Presently, these capabilities help optimize maintenance scheduling and forecast equipment failures.
Key Takeaways
Digital twin technology is revolutionizing supply chain and healthcare operations by creating virtual replicas that enable real-time optimization, predictive analytics, and risk-free testing environments.
* Digital twins combine IoT sensors, AI analytics, and real-time data to create dynamic virtual models that mirror physical assets throughout their entire lifecycle.
* Supply chains achieve 40% labor productivity gains and 15% space optimization through warehouse digital twins that test layouts without disrupting operations.
* Healthcare digital twins enable personalized treatment planning, surgical simulation, and hospital resource optimization while reducing patient risk.
* The global digital twin market is projected to reach $259.32 billion by 2032, driven by 95% of IoT platforms offering digital twinning capabilities by 2029.
* Leading platforms like Ansys Twin Builder, ThingWorx, and Oracle IoT Cloud provide specialized tools for implementing digital twins across different industry applications.
Digital twin technology represents a fundamental shift from reactive to proactive management, enabling organizations to predict problems, optimize performance, and innovate safely in virtual environments before implementing changes in the physical world.
FAQs
Q1. What is a digital twin and how does it work in supply chain management?
A digital twin is a virtual replica of a physical object or system that uses real-time data and AI to simulate and optimize operations. In supply chain management, digital twins can model warehouses, transportation networks, and inventory systems to improve efficiency and predict potential issues.
Q2. How are digital twins being used in healthcare?
Digital twins in healthcare are used to create virtual models of organs, simulate surgeries, optimize hospital resources, and personalize treatment plans. They allow medical professionals to test scenarios and improve patient outcomes without risk to actual patients.
Q3. What are the key components of digital twin technology?
The key components of digital twin technology include Internet of Things (IoT) sensors for data collection, artificial intelligence for analysis, real-time data processing, and simulation capabilities. These elements work together to create accurate, dynamic virtual models of physical assets.
Q4. How can digital twins improve warehouse operations?
Digital twins can optimize warehouse layouts, enhance picking and put-away operations, and improve space utilization. By creating virtual replicas of facilities, companies can test different configurations to identify the most efficient setups without disrupting ongoing operations.
Q5. What are some popular digital twin tools for supply chain and healthcare applications?
Some popular digital twin tools include Ansys Twin Builder for medical device simulation, ThingWorx for logistics and asset tracking, and Oracle IoT Cloud for predictive analytics. These platforms offer specialized capabilities for creating and managing digital twins in various industry applications.