Digital Twins and the Evolution of Smart Operations
- International Academy
- Dec 1, 2025
- 10 min read
Author: Lina M. Farouk
Affiliation: Independent Researcher
Abstract
Digital twins—virtual representations of physical assets, processes, or systems continuously updated with real-time data—have quickly become central to the global movement toward smart operations. Their adoption has accelerated across manufacturing, logistics, infrastructure, energy systems, aviation, healthcare, and urban planning. The convergence of Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge computing, and cyber-physical systems has transformed digital twins from theoretical constructs into practical solutions that significantly improve predictive maintenance, system optimization, risk assessment, sustainability performance, and operational resilience.
This article examines the evolution of digital twin technology and its role in shaping the next generation of smart operations. It builds a multidisciplinary analytical framework grounded in Bourdieu’s theory of field, capital, and habitus, world-systems theory, and institutional isomorphism. These theories illuminate how digital twins redistribute forms of digital and symbolic capital, realign global production hierarchies, and drive convergence in managerial practices. While technical studies highlight performance gains, this article emphasizes the socio-institutional dimensions: power asymmetries in global supply chains, the institutional pressures that normalize certain digital twin architectures, and the uneven distribution of digital capital across nations and firms.
Using a conceptual methodology based on a structured review of recent academic literature (2020–2025), industry reports, and theoretical works, this article synthesizes four major themes: (1) digital twins as operational intelligence systems, (2) digital twins as instruments of capital formation and competitive advantage, (3) digital twins as artifacts shaped by global economic hierarchies, and (4) digital twins as institutional norms driving uniformity in “smart operations” practices. The analysis demonstrates that while digital twins enhance efficiency, sustainability, and resilience, they also raise questions about governance, labor impacts, data ownership, algorithmic opacity, and global inequality.
The conclusion argues that digital twins will define the next stage of operational excellence, but their long-term value depends on inclusive capability building, transparent governance structures, and attention to global disparities in digital infrastructure. It calls for interdisciplinary research and policy frameworks that ensure digital twin technologies contribute not only to smart operations but also to equitable and sustainable development.
1. Introduction
Digital twins have transitioned from visionary ideas to strategic assets underpinning Industry 4.0 and the broader transformation toward data-driven operations. Modern operations generate massive amounts of data from sensors, machines, logistics flows, customer interactions, and environmental conditions. Digital twins integrate these data streams to create dynamic, evolving models capable of simulation, prediction, optimization, and automated decision support.
Originally conceptualized in the aerospace industry, where NASA used early forms of virtual replicas to monitor spacecraft systems, digital twins have now expanded to integrate real-time data, machine learning, and sophisticated simulation environments. Advances in cloud computing, cyber-physical systems, and industrial analytics make it possible to model not only individual machines but entire factories, supply chains, cities, and ecological systems.
While the technical benefits—such as predictive maintenance, higher equipment availability, or optimized logistics—are widely recognized, deeper socio-institutional dimensions are often overlooked. Technologies do not operate in a vacuum: they are embedded within social fields, institutional structures, and global economic hierarchies. Therefore, the adoption of digital twins involves not only engineering decisions but also organizational politics, cultural change, and international power dynamics.
This article offers an expanded and interdisciplinary understanding of digital twins in smart operations by:
Reviewing their current capabilities and applications across major industries.
Applying Bourdieu’s theory, world-systems analysis, and institutional isomorphism to interpret adoption patterns.
Analyzing how digital twins reshape forms of capital, competitive advantage, and global interdependencies.
Discussing governance, ethical, and labor-related challenges.
Presenting implications for managers, educators, researchers, and policymakers.
By situating digital twins within broader societal structures, this article seeks to advance theoretical, managerial, and policy-oriented conversations about the future of smart operations.
2. Background and Theoretical Framework
2.1 The Concept and Evolution of Digital Twins
A digital twin is typically defined as a high-fidelity virtual representation of a physical entity (machine, process, building, network, or system) that maintains a continuous, bidirectional connection with its real-world counterpart. Digital twins integrate three core components:
The physical system: machines, assets, processes, or networks.
The digital model: computational models, 3D representations, simulation engines, and AI algorithms.
The data connection: real-time data flows through IoT sensors, enterprise systems, and external sources.
Modern digital twins leverage multiple technological layers:
IoT and sensors: enabling real-time monitoring.
Cloud and edge computing: providing scalable computation.
AI and machine learning: enabling prediction, anomaly detection, and optimization.
Simulation engines: supporting scenario planning and virtual experimentation.
Visualization tools: offering dashboards, 3D models, and decision interfaces.
Digital twins differ from traditional simulation models because they continuously reflect the current state of the physical system, enabling both predictive and prescriptive analytics. For example, an automotive digital twin can simulate wear patterns on components, predict failures, and autonomously adjust operating conditions to extend component lifespan.
In recent years, digital twins have expanded beyond single-asset modeling to:
Process twins: modeling end-to-end production lines.
System twins: modeling integrated networks (e.g., energy grids).
Supply chain twins: representing multi-tier networks of suppliers and logistics partners.
City twins: capturing complex interactions among urban infrastructure, traffic, energy, and environmental systems.
This expansion reflects the growing ambition to achieve global situational awareness—an overarching capability central to smart operations.
2.2 Bourdieu’s Theory: Field, Capital, Habitus, and Digital Capital
Pierre Bourdieu’s theoretical framework helps interpret why certain organizations lead digital twin adoption and how digital transformation reshapes power relations.
Field
A field is a structured space in which actors compete for resources. The field of smart operations includes manufacturers, logistics providers, technology vendors, consultants, regulators, and research institutions. Within this field, digital twin capability becomes a differentiating factor.
Capital
Bourdieu’s forms of capital provide a lens to understand digital transformation:
Economic capital: financial resources to invest in digital infrastructure.
Cultural capital: technical knowledge, engineering skills, and digital literacy.
Social capital: strategic partnerships in supply chains and technology ecosystems.
Symbolic capital: recognition as an innovator, leader, or “smart operations” pioneer.
Digital twin adoption generates and reinforces these capitals. Firms with higher economic and cultural capital can more easily build advanced digital twin ecosystems, reinforcing their competitive positions.
Digital Capital
Contemporary scholars extend Bourdieu’s framework to include digital capital, comprising:
access to digital tools and infrastructure
data literacy
computational skills
digital culture and organizational readiness
Digital twins embody digital capital at an institutional level. They encode knowledge and skill into models and interfaces, centralizing expertise and decision-making power.
Habitus
Habitus refers to internalized dispositions that guide behavior. Digital twins reshape managerial habitus by encouraging reliance on data-driven decision-making rather than intuition or experiential judgment. Engineers and operators develop new ways of “seeing” operations through simulations, dashboards, and predictive indicators.
2.3 World-Systems Theory: Core, Periphery, and Digital Inequality
World-systems theory interprets global economic structures as hierarchical:
Core countries: technologically advanced economies with high capital and innovation capacity.
Semi-peripheral regions: emerging economies seeking technological upgrading.
Peripheral regions: low-income economies supplying raw materials or labor.
Digital twins tend to originate and evolve in the core economies, diffusing outward along global supply chains. This creates several dynamics:
Firms in peripheral regions may face pressure to adopt compatible digital systems despite limited resources.
Global manufacturers may mandate digital reporting that small suppliers cannot easily provide.
Data extracted from peripheral regions may generate disproportionate value in the core.
Thus, digital twins can reinforce existing hierarchies unless complemented by capacity-building efforts.
2.4 Institutional Isomorphism and the Diffusion of Smart Operations
Institutional isomorphism explains why organizations adopt similar technologies and practices. Digital twins spread through:
Coercive pressures: compliance with safety, traceability, or sustainability regulations.
Mimetic pressures: imitation of successful early adopters during periods of uncertainty.
Normative pressures: professional standards promoted by consultants, industry associations, and academic programs.
As a result, global industries are converging toward similar “smart operations” models centered on digital twins, predictive analytics, and integrated data platforms.
3. Method
This research uses a conceptual and integrative method rather than empirical data collection. It involves:
Structured literature review:Academic publications from 2020–2025 in operations management, manufacturing, logistics, infrastructure, and digital transformation.
Theoretical integration:Cross-disciplinary synthesis incorporating Bourdieu’s theory, world-systems theory, and institutional isomorphism.
Analytical categorization:Identification of themes linking digital twin capabilities with socio-institutional structures.
Interpretive reasoning:Drawing conclusions on how digital twins reshape smart operations and influence power, inequality, and institutional conformity.
The objective is not to test hypotheses but to consolidate knowledge, articulate patterns, and propose conceptual insights.
4. Analysis
4.1 Digital Twins as Engines of Operational Intelligence
Digital twins enable unprecedented levels of operational intelligence across industries:
Manufacturing: simulate line balancing, reduce downtime, and improve product quality.
Aviation: monitor engines and aircraft systems to predict component wear and optimize flight paths.
Healthcare: model organ behavior, personalize treatment plans, and simulate patient outcomes.
Energy: optimize grid stability, forecast energy consumption, and integrate renewable sources.
Construction and infrastructure: support asset lifecycle planning, safety assessments, and structural monitoring.
The ability to conduct “what-if” simulations without interrupting physical operations represents a profound shift in how decisions are made. Managers can test multiple scenarios, such as:
How would changing production speed affect defect rates?
What happens if a supplier fails to deliver on time?
How would infrastructure respond to extreme weather events?
What is the impact of energy fluctuations on system stability?
Such predictive insights generate efficiency gains, reduce risks, and support long-term planning.
4.2 Predictive Maintenance and Smart Manufacturing
Digital twins significantly enhance maintenance capabilities:
Anomaly detection through analysis of real-time sensor data.
Remaining Useful Life (RUL) prediction of components.
Failure forecasting enabling early intervention.
Automated optimization of operating parameters to reduce wear.
For asset-intensive industries—automotive, mining, chemicals, oil and gas—predictive maintenance can save millions annually by preventing unplanned downtime.
From a Bourdieusian perspective, predictive maintenance capability becomes a form of technical capital that strengthens a company’s competitive position and symbolic power within the field. Firms recognized for reliability and innovation attract customers, talent, and partners—reinforcing cycles of capital accumulation.
4.3 Supply Chain Twins: Visibility, Resilience, and Sustainability
Supply chain digital twins provide end-to-end visibility across procurement, inventory, logistics, and distribution. They enable:
Risk modeling: simulating disruptions such as port congestion, geopolitical instability, or pandemics.
Routing optimization: reducing fuel consumption, emissions, and delays.
Inventory balancing: aligning stock levels with demand fluctuations.
Sustainability modeling: tracking carbon footprints and resource usage across the lifecycle.
However, these benefits often require extensive data-sharing across suppliers. Small and medium enterprises (SMEs) may be compelled to adopt digital systems mandated by multinational buyers, generating asymmetric burdens. This reflects world-systems dynamics in global production networks.
4.4 Infrastructure and Urban Systems
Infrastructure digital twins are increasingly deployed in:
transportation networks
rail systems
bridges and tunnels
water and sanitation systems
smart energy grids
urban planning and zoning
They support condition monitoring, safety inspections, asset renewal decisions, and emergency response planning. Urban digital twins combine traffic data, environmental data, land-use data, and population flows to model entire city ecosystems.
These applications highlight governance complexity: public agencies must balance transparency, privacy, public accountability, and multi-stakeholder collaboration.
4.5 Digital Twins as Digital and Symbolic Capital
Digital twins embody multiple forms of capital:
Digital capital: skills, computational resources, data architecture.
Human capital: data scientists, AI engineers, modelers, and operational experts.
Organizational capital: workflows built around data-driven decision-making.
Symbolic capital: reputation as a technologically advanced, resilient, or sustainable organization.
These capitals reinforce one another and shape competitive dynamics. Firms lacking economic or digital capital may struggle to adopt digital twins, deepening competitive inequality.
4.6 Institutional Convergence and Standardization
Institutional pressures contribute to the standardization of smart operations:
Certifications and compliance frameworks increasingly assume digital traceability.
Industry associations promote common architectures.
Consultants push identical “maturity models” across industries.
This results in global convergence around similar digital twin frameworks—even when local contexts differ.
4.7 Governance, Ethical, and Labor Considerations
Digital twins raise important governance questions:
Data ownership: Who owns the data generated by machines, workers, and supply chains?
Algorithmic transparency: How are decisions explained?
Labor impacts: What happens when models replace tacit knowledge?
Surveillance concerns: How is worker monitoring regulated?
Environmental impact: How to balance computing energy use with sustainability gains?
These issues require comprehensive governance frameworks.
5. Findings
Digital twins significantly enhance efficiency and resilience, but adoption remains uneven across sectors and regions.
Digital twins act as instruments of capital accumulation, reinforcing existing advantages of high-capital firms.
Global inequalities influence diffusion, with core economies dominating platform development.
Institutional pressures drive convergence, sometimes at the expense of locally appropriate solutions.
Ethical and governance gaps persist, especially concerning data rights, labor impacts, and transparency.
6. Conclusion
Digital twins represent a fundamental evolution in how organizations monitor, analyze, and optimize operations. They offer substantial benefits in performance, resilience, safety, and sustainability. However, digital twins are not purely technical objects—they are socio-institutional constructs shaped by capital, power, and global economic structures.
For digital twins to contribute to inclusive and sustainable smart operations, organizations must invest in digital skills, ethical governance, transparent data frameworks, and capacity-building across global supply chains. Future research should explore empirical aspects of digital twin adoption, the lived experiences of workers, cross-country disparities in digital capacity, and the long-term environmental consequences of large-scale digitalization.
Digital twins will continue to evolve, eventually integrating with autonomous systems, generative AI, quantum computing, and hyper-realistic simulations. Their transformative potential depends on aligning technological innovation with ethical responsibility and global equity.
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