Special Issue in the International Journal of Production Economics

Organizations continue to transform their operations by adopting a suite of digital and artificial intelligence technologies. One emerging application is that of the digital twin within the firm’s own operations as well as the broader supply chain. A digital twin in operations and supply chain management is a virtual system comprised of (i.) a computerized representation of physical internal operations and extended supply chains; (ii.) connective technologies (e.g., Internet of Things (IoT) sensors, blockchain, cloud computing, etc.) which collect and transmit data about physical objects; (iii.) predictive, prescriptive, and descriptive analytics to support improved decision making (Ivanov, 2023a); and (iv) autonomous procedural decision-making abilities in industrial systems (Villalonga et al., 2021).

The original idea of a digital twin is to represent in digital form an object, a product, an asset, a process, a network, or any combination of these, for superior prognostics and efficient resource utilization in the management and control of complex systems that are usually separated by distance (Grieves & Vickers, 2017). New, innovative models could use digital twin concepts to establish cloud -based organizations, digital platforms, service-oriented value creation, and dynamic process composition (Ivanov et al. 2022, Papanagnou et al. 2022). Simulation, AI, and machine learning tools can be applied to the design and usage of digital twins to evaluate decision alternatives and facilitate real-time decision making (Brusset et al., 2022; Granacher et al., 2022; MacCarthy et al., 2022; Nguyen et al., 2022; Sharma et al., 2022; Tozanli and Saénz, 2022). Digital twins have the potential to reduce costs while at the same time increase supply chain information sharing, connectivity, and resilience (Bhandal et al., 2022). Digital twins will play a crucial role in the digitalization of supply chain, operations management, and in building the industrial Metaverse (Dolgui and Ivanov, 2023; Lee and Kundu, 2022). A digital twin also directs decision makers as they navigate the potential solution space and guides them toward improved system designs that are robust to uncertain parameters and consider multiple important decision criteria (Granacher et al., 2022).

Digital twins are complex systems that typically possess several defining characteristics. Digital twins employ a combination of analytical models such as visualization, optimization, and simulation models. Digital twins also include capabilities for integrating with external systems to gather and process data as well as capabilities to process data internally within predictive and prescriptive models. They are driven by knowledge and use human-machine interfaces to transfer knowledge to different parties within the organization or the supply chain. Underlying technology supports a digital twin by facilitating data collection and processing, visualization, and integration (Ivanov, 2023b). Recent literature indicates different levels of digital twins, including cognitive digital twins (Zheng et al., 2022; Wang et al., 2024) and intelligent digital twins, representing a collaboration between human- and AI-decision-making which virtually represents physical supply chains, gathers and analyses data within decision-making models, replicates typical human decision-making policies, and generates new knowledge and procedures for making decisions (Ivanov, 2023b; Kinra et al., 2020).

Hype already surrounds the advent and use of digital twins within plants and other industrial facilities as well as supply chains, though some major challenges and issues related to their adoption remain (Berti and Finco, 2022). There is a need to theorize the digital twins’ concepts and to propose concrete methods and technologies for their design and implementation in the fields of supply chain, production, and engineering management.

The following problems have emerged and should be examined, though not limited to:

(a) Which new operations and supply chain models emerge using digital twins and how and where can new value be created?

(b) How do predictive, prescriptive, decision-making, and descriptive analytics in digital twins help to improve firms’ performance?

(c) How, to what extent, and when are operational decision-making capabilities (e.g., visibility, transparency, collaboration) enhanced through the real-time predictive and prescriptive models provided by digital twins?

(d) Which problems arise and how they can be solved using sophisticated representation and interpretation of data through AI algorithms, including advanced human-machine interaction and their implications?

(e) How can digital twins be designed and implemented for different systems (warehouse, production system, supply chain, energy and other transport and distribution networks) to improve efficiency and increase supply chain information sharing, connectivity, and resilience?

(f) Which human factors and behavioral or ethical problems emerge in the context of the application of digital twins?

(g) How do digital twins impact organizational decentralization due to customer and supplier collaboration generating process intelligence, integration intelligence, and collaboration intelligence in relation to work practices? Which operations and supply chain transformations are required for the implementation of digital twins?

(h) When do digital twins act as enablers of system and process resilience, and what is their role in achieving operations and supply chain flexibility, adaptability, and sustainability?

(i) What is the strategic role of ancillary emerging technologies (e.g., Blockchain, IoT, etc.) in the design of digital twins? How can this role be conceptualized within the framework of more unifying operations technologies?

(j) Which tradeoffs exist between the time and effort required to design digital twins and changing the organizational and decision-making structure and the gains from transitioning to the digital companion and control tower-based decision-making support? How can these tradeoffs be better categorized and resolved as optimization problems, operations and supply chain tensions, and paradoxes?

The scope of the special issue will encompass the organizational, modeling, and technology perspectives. We are equally interested in papers looking at theorizing digital twins, designing digital twins for solution of supply chain and operations management and engineering problems (e.g., network configuration (or reconfiguration), inventory management, production systems, resilience, and sustainability), and using digital twins (e.g., the control tower concept) with the infrastructure description as well as the mapping tools. The scope of the special issue encompasses all manuscripts concerning the impact, development, implementation, and use of digital twins in manufacturing and service operations and supply chains.

The application areas could involve but are not limited to the following sub-topics:

Human-AI decision-making collaboration in digital twins

Mastering complexity in digital twins

Predictive, prescriptive, and descriptive analytics in digital twins

Enhancing forecasting, visibility, and collaboration capabilities with digital twins

Ethical and sustainability concerns in digital twins

Digital twins in e-commerce

Technology for design and implementation of digital twins

Digital supply chain twins

Decision making and digital twins

Warehousing and manufacturing digital twins

Cognitive and intelligent digital twins

Digital twins and supply chain resilience

Data aggregation and validation in digital twins

Digital transformation, Industry 5.0, and digital twins

Model calibration in digital twins

Digital twins and the industrial Metaverse

Generative AI (e.g., ChatGPT) and digital supply chain and operations twins

Optimization and simulation using digital twins

While the special issue may seem well suited to Design Science Research (Bagni et al., 2024), it is open to any other methodological approach within operations, supply chain management, industrial engineering, and data science that fits the scope of IJPE. Our ultimate objective is to produce a special issue which would serve as a reference point presenting digital twins in operations and supply chain management not merely as digital replica of some real objects but as a complex socio-technical phenomenon involved with continuous human-artificial intelligence interactions. This leads to an understanding, designing, and implementing the digital twins in operations and supply chain management through the lens of Industry 5.0, reconfigurable, and viable supply chains.

The suggested timeline is:

o Call opens April 1, 2024

o Deadline for submission December 31, 2024

o First round of reviews March 31, 2025

o Publication online immediately after acceptance

References

Bagni, G., Godinho Filho, M., Finne, M., & Thürer, M. (2024). Design science research in operations management : Is there a single type? Production Planning & Control, 1‑19. https://doi.org/10.1080/09537287.2024.2310230

Berti N., Finco S. (2022). Digital Twin and Human Factors in Manufacturing and Logistics Systems: State of the Art and Future Research Directions. IFAC-PapersOnLine 55(10), 1893-1898.

Bhandal, R., Meriton, R., Kavanagh, R.E. and Brown, A. (2022). The application of digital twin technology in operations and supply chain management: a bibliometric review. Supply Chain Management, 27(2), 182-206

Brusset, X., La Torre, D., & Broekaert, J. (2022). Algorithms, Analytics, and Artificial Intelligence. In The Digital Supply Chain (p. 93‑110). Elsevier. https://doi.org/10.1016/B978-0-323-91614-1.00006-X

Chen, Z., & Huang, L. (2021). Digital twins for information-sharing in remanufacturing supply chain : A review. Energy, 220, 119712. https://doi.org/10.1016/j.energy.2020.119712

Dolgui A., Ivanov D., (2022). 5G in Digital Supply Chain and Operations Management: Fostering Flexibility, End-to-End Connectivity and Real-Time Visibility through Internet-of-Everything. International Journal of Production Research,60(2), 442-451.

Dolgui A., Ivanov D. (2023). Metaverse supply chain and operations management. International Journal of Production Research, DOI: 10.1080/00207543.2023.2240900

Granacher, J., Nguyen, T.-V., Castro-Amoedo, R., & Maréchal, F. (2022). Overcoming decision paralysis—A digital twin for decision making in energy system design. Applied Energy, 306, 117954. https://doi.org/10.1016/j.apenergy.2021.117954

Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In F.-J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches (pp. 85-113). Springer International Publishing. https://doi.org/10.1007/978-3-319-38756-7_4

Ivanov, D. (2023a). Conceptualisation of a 7-element digital twin framework in supply chain and operations management. International Journal of Production Research, 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, 263, 108938.

Ivanov D., Dolgui A., Sokolov B. (2022). Cloud Supply Chain: Integrating Industry 4.0 and Digital Platforms in the “Supply Chain-as-a-Service”. Transportation Research – Part E: Logistics and Transportation Review, 160, 102676.

Ivanov D., Dolgui A. (2021). A digital supply chain twin for managing the disruptions risks and resilience in the era of Industry 4.0. Production Planning and Control, 32(9), 775-788.

Kinra, A., Hald, K. S., Mukkamala, R. R., & Vatrapu, R. (2020). An unstructured big data approach for country logistics performance assessment in global supply chains. International Journal of Operations & Production Management, 40(4), 439‑458. https://doi.org/10.1108/IJOPM-07-2019-0544

Lee, J., & Kundu, P. (2022). Integrated cyber-physical systems and industrial metaverse for remote manufacturing. Manufacturing Letters, 34, 12‑15. https://doi.org/10.1016/j.mfglet.2022.08.012

MacCarthy B., Ivanov D. (2022b). The Digital Supply Chain—emergence, concepts, definitions, and technologies. In: MacCarthy B., Ivanov D. (Eds.). The Digital Supply Chain. Elsevier, Amsterdam, pp. 3-14.

MacCarthy, B., Ahmed, W., Demirel, G. (2022). Mapping the supply chain: Why, what and how? International Journal of Production Economics, 250, 108688.

Nguyen, T., Duong, QH., Nguyen, TV., Zhu, Y., Zhou, L. (2022). Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review. International Journal of Production Economics, 244, 108381.

Papanagnou, C., Seiler, A., Spanaki, K., Papadopoulos, T., & Bourlakis, M. (2022). Data-driven digital transformation for emergency situations: The case of the UK retail sector. International Journal of Production Economics, 250, 108628.

Sharma, A., Kosasih, E., Zhang, J., Brintrup, B., Calinescu, A. (2022). Digital Twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration, 30, 100383.

Tozanli, Ö, Saénz, M.J. (2022). Unlocking the Potential of Digital Twins in Supply Chains. MIT Sloan Management Review, 63(4), 1-4.

Villalonga, A., Negri, E., Biscardo, G., Castano, F., Haber, R. E., Fumagalli, L., & Macchi, M. (2021). A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins. Annual Reviews in Control, 51, 357‑373. https://doi.org/10.1016/j.arcontrol.2021.04.008

Wang, J., Li, X., Wang, P., & Liu, Q. (2024). Bibliometric analysis of digital twin literature : A review of influencing factors and conceptual structure. Technology Analysis & Strategic Management, 36(1), 166‑180. https://doi.org/10.1080/09537325.2022.2026320

Zheng, X., Lu J., Kiritsis D. (2022). The emergence of cognitive digital twin: vision, challenges and opportunities. International Journal of Production Research, 60(24), 7610-7632.