Digital Twins in Supply chain:Challenges Authors: Merilyn Mudzingaidzwa Gimantha Wijegunawardena Supervisor: Shahryar Soroshian Masters`Thesis in Logistics and Transport Management Spring 2025 Graduate School, School of Business, Economics and Law,University of Gothenburg,Sweden Table of Contents 1 Introduction…………………………………………………………………………………..6 1.1 Problem statement…………………………………………………………………………...7 1.2 Purpose of study……………………………………………………………………………..8 1.3 Research Question…………………………………………………………………………...9 2 Methodology………………………………………………………………………………….10 2.1 Research Approach………………………………………………………………………...10 2.2 Data Sources and Search Strategy………………………………………………………...11 2.3Screening and Selection Process (PRISMA Framework)...................................................11 2.3.1 Identification Stage………………………………………………………………………..12 2.3.2 Screening Stage……………………………………………………………………………12 2.3.3 Eligibility Stage……………………………………………………………………………12 2.3.4 Inclusion Stage…………………………………………………………………………….12 2.4 Data Extraction and Analysis…………………………………………………………….. 13 2.4.2 Thematic Analysis…………………………………………………………………………13 2.4.3 Use of Bibliometrics Software…………………………………………………………….13 2.4.4 Keyword co-occurrence analysis…………………………………………………………..14 2.5 Validation Process…………………………………………………………………………..15 2.5.1 Selection of Experts………………………………………………………………………..15 2.7 Limitations…………………………………………………………………………………..17 2.8.Final PRISMA workflow according to the literature extracted…………………………18 3 Systematic Literature Review………………………………………………………………..19 3.2 Categorization of Articles……………………………………………………………………27 3.3 Industry Application…………………………………………………………………………28 3.4 Methodologies used for DT research………………………………………………………...28 3.5 Geographical Distribution of publication……………………………………………………30 4 Challenges…………………………………………………………………………………….32 4.1 Technological Challenges……………………………………………………………………32 4.2 Implementation and Adaptation Challenges………………………………………………...35 4.3 Supply Chain Specific Challenges…………………………………………………………..38 4.4Ethical and Regulatory Challenges…………………………………………………………..41 4.5 Business and Economic Challenges…………………………………………………………43 5 Discussion…………………………………………………………………………………….45 5.1Introduction………………………………………………………………………………….45 5.2 Validation……………………………………………………………………………………45 5.2.1 Technological challenges………………………………………………………………….45 5.2.2 Implementation and Adaptation Challenges………………………………………………46 5.2.3 Supply Chain Specific Challenges………………….……………………………………..47 5.2.4 Ethical and Regulatory Challenges………………………………………………………..48 5.2.5 Business and Economic Challenges……………………………………………………….49 5.3 Comparative Analysis Vs Practitioner Perspectives…………………………...……………50 6 Conclusion………………………………………………………………………………….…51 6.1 Future Research Directions………………………………………………………….………51 List of figures Figure 1.1 Prisma flow diagram………………………………………………………………..13 Figure 2.1 Co-occurrence analysis from VOSviewer………………………………………….15 Fig 2.3 Experts and their professions……………………………………………………….17 Fig 2.4 Final PRISMA Flow Diagram showing the stages in the qualitative synthesis……..18 Fig 3 : Overview of literature used…………………………………………………………...19 Fig 3.1 Publication per year from 2019……………………………………………………….27 Fig 3.2 Publication according to Industries…………………………………………………..28 Fig 3.3 Research methodologies used in digital twins studies…………………………..………29 Fig 3.4 Top 10 countries by number of publications…………………………………………..30 Fig 4 Technological Challenges list down……………………………………………...……37 Fig 4.1 Implementation and Adaptation Challenges………………………………………….39 Fig 4.2 Supply Chain Specific Challenges…………………………………………………….40 Fig: 4.3 Ethical and Regulatory Challenges………………………………………………...…42 Fig 4.4 Business and Economics Challenges…………………………………………………..44 Abbreviations SCM- Supply chain management DT(s) - Digital Twin(s) ROI- Return On Investment SLR- Systematic Literature Review SC -Supply Chain IoT-Internet of Things DTCD- Digital Twins of Customer Demands OEM- Original Equipment Manufacturer. DSCT- Digital Supply Chain Twin. Abstract This thesis explores the integration of Digital Twin (DT) technology within the supply chain management and mainly focuses on the challenges which hinders its wide extended adoption. DTs offers promising applications that are able to enhance real-time decision-making and operational efficiency.The implementation of these systems across global SCs remains complex.Through the use of a Systematic Literature Review (SLR) combined with experts validation , this study examines the critical barriers to the adoption and also highlights the areas that requires further research. This study contributes to a deeper understanding of how the DTs intersect with SC dynamics and offers guidance for future strategies that aim for effective and scalable implementation. 1 Introduction Due to the fast pace of Industry 4.0 technologies, there has been an increase in the adoption of digital twins in many sectors such as manufacturing, healthcare and SC.Recognizing the substantial advantages of digitalization, such as quicker access to information, improved customer experiences, more production and better decision-making, many industries are moving toward it making all the while improving safety and cutting operating expenses (Rosin et al., 2020). Digitization offers various advantages in dealing with today’s business challenges, particularly in volatile markets that require rapid responses to unexpected changes. The early 2020 COVID-19 epidemic, which disrupted the world, is a prime example of this unpredictability. SCs were affected by panic buying and delays brought on by a lack of workers in production and logistics as the virus spread.(Chowdhury et al., 2021). This unexpected crisis highlighted the essential need for real-time data integration, automation and predictive analytics which are the major key components of digitalization. Digital twins has as an advanced digitalization, has enhanced the capabilities by creating virtual replicas for SC processes thus allowing businesses to be able to simulate disruptions, optimize their operations and improve their decision-making (Ivanov & Dolgui, 2020). Through closing the gap between the physical and the digital SCs, digital twins have extended the impact of digitalization by enabling greater resilience, efficiency and adaptability in the case of any uncertainties (Leng et al., 2021). A digital twin is defined as a virtual replica of a physical system that enables updates to real time using data from devices like sensors , IOT devices and advanced analytical according to Tao et al., 2019).In SCM, the function of digital twins is to ensure real time monitoring, to have predictive analytics and to provide stimulated decision making, thus offering companies with the ability to improve their decision making, reduce risks and increasing operations efficiency(Negri et al., 2017; Kritzinger et al., 2018).For instance, global logistics company DHL has adopted digital twins to optimize its warehouse operations and predict potential disruptions in its SC (DHL, 2020). Similarly, Siemens has also adopted digital twins to simulate and optimize its SC networks and this has enabled Siemens to respond more effectively to changing market situations. (Siemens, 2021) The digital twin concept was initially introduced in the product lifecycle management(PLM)so as to increase and optimize manufacturing processes(Grieves, 2014).The rapid technological advancements, with the introduction of big data, artificial intelligence and cloud computing has resulted in SC networks becoming more complex(Leng et al., 2020).Comparing to the traditional SCM approach,digital twins gives companies the space to create dynamic data driven models that makes a copy of the real world behavior of SCs(Barricelli et al., 2019). 1.1. Problem Statement Over the past decade the complexity of global supply chain have been increase due to globalization.,evolving of customer expectations, geopolitical instability and unpredictable disruptions like the COVID-19 pandemic.Therefore businesses have started adopting advanced technologies such as digital twins that provides potential benefits such as improved visibility, efficiency and resilience.However, the adoption of digital twins is still limited.Many organizations are still relying on the legacy data systems which lacks real-time synchronization, preventing effective use of the digital twins for end-to-end supply chain visibility and also predictive insights (Toobler, 2024; Forbes, 2025).Additionally, the absence of standardized frameworks and implementation best practices has made it difficult for firms to assess return on investment and realize long-term value (Lambda SCS, 2023). The integration of digital twins into SCs raises concerns in regards to data accuracy, security and interoperability.Supply chains generate large volumes of real-time data through IoT devices, logistics networks and enterprise systems.Having accuracy, synchronization and the protection of this data across digital twin environments is still the major challenge.The inconsistency between the digital representations and the real-world operations can lead to poor decision-making and operational risks.Moreover, the increased reliance on digital models introduces new vulnerabilities to supply chain Marques et al., 2025; Razzaque et al., 2025).Apart from the theoretical benefits, the practical difficulties associated with implementing digital twins reveals a critical research gap that must be addressed so as to enable resilient, agile and data-driven supply chains in the today’s uncertain global economy. 1.2.Purpose of study The existing research shows the applicability of digital twins in the modern SC. Tao & Zhang (2022) explains how organizations are benefiting from digital twins from real time data from suppliers, manufacturers and distribution networks.This has benefited the organization in their production and decision making.There is emphasis of their toilet in resilient SC strategies thus enabling organizations to test various situations and develop risk mitigation measures Jones et al. (2020).Moreover Sharma et al. (2021) explores the benefits of digital twins in improving the demand forecasting accuracy, better inventory management thereby reducing operational costs. Despite the all the advancement, the adoption of digital twins in SCM remains limited.There are some companies which do recognizes the potential .Meanwhile other companies do not recognize the potential in digital twins due to factors hindering the implementation.Therefore, this study aims to explore the current state of research on digital twin technology in SCM identifying the key challenges the organizations are facing.By focusing specifically on these barriers, the research seeks to provide a clearer understanding of why the adoption still remains limited, despite the technology's proven benefits.By addressing these challenges will help inform future strategies for more effective implementation and integration of digital twins into supply chain operations. 1.3.Research questions Q1, What are the major challenges hindering the adoption of digital twins in SCM? Q2 What are the challenges of DT in SCM in the context of Sweden. To answer the first question, it is necessary to determine what factors influence the adoption of digital twins in SCM.There have been researches that examined the implementation of DT in SCM operations, However there is limited focus on specific barriers preventing their adoption. For the second question, the aim is to gather insights from professionals to understand the unique barriers which companies face in Sweden. 2 2. Methodology 2.1. Research Approach. The systematic review of Digital Twins in SCM provides researchers with a systematic approach to analyze existing research in order to obtain comprehensive knowledge. SLRs systematize research evaluation to produce organized summaries that reveal present knowledge in a field. As a newly adopted technology that applies across logistics and artificial intelligence and industrial engineering the identification of vital citations and research limitations in current literature becomes necessary (Le & Fan, 2023). The systematic evaluation method permits researchers to minimize bias while creating results from reliable evidence instead of selective preferences as reported in Liu et al. (2022). The major benefit of doing an SLR involves illustrating present research directions as researchers examine various SC operations perspectives on Digital Twins. Research scholars adopt different viewpoints about digital twin applications through emphasizing predictive analytics in combination with risk management as well as cost reduction and sustainability features (Ivanov & Dolgui, 2020). Research finds better direction through structured examination of different perspectives by identifying consensual aspects and discovery opportunities. Digital Twins boost SC resilience because they help organizations detect and manage operational risks better according to Ivanov and Dolgui (2020). Kapil et al. (2024) studied Digital Twin implementation at different industrial levels by developing insights about effective methods and facing difficulties in adoption. The structured approach of SLRs enhances research reliability while ensuring credibility because researchers define their questions, choose databases and apply strict selection guidelines and use strict analytical procedures. The systematic research method enables researchers to base their conclusions on a wide range of representative data sources (Kapil et al., 2024). Systematic narrative reviews carry fewer biases in comparison to traditional narrative reviews because SLRs establish a method that creates stronger reliability of field overviews according to Le and Fan (2023). The SLR approach tracks Digital Twin applications and frameworks as well as SCM challenges to build up solid theoretical research foundations for research ahead (Liu et al., 2022). Researchers as well as practitioners have access to empirical data and tested methodologies through its applications. Le and Fan (2023) investigated the functional and technical aspects of Digital Twin deployment in logistics while presenting real-world deployment examples. The systematized review process helps researchers locate unexplored areas for future research through identified knowledge gaps (Kapil et al., 2024). An SLR demonstrates great worth in connecting scholarly investigations with industrial practice. Leading companies such as Siemens and Amazon have adopted Digital Twins into their operations although academic research about their complete effects is in development (Siemens, n.d.). Digital Twin technology at Siemens enables process and SC optimization (Siemens, n.d.) alongside existing Amazon Digital Twin applications for warehouse and inventory management (Toobler, n.d.). The analysis of suitable industry research through an SLR provides organizations with usable information to improve their approach to implementing Digital Twins. 2.2. Data Sources and Search Strategy. There are many databases among those databases to maintain high academic credibility, Scopus is selected as the main database for this thesis. Scopus is one of the largest and most reliable academic databases, covering a wide range of high-quality scientific publications. It ensures credibility by carefully selecting and regularly reviewing its indexed content (Baas et al., 2020). However, databases like Web of Science and Google Scholar also be used in some parts where necessary. 2.3. Screening and Selection Process (PRISMA Framework). To maintain transparency and ensure reproducibility, the selection process follows the PRISMA methodology, which includes four key stages shown as below in Fig 1. To ensure transparency and reproducibility, for the selection process PRISMA methodology was used. The PRISMA statement is designed to improve the clarity and scientific quality of systematic reviews and meta-analyses. It is widely recognized and endorsed by many academic journals as a guideline for authors (Swartz, 2011). A PRISMA flow diagram helps researchers keep track of their study selection process in a structured and transparent way. This is important for ensuring that the review is accurate, reliable and valid as shown in Fig1 below. Figure 2 PRISMA Framework (How to Create an Effective PRISMA Flow Diagram | AJE, n.d.) 2.3.1. Identification Stage. A thorough Scopus search took place during the selection process by investigating research. A total of 931 documents included in the Digital Twins and Supply Chain as a main topic during the primary database search. And the keywords used when searching was ( "digital twin*" AND "supply chain*" ) Because of the language barriers only english results were included, after filtering english 906 documents were found. 2.3.2. Screening Stage. From the initial 906 articles from every subject, we decided to concentrate on the 180 entries found under Business, Management and Accounting only. Since our thesis looks at management and strategy in Digital Twins for supply chains, we decided to examine the Business, Management and Accounting subject. This area covers main points such as adopting new ideas, managing changes, using digital approaches, and planning a supply chain, all of which align with the learning outcomes of our Logistics and Transport Management Master’s programme. Kouhizadeh et al. (2021) say that for innovations in supply chains to work, technical aspects are necessary, but companies should also focus on how managers decide, prepare the corporation, and adjust their business model, areas primarily studied in Business and Management. Our main interest lies in observing how companies use these tools and achieve real operational benefits. To ensure the research was useful, we scanned for articles that study Digital Twins in a business context including decisions, costs and benefits, how read the staff is and organizational planning. It is also advised in systematic reviews to use this method to enhance both the quality and relevance of findings (Tranfield et al., 2003). Among the different types of documents from our database search (conference papers, book chapters and reviews), we kept exclusively journal articles (88 in all) for our analysis. The reviews of journal articles are more detailed, so the information in them is described in a more thorough manner. To give an example, conference papers might simply outline an idea, whereas book chapters often present just a summary of a study, while journal articles provide the whole research, including how it was conducted and what was found. According to Tranfield et al. (2003), limiting your information to journal articles makes the study more reliable and uniform. Because of these articles, we are confident the review will be research driven and match the main points of our thesis. 2.3.3. Eligibility Stage & Inclusion Stage When the screening ended, 88 studies were eligible for further study. Eligible articles were chosen reading through their abstracts to identify those related to our research goal of finding the challenges of using DTs in supply chain systems.To support this process, AI-assisted tools were used to help efficiently read and filter the abstracts based on relevance and keywords. We then looked at the selection criteria and found that 34 articles gave clear insights into the issues, barriers or challenges of DTs in supply chain environments. The articles were selected because their abstracts pointed out the challenges of DT and the motives. The reason the other 54 articles were not included is explained by what was recorded in their abstracts. Certainly, some research concentrated heavily on simulations, designing systems or engineering models, without considering adoption or supply chain uses. A few studies made brief references to digital twins together with other digital transformation topics, not focusing on the main obstacles of digital twins. Some instances, digital twin expected results were pointed out in other sectors such as healthcare, aerospace and construction, but were not tied to the supply chain. By using this approach, we were able to assemble a collection of articles that most fitted our systematic review and made our selection process clear.At the final confirmation step, we included 34 articles as our main literature for the SLR. 2.5. Validation Process. 2.5.1. Selection of Experts. Three to five experts from a small team evaluate the findings to improve their practical usefulness and increase reliability. The method of expert validation serves as a standardized approach for theoretical concept evaluation according to Johansen and Fischer-Hübner (2023) in academic laboratories as well as industrial facilities. Instead of data analysis only experts engage in critical assessment of key concepts by refining definitions while identifying inconsistencies and proposing improvements. The experts verify that findings from the literature properly mirror realistic SCM problems alongside digital twin adoption issues. The chosen experts possess experience in SCM and digital twins and their professional background in these areas formed the basis of selection. Small groups of around three to five people serve qualitative research through in-depth evaluation of complex matters within a manageable sample scope according to Collis and Hussey (2021). This validation system achieves balance through the combination of academic researchers and practitioners from industry. Specialist researchers from digital twins and SC resilience and logistics innovation sectors will form the expert panel that will contribute theoretical work and research data and findings. Logistics, manufacturing and retail sector professionals will give simultaneous feedback about the challenges of digital twins and their possible commercial use cases. The research validity increases through a combination of conceptual analysis with practical industrial insights which results in a full assessment of SC digital twin deployment methods. 2.5.2. Validation Method. Expert assessments will validate the list of identified challenges and benefits extracted from the literature review during this process. The experts will review the identified findings to compare them with field experience before they indicate which other factors should be included. The validation follows Johansen & Fischer-Hübner (2023) by obtaining qualitative information through expert interviews involving direct experience in SC challenges in digital twin professionals.For the experts we had 3 main criteria when choosing them which are 1.Experience in industries in Sweden, 2.Field related in supply chain and logistics and experience, 3.enough understanding in digital twin and digitalization,eg implementing digital twin, managing digital twin, something related to digital twin The expert will evaluate the findings, identify potential missing factors and the framework will be assessed by these experts. Our study's credibility and research conclusions gain added strength because this verification process links academic research and industry practice. Adjustments will be made when experts detect inconsistencies to maintain precise modeling of SC real-world conditions. The collected data will undergo qualitative evaluation to obtain significant expert feedback which will finalize and enhance the research conclusions. All interviews were conducted online using Microsoft Teams ensuring a consistent and efficient process.Digital platforms helped us to interview company personnel regardless of their geographical location.The experts chosen are from Sweden working under SCM in different industries so the validation is in context of Sweden.Before all interviews, nd detailed notes were taken during each interview. Due to the informal nature of some conversations and the preference of participants, recordings were not retained. To maintain consistency and accuracy in the information gathered, a standard set of questions was used for each group.The experts were asked the same set of questions as the company personnel so as to ensure that the data collected will be comparable and easily analyzable.Below is the table showing the experts which were interviewed and their professions in fig 2.3 Fig 2.3 Experts and their professions Experts Profession 1 Logistics Development Manager 2 Senior Data Scientist 3 Data Scientist 4 Supply chain Coordinator 5 Need Planner 2.6. Ethical Considerations. The research operates under ethical boundaries by drawing information solely from open-access databases and institutional sources to guarantee legal and ethical compliance for all chosen studies (Bhandari, 2024). The research protocol follows BMC Medical Ethics' ethical standards (Al-Madaney & Fässler, 2023) which require voluntary participation and total participant confidentiality. The experts taking part in validation will receive information about research goals but must decide to participate freely and keep their private details secure from disclosure for protecting their personal information. The findings of this study originate from expert contributions and public research data sources only (MDPI, 2024). The research standards for integrity require this commitment which maintains academic research with complete transparency as well as full honesty and accountability. The study follows privacy protection standards that allow the researchers to conduct data research responsibly and ethically (City University of Hong Kong Library, 2024). This research protects participant privacy and security along with academic standards by implementing the specified ethical protocols. 2.7. Limitations. This study acknowledges several limitations. This research method depends on secondary data from a SLR because it fails to grasp current technological developments and forthcoming digital twin industry trends. Furthermore, the specified research approach omits critical investigations published after the review time and non-English works thus leading to possible geographic predispositions. The expert validation procedure uses a restricted group of analysts (3–5 experts) because of time requirements. Expert insights contribute practical value to the study, yet the small number of experts can weaken both internal and external research validity (Faber & Fonseca, 2014). The study remains academically valid through its systematic literature approach combined with assessing high-quality scientific publications 2.8.Final PRISMA workflow according to the literature extracted Fig 2.4 Final PRISMA Flow Diagram showing the stages in the qualitative synthesis 3 3.1.Systematic Literature Review The SLR is an essential component of this research because it aids in the identification of existing knowledge and gaps in the field of study. For this Master’s thesis, the literature review was conducted using two primary sources: Google Scholar and Scopus. These two platforms have played a significant role in gathering all the necessary literature relevant to the topic. To comprehend the concept of Digital Twin in the SC and its applications, extensive use of Scopus was made. Specific keywords and literature were utilized to search. This approach facilitated the identification and understanding of the current state of research and applications of Digital Twins in the realm of SC and logistics. The objective was to find the challenges hindering the adoption of digital twins in the SC.Literature related to this area was gathered using both Google Scholar and Scopus. The utilization of reputable sources such as Google Scholar and Scopus lends credibility to the whole process. A quick overview for the literature used is in Fig 3 below Authors Name of Article Description Application Area (Inferred) Defining Information Defining Information Requirements for Requirements for Off-Site Lei Z.; Chen Q.; Altaf M.S.; Off-Site Construction Journal of Construction Construction Cao K. Management: An Engineering and Management Management: An Industry Industry Case Study Case Study from Canada from Canada Toward digital twins for Toward digital twins Chabanet S.; Bril El-Haouzi sawmill production for sawmill production International Journal of H.; Morin M.; Gaudreault J.; planning and control: planning and control: Production Research Thomas P. benefits, opportunities, benefits, opportunities, and challenges and challenges The architectural The architectural framework of a cyber framework of a cyber Park K.T.; Son Y.H.; Noh physical logistics International Journal of physical logistics system S.D. system for Production Research for digital-twin-based digital-twin-based supply chain control supply chain control An analysis of digital An analysis of digital twin technologies twin technologies Stadtfeld G.M.; Lienemann enhancing supply chain enhancing supply chain Production Planning and Control R.; Gruchmann T. viability: empirical viability: empirical evidence from multiple evidence from multiple cases cases Digital Supply Chain Digital Supply Chain Twins in Urban Tasche L.; Bähring M.; Twins in Urban Logistics Logistics System - Logistics Gerlach B. System - Conception of Conception of an an Integrative Platform Integrative Platform State of the Art of Roman E.-A.; Stere A.-S.; State of the Art of Digital Digital Twins in Roșca E.; Radu A.-V.; Twins in Improving Logistics Improving Supply Codroiu D.; Anamaria I. Supply Chain Resilience Chain Resilience Digital Supply Chain Digital Supply Chain Zhang H.; Lv Y.; Zhang S.; Management: A Journal of Global Information Management: A Review Liu Y.D. Review and Management and Bibliometric Analysis Bibliometric Analysis Digital twin design and Digital twin design and analytics for scaling up analytics for scaling up International Journal of Sharma A.; Kumar Tiwari M. electric vehicle battery electric vehicle battery Production Research production using robots production using robots Transforming Transforming humanitarian supply humanitarian supply International Journal of Logistics Singh R.K. chains with digital twin chains with digital twin Management technology: a study on technology: a study on resilience and agility resilience and agility Applying digital twins Applying digital twins for for inventory and cash inventory and cash management in supply International Journal of Badakhshan E.; Ball P. management in supply chains under physical Production Research chains under physical and and financial financial disruptions disruptions Driving Driving competitiveness competitiveness with with RFID-enabled digital Voipio V.; Elfvengren K.; RFID-enabled digital twin: case study from a Measuring Business Excellence Korpela J.; Vilko J. twin: case study from a global manufacturing global manufacturing firm’s supply chain firm’s supply chain IoT & digital twins IoT & digital twins concept integration concept integration Simchenko N.A.; Tsohla S.Y.; International Journal of Supply effects on supply chain effects on supply chain Chyvatkin P.P. Chain Management strategy: Challenges and strategy: Challenges effect and effect What impedes digital What impedes digital twin from twin from revolutionizing revolutionizing agro-food supply chain? agro-food supply Yadav V.S.; Majumdar A. Operations Management Research Analysis of barriers and chain? Analysis of strategy development for barriers and strategy mitigation development for mitigation Exploring critical success Exploring critical Deepu T.S.; Ravi V. factors influencing success factors Digital Business adoption of digital twin influencing adoption of and physical internet in digital twin and electronics industry using physical internet in grey-DEMATEL electronics industry approach using grey-DEMATEL approach From Theory to From Theory to Practice: Practice: Leveraging Leveraging Digital Twin Digital Twin Technologies and Supply Technologies and Hossain M.I.; Talapatra S.; Chain Disruption Supply Chain Global Journal of Flexible Saha P.; Belal H.M. Mitigation Strategies for Disruption Mitigation Systems Management Enhanced Supply Chain Strategies for Enhanced Resilience with Strategic Supply Chain Fit in Focus Resilience with Strategic Fit in Focus Deploying hybrid Deploying hybrid modelling to support the modelling to support development of a digital the development of a International Journal of Badakhshan E.; Ball P. twin for supply chain digital twin for supply Production Research master planning under chain master planning disruptions under disruptions Digital Twinning for Digital Twinning for Resilient Global Supply Resilient Global Markets, Globalization and Yun G.; Hales D.N.; Hong L. chains: Three Case Supply chains: Three Development Review Studies Case Studies Technologies, Technologies, opportunities and opportunities and challenges of the challenges of the Entrepreneurial Business and Rymarczyk J. industrial revolution 4.0: industrial revolution Economics Review Theoretical 4.0: Theoretical considerations considerations A digital twin-enabled A digital twin-enabled International Journal of Peron M. digital spare parts supply digital spare parts Production Research chain supply chain Incorporating supply and Incorporating supply production digital twins and production digital Lim K.Y.H.; Dang L.V.; Chen International Journal of to mitigate demand twins to mitigate C.-H. Production Economics disruptions in demand disruptions in multi-echelon networks multi-echelon networks Conceptualisation of a Conceptualisation of a International Journal of Ivanov D. 7-element digital twin 7-element digital twin Production Research framework in supply framework in supply chain and operations chain and operations management management Harnessing digital twin Harnessing digital twin technology to enhance technology to enhance resilience in Srivastava G.; Bag S. resilience in humanitarian Benchmarking humanitarian supply supply chains: an chains: an empirical empirical study study Busse A.; Gerlach B.; Towards Digital Twins of Towards Digital Twins Lengeling J.C.; Poschmann Multimodal Supply of Multimodal Supply Logistics P.; Werner J.; Zarnitz S. Chains Chains Acceptance of digital Acceptance of digital twins of customer twins of customer demands for supply Oehlschläger D.; Glas A.H.; demands for supply chain Industrial Management and Data chain optimisation: an Eßig M. optimisation: an analysis Systems analysis of three of three hierarchical hierarchical digital twin digital twin levels levels Emergency Supply Emergency Supply Chain Rinaldi M.; Caterino M.; Chain Resilience Resilience Enhanced Riemma S.; Macchiaroli R.; Enhanced Through Logistics Through Blockchain and Fera M. Blockchain and Digital Digital Twin Technology Twin Technology A conceptual A conceptual framework framework for supply for supply chain digital International Journal of Logistics Freese F.; Ludwig A. chain digital twins–development and Research and Applications twins–development and evaluation evaluation Socially responsible Socially responsible operations in the operations in the Industry Industry 4.0 era: International Journal of Asokan D.R.; Huq F.A.; 4.0 era: post-COVID-19 post-COVID-19 Operations and Production Smith C.M.; Stevenson M. technology adoption and technology adoption Management perspectives on future and perspectives on research future research Barriers and Challenges Barriers and for Digital Twin Adoption Challenges for Digital Sharma A.K.; Srivastava in Healthcare Supply Twin Adoption in Global Business Review M.K.; Sharma R. Chain and Operations Healthcare Supply Management Chain and Operations Management Digital twins' readiness Digital twins' readiness and its impacts on Patil A.; Srivastava S.; Paul and its impacts on supply supply chain Industrial Management and Data S.K.; Dwivedi A. chain transparency and transparency and Systems sustainable performance sustainable performance Digital twin for Digital twin for sustainable manufacturing sustainable Kamble S.S.; Gunasekaran supply chains: Current manufacturing supply Technological Forecasting and A.; Parekh H.; Mani V.; trends, future chains: Current trends, Social Change Belhadi A.; Sharma R. perspectives, and an future perspectives, and implementation an implementation framework framework A digital supply chain A digital supply chain twin for managing the twin for managing the Ivanov D.; Dolgui A. disruption risks and disruption risks and Production Planning and Control resilience in the era of resilience in the era of Industry 4.0 Industry 4.0 Embracing resilience in Embracing resilience in pharmaceutical pharmaceutical International Journal of manufacturing: “digital manufacturing: “digital Avinash B.; Joseph G. Pharmaceutical and Healthcare twins” – forging a twins” – forging a Marketing resilient path in the resilient path in the VUCA maze VUCA maze Analyzing the role of Analyzing the role of digital twins in digital twins in developing a resilient developing a resilient Singh G.; Rajesh R.; Misra sustainable Technological Forecasting and sustainable manufacturing S.C.; Singh S. manufacturing supply Social Change supply chain: A grey chain: A grey influence influence analysis analysis (GINA) (GINA) approach approach Navigating the digital Navigating the digital landscape: prioritizing landscape: prioritizing International Journal of System Agarwal V.; Sahai S.; Sahay challenges in supply challenges in supply Assurance Engineering and N. chain management of chain management of Management digital twin digital twin implementation implementation The above articles are the reviewed literature which have provided a comprehensive understanding of digital twins in SCM with key focus themes such as challenges , applications , methodologies and industry-specific implementations.These articles were sourced from Scopus and filtered according to the relevance in digital twins in SCs thus ensuring that we gather high quality and up to date dataset. 4 Challenges 1st article Q1, What are the major challenges hindering the adoption of digital twins in SCM? 4.1. Technological challenges The main difficulty for SCM to adopt Digital Twin (DT) technology comes from technological problems. Several research investigations point to technical challenges acting as barriers against the practical deployment of DTs among different sectors and industries. The main barriers to adopting Digital Twin technology consist of complex data integration and interoperability problems along with cybersecurity risks combined with restricted infrastructure capabilities and problems with real-time processing and missing standards. System interoperability and data integration stand as the main problems that limit practical uses. Sharma et al. (2025) identify integration problems which include both incompatible systems and the absence of shared data standards during the process of uniting different data sources. Simulation tools and optimization platforms and machine learning models operate with independent infrastructure structures which creates integration challenges according to the findings of Badakhshan and Ball (2024). Agarwal et al. (2024) bring attention to the predominant technology challenge of data integration and interoperability stemming from legacy systems and architects that prevent smooth digital twin deployment. The articles stress out the significance of real-time data processing along with the computational power that comes with it. High processing power represents a critical requirement within DT systems to support multiscale simulations of large data volumes according to Lv et al. (2024) and Ivanov (2023). Advanced infrastructure requirements for simulations include cloud computing along with IoT networks and AI capabilities but such facilities frequently struggle to meet current development standards. Chabanet et al. (2023) state that even though Optitek shows effectiveness in simulations it operates too intensively for real-time implementation thereby restricting its capabilities for live DT system integration. Among the major concerns exist both cybersecurity threats alongside data privacy requirements. DT systems become more exposed to cyber threats because researchers Gerlach et al. (2021) and Avinash and Joseph (2024) indicate that AI along with IoT and cloud infrastructure leads to increased system vulnerabilities. DTCDs base their operations on sensitive user data according to Oehlschläger et al. (2024). The task of uniting smart devices with the challenge of safeguarding continuously obtained data presents an important threat to systems. Surrounding the implementation of Decision Technologies stands a common challenge from the absence of standardized deployment frameworks. The digital simulation of electric vehicle battery production remains hindered by insufficient standardized robotic approaches according to Sharma and Tiwari (2023). This shortage restricts both the widespread adoption and scalability potential of the simulation process. The authors Freese and Ludwig (2024) explain how differing data semantics between different platforms creates barriers to meaningful system to system communication and data analysis.DT implementation depends equally on having ready infrastructure in place. DT adoption faces fundamental hurdles because of insufficient technological readiness which includes bad data connections together with skilled personnel shortages according to Patil et al. (2024). The deployment of DSCTs involves substantial IoT and AI along with cloud infrastructure expenses which multiple organizations find costly according to Tasche et al. (2023). Multiple sources within these publications demonstrate that current digital twin implementations in the industry tend to work as independent systems. According to Busse et al. (2021) together with Lim et al. (2024) existing DT systems end up being built for individual functions in the SC without achieving an all-encompassing holistic perspective. The separated implementation of DT systems prevents these platforms from achieving their maximum capability to predict disruptions in real time. Simulation accuracy together with qualified input data management stands as a final technical aspect of designing Digital Twins. The available simulation outcomes depend on reliable and thoroughly calibrated data sources according to both Binsfeld and Gerlach (2022) and Gerlach et al. (2021). Zhang et al. (2024) demonstrate that humanitarian SCs operate with limited history because it reduces the ability to produce accurate prediction models which affects the operational reliability of these discrete systems in practical implementations. The research establishes that technological obstacles extend beyond easy solutions because they exist throughout existing SC system designs. The successful management of these challenges demands innovative technology alongside strategic arrangements between SC systems, tools and procedures for handling data throughout the network. Technological Challenges list down Fig 4:Technological Challenges list down Challenge Authors Data Integration & Sharma et al. (2025), Badakhshan & Ball (2024), Interoperability Gerlach et al. (2021), Agarwal et al. (2024), Bossert et al. (2020), Freese & Ludwig (2024) Data Security & Privacy Risks Sharma et al. (2025), Avinash & Joseph (2024), Oehlschläger et al. (2024), Agarwal et al. (2024) Real-Time Data Processing Lv et al. (2024), Sharma et al. (2025), Ivanov (2023), Challenges Xiao et al. (2024) Infrastructure Limitations (IoT, Tasche et al. (2023), Ivanov (2023), Dolgui & Ivanov AI, Cloud, 5G) (2023), Patil et al. (2024) System Interoperability Issues Sharma et al. (2025), Gerlach et al. (2021), Badakhshan & Ball (2024), Bossert et al. (2020), Busse et al. (2021) Simulation & Modeling Lv et al. (2024), Chabanet et al. (2023), Zhang et al. Complexity (2024), Freese & Ludwig (2024) Lack of Standardized Sharma & Tiwari (2023), Agarwal et al. (2024) Methodologies Scalability of Digital Twins Agarwal et al. (2024), Patil et al. (2024) Cybersecurity & System Avinash & Joseph (2024), Dolgui & Ivanov (2023), Vulnerabilities Oehlschläger et al. (2024) Data Quality & Availability Binsfeld & Gerlach (2022), Bossert et al. (2020), Freese Issues & Ludwig (2024) ML Integration Difficulties Singh et al. (2024), Zhang et al. (2024), Ashraf et al. (2024) Legacy System Compatibility Sharma et al. (2025), Lim et al. (2024) Human-Machine Interface & Agarwal et al. (2024), Patil et al. (2024) Technical Skill Gaps . 4.2. Implementation and Adaptation Challenges. The literature review reveals that implementation and adoption issues remain the most commonly reported barrier to digital twin (DT) applications adoption within SCM systems. Multiple obstacles impede DT implementation within SCM according to examined research with points including the reluctance of organizations to change, technical limitations of infrastructures, excessive project expenses, insufficient employee training and the absence of clear standards. Research consistently emphasizes the importance of both insufficient managerial backing and employee hesitation to alteration which creates hurdles to digital twin adoption according to multiple studies. The conventional organizational frameworks and cultural stability inside organizations create obstacles to digital technology adoption according to Sharma et al. (2025) and Gerlach et al. (2021). The adoption processes are delayed and the obtained benefits become limited by employee deficiency in digital capabilities according to Sharma et al. (2025) and Sharma et al. (2023). Under normal circumstances, the integration of digital transformation systems with existing legacy networks requires substantial workflow restructuring and proven managerial expertise as well as technical capability to overcome operational obstacles (Bossert et al., 2020; Freese & Ludwig, 2024). The implementation faces limitations from multiple infrastructure-related obstacles according to various experts. The authors Lv et al. (2024) and Badakhshan & Ball (2024) explain how existing IT infrastructure needs major updates to support real-time analytics and synchronize data with the addition of machine learning capabilities to digital transformation systems. Companies especially those in small and medium enterprises face substantial barriers in DT deployment due to lacking simulation capacity, sensor networks and cloud computing systems according to Tasche et al. (2023) and Chabanet et al. (2023). Integration costs along with unclear ROI stand as common implementation challenges which companies frequently encounter. The implementation of DT systems initially requires investments between 2.5 and 5.5 million dollars which must be supplemented by expenses for workforce education according to Avinash & Joseph (2024). Chabanet et al. (2023) and Sharma & Tiwari (2023) show how numerous organizations avoid adopting DTs since they lack clarity on project ROI particularly in margins tight businesses or challenging operational environments. The scalability issue presents itself since controlled-environment functioning models struggle to adapt to diverse real-world applications (Binsfeld & Gerlach, 2022; Singh et al., 2024). The problem of implementation and conceptual clarity emerges as a main challenge in the perspective of multiple authors. Most Digital Transformation concepts exist at theoretical or experimental stages according to Badakhshan & Ball (2024) and Busse et al. (2021) and Dolgui & Ivanov (2025) while experiencing minimal practical implementation. Standard methodologies along with frameworks stand in the way of successful adoption across organizations. According to Oehlschläger et al. (2024) successful deployment needs more than just technological infrastructure since acceptance from users demands both their motivation and the establishment of trust alongside positive perceptions of the system. The combination of stringent regulations creates additional hurdles for companies working in the healthcare and pharmaceuticals industries. Agarwal et al. (2024) present regulatory alignment, data governance together with strategic alignment as fundamental barriers that organizations face during DT deployment success. Golan et al. (2021) state that companies fail to integrate digitized technologies into their SCs even though these platforms enhance vaccine supply systems because they prioritize operational efficiency over resilience. The difficulty stemming from human-AI partnership complexity acts as an obstacle in certain situations. According to Ivanov (2023) real-world integration of human-AI decision-support systems remains limited due to the absence of these systems thus preventing their valuable contribution to SC agility and resilience. The implementation of digital twins faces challenges from technical requirements and managerially as well from cultural and organizational readiness elements. The adoption of digital twins remains slow across different industries and SC environments because of poor employee training and unclear returns on investments as well as deficient infrastructure and conceptual understanding ambiguities. Things must be handled from a comprehensive standpoint where technology functions alongside change management practices together with policy adherence and worker skill building. Figure 4.1: Implementation and Adaptation Challenges. Challenge Authors Lack of Management Support Sharma et al. (2025), Gerlach et al. (2021), Patil et al. and Resistance to Change (2024), Oehlschläger et al. (2024) Lack of Employee Skills and Sharma et al. (2025), Tasche et al. (2023), Oehlschläger Training et al. (2024), Avinash & Joseph (2024), Patil et al. (2024) Infrastructure and System Sharma et al. (2025), Bossert et al. (2020), Patil et al. Readiness (2024), Dolgui & Ivanov (2023) Unclear ROI and Cost–Benefit Tasche et al. (2023), Chabanet et al. (2023), Binsfeld & Analysis Gerlach (2022) Data Governance and Ownership Agarwal et al. (2024), Freese & Ludwig (2024) Issues Legacy Systems and Integration Bossert et al. (2020), Sharma et al. (2025), Freese & with Existing Operations Ludwig (2024) Conceptual Gaps and Lack of Busse et al. (2021), Dolgui & Ivanov (2025), Ashraf et Real-World Validation al. (2024), Singh et al. (2024) High Complexity and Binsfeld & Gerlach (2022), Sharma & Tiwari (2023), Customization Requirements Bossert et al. (2020) User Acceptance and Oehlschläger et al. (2024) Organizational Culture Strategic Misalignment Agarwal et al. (2024), Singh et al. (2024), Patil et al. (2024) Fragmented Implementation Busse et al. (2021), Ivanov & Dolgui (2021) 4.3 Supply Chain Specific Challenges. DT systems deployed within SC environments create new operational difficulties which surpass organizational and technical elements. Various SC obstacles exist according to research findings which include complex coordination requirements and unpredictable demand patterns and restricted system flexibility as well as industry-specific limitations. The deployment of Digital Twins in real-world SCs faces challenges because of multiple operational factors that affect multi-tier networks operating in turbulent conditions or those requiring customized solutions. Multiple studies show data visibility alongside SC data synchronization as a fundamental operational issue. According to Sharma et al. (2025) defective decision-making and forecasting failures start when input information falls behind or lacks consistency in real-time systems. Gerlach et al. (2021) add weight to this problem by explaining that time-sensitive data exchange across supply network nodes requires extensive efforts to maintain, while the physical to digital process synchronization remains a significant barrier. Freese and Ludwig (2024) show that global SCs face problems with inadequate end-to-end data visibility alongside misaligned data during Suez Canal blockage disruptions. Tasche et al. (2023) and Chabanet et al. (2023) demonstrate that last-mile logistics services alongside sawmills face challenges with poor integration of real-time data inputs from weather or historical log records which reduces Digital Transformation efficiency. Many experts point to scalability problems while working with multiple stakeholders as a major concern. SC actors whose network spans multiple nodes typically experience fragmented digital threads because their DT implementations fail to achieve proper alignment (Sharma et al. 2025 and Busse et al. 2021). The entire system suffers SC disruptions according to Badakhshan and Ball (2024) when master planning environments become complex thus demanding adaptive DT solutions. According to their research on healthcare SCs (2025) Sharma et al. established that adaptable frameworks are necessary for working alongside logistics providers, suppliers and hospital institutions. The research from Lim et al. (2024) investigates how demand spikes within fast-moving consumer goods FMCG networks trigger upstream capacity problems that reveals inadequacies in current decision technology systems regarding planning and replanning abilities. SCs face pressing challenges regarding how to deal with operational disruptions while building stronger resilience capabilities. During crisis situations like COVID-19 Lv et al. (2024) demonstrate that managing DT functions becomes challenging because SC nodes must cope with parallel disruptions. According to Ivanov (2023) and Badakhshan and Ball (2023) traditional risk management systems display weaknesses when upstream suppliers lack financial and operational resilience. Ashraf et al. (2024) discuss disruption detection processes by explaining that prediction models falter at initial warning identification and thereby extend the response delay of the system to shocks. The implementation of digital threads becomes more complex because of specific industry requirements. The “two-speed” EV battery SC faces scalability problems because of manual labor alongside immature processes which are described by Sharma and Tiwari (2023). The sawmill industry demands specialized non-portable DT models because raw material variations combined with slow time-based coordination between process steps require individual solutions according to Chabanet et al. (2023). The modeling of organic food chains faces two main issues according to Binsfeld and Gerlach (2022): perishability of products alongside unstable demand. Oehlschläger et al. (2024) demonstrate that DTs in apparel manufacturing and emergency response require specific customizations because they address distinct individual requirements. CPPUNIT employs challenging models in complex uncertain fragmented environments which restrict the potential adoption of DT. The VUCA situation that SCs operate within today poses challenges for accurate modeling according to Zhang et al. (2024). The authors Golan et al. (2021) and Avinash and Joseph (2024) demonstrate that SCs need better real-time capabilities and responsiveness for pharmaceutical products despite known requirements for resilient supply systems. Global sourcing activities lack diffusion-type technologies to track upstream labor conditions which results in responsible SCM weaknesses according to Asokan et al. (2022). The implementation of digital twins faces major obstacles within SCs from four key operational factors including instantaneous data perception combined with operational disruption coordination and network growth complications and sector-based unique requirements. The application of digital twin solutions in operational supply networks encounters extensive challenges due to factors like live infrastructure complexity and multi-stage supply network structures which necessitates tailored operational frameworks in each sector. Figure 4.2: Supply Chain Specific Challenges Supply Chain-Specific Challenge Authors Fragmented digital threads and alignment issues in Sharma et al. (2025), Busse et al. multi-node networks (2021) Complex master planning requiring adaptive DT Badakhshan & Ball (2024) solutions Need for scalable frameworks in healthcare supply Sharma et al. (2025) chains Capacity overloads from demand spikes in FMCG Lim et al. (2024) networks Difficulty managing DTs during simultaneous Lv et al. (2024) disruptions (e.g., COVID-19) Lack of financial and operational resilience among Ivanov (2023), Badakhshan & upstream suppliers Ball (2023) Prediction model failures in early disruption detection Ashraf et al. (2024) Scalability issues in EV battery production Sharma & Tiwari (2023) Need for non-portable DT models in sawmills Chabanet et al. (2023) Modeling challenges in organic food chains Binsfeld & Gerlach (2022) (perishability and demand) Customization requirements in apparel and emergency Oehlschläger et al. (2024) services Modeling limitations in VUCA conditions Zhang et al. (2024) Limited real-time responsiveness in pharmaceutical Golan et al. (2021), Avinash & supply chains Joseph (2024) Insufficient monitoring of upstream labor conditions in Asokan et al. (2022) global sourcing 4.4 Ethical and Regulatory Challenges The deployment of Digital Twins (DTs) in SCM introduces a spectrum of ethical and regulatory challenges that have been critically discussed across the literature. A key concern is data privacy and protection, especially when DTs rely on real-time, sensitive operational data. For instance, Batty et al. (2021) and Lu et al. (2020) stress the ethical implications of continuously collecting and processing large volumes of personal and organizational data. As DT systems become more interconnected across global SCs, the risk of breaches and misuse increases, requiring firms to uphold stringent data governance protocols. Another major issue is lack of standardized regulations which has been highlighted in studies of Bosona (2020) and Kritzinger et al. (2018). The literature points out that many regions and sectors lack unified standards for implementing DT technologies thus creating fragmentation in regulatory compliance.The inconsistency prevents cross-border operations and it limits the scalability of digital twin systems. Moreover, companies and organizations often struggle to interpret and apply multiple legal frameworks simultaneously, especially when handling international logistics and supplier data. The literature also explores that there is ethical responsibility in decision-making which is mostly driven by AI and simulations embedded in DTs. Wang et al. (2019) and Grieves & Vickers (2017) highlights the concerns about the transparency and accountability of decisions that are made through automated digital twin models. These models may unintentionally result in biases or errors, which could lead to unethical consequences if there is no carefully monitored. Furthermore, intellectual property and data ownership are frequently discussed. Negri et al. (2017) and Fuller et al. (2020) discusses the uncertainty over who owns the data which is being generated by DTs, especially in collaborative SC ecosystems.Therefore without a clear ownership agreements, partners may hesitate to share valuable data which is leading to fragmented or less effective digital twin applications. Lastly, ethical dilemmas such as who will be accountable if AI makes biased decisions and arise in the workforce transformation like the changes which come with the new automation initiated by DT implementation. Automation and AI may replace certain employee roles in companies, as discussed in Madni et al. (2019) which might lead to workforce displacement and raising questions about corporate responsibility and fairness in digital transitions. Figure 4.3 Ethical and Regulatory Challenges Author(s) Identified Ethical & Regulatory Challenge(s) Jones et al. (2020) Data ownership and privacy issues in collaborative manufacturing environments. Ivanov & Dolgui (2021) Concerns over data integrity and the secure transmission of real-time data in global supply chains. El Saddik (2018) Ethical implications of continuous monitoring, especially concerning worker surveillance and data consent. Madni et al. (2019) Need for governance frameworks and legal policies to ensure responsible use of digital twins across industries. Wuest et al. (2022) Regulatory uncertainty in sustainability targets and the ethical implications of predictive modeling on emissions compliance. Yang et al. (2021) Regulatory gaps concerning cross-organizational data sharing and compliance with local data protection laws. Batty (2018) Risks around unintended biases in algorithms and the lack of ethical oversight in complex system modeling. Kritzinger et al. (2018) Ethical concerns regarding AI decision-making transparency and responsibility in supply chain automation. Tao et al. (2019) Regulatory mismatch between digital innovation pace and construction standards; lack of clear ethical frameworks for deployment. Lee et al. (2020) Regulatory inconsistencies across borders in biotechnology and logistics, complicating digital twin implementation in global chains. 4.5. Business and Economic Challenges The systematic analysis for the selected academic articles shows a range of business and economic challenges that hinder the implementation and adoption of Digital Twins (DTs) in SCM. One of the repeatedly mentioned challenges across the several studies is the high cost of implementation,specifically for small and medium-sized enterprises (SMEs). For instance, Jones et al. (2020) and Yang et al. (2021) highlights the financial costs that are associated with implementing digital twin infrastructure and also the maintenance of data integration across the complex systems.This cost barrier makes the adoption of DTs economically nonviable for smaller firms, even though there might be significant long term benefits. Another mentioned critical challenge is the difficulty in quantifying the return on investment. According to Ivanov & Dolgui (2021), the economic gains found from digital twin models are often uncertain.This is due to when predictive models are used to simulate complex disruptions. This uncertainty results in reduction in organizations management's willingness to commit to large investments without tangible short-term outcomes. Additionally, several studies including that includes Madni et al. (2019) and Tao et al. (2019) have emphasized the need for new business models and strategic alignment. Organizations often struggle to integrate digital twins into their operations due to lack of readiness and resistance to change.Therefore the business culture and digital maturity becomes limiting factors when adjusting DT initiatives with broader strategic objectives. Furthermore, punlications like El Saddik (2018) have discussed the economic risks of technological dependency as companies may become locked into rapidly evolving ecosystems that require frequent updates, re-skilling of employees and increase in capital investment. Therefore the fast-paced environment introduces uncertainty which adds pressure on financial planning. Finally, the regulatory and economic inefficiencies which are associated with cross-border operations are also mentioned, specifically by Lee et al. (2020). These inefficiencies come from the delays in coordination, lack of harmonized standards and inconsistent economic policies, all of which have an impact on the feasibility of deploying digital twins across international SCs. The table below shows the business and economic challenges explained above. Fig 4.4 Business and Economic Challenges Author(s) Identified Business and Economic Challenge(s) Jones et al. (2020) High initial costs of implementation and integration across existing systems, especially for SMEs. Ivanov & Dolgui (2021) Difficulties in justifying ROI due to uncertainty in economic gains from predictive models. El Saddik (2018) Economic risk due to dependency on continuously evolving technologies, increasing costs and uncertainty. Madni et al. (2019) Need for new business models and digital capabilities to remain competitive in digitally integrated environments. Tao et al. (2019) Organizational resistance and misalignment between digital investment strategies and business culture. Yang et al. (2021) High implementation costs for SMEs and limited scalability due to lack of economic incentives or funding. Batty (2018) Managing the economic implications of real-time simulations and long-term cost-benefit analysis in uncertain environments. Lee et al. (2020) Economic inefficiencies due to regulatory delays and the complexity of international coordination in high-tech industries. Wuest et al. (2022) Financial pressure to meet sustainability goals using expensive digital tools, often without clear short-term economic payoff. Kritzinger et al. (2018) Lack of clear economic frameworks to measure the long-term business impact of digital twins. 5 Q2 What are the challenges of DT in SCM in the context of Sweden. Discussion 5.1. Introduction. An expert interview validation process serves to analyze the Chapter 4 discoveries throughout this chapter. Professional insights and academic literature are compared to establish how well research-based challenges influence digital twin (DT) adoption within SCM. The current step strengthens research reliability through its method of combining academic theories with on-site observations of industry experts. Our research design required five experts having experience in SC logistics and digital transformation and operations management to conduct interviews. The experts who participated came from Sweden in different sectors that included manufacturing and service industry roles at organizations with operations in logistics, retail and production planning. The participants verify or question or extend the research problems found throughout the literature review. 5.2. Validation of Literature Findings Through Expert Interviews 5.2.1. Technological Challenges The literature review in Chapter 4 showcased technological obstacles which included data integration problems, system interoperability boundaries and cybersecurity threats along with infrastructure limitations and real-time processing limitations. A validation process including expert interviews proved the essential role of these technological barriers when applied in current SC operations. All five experts unanimously declared data integration problems as the most substantial technological barrier they encounter. The interview participants pointed out that systems like warehouse management platforms, transport management systems and supplier data sources operate independently due to missing interoperability. The expert expressed difficulty for operational systems to achieve real-time data synchronization because otherwise-compatible data structures cannot work together within legacy ERP systems. Expert backing grew strong for infrastructure limitations as one of the main challenges during the study. Different experts reported that cloud systems together with IoT solutions grow in use yet struggle to reach comprehensive time-sensitive integration between all SC components. Multiple experts identified the problem of data lag since updates took up to 24 hours which compromised the performance of decision-making resources. The participants in the interviews validated the scope of cybersecurity threats. Several experts highlighted security risks which develop when physical SC equipment links to cloud-based data management platforms. Lack of suitable cybersecurity protections and non-existing certified personnel who handle cybersecurity brought greater levels of organizational risk. The interviews with experts showed the same concern for the absence of consistent data governance standards which agree with research results. Each platform generates unreliable data output because incompatible data specifications create obstacles for analysis and operational forecasting. The experts stated that legacy systems which are incompatible present a major obstacle to digital twin adoption. Displays of older systems proved to be a main technical challenge with special consideration for customs and external logistics partners involved. The technical obstacles described by industry experts in direct interviews confirm the academic research which demonstrates real-world validation for data integration issues along with real-time system restrictions as well as cybersecurity threats and a need for standardized methods and the challenges of deploying across complex SCs. 5.2.2. Implementation and Adaptation Challenges. The review of existing literature shows that digital twin (DT) implementation success often fails due to employee skill deficits alongside organizational resistance and managerial support inadequacy and unidentified return on investment. Distinct expert interviews confirmed the existing barriers by illustrating practical scenarios that illustrate their impact on actual deployment situations. Most experts pointed out that employee resistance to change emerged as a vital theme. Employee resistance emerged as a common problem when digital tools were meant to enhance operations according to the information gathered from multiple interviewees. People revealed their resistance to new technology systems due to technological anxieties and insufficient motivation levels as well as unfavorable perspectives about operational modifications. The essential findings in research support that buyer-supplied elements stand as major impediments during implementation success efforts. Multiple participants from the interviews identified insufficient training programs as well as inadequate employee digital competencies as major challenges. Experts indicated employees do not possess sufficient technical abilities to function with sophisticated simulation and analytics systems along with real-time monitoring tools. Businesses experience protracted adaptation duration while using digital technology inadequately when they lack effective upskilling programs. The interviews demonstrated organizational readiness to be a fundamental variable. Most companies demonstrated restricted advanced digital system usage as experts noted that only selected motivated staff members actively engage with these technologies, but overall adoption continues to remain restricted throughout the organization. Digital twin developments rest primarily at early implementation levels throughout industries because organizations lack internal integration between different operational groups. The interviews supported the notion that doubting the financial ROI functions as a substantial obstacle. The use of digital tools receives significant funding, but management teams often discover it is challenging to identify immediate monetary gains or process enhancement benefits. The research shows that organizational hesitation for DT implementation stems from uncertainty about ROI whereas executive backing and initiation are further delayed by this unclarity. Some experts observed maintenance and support problems following initial platform installation because organizations struggled to find personnel with the skills needed to handle and debug DT systems. The identified issue causes additional challenges for organizational adaptation. The interview responses validated the finding that implementation problems stem from deep cultural and educational elements which also interact with organizational structures. The obtained insights demonstrate the importance of developing comprehensive change management programs and skilled-worker training together with establishing clear digital-twin value propositions. 5.2.3. Supply Chain Specific Challenges. Research findings identified supply chain-specific issues which include inadequate end-to-end visibility and poor data synchronization among stakeholders and scalability constraints with complex multisystem networks along with industry-specific requirements. Expert interviews confirmed the existence of these supply chain-specific challenges while bringing practical expertise to develop them further. The experts agreed that SC digital systems operate in separate network segments. Anecdotal evidence collected from various interviewees shows that data transfers happen in an erratic manner between different departments as well as suppliers and logistics partners and third-party providers. The experts explained that extended delays in inventory and transportation data updates reaching up to 24 hours create major problems for both forecasting and coordination functions. Real-time systems integration problems prove that literature experts are correct about difficulties in adopting SC digital twins effectively. Scalability challenges were also emphasized. Many present-day DT systems function only at departmental and local operational levels because they lack the capability to efficiently extend their reach to full SC networks. The introduction of DT systems years ago proved inadequate for complete implementation because they lacked the ability to cross through diverse operational domains according to an interviewee. The findings regarding adaptable scalable frameworks for multi-node deployments which are insufficient in practice were confirmed through field observations. The experts mentioned industry obstacles which restrict the use of dynamic transportation. According to the interviewed healthcare professional, end-to-end visibility remains absent while counterfeit goods continue causing problems alongside stock expiration and pilferage. The perishability of organic products and changing quality characteristics of raw materials along with food and sawmill industries create difficulties in modeling dynamic transportation systems according to the experts. These difficulties also exist in academic research. The automotive industry reported separated digital tools which prevented SC entities from uniting their digital SC twin across OEMs and logistics providers and suppliers. The study also confirmed that disruption management has become a substantial practical obstacle. Experts explained the challenges regarding fast response time to unexpected events including increased demand or delayed supply or unreliable inventory levels caused by insufficient simulation and forecasting capabilities. Existing DT systems show weaknesses in real-time disruption controls while lacking complete stress tests for complete SC functions according to research findings. The expert interviews verified that SC particular challenges comprise operational as well as structural issues. Multiple challenges prevent the successful implementation of digital twins including differential data dissemination patterns and particular sector restrictions and scalability obstacles and restricted system response to disruptions. The findings demonstrate the necessity to develop DT architectures which function across diverse systems for SC visibility across multiple tiers. 5.2.4. Ethical and Regulatory Challenges. The ethical and regulatory issues detailed through literature review in Chapter 4 received support from expert interviews which demonstrated partial validation. The practitioners acknowledged ethical along with regulatory issues as significant points during digital twin (DT) implementation despite having other primary duties. Most specialists identified privacy threats with data combined with cybersecurity risks when organizations work with various partners across external platforms. The lack of secure connection between cloud systems and SC data including order movements, stock quantities and partner messaging has resulted in raised exposure risks according to expert analysis. Multiple research participants expressed doubts about the personnel managing data assets and the authorized parties who may access the information together with their security protocols thus validating previous literature on data ownership and security regulations. Some participants spent a limited amount of time addressing the problem of regulatory uncertainty in the implementation of digital transformation solutions. The study participants underscored difficulties that emerge from regulatory inconsistencies when enterprises operate overseas and work with international suppliers. Previous studies showed that diverse industry and national standards create difficulties for implementers who want to scale DT solutions across organizational boundaries. The issue of algorithmic bias and the implementation of ethical AI governance framework did not come up directly through interview discussion though concerns about system accountability alongside skill gaps in AI decision management emerged. A participant mentioned that insufficient internal personnel knowledge about managing these sophisticated systems creates the risk of making poor decisions through inadequate system oversight. The shortage of professionals skilled in system oversight results in ethical implications for companies. Numerous companies depend on only a handful of experts who sustain their DT infrastructure, yet this practice exposes them to risk when these specialists are unreachable or when untraceable decisions get made. Even though ethical concerns and regulatory concerns appeared less often than technical or operational issues during the interviews they represented actual challenges that industry faces today. Experts validated three primary risks connected to data protection alongside governance uncertainties and insufficient regulatory backing which mainly affects worldwide and sensitive SC systems. The expanding use of digital technology requires more defined control systems and better structural methods and more rigorous ethical supervision protocols. 5.2.5. Business and Economic Challenges. Chapter 4 demonstrated various important economic and business hurdles that prevent SCs from implementing digital twin technology. Traditional firms face two major obstacles when implementing digital twins which are exorbitant setup costs combined with dubious ROI calculations and the need for technological development and strategic reevaluation. The interviews conducted with experts verified the mentioned adoption hurdles and added practical experience-based insights about cost-related adoption barriers. The interview participants often mentioned how DT implementation costs became a significant concern during their initial implementation phase. The experts observed that implementing DT systems necessitates substantial spending on infrastructure as well as training and systems integration. The expert asserted their system experienced limited utilization because of deployment barriers caused by substantial financial expenses and system complexity in subsequent years. Economic uncertainty becomes a major obstacle to DT scaling according to the research findings which mainly affect firms running operations in financial sensitive settings and developing markets. Experts noted the expensive nature of hiring workers who possess needed competencies as a core concern. Professional experts explained the intense difficulty alongside substantial expenses associated with finding qualified workers who can work with DT systems. The issue extends past technical skills to create challenges about long-term DT sustainability through economic feasibility of developing internal solutions. Explaining ROI proved challenging because of factors that emerged during the interviews. Multiple experts noted that DTs may show substantial long-term advantages but identifying their short-term monetary value remains complex especially when digital instruments lack rapid performance improvements. The interviews support research findings which show that unclear cost-benefit relationships at first create doubts among managers who then split their decision-making on new implementation. An economic danger exists when organizations depend too heavily on advancing technologies because they become susceptible to various risks. Technological systems that use cloud infrastructure with machine learning capabilities along with sensor-based analytics create an economic risk for businesses because they could become trapped in expensive technological networks. Bearing costs from sustained software update requirements together with employee training expenses along with vendor service requirements proved to be enduring expenses for the experts. The research interviews confirmed that business culture together with strategic alignment determines how successful organizations can implement adoption programs. Organizations having a budget for digital twin deployment usually fail to support its implementation effectively since the connection to company objectives remains obscure. The expert interviews confirmed how business and economic factors act as main obstacles against digital twin adoption within SC domains. The deployment of digital twins requires resolving critical barriers such as expensive implementation costs along with uncertain ROI calculations and lack of competent staff paired with strategy-technology alignment differences. 5.3. Comparative Analysis of Literature vs. Practitioner Perspectives A critical evaluation examines the information discovered in the SLR of Chapter 4 versus the data collected from five expert interviews featured in Chapter 5.2. This research evaluates academic digital twin (DT) adoption theory in comparison to SCM practices to determine their alignment and detect practical field observations which were developed during validation. Expert interviews validated that SCs face the seemingly complete set of technological barriers and implementation barriers as well as barriers stemming from SCs themselves along with ethical and economic barriers. Practitioners from different industries validated that technical barriers such as data system interoperability and legacy systems and fragmented databases along with real-time integration difficulties are widespread recurring factors. All experts strongly confirmed the existence of implementation and adaptation difficulties. The deployment of digital twin technologies faces daily implementation barriers due to employee reluctance to embrace change along with insufficient digital competency and a lack of organizational preparedness. Numerous supply chain-specific problems from academic literature directly apply to operational environments. Numerous experts validated that challenges regarding real-time visibility and end-to-end data connectivity and complex multi-node supply network scalability persist. The system infrastructure within many organizations operates separately from other units which keeps them from establishing one combined digital SC framework in real environments. Interview participants identified ethics along with regulatory matters even though they stood in the background of their conversations. Data ownership conflicts and privacy risks together with cybersecurity threats emerged within the discussions while academics worried about these matters even though experts themselves did not prioritize them at the same level. The implementation costs along with proving ROI provided key business and economic obstacles that many stakeholders acknowledged. Business experts shared their concerns about the unsatisfied benefits from digital transformation solutions in addition to stating that strategic alignment and skilled personnel shortage causes underutilization of digital systems. The current data indicates organizations encounter exceptionally challenging tasks to combine technology with long-term economic viability when operating complex systems. The interview data validated research evidence from academics, yet it revealed additional complex observations. The professionals operating systems with digital twin capabilities did not recognize the technical name for their systems. The professional community demonstrates a difference in terminology with university researchers because they perform Digital Twin-style work but not under the technical name. Organizational employees openly discussed traditional habits within their institutions that negatively impact digital transformation successfully while published research downplays these issues. Existing research studies regarding DT adoption maintain its validity because there is a strong match between academic knowledge and real-world practice. Upcoming research must base its implementation on problems that users encounter because findings from interviews demonstrate the necessity of addressing training protocols and ongoing support and communication methods. Studies indicate that digital twin adoption across SCs involves more than technical and economic procedures since it drives fundamental changes that need collective organization-wide participation. 6 Conclusion In summary, despite the Digital Twin (DT) technology having the potential to revolutionize SC operations through real-time modeling, predictive analytics and decision support, its wide extended integration still faces challenges.The application of Digital Twins in SCs can benefit the companies to visualize and simulate complex logistics, monitor assets in real time and to respond more effectively incase of any disruptions.However, the full-scale deployment across global and multi-tier supply networks is not yet fully feasible due to several technical, organizational and contextual barriers.One of the primary main challenges lies in the fragmented digital infrastructure across SCs.Many organizations are still operating with legacy systems and manually driven processes thus making automated data capture and integration difficult.Real-time data transfer is essential for the functioning of DTs and it is further constrained by infrastructure gaps, especially in remote or resource-limited regions where certain assets or facilities lack connectivity or compatible sensor technologies. Therefore, as much as the DTs are highly effective when applied to specific segments of the SC such as warehouse operations, transport route optimization or predictive maintenance of machinery, extending them across the entire SC ecosystems is still a complex endeavor.In cases like these , using simplified digital models or hybrid approaches may preferably offer a more manageable alternative.These are able to support decision-making through periodically updated data, rather than relying on real-time, bi-directional data exchange that may not be feasible for all assets. 6.1 Future Research Directions So as to overcome the current limitations and enhance the successful implementation of Digital Twins in SCs, several critical areas should be prioritized in future research and industrial practice.Standardization of Digital Twin Frameworks is very essential. Although the concept of Digital Twins has matured, still there is no universally accepted framework or vocabulary for describing DT systems across industries.Therefore establishing a consistent structure for developing, validating and integrating DTs will improve communication, reduce redundancy and promote interoperability across SC networks. Secondly, transparency in Data Governance and Collection is another vital area.As DTs are increasingly interacting with product usage and consumer behavior data, companies must ensure transparent data practices.Clearly communicating what data is collected, how it is used and how it is protected will be essential for when it comes to building trust and enabling long-term acceptance of DT-enabled SC services. Finally, organizations need to develop a clear strategic understanding of the DT implementation first before adopting DTs for the sake of digital transformation.Therefore companies have to define the specific use cases, their expected outcomes and performance benchmarks.Aligning the DT projects with broader SC and business goals will ensure focused investment and measurable benefits. Bibliography Accorsi, R., Battarra, I., Guidani, B., Manzini, R., Ronzoni, M. and Volpe, L. (2022). Augmented spatial LCA for comparing reusable and recyclable food packaging containers networks. Journal of Cleaner Production, 375, p.134027. doi:https://doi.org/10.1016/j.jclepro.2022.134027. Agarwal, V., Sahai, S. and Sahay, N. (2024). Navigating the digital landscape: prioritizing challenges in supply chain management of digital twin implementation. International Journal of System Assurance Engineering and Management. doi:https://doi.org/10.1007/s13198-024-02553-y. Al-Madaney, M.M. and Margrit Fässler (2023). Development and validation of a tool to assess researchers’ knowledge of human subjects’ rights and their attitudes toward research ethics education in Saudi Arabia. BMC Medical Ethics, 24(1). doi:https://doi.org/10.1186/s12910-023-00968-z. Ashraf, M., Amr Eltawil and Ali, I. (2024). Disruption detection for a cognitive digital supply chain twin using hybrid deep learning. Operational Research, 24(2). doi:https://doi.org/10.1007/s12351-024-00831-y. Asokan, D.R., Huq, F.A., Smith, C.M. and Stevenson, M. (2022). Socially responsible operations in the Industry 4.0 era: post-COVID-19 technology adoption and perspectives on future research. International Journal of Operations & Production Management, 42(13). doi:https://doi.org/10.1108/ijopm-01-2022-0069. Avinash B and Joseph, G. (2025). Embracing resilience in pharmaceutical manufacturing: ‘digital twins’ – forging a resilient path in the VUCA maze. International Journal of Pharmaceutical and Healthcare Marketing. doi:https://doi.org/10.1108/ijphm-03-2024-0024. Baas, J., Schotten, M., Plume, A., Côté, G. and Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), pp.377–386. doi:https://doi.org/10.1162/qss_a_00019. Badakhshan, E. and Ball, P. (2022). Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions. International Journal of Production Research, 61(15), pp.1–23. doi:https://doi.org/10.1080/00207543.2022.2093682. Bakhshi, S., Ghaffarianhoseini, A., Amirhosein Ghaffarianhoseini, Najafi, M., Rahimian, F., Park, C. and Lee, D. (2024). Digital twin applications for overcoming construction supply chain challenges. Automation in Construction, 167, pp.105679–105679. doi:https://doi.org/10.1016/j.autcon.2024.105679. Bandara, Lahiru Vimukthi and Buics, L. (2024). Digital Twins in Sustainable Supply Chains: A Comprehensive Review of Current Applications and Enablers for Successful Adoption. Engineering Proceedings, [online] 79(1), p.64. doi:https://doi.org/10.3390/engproc2024079064. Barykin, S.Y., Aleks, A., Bochkarev, R., Dobronravin, E. and Sergeev, S.M. (2021a). The Place and Role of Digital Twin in Supply Chain Management. Academy of Strategic Management Journal, [online] 20(2S), pp.1–19. Available at: https://www.abacademies.org/articles/the-place-and-role-of-digital-twin-in-supply-chain-manage ment-11000.html. Barykin, S.Y., Bochkarev, A.A., Kalinina, O.V. and Yadykin, V.K. (2020). Concept for a Supply Chain Digital Twin. International Journal of Mathematical, Engineering and Management Sciences, 5(6), pp.1498–1515. doi:https://doi.org/10.33889/ijmems.2020.5.6.111. Bhandari, P. (2021). Ethical considerations in research | types & examples. [online] Scribbr. Available at: https://www.scribbr.com/methodology/research-ethics/. Binsfeld, T. and Gerlach, B. (2022). Quantifying the Benefits of Digital Supply Chain Twins—A Simulation Study in Organic Food Supply Chains. Logistics, 6(3), p.46. doi:https://doi.org/10.3390/logistics6030046. Braun, V. and Clarke, V. (2006). Using Thematic Analysis in Psychology. Qualitative Research in Psychology, [online] 3(2), pp.77–101. doi:https://doi.org/10.1191/1478088706qp063oa. Burgos, D. and Ivanov, D. (2021). Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions. Transportation Research Part E: Logistics and Transportation Review, 152, p.102412. Busse, A., Gerlach, B., Lengeling, J.C., Poschmann, P., Werner, J. and Zarnitz, S. (2021). Towards Digital Twins of Multimodal Supply Chains. Logistics, 5(2), p.25. doi:https://doi.org/10.3390/logistics5020025. Cavalcante, I.M., Frazzon, E.M., Forcellini, F.A. and Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, pp.86–97. doi:https://doi.org/10.1016/j.ijinfomgt.2019.03.004. Chabanet, S., Bril El-Haouzi, H., Morin, M., Gaudreault, J. and Thomas, P. (2022). Toward digital twins for sawmill production planning and control: benefits, opportunities, and challenges. International Journal of Production Research, pp.1–24. doi:https://doi.org/10.1080/00207543.2022.2068086. Chen, Z. and Huang, L. (2022). The Impact of Digital Twins on Local Industry Symbiosis Networks in Light of the Uncertainty Caused by the Public Crisis. International Journal of Information Systems and Supply Chain Management, [online] 15(1). doi:https://doi.org/10.4018/IJISSCM.20220101.oa1. Chowdhury, P., Paul, S.K., Kaisar, S. and Moktadir, Md.A. (2021). COVID-19 pandemic related supply chain studies: A systematic review. Transportation Research Part E: Logistics and Transportation Review, 148(102271), p.102271. doi:https://doi.org/10.1016/j.tre.2021.102271. Cimino, C., Negri, E. and Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, [online] 113, p.103130. doi:https://doi.org/10.1016/j.compind.2019.103130. City University of Hong Kong (2015). Research Guides: Research Methods: Ethics in Research. [online] Cityu.edu.hk. Available at: https://libguides.library.cityu.edu.hk/researchmethods/ethics. DHL (2025). [online] Dhl.com. Available at: https://www.dhl.com/us-en/home/innovation-in-logistics.html?locale=true&error=CANDIDATE _PAGE_NOT_FOUND [Accessed 28 Feb. 2025]. DiNapoli, J. (2024). Toothpaste maker Colgate testing new product ideas on ‘digital twins’. Reuters. [online] 10 Dec. Available at: https://www.reuters.com/business/retail-consumer/toothpaste-maker-colgate-testing-new-product -ideas-digital-twins-2024-12-10/. Discover Digital Twin Applications in Various Industries. (2022). Discover Digital Twin Applications in Various Industries. [online] Available at: https://www.toobler.com/blog/digital-twin-applications-industries. Dolgui, A. and Ivanov, D. (2024). Internet of behaviors: conceptual model, practical and theoretical implications for supply chain and operations management: International Journal of Production Research. International Journal of Production Research, [online] pp.1–8. doi:https://doi.org/10.1080/00207543.2024.2372008. Dominik Oehlschläger, Glas, A.H. and Eßig, M. (2023). Acceptance of digital twins of customer demands for supply chain optimisation: an analysis of three hierarchical digital twin levels. Industrial Management and Data Systems. doi:https://doi.org/10.1108/imds-07-2023-0467. Egan, S. and Tutos, N.C. (2023). Digital-Age Construction – Manufacturing Convergence. Springer eBooks, pp.849–900. doi:https://doi.org/10.1007/978-3-031-21343-4_29. Ehsan Badakhshan and Ball, P.D. (2023). Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions. International Journal of Production Research, pp.1–32. doi:https://doi.org/10.1080/00207543.2023.2244604. El-Agamy, R.F., Sayed, H.A., Akhatatneh, A., Mansourah Aljohani and Mostafa Elhosseini (2024). Comprehensive analysis of digital twins in smart cities: a 4200-paper bibliometric study. Artificial intelligence review, 57(6). doi:https://doi.org/10.1007/s10462-024-10781-8. Freese, F. and Ludwig, A. (2024). A conceptual framework for supply chain digital twins – development and evaluation. International Journal of Logistics: Research and Applications, pp.1–23. doi:https://doi.org/10.1080/13675567.2024.2324895. Fuller, A., Fan, Z., Day, C. and Barlow, C. (2020). Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access, [online] 8, pp.108952–108971. doi:https://doi.org/10.1109/access.2020.2998358. Gerlach, B., Zarnitz, S., Nitsche, B. and Straube, F. (2021). Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits. Logistics, 5(4), p.86. doi:https://doi.org/10.3390/logistics5040086. Golan, M.S., Trump, B.D., Cegan, J.C. and Linkov, I. (2021). Supply chain resilience for vaccines: review of modeling approaches in the context of the COVID-19 pandemic. Industrial Management & Data Systems. doi:https://doi.org/10.1108/imds-01-2021-0022. Green, E. (2023). Digital Twins Across Manufacturing. Springer eBooks, pp.735–771. doi:https://doi.org/10.1007/978-3-031-21343-4_26. Grieves, M. and Vickers, J. (2016). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. Transdisciplinary Perspectives on Complex Systems, [online] pp.85–113. doi:https://doi.org/10.1007/978-3-319-38756-7_4. Hong, Y., Le Van Dang and Chen, C.-H. (2024). Incorporating supply and production digital twins to mitigate demand disruptions in multi-echelon networks. International Journal of Production Economics, pp.109258–109258. doi:https://doi.org/10.1016/j.ijpe.2024.109258. Ivanov, D. (2023a). Conceptualisation of a 7-element digital twin framework in supply chain and operations management. pp.1–13. doi:https://doi.org/10.1080/00207543.2023.2217291. Ivanov, D. (2023b). Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability. International Journal of Production Economics, [online] 263, p.108938. doi:https://doi.org/10.1016/j.ijpe.2023.108938. Ivanov, D. and Dolgui, A. (2020). A Digital Supply Chain Twin for Managing the Disruption Risks and Resilience in the Era of Industry 4.0. Production Planning & Control, 32(9), pp.775–788. doi:https://doi.org/10.1080/09537287.2020.1768450. Ivanov, D., Dolgui, A., Das, A. and Sokolov, B. (2019). Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility. Lecture Notes in Mechanical Engineering, [online] 276, pp.309–332. doi:https://doi.org/10.1007/978-3-030-14302-2_15. Jesus, V., Kalaitzi, D., Batista, L. and Lopez, N.L. (2024). Digital Twins of Supply Chains: A Systems Approach. IEEE Transactions on Engineering Management, 71, pp.14915–14932. doi:https://doi.org/10.1109/tem.2024.3468177. João, A., Giese, T., Klaus Schützer, Anderl, R., Schleich, B. and Vilson Rosa Almeida (2024). Digital Twins in Industry 4.0 – Opportunities and challenges related to Cyber Security. Procedia CIRP, 121, pp.25–30. doi:https://doi.org/10.1016/j.procir.2023.09.225. Johansen, J. and Fischer-Hübner, S. (2023). Expert Opinions as a Method of Validating Ideas: Applied to Making GDPR Usable. Springer eBooks, pp.137–152. doi:https://doi.org/10.1007/978-3-031-28643-8_7. Jones, D., Snider, C., Nassehi, A., Yon, J. and Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, [online] 29(1755-5817), pp.36–52. doi:https://doi.org/10.1016/j.cirpj.2020.02.002. Kapil, D., Raut, R., Kirti Nayal, Kumar, M. and Akarte, M.M. (2024). A multisectoral systematic literature review of digital twins in supply chain management. Benchmarking An International Journal. doi:https://doi.org/10.1108/bij-04-2024-0286. Kritzinger, W., Karner, M., Traar, G., Henjes, J. and Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, [online] 51(11), pp.1016–1022. doi:https://doi.org/10.1016/j.ifacol.2018.08.474. Lam Weng Siew, Weng Hoe Lam and Pei Fun Lee (2023). A Bibliometric Analysis of Digital Twin in the Supply Chain. Mathematics, 11(15), pp.3350–3350. doi:https://doi.org/10.3390/math11153350. Lattanzi, L., Raffaeli, R., Peruzzini, M. and Pellicciari, M. (2021). Digital twin for smart manufacturing: a review of concepts towards a practical industrial implementation. International Journal of Computer Integrated Manufacturing, pp.1–31. doi:https://doi.org/10.1080/0951192x.2021.1911003. Le, T.V. and Fan, R. (2023). Digital Twins for Logistics and Supply Chain Systems: Literature Review, Conceptual Framework, Research Potential, and Practical Challenges. [online] arXiv.org. Available at: https://arxiv.org/abs/2311.17317. Leng, J., Zhang, H., Yan, D., Liu, Q., Chen, X. and Zhang, D. (2018). Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. Journal of Ambient Intelligence and Humanized Computing, [online] 10(3), pp.1155–1166. doi:https://doi.org/10.1007/s12652-018-0881-5. Lv, Z., Qiao, L., Mardani, A. and Lv, H. (2022). Digital Twins on the Resilience of Supply Chain Under COVID-19 Pandemic. IEEE Transactions on Engineering Management, pp.1–12. doi:https://doi.org/10.1109/tem.2022.3195903. Maheshwari, P., Kamble, S., Belhadi, A., Venkatesh, M. and Abedin, M.Z. (2023). Digital twin-driven real-time planning, monitoring, and controlling in food supply chains. Technological Forecasting and Social Change, [online] 195, p.122799. doi:https://doi.org/10.1016/j.techfore.2023.122799. Malcolm, J. (2025). Walmart CEO reveals how chain is using ‘digital twin’ to plan store changes – and it’s been used for over 1... [online] The US Sun. Available at: https://www.the-sun.com/money/13304708/walmart-ceo-john-furner-nrf-inflation-ai-digital-twin /?utm_source=chatgpt.com. MDPI (2024). Research Integrity Promotes Open Access Practices. [online] MDPI Blog. Available at: https://mdpiblog.wordpress.sciforum.net/2024/10/23/research-integrity-open-access [Accessed 30 Apr. 2025]. Mecalux (2021). Digital twin examples: 3 cases in the logistics sector. [online] Mecalux.com. Available at: https://www.mecalux.com/blog/digital-twin-examples?utm_source=chatgpt.com [Accessed 24 Mar. 2025]. Moosapour, H., Saeidifard, F., Aalaa, M., Soltani, A. and Larijani, B. (2021). The Rationale behind Systematic Reviews in Clinical medicine: a Conceptual Framework. Journal of Diabetes & Metabolic Disorders, [online] 20(1), pp.919–929. doi:https://doi.org/10.1007/s40200-021-00773-8. N.A. Simchenko, S.Y. Tsohla and P.P. Chyvatkin (2019). IoT & Digital Twins Concept Integration Effects on Supply Chain Strategy: Challenges and Effects. International Journal of Supply Chain Management, 8(6), pp.803–808. Nalen, C.Z. (2023). How to Create an Effective PRISMA Flow Diagram | AJE. [online] www.aje.com. Available at: https://www.aje.com/arc/how-to-create-prisma-flow-diagram/. Nowell, L.S., Norris, J.M., White, D.E. and Moules, N.J. (2017). Thematic analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, [online] 16(1), pp.1–13. doi:https://doi.org/10.1177/1609406917733847. Nuttah, M.M., Roma, P., Lo Nigro, G. and Perrone, G. (2023). Understanding blockchain applications in Industry 4.0: From information technology to manufacturing and operations management. Journal of Industrial Information Integration, 33, p.100456. doi:https://doi.org/10.1016/j.jii.2023.100456. Oca, A., Cosmas, A., Cenk Tunasar and Shah, K. (2024). Digital twins: The key to unlocking end-to-end supply chain growth. [online] McKinsey & Company. Available at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/digital-twins-the-key-to-unlo cking-end-to-end-supply-chain-growth?utm_source=chatgpt.com. Onaji, I., Tiwari, D., Soulatiantork, P., Song, B. and Tiwari, A. (2022). Digital twin in manufacturing: conceptual framework and case studies. International Journal of Computer Integrated Manufacturing, 35(8), pp.1–28. doi:https://doi.org/10.1080/0951192x.2022.2027014. Papadonikolaki, E. and Anumba, C.J. (2024). Mapping the Complexity of Net Zero Transition Through a System of Digital Twin Systems. IEEE Transactions on Engineering Management, 71, pp.13949–13962. doi:https://doi.org/10.1109/tem.2024.3428641. Patil, A., Srivastava, S., Sanjoy Kumar Paul and Dwivedi, A. (2024). Digital twins’ readiness and its impacts on supply chain transparency and sustainable performance. Industrial management + data systems/Industrial management & data systems. doi:https://doi.org/10.1108/imds-10-2023-0767. Rasheed, A., San, O. and Kvamsdal, T. (2020). Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. IEEE Access, [online] 8(1), pp.21980–22012. doi:https://doi.org/10.1109/access.2020.2970143. Richter, E., Blasco, V., Antonini, F., Rey, M., Laurent Reydellet, Karim Harti, Nafati, C., Albanèse, J. and Leone, M. (2014). Sleep disorders among French anaesthesiologists and intensivists working in public hospitals. European Journal of Anaesthesiology, 32(2), pp.132–137. doi:https://doi.org/10.1097/eja.0000000000000110. Roberts-Islam, B. (2024). The fashion exec’s guide to digital product passports. [online] Vogue Business. Available at: https://www.voguebusiness.com/story/sustainability/the-fashion-execs-guide-to-digital-product-p assports?utm_source=chatgpt.com. Rosen, R., Wichert, G. von , Lo, G. and Bettenhausen, K.D. (2015). About The Importance of Autonomy and Digital Twins for the Future of Manufacturing. IFAC-PapersOnLine, [online] 48(3), pp.567–572. doi:https://doi.org/10.1016/j.ifacol.2015.06.141. Rosin, A.F., Proksch, D., Stubner, S. and Pinkwart, A. (2020). Digital new ventures: Assessing the benefits of digitalization in entrepreneurship. Journal of Small Business Strategy, [online] 30(2), pp.59–71. Available at: https://jsbs.scholasticahq.com/article/26335-digital-new-ventures-assessing-the-benefits-of-digita lization-in-entrepreneurship [Accessed 20 Mar. 2025]. Salvo, J.J. (2023). Welcome to the Complex Systems Age: Digital Twins in Action. Digital Twin, pp.559–575. doi:https://doi.org/10.1007/978-3-031-21343-4_20. Schmitt, L. and Copps, D. (2023). The Business of Digital Twins. Springer eBooks, pp.21–63. doi:https://doi.org/10.1007/978-3-031-21343-4_2. Sergey Yevgenievich Barykin, Kapustina, I.V., Sergey Mikhailovich Sergeev, Kalinina, O.V., Viktoriia Valerievna Vilken, Elena, Putikhin, Y.Y. and Volkova, L.V. (2021b). Developing the Physical Distribution Digital Twin Model within the Trade Network. Academy of strategic management journal/Academy of Strategic Management journal, 20. Sharma, A. and Kumar Tiwari, M. (2022). Digital twin design and analytics for scaling up electric vehicle battery production using robots. International Journal of Production Research, pp.1–35. doi:https://doi.org/10.1080/00207543.2022.2152896. Sharma, A.K., Srivastava, M.K. and Sharma, R. (2025). Barriers and Challenges for Digital Twin Adoption in Healthcare Supply Chain and Operations Management. Global Business Review. doi:https://doi.org/10.1177/09721509251314795. Siemens (n.d.). Press. [online] press.siemens.com. Available at: https://press.siemens.com/global/en. Siemens Digital Industries Software. (n.d.). Digital Twin | Siemens Software. [online] Available at: https://www.sw.siemens.com/en-US/technology/digital-twin/. Singh, D., Sharma, A. and Rana, P.S. (2024). Machine Learning and Digital Twins-enabled Supply Chain Resilience: A Framework for the Indian FMCG Sector. Global Business Review. doi:https://doi.org/10.1177/09721509241275751. Singh, G., Rajesh, R., Misra, S.C. and Singh, S. (2024). Analyzing the role of digital twins in developing a resilient sustainable manufacturing supply chain: A grey influence analysis (GINA) approach. Technological Forecasting and Social Change, [online] 209, p.123763. doi:https://doi.org/10.1016/j.techfore.2024.123763. Swartz, M.K. (2011). The PRISMA Statement: A Guideline for Systematic Reviews and Meta-Analyses. Journal of Pediatric Health Care, [online] 25(1), pp.1–2. doi:https://doi.org/10.1016/j.pedhc.2010.09.006. Tao, F., Zhang, H., Liu, A. and Nee, A.Y.C. (2019). Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, [online] 15(4), pp.2405–2415. doi:https://doi.org/10.1109/tii.2018.2873186. Tasche, L., Maximilian Bähring and Gerlach, B. (2023). Digital Supply Chain Twins in Urban Logistics System. Tehnički glasnik, 17(3), pp.405–413. doi:https://doi.org/10.31803/tg-20230518081537. Uhlemann, T.H.-J. ., Lehmann, C. and Steinhilper, R. (2017). The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0. Procedia CIRP, 61, pp.335–340. doi:https://doi.org/10.1016/j.procir.2016.11.152. van der Valk, H., Strobel, G., Winkelmann, S., Hunker, J. and Tomczyk, M. (2022). Supply Chains in the Era of Digital Twins – A Review. Procedia Computer Science, [online] 204, pp.156–163. doi:https://doi.org/10.1016/j.procs.2022.08.019. van Eck, N.J. and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), pp.523–538. doi:https://doi.org/10.1007/s11192-009-0146-3. Wang, W., Yang, S., Xu, L. and Yang, X. (2019). Carrot/stick mechanisms for collection responsibility sharing in multi-tier closed-loop supply chain management. Transportation Research Part E: Logistics and Transportation Review, 125, pp.366–387. doi:https://doi.org/10.1016/j.tre.2019.03.002. Xiao, J., Ma, S., Wang, S. and Huang, G.Q. (2024). META-INVENTORY MANAGEMENT DECISIONS: A THEORETICAL MODEL. International Journal of Production Economics, pp.109339–109339. doi:https://doi.org/10.1016/j.ijpe.2024.109339. Xin Ma, Lyu, D. and Mengmeng Jiang (2022). Cross Border Supply Chain Coordination Evaluation Model of biotech Logistics Industry based on Big Data Drive. Journal of Commercial Biotechnology, 26(4). doi:https://doi.org/10.5912/jcb1044. Zhang, T., Matthieu Lauras, Zacharewicz, G., Rabah, S. and Benaben, F. (2024). Coupling simulation and machine learning for predictive analytics in supply chain management. International journal of production research, pp.1–18. doi:https://doi.org/10.1080/00207543.2024.2342019.