Review Articles Open Access

IoT and Digital Twins for Enterprise Digital Transformation: A Structured Review of Architectures, Enabling Technologies, Applications, and Research Challenges

Saira Shairi , Saqib Dar
Available online January 1, 2026
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Abstract

Enterprises that operate physical assets increasingly use IoT devices to collect operational data and Digital Twins (DTs) to represent the condition of assets and processes in digital form. IoT provides measurements from the physical environment and, in some applications, also carries commands back to equipment. A DT uses these data to maintain and update a representation of the corresponding physical entity. Previous reviews have usually examined DT architecture, IoT, edge/cloud computing, artificial intelligence, cybersecurity, or individual application domains as separate topics. In this paper, we review 45 peer-reviewed papers between 2017 and 2025 and examine how these technologies are combined in enterprise DT systems. Each paper was examined according to its architectural role, enabling technologies, application domain, decision function, and reported technical limitations. The comparison resulted in a seven-layer architecture covering the physical and IoT layer, communication, edge/fog processing, cloud and enterprise data, DT models and services, intelligence and decision support, and enterprise applications and governance. We also identified five forms of DT integration. They range from systems used mainly for monitoring to implementations in which DT information is connected to operational decisions or control. Across the reviewed papers, recurring problems include interoperability, synchronization between physical and digital states, model validity, cybersecurity, lifecycle management, computational requirements, and integration with existing enterprise systems. The review indicates that the usefulness of an enterprise DT depends on how well physical observations, the digital representation, decision functions, and operational actions are connected within the same system.

Keywords

Digital transformation Digital twin Internet of Things Edge and cloud computing Enterprise architecture

Introduction

Replacing a manual activity with a digital interface does not by itself constitute digital transformation. Digital transformation involves changes in how an organization collects and uses information, coordinates its processes, makes decisions, and creates value. These changes may involve technology, but they also affect operational procedures and the way different parts of an organization interact. Vial described digital transformation as a process in which digital technologies create disruptions that trigger strategic responses and changes in value creation paths . Verhoef et al. similarly separated digitization, digitalization, and digital transformation, and emphasized that transformation has implications beyond the information technology function . Nadkarni and Pruegl showed that the concept spans technological and organizational research streams and therefore needs a clear link between technical capability and organizational change . These distinctions matter in cyber-physical enterprises because collecting more data does not, by itself, change how an organization operates. One component of the connection is provided by the Internet of Things (IoT). Physical process states can be recorded by sensors, embedded controllers, mobile devices, machines, and gateways, which can then share data with nearby or distant computing systems. IoT was positioned as a technology foundation for linked industrial processes alongside cloud computing, cyber-physical systems, analytics, and automation in Industry 4.0 research . However, an IoT implementation primarily addresses issues related to actuation, observation, and communication. A stream of measurements does not automatically provide a model of the physical entity, explain how its state is evolving, or support a controlled evaluation of future actions. DT research addresses this second requirement. Early manufacturing work distinguished a digital model, a digital shadow, and a DT according to the direction and degree of automated data exchange between physical and digital entities . Later papers described DTs as combinations of physical entities, virtual models, data, services, and connections that can support monitoring, simulation, prediction, and control . In practice, IoT and DTs perform different but connected functions. IoT devices report the state of the physical system through measurements collected from sensors and controllers. A DT relates these measurements to a model of the corresponding asset or process. In applications that support decision making or control, information may also move in the opposite direction. A recommendation may be returned to an operator, while applications with actuation capability can send a command to a controller or physical device. Previous DT surveys have approached the field from different directions. Previous surveys examine different parts of the DT research space. Fuller et al. discuss enabling technologies and open research issues, while Jones et al. focus on DT definitions and classification. Industrial applications are reviewed by Liu et al. , and Qi et al. examine technologies and tools used in DT implementation. Qian et al. examine the architectural organization of DT systems. Sharma et al. consider the relationship between DT theory and practice, while Liu et al. organize DT systems around physical entities, virtual models, twin data, and applications. More focused reviews address particular technical or application concerns. Hakiri et al. examine DTs in future networks and IoT environments. Cybersecurity is considered from general DT and industrial perspectives in , and production-related implementation challenges are discussed in . Standards and interoperability are examined in . Other reviews focus on DT Networks , smart-city applications , IIoT and Industry 5.0 , Industry 4.0 reference architectures , and DT-assisted edge offloading . The surveys summarized above are organized around different technologies, architectural concerns, or application domains. Across this selected set, the full path from physical observation to enterprise use is not consistently treated as a single organizing frame. In an operational system, data may pass through sensing devices, communication networks, edge or cloud resources, DT models, analytical functions, and enterprise software before contributing to a decision. This review examines how information remains associated with the correct physical entity and how state is maintained as data move through these parts of the system. The physical-to-enterprise path is therefore used as the organizing perspective, from observation and DT processing to decision support and, where applicable, operational action. In this review, enterprise refers to an organization that operates physical or operational systems and connects them with digital technologies. The represented environment may include assets, facilities, infrastructure, products, logistics networks, or human-centered services. The application scope covers manufacturing, healthcare, smart cities and infrastructure, buildings and energy systems, agriculture, and supply chains. Purely virtual consumer applications are excluded unless they contribute an architectural concept that can be transferred to a cyber-physical enterprise setting. The review addresses six research questions:

  • RQ1: What roles do IoT and DTs perform in enterprise digital transformation?

  • RQ2: How can physical assets, IoT data, computing resources, DT models, decision functions, and enterprise applications be organized within a common architecture?

  • RQ3: Which enabling technologies recur in IoT–DT systems, and where do they operate within the architecture?

  • RQ4: How do application domains differ in the entity being represented, the decision being supported, and the operational requirements placed on the DT?

  • RQ5: Which technical and governance barriers recur in DT deployment, scaling, and reuse?

  • RQ6: What problems remain when separate DT implementations are connected into interoperable and governed enterprise systems?

The analysis links IoT, DTs, edge/cloud computing, AI, and enterprise systems along the path from physical observation to operational use. From this analysis, the paper defines a seven-layer architecture that separates the responsibilities of physical devices, communication, computing resources, DT models, decision functions, and enterprise applications. The 45 reviewed papers are also grouped into five integration patterns according to how DT information is used for monitoring, diagnosis, prediction, decision support, and closed-loop action. Reported deployment barriers are organized into technical and governance categories and are used to identify research directions involving interoperability, uncertainty, cybersecurity, lifecycle management, sustainability, and enterprise-level evaluation.

The rest of the paper is organized as follow: Section 2 defines the main concepts and positions the paper relative to earlier DT surveys. Section 3 describes the review design, paper selection, and coding procedure. Section 4 examines the technical foundations while Section 5 presents the seven-layer architecture. Section 6 discusses the enabling technologies and integration patterns. Section 7 compares the application domains, and Section 8 examines the cross-domain research challenges. Section 9 presents the research agenda. Section 10 discusses the limitations of the review, and Section 11 concludes the paper.

Complete Article

The complete article, including all figures, tables, equations and algorithms, is available in the official publication PDF.

Conclusion

IoT and DTs play related but different roles in enterprise systems. IoT infrastructure provides the connection to physical entities through sensing, identification, communication, and, where required, actuation. The DT uses information from that connection to maintain a representation of the corresponding asset or process, including its state, behavior, relationships, and associated models. Processing may take place at the edge, in the cloud, or across both environments according to the requirements of the application. Twin information can then be used by AI, simulation, optimization, or rule-based methods for analysis and decision support. In this review, the enterprise role of these technologies becomes most visible when their outputs are incorporated into an operational workflow or control process. The analysis resulted in seven logical layers: physical assets and IoT, connectivity, edge/fog computing, cloud and enterprise data, DT models and services, intelligence and decision support, and enterprise applications and governance. The reviewed papers were also grouped into five integration patterns: connected monitoring, diagnostic twins, predictive twins, prescriptive twins, and governed closed-loop twins. These patterns describe different uses of DT information and are intended as classification categories rather than mandatory stages of development. Systems that connect DT-generated decisions to an operational workflow or physical process bring additional concerns related to validation, cybersecurity, clear decision authority, governance, and audit. The reviewed applications cover manufacturing, healthcare, smart cities, buildings and energy, agriculture, and supply chains. Across these domains, recurring issues include aligning entity identities and semantics, maintaining sufficient consistency between digital and physical state, and determining where data and computation should be placed. Other concerns involve data and decision integrity, model changes over the system lifecycle, and integration with existing enterprise systems. These issues also influence one another. A change in synchronization policy may affect both communication load and the accuracy of the maintained state. Moving a model between edge and cloud resources may change privacy and resilience requirements. Similarly, when DT output is given greater authority to influence an operational action, validation and audit requirements may become more demanding. Taken together, the findings support an observation-model-decision-action view of enterprise DT systems. Physical observations are associated with a maintained digital representation, that representation is used within a defined decision process, and the resulting information may be connected to an operational action under appropriate governance. Within the scope of this review, the usefulness of an enterprise DT therefore depends on how these parts work together across the complete information and decision path. A DT contributes to organizational processes when its information can be used for a defined decision and the resulting action can be traced, evaluated, and revised.

Future research should therefore focus on composable twins, edge-native execution, uncertainty-aware operation, secure decision provenance, human-governed AI, sustainability, and enterprise-level evaluation.

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