Metaverse supply chain and operations management

ABSTRACT The metaverse and Web 3.0 have created a new digital world with specific properties and behaviours replicating and influencing the behaviours and processes of physical entities. This study aims to advance our understanding of how the metaverse will impact supply chain and operations management (SCOM). Using elements of a structured literature search and building on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multiple socio-technological dimensions. We conclude that further metaverse developments could result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of the physical and metaverse worlds. We offer a structured future research agenda pointing to new research questions and topics stemming from metaverse-driven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can emerge for the novel metaverse SCOM processes and decision-making areas (e.g. joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for the metaverse and physical worlds, and pricing and contracting for digital products), as well as new performance measures (e.g. virtual customer experience level, availability of digital products, and digital resilience and sustainability).

[1]  D. Ivanov Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability , 2023, International Journal of Production Economics.

[2]  D. Ivanov,et al.  Circular supply chain management with blockchain technology: A dynamic capabilities view , 2023, Transportation Research Part E: Logistics and Transportation Review.

[3]  D. Ivanov Conceptualisation of a 7-element digital twin framework in supply chain and operations management , 2023, Int. J. Prod. Res..

[4]  D. Battini,et al.  Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments , 2023, Omega.

[5]  Denish Shah,et al.  Marketing in the Metaverse: Conceptual understanding, framework, and research agenda , 2023, Journal of Business Research.

[6]  H. Boyes,et al.  Digital twins: An analysis framework and open issues , 2022, Comput. Ind..

[7]  T. Choi,et al.  Can we work more safely and healthily with robot partners? A human‐friendly robot–human‐coordinated order fulfillment scheme , 2022, Production and Operations Management.

[8]  D. Ivanov,et al.  A review on reinforcement learning algorithms and applications in supply chain management , 2022, Int. J. Prod. Res..

[9]  B. Keskin,et al.  Post-pandemic Adaptation and Development of Supply Chain Viability Theory , 2022, Omega.

[10]  D. Mourtzis,et al.  Human centric platforms for personalized value creation in metaverse , 2022, Journal of Manufacturing Systems.

[11]  B. MacCarthy,et al.  Mapping the supply chain: Why, what and how? , 2022, International Journal of Production Economics.

[12]  A. Kusiak Predictive models in digital manufacturing: research, applications, and future outlook , 2022, Int. J. Prod. Res..

[13]  D. Ivanov The Industry 5.0 framework: viability-based integration of the resilience, sustainability, and human-centricity perspectives , 2022, Int. J. Prod. Res..

[14]  D. Ivanov,et al.  The shortage economy and its implications for supply chain and operations management , 2022, Int. J. Prod. Res..

[15]  W. Reim,et al.  Enabling collaboration on digital platforms: a study of digital twins , 2022, Int. J. Prod. Res..

[16]  Jay Lee,et al.  Integrated Cyber-Physical System and Industrial Metaverse for Remote Manufacturing , 2022, Manufacturing Letters.

[17]  P. Ball,et al.  Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions , 2022, Int. J. Prod. Res..

[18]  D. Ivanov,et al.  Blockchain-supported business model design, supply chain resilience, and firm performance , 2022, Transportation Research Part E: Logistics and Transportation Review.

[19]  Jonathan Gaudreault,et al.  Toward digital twins for sawmill production planning and control: benefits, opportunities, and challenges , 2022, Int. J. Prod. Res..

[20]  D. Ivanov,et al.  Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service” , 2022, Transportation Research Part E: Logistics and Transportation Review.

[21]  Quoc-Viet Pham,et al.  Artificial Intelligence for the Metaverse: A Survey , 2022, Eng. Appl. Artif. Intell..

[22]  E. Pesch,et al.  OR and analytics for digital, resilient, and sustainable manufacturing 4.0 , 2022, Annals of Operations Research.

[23]  Li Zhou,et al.  Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review , 2022, International Journal of Production Economics.

[24]  D. Ivanov,et al.  5G in digital supply chain and operations management: fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything , 2021, Int. J. Prod. Res..

[25]  Subodha Kumar,et al.  Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond , 2021, Production and Operations Management.

[26]  M. Tiwari,et al.  Machine learning in manufacturing and industry 4.0 applications , 2021, Int. J. Prod. Res..

[27]  D. Ivanov,et al.  Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions , 2021, Transportation Research Part E: Logistics and Transportation Review.

[28]  D. Ivanov,et al.  Stress testing supply chains and creating viable ecosystems , 2021, Operations Management Research.

[29]  Daria Battini,et al.  Costs of resilience and disruptions in supply chain network design models: A review and future research directions , 2021 .

[30]  Stephen M. Disney,et al.  Dual Sourcing and Smoothing Under Nonstationary Demand Time Series: Reshoring with SpeedFactories , 2021, Manag. Sci..

[31]  Dimitris Kiritsis,et al.  Actionable cognitive twins for decision making in manufacturing , 2021, Int. J. Prod. Res..

[32]  Ajay Das,et al.  Supply chain viability: conceptualization, measurement, and nomological validation , 2021, Ann. Oper. Res..

[33]  Tadeusz Sawik,et al.  A linear model for optimal cybersecurity investment in Industry 4.0 supply chains , 2020, Int. J. Prod. Res..

[34]  Guoxin Wang,et al.  Building blocks for digital twin of reconfigurable machine tools from design perspective , 2020, Int. J. Prod. Res..

[35]  Enzo Morosini Frazzon,et al.  Intelligent methods and systems for decision-making support: Toward digital supply chain twins , 2020, Int. J. Inf. Manag..

[36]  A. Brintrup,et al.  Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions , 2020, J. Ind. Inf. Integr..

[37]  Dmitry Ivanov,et al.  Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management , 2020, Int. J. Prod. Res..

[38]  Tsan-Ming Choi,et al.  Platform Supported Supply Chain Operations in the Blockchain Era: Supply Contracting and Moral Hazards , 2020, Decis. Sci..

[39]  Chao Liu,et al.  Web-based digital twin modeling and remote control of cyber-physical production systems , 2020, Robotics Comput. Integr. Manuf..

[40]  Boris V. Sokolov,et al.  Reconfigurable supply chain: the X-network , 2020, Int. J. Prod. Res..

[41]  Andrew Kusiak,et al.  Open manufacturing: a design-for-resilience approach , 2020, Int. J. Prod. Res..

[42]  D. Ivanov Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic , 2020, Ann. Oper. Res..

[43]  Alexandre Dolgui,et al.  A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 , 2020, Production Planning & Control.

[44]  Jiankun Sun,et al.  Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations , 2020, Manag. Sci..

[45]  Alexandre Dolgui,et al.  Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak , 2020, Int. J. Prod. Res..

[46]  Ruomeng Cui,et al.  AI and Procurement , 2020, Manuf. Serv. Oper. Manag..

[47]  Henrik S. Sternberg,et al.  Distributed ledger technology in supply chains: a transaction cost perspective , 2020, Int. J. Prod. Res..

[48]  Jennifer Blackhurst,et al.  Industry 4.0 and resilience in the supply chain: a driver of capability enhancement or capability loss? , 2020, Int. J. Prod. Res..

[49]  Enzo Morosini Frazzon,et al.  A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing , 2019, Int. J. Inf. Manag..

[50]  Duncan McFarlane,et al.  Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing , 2019, Int. J. Prod. Res..

[51]  Lucas P. Veelenturf,et al.  The strategic role of logistics in the industry 4.0 era , 2019, Transportation Research Part E: Logistics and Transportation Review.

[52]  Param Vir Singh,et al.  'Un'Fair Machine Learning Algorithms , 2019, Manag. Sci..

[53]  E. H. Grosse,et al.  Logistics 4.0: a systematic review towards a new logistics system , 2019, Int. J. Prod. Res..

[54]  Elliot Bendoly,et al.  Behavioral Operations and Supply Chain Management-A Review and Literature Mapping , 2019, Decis. Sci..

[55]  Jan A. Van Mieghem,et al.  Dual Sourcing and Smoothing Under Non-Stationary Demand Time Series: Re-Shoring with Speedfactories , 2019 .

[56]  Daria Battini,et al.  Big size highly customised product manufacturing systems: a literature review and future research agenda , 2019, Int. J. Prod. Res..

[57]  Angappa Gunasekaran,et al.  Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience , 2019, Int. J. Prod. Res..

[58]  Guy Van den Broeck,et al.  Siemens , 2018, Definitions.

[59]  Stephan Biller,et al.  The Internet of Things and Information Fusion: Who Talks to Who? , 2018, Manuf. Serv. Oper. Manag..

[60]  K. Stecke,et al.  The evolution of production systems from Industry 2.0 through Industry 4.0 , 2018, Int. J. Prod. Res..

[61]  Shimon Y. Nof,et al.  Collaborative service-component integration in cloud manufacturing , 2018, Int. J. Prod. Res..

[62]  Adam N. Elmachtoub,et al.  Smart "Predict, then Optimize" , 2017, Manag. Sci..

[63]  George Q. Huang,et al.  Physical Internet and interconnected logistics services: research and applications , 2017, Int. J. Prod. Res..

[64]  Hamideh Afsarmanesh,et al.  Collaborative networks: a new scientific discipline , 2005, J. Intell. Manuf..

[65]  C. Fagin The research agenda. , 1986, The American journal of orthopsychiatry.

[66]  D. Ivanov,et al.  Supply chain resilience: a tertiary study , 2023, International Journal of Integrated Supply Management.

[67]  F. Sgarbossa,et al.  Additive or Conventional Manufacturing for Spare Parts: Effect of Failure Rate Uncertainty on the Sourcing Option Decision , 2022, IFAC-PapersOnLine.

[68]  Berti Nicola,et al.  Digital Twin and Human Factors in Manufacturing and Logistics Systems: State of the Art and Future Research Directions , 2022, IFAC-PapersOnLine.

[69]  Dmitry Ivanov,et al.  Digital Supply Chain Management and Technology to Enhance Resilience by Building and Using End-to-End Visibility During the COVID-19 Pandemic , 2021, IEEE Transactions on Engineering Management.

[70]  D. Ivanov,et al.  Global Supply Chain and Operations Management , 2021, Springer Texts in Business and Economics.

[71]  Fabio Sgarbossa,et al.  Human factors in production and logistics systems of the future , 2020, Annu. Rev. Control..

[72]  Benoît Iung,et al.  Challenges for the cyber-physical manufacturing enterprises of the future , 2019, Annu. Rev. Control..

[73]  Alexandre Dolgui,et al.  Sustainable and Energy Efficient Reconfigurable Manufacturing Systems , 2019, Springer Series in Advanced Manufacturing.

[74]  Luca Fumagalli,et al.  Flexible Automation and Intelligent Manufacturing , FAIM 2017 , 27-30 June 2017 , Modena , Italy A review of the roles of Digital Twin in CPS-based production systems , 2017 .

[75]  Alexandre Dolgui,et al.  Supply Chain Engineering , 2010 .

[76]  J. Proth,et al.  Supply Chain Engineering : Useful Methods and Techniques , 2009 .

[77]  Cynthia L. Perrine,et al.  Accenture , 2002, INTR.