Digital Twins in the Software Industry: Comparison
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Please note this is a comparison between Version 2 by Fanny Huang and Version 1 by Juan A. Holgado-Terriza.

Digital twins are a powerful consequence of digital transformation. In fact, they have been applied to many industries to enhance operations, predict needs, improve decision making, or optimize performance, even though the definition of digital twins is still evolving. Digital twins are already influencing and will significantly affect the software industry, revolutionizing various aspects of the software development lifecycle. 

  • digital twin
  • software industry
  • software development

1. Digital Twins Concept and Its Adoption

The term digital twin (DT) was coined by Michael Grieves and presented in the first executive PLM (product lifecycle management) courses at the University of Michigan in early 2002. The initial definition by Grieves [4][1] is based on the idea that a digital informational construct about a physical system could be created independently. Then, this digital information construct would be a twin of the information embedded within the physical system itself and would be linked with the physical system throughout the entire lifecycle of the system. From this definition, three components that conform to a DT can be distinguished: the physical system, the digital informational construct, and the connectivity between both.
According to Kritzinger, three levels of DT integration can be defined based on the type of connectivity, as in Kritzinger et al. [8][2]: a digital model, where the DT does not exchange data within the digital model and the physical system; a digital shadow, where changes in the physical system are communicated one way to the DS; and the digital twin, in which there is complete communication in both directions and a change in one affects the other.
Conversely, Korenhof et al. [9][3] describe a DT as a type of emerging technology able to discern dependencies between product, process, and operations, characteristics that remained hidden before. Moreover, a DT can make issues visible before they become critical, being able to predict trends and behaviors to optimize them. Then, in this vision, a DT can capture a digital representation of a system’s operation, determine the critical elements influencing its performance, and consequently find different optimized approaches.
Complementary to DTs, several technologies (the IoT, big data, mixed reality, blockchain, 5G, machine learning, cloud computing, or cyber–physical systems) appeared simultaneously in the context of Industry 4.0 that support the development of DTs. Specifically, the cyber–physical system (CPS) concept was coined in 2006 by Helen Gill at the National Science Foundation (NSF) as a system that integrates computing and physical processes [10][4]. In these systems, embedded computers with sensors, actuators, controllers, and software in robots, humans, and network connectivity are combined to monitor and control physical processes, usually with real-time feedback loops of sensing, decision making, and evaluations of network compatibility, as commented by Dafflon et al. [11][5]. Currently, the CPS designs are supported by many technological platforms such as the IoT, Fog, Edge Cloud, or 5G [12][6]. Furthermore, human–robot cooperation (HRC), for instance, studies the interactions between humans and robots in critical scenarios in a factory where humans and robots must work together, applying dynamic planning and safe routes for autonomous robots, as in the study by Maruyama et al. [13][7].
DTs and CPSs are interrelated concepts. The critical difference between DTs and CPSs relies on a DT as a virtual model, i.e., an informational construct of a physical system. In contrast, a CPS is an enhanced physical system integrating digital intelligence and connectivity, as stated by Tao et al. [2][8]. CPS support DTs through their capacity to integrate and synchronize the physical world with the digital world. This is achieved by collecting, transmitting, and processing the real-time data generated by sensors in the physical world. Once these data are processed, they might be used to create and update a DT, covering a virtual simulation of the physical world and all its relevant aspects. The relationship between a CPS and a DT is bidirectional, since the insights obtained by a DT can be used to change the behavior of the physical world through actuators—for instance. Even though the DT concept was coined in early 2002, its maturity was gained in the 2010s when it started to gain traction, as CPSs offered a bridge between the physical and digital worlds. Somers et al. [14][9] proposed using the emerging concept of DTs to help test and enhance the CPS development phases.
The definition of a DT continues to evolve as its usage is widespread in different areas. Semeraro et al. [15][10] analyzed different DT definitions and the specific issues that characterize the term. They reviewed 30 DT definitions, grouping them into five clusters to extract the primary features, and summarized DTs as follows: “A set of adaptive models that emulate the behavior of a physical system in a virtual system getting real-time data to update itself along its life cycle. The DT replicates the physical system to predict failures and opportunities for change and prescribe real-time actions for optimizing or mitigating unexpected events by observing and evaluating the operating system profile”.
The adoption of the DT from its origin was oriented mainly toward manufacturing, where it is applied extensively. For this reason, the first step was to analyze DTs’ impact on manufacturing, specifically in smart manufacturing. Then, a literature search was performed in research databases such as Scopus and the Web of Science, using the terms “DT” and “manufacturing”. In this case, only open-access research publications from 2020 were extracted for the analysis.
The first study analyzed DT usage in manufacturing, considering the most relevant issues and the primary purposes. Accordingly, a taxonomy (Table 1) was elaborated, collecting a list of these issues, which are summarized below:
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Monitoring and control: DTs mainly focus on monitoring assets to gain knowledge about decisive factors that can impact them. This asset understanding can be applied for different usages, such as anomaly detection, as for Calvo-Bascones et al. or Latsou et al. [16[11][12],17], or evaluating the status, history, or need for maintenance during the industrial process, especially in the supply chain, as with Dietz et al. [18][13].
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Quality: Research related to quality in its distinct aspects, such as inspections, verification, or defect classification, are often areas where DT applications can be involved. Sommers et al. [14][9] propose using DTs for CPS testing. Zheng et al. [19][14] define an approach to building a quality-oriented DT for manufacturing processes by combining them with multiple agents.
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The intelligent design of products and manufacturing processes: A significant body of work is focused on DTs’ applicability in collaborative design, modeling, prototyping, and simulation at different stages, as well as team-based scrutiny of manufacturing processes. They also include frameworks or methods that combine or integrate the use of DTs in the design steps of manufacturing processes. For example, Nielsen et al. [20][15] research optimizing product design in product families to fit MMSs (matrix-structured manufacturing systems). In contrast, Cimino et al. [21][16] focus on the practical design of production lines.
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Intelligent planning, process, and production control: In these works, the building of the outcome of the value stream starting from the initial plan, the scheduling of the process chain at different steps, and its adaptation to produce variations and control over the process were the relevant issues for manufacturing. Chiurco et al. [22][17] used rover data modeling and machine learning (ML) to enable DTs in adaptive planning and control, as they are a good fit for dynamic production scheduling, dynamic performance optimization, process automation, and control. Likewise, Negri et al. [23][18] focus on production scheduling.
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Intelligent maintenance: Maintenance is a complementary issue linked to the design and building of manufacturing processes. Then, assuring and improving the maintenance of assets during the building of the products and in the post-building phase was recommended. Every unplanned stop in the product manufacturing process could mean a significant amount of time and cost increments. Neto et al. [24][19] is an excellent example of running simulations for opportunistic preventive maintenance scheduling.
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Decision making/support: DTs can help to assist in the decision or support of manufacturing products actively or passively. For instance, Villalonga [25][20] describes a dynamic scheduling decision-making framework based on DTs.
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Extension of product as service: Some works, as Laukotka [26][21] suggests, use DTs to enable product service strategy (PSS) in organizations to have more stages or steps in their product lifecycle. They also provide variations in the final product and empower digital versions to extract customer data, as with Wilking et al. [27][22].
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Value and supply chain: Many DTs are focused on resource procurement and supply management. For instance, Rasor et al. [28][23] use a systematic framework to address the collaborative development of DTs in manufacturing value chains. On the other hand, Moder et al. [29][24] analyze the relevant usage of semantic web technologies on DTs for the digitalization of supply chain processes. DTs can help select alternatives to increase resilience to be sure there is no stop in the manufacturing process when the simulation predicts potential issues.
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Resilience, cybersecurity: The improvements in product security and resiliency concerning the availability of assets and processes are usually recurrent concerns in designing and building manufacturing processes that DTs can validate before the actual deployment and start-up of the system. Papacharalampopoulos et al. [30][25] specify a roadmap for designing and implementing DTs to add agility and resilience to manufacturing. In particular, Empl et al. [31][26] developed a cybersecurity framework based on DTs to analyze the vulnerabilities of IoT systems applying the SOAR (security orchestration, automation, and response) paradigm.
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Continuous improvement and optimization: DTs are specialized in continuous improvement methods such as kaizen and optimization. Umeda [32][27] introduces the extension of DTs as digital triplets to add kaizen activities for continuous improvement between engineering cycles with educational purposes. On the other hand, Ferriol-Galmés et al. [33][28] cover building a DT for network optimization using neural networks so the DT can accurately estimate relevant SLA metrics for network optimization, as well as performance and optimization, like for Petri et al. [34][29], which use DTs better to understand the complex interplay between environmental variables and performance so the infrastructure gains resilience.
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General purpose and design of DTs: Some works are focused on the techniques and architectures required for DT generation. Efforts in this regard, like by Duan et al. [35][30], try to propose developing a standardized DT model. On the other hand, Göllner [36][31] presents guidelines for modeling DTs and their content to be interoperable and collaborative as a production plant can be seen as a system of systems (SoS) that works together towards a purpose. From another perspective, Kugler [37][32] provides a method for visualizing and defining use cases for DTs.
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Project management, cost reduction, and ROI: Some approaches propose using DTs related to project management for cost estimation (such as Farsi et al. [38][33]) and reduction, return on investment (ROI), and evolution measurement. Hickey et al. [39][34] discuss, on the other hand, the support that DTs can offer to project managers with more visual and effective communication methods. They also remark on the potential of DTs in risk and resource management.
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Sustainability: A claim on the importance of efficiency, which can be gained in the manufacturing process with energy consumption, recycling, or reusing, is appreciated in different articles. For example, Mouthaan et al. [40][35] discuss how twin transition and digitalization can contribute to sustainability and progress. Decarbonization and dematerialization are increasingly applied. Chen et al. [41][36] propose a framework to support environmental sustainability through lean principals.
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Training/knowledge transfer: DTs can help teach engineering and transfer knowledge at different steps of the process and across departments. Maschler et al. [42][37] cover a positive feedback contribution to learning process acceleration through DTs.
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Emotion-aware processes: In environments where robots and humans interact, an awareness of fatigue levels and emotions are essential to avoid accidents, defects, and to protect people and assets, contributing to employee satisfaction by following human-centered processes. Florea et al. [43][38] describe many use cases: improved information delivery, ergonomics, professional development at enterprise scale.
Table 1.
Main usages of DTs in smart manufacturing.
[55].

2. Digital Twins in the Software Industry

Regarding the software industry, the first company founded to provide software products and services was the Computer Usage Company in 1955, as stated by Kubie [108][103]. The global software product market in the software industry amounted to USD 968.25 billion (about USD 3000 per person in the US) in 2021. The market is expected to reach USD 1493.07 billion (about USD 4600 per person in the US) in 2025, according to the Software Products Global Market Report 2023 Edition. Digital transformation impacts most companies’ transition to the cloud, data governance, and regulation adaptation. However, there are other issues to learn from smart manufacturing to reach Software Industry 4.0 as the industry is already thinking about Industry 5.0.
Historically, the software industry has adopted many concepts from traditional industries to improve efficiency and productivity. Nevertheless, both sides have crucial differences because the extreme adaptability and changing market of the software industry make achieving stable requirements nearly impossible. Then, it may be impossible to reach an objective conclusion about whether a software system meets its specifications, as stated by Sommerville [109][104].
Some of the areas where the methods and techniques of traditional industries are successfully adopted in the software industry are as follows:
  • Project management: A key concept adapted with different approaches and frameworks based on lean principles from the Japanese industry. An example could be Kanban from Toyota production lines.
  • Quality assurance and quality control: Although software quality is not directly comparable with quality in manufacturing, disciplined approaches such as Six Sigma—one of the most prevalent manufacturing philosophies—are applied in the software industry.
  • Software engineering: This field has been applied to the software industry, bringing principles and methods from traditional engineering. Lean manufacturing principles have been translated into software engineering [110][105].
  • Continuous improvement: Inspired by the Deming cycle. This is the spirit of many software industry processes, techniques, and standards, such as the security information management system (ISO27001 [111][106]).
  • Operations: Advanced manufacturing has impacted the software industry in process automation and delivery, automatized testing, reliability, and supply chain management, among others. Integration into the software development process opened the recent DevOps paradigm.
  • Security: Reaching high-level IT security is mandatory for current software products from their inception to avoid possible cyber-attacks or information theft. Security directly impacts a software product through the inclusion of development practices to strengthen security and compliance and the application of tools to improve products through a continuous static and dynamic analysis of the potential vulnerabilities at any stage of the software development pipeline.
Area Articles References
Table 2.
DT’s main drawbacks in smart manufacturing.
Area Articles Ref.
Monitoring and Control 11 [16,17,18,41,44,45,46,47,48,49,50][11][12][13][36][39][40][41][42][43][44][ 1645]
[17,18,24,28,1332,36,]46,53,[19][23][27][31][41][48]62,[57]63,[58]77,[72]81,[76]85,[80]86,[81]87,105][12][[82][100] Quality 6 [14,19,,53][944,][1451,][39][46]52[47][48]
Preparation and redefinition of human interaction, culture acceptance 5 [26,44,45,49,67][21][39][40][44][62] Intelligent Design 12 [20,21,44,52,54,55,56,57,58,59,60,[1561][15][16
Data quality: Incomplete documentation, binary data, real historical data on assets/Cold start, dark data, siloed info 16 [17]][39][18][47][24][49][50,20],23[,2951][52][53][54][55][56]
,31,37,42,51,63,71,80,82,[2684,][32][3789,90,107]][46][58][66][75][77][79][84][85][102] Intelligent Planning, Process and Production Control 23 [
Privacy/Cybersecurity/Ethics22, 723,24, [27,31,45,55,72,90,106][22][25,44,52,]59,62,[54]63,26[64,65,57][66,5867,68,]][59][60][61][62]69,70,71,,77][72,17][73,1874,75,][19][40][20][50][39][67][47[85][101][63][64][65][66][67][68][69][70]76[71][72]
Intelligent Maintenance 12 [24,44,84,85][1954,][3955,]
Lack of General Framework, DT definition, and benefits 14 [16[49,34,35,98,101][11][3978,,29][504179,,45,][30][7380,54,]]61,68,[34]81,[3682,][83,74][40][75][49][76][[77][72,5678][79]79[80]
,92,][63][67][74][87][93][96] Decision Making/Support 7 [24
The complexity of systems/products, continuous change,25 9,64,86,87,88,89][19][20][59][ [32,41,50,52,]81][47[59,][5464,][5981,][7691,93][27][36][4582][83][84]
][86][88] Extension of Product as a service 5 [26,27,][21][90,22][91,
Lack of real-world applications implemented 8 [22,4185,,95][45,17][58,36]74,75,][40][86][53]92[87]
[69][70]83[78][90] Value and Supply chain with suppliers and third parties 8 [28,29,52,93,[94,95
Data infrastructure, talent in data science knowledge, high fidelity in mirrored information:,2396,97] 12][24][47][88][89][90][91][92]
[25,34,45,47,65,69,2070,72,][2973,][4078,]81,97][[42][60][64][65][67][68][73][76][92] Resilience, cybersecurity 4 [30,[31,2552,
Lack of more research results73]][26] 12[47][68]
[19,21,57,66,68,88,94,96,100,102,103,104][14][16][52][61][63][83][89][91][95][97][98][99] Continuous Improvement, Optimization 9 [27,]32,33,[34,62,27][28]
The complexity of the DT model design and interpretation94][95]
[12][29]90 8,98,99, [30,38,40,48,56,76,81,99][25][33][35]100][22[43][57][51][85][71][93][76][[94] General Purpose, Design of DT 3 [35,36,
Bias, Coding bias 2 [1437][30][31],60][9][55[32]
] Project Management, Cost Reduction, ROI 3 [38,39,101][33][34][96]
Sustainability
Total analyzed articles 94 5 [34,40,41,102,103][29][35][36][97][98]
Training, Knowledge transfer 3 [42,104,105][37][99][100]
Emotion-aware processes 2 [43,38]106][[101]
Total analyzed articles 94
This list gives us an overview of how DTs have been used in manufacturing and what aspects may be covered in the software industry, particularly in ALM. However, the application of DTs in manufacturing may pose some drawbacks that limit their usage. The main drawbacks are described in Table 2.
Heterogeneous data, harmonization, integrations, interoperability
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Heterogeneous data, harmonization, integrations, and interoperability: Despite the efforts of the industry to set standards, there are issues connected to the diversity of data when integration from manufacturing process sources needs to be consolidated for a high-fidelity representation. These data from different value-chain layers become more relevant when harmonized at different scales and semantically structured to simplify the conversion into valuable information. Talkhestani et al. [63][58] mention the heterogeneity between models and their relationship in the DT as one of the top challenges observed in the field.
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Preparation and redefinition of the human role, interaction, and cultural acceptance: The human factor in any part of the manufacturing process and the active role of this issue can reduce the ability to create human-centered processes with less friction on cyber–physical systems. Ahmadi et al. [55][50] discuss the role and evolution of humans as they interact with recent technologies and how future skills might fit with existing roles differently.
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Data quality: Incomplete documentation, binary data, accurate historical data on assets, cold starts, dark data, and siloed information cannot be collected appropriately. Apart from identifying the sources, data preparation could be a complex issue with a lack of documentation. There is no previous knowledge about the availability of certain types of data and no previous experience integrating the data from various parts of the organization. Ehrhardt et al. [107][102] share the difficulties with data quality since data are recorded manually from the production systems. The accuracy of these data for optimization purposes may lead to wrong actions and decisions.
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Privacy/cybersecurity/ethics: Personally identifiable information (PII) data and information deducted from DTs or other sources raise significant concerns among the articles reviewed. For example, Neguina et al. [106][101] comments that developing these systems involving personal data is subject to cybercrime and non-ethical usage opportunities.
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Lack of a general framework, DT definition, and benefits: Kuehner [45][40] reports gaps in the DT definition and the importance of establishing a standard framework for DTs’ definition. For instance, Calvo-Bascones [16][11] introduces variations in the definition of DTs and provides different methods to detect anomalies with DTs, but none are accepted as a general approach.
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The complexity of systems/products, continuous change: The evolution of customer demands and the need for efficiency result in a changing manufacturing process ecosystem with increasingly complex and fully automated behaviors. As a reference, Van Dinter [81][76] mentions that the complexity of models is one of the key issues to cover, together with the computational workload due to the variety of data, assets, and components. As Ruzsa [52][47] considers, DTs can help to tackle this continuous change, but to build them, the article recognized a considerable effort in organization architecture, big data solutions, and digital transformation.
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Lack of real-world applications: Many works indicate a lack of tested initiatives for long-term real-world scenarios, and existing scenarios have many constraints to verify their efficacy. Chen et al. [41][36] describe the lack of practice-based frameworks and operational and implementation guidelines in the existing scenarios as a top issue.
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Data infrastructure, talent in data science knowledge, and high fidelity in mirrored information: The explosion of big data can pose problems in capturing a sufficient variety of data to mirror physical systems into a DT. Data science can minimize its impact. Kumbhar [69][64] believes that data science knowledge is a critical capability for industries to implement DTs-related technologies and is a potential constraint. The main reasons are that the infrastructure costs grow remarkably, and the available talent to apply data science remains limited.
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Lack of research results: There are insufficient research results in specific areas to compare and build better proposals for setting the basis for DTs. Ragazzini et al. [66][61] summarize a lack of concerns in specific applicability areas. Meanwhile, Langlotz [103][98] highlights the lack of research for DTs operating in physics and data-driven models required for industrial cases.
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The complexity of DT design and interpretation: The interpretation and design of DT dynamics are rather complex issues when used in automatic decisions. Farsi et al. [38][33] show complex scenarios for DTs due to a lack of data or uncertainty. This makes the design of the techniques and their interpretation more complex.
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Bias and coding bias: Simulations and results from DTs may include undesirable constraints or limitations based on training data and the process selected to reflect reality and generate simulation-based services from the virtual models. Creating tech debt in DTs can be easy from the first iteration by introducing bias towards specific options. This can be risky for the success of their implementations, as stated by Ng et al. [60]
Even though there are distinct categories in software development, such as programming services, system services, open-source tools, or SaaS (Software as a Service), all share common practices to adapt software products and value streams to the exigency level of customers. In this context, ALM is the PLM of computer programs, whereas PLM focuses more on hardware. This is precisely the origin of DTs, where Grieves [4][1] created the concept of the DT. Consequently, researchers hypothesize that all work coming from smart manufacturing related to PLM could be an excellent input for ALM.
Traditionally, the products in the industry, including hardware and software, were managed by PLM. However, the software industry shift starts with managing software products with an ALM paradigm. Research such as that by Deuter et al. [6][107] pointed out that the integration mechanisms between ALM and PLM can be achieved through an apparent convergence with DTs.
ALM covers the entire process from inception to the end of an application’s life. It comprises several disciplines: project management, requirements management, architecture and design, software development, testing and quality assurance, deployment, maintenance, and decommissioning. SDLM is a subset of ALM covering only the phases of software development. While ALM helps to make better and brighter decisions about efficiently managing software, the software development life cycle (SDLC) helps to create robust software. ALM continues after development until the application is no longer used and may span many SDLCs.
Chapell [112][108] identifies mainly three areas in ALM: governance, which includes all of the decision making and project management for the application; development, the process of creating the application, which can reappear several times and which is linked directly with the SDLC; and operations, that is, all the work required to run and manage the application. From a standards perspective, ISO/IEC 12207 [113][109] can be taken as a reference for ALM. It includes not only main processes such as acquisition, procurement, development, operation, or maintenance, but also support processes such as documentation, configuration management, quality assurance, V&V (verification and validation), joint revision, auditing, and problem resolution, as well as organizational processes such as management, infrastructure, improvement, and human resources. Some people also refer to ADLM (application development lifecycle management) to include DevOps as a valuable piece of collaborative culture, principles, and practices towards products. Furthermore, including good practices for guaranteeing security in software design and continuous vulnerability analysis promotes the DevSecOps paradigm.
Although DTs are particularly useful for simulating and modeling the behavior of complex systems, including software and hardware, they can provide potential advantages in the context of ALM or the SDLC. DTs can enhance software applications’ understanding, development, and management through their lifecycle. DTs may be used to create detailed models of software systems and the dynamics around them over their changing lifecycle flow. DTs can predict performance, improve maintainability, and evaluate changes before updating and deploying software.
Antonino et al. [114][110] and Nakagawa et al. [115][111] offer excellent examples of how Industry 4.0 requires continuous engineering monitoring practices for quality properties over a software or system architecture, and the applicability of DTs to simulate the evolving architecture and its evaluation.Likewise, Jones et al. [116][112] apply version control and DTs from conceptual design phases to physical prototypes using DTs to maintain synchronization. These works show how traditional and software industries converge within PLM and ALM, and how DTs can help to extend the capabilities of the value stream in both PLM and ALM. 

References

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