Conceptualisation of a 7-element digital twin framework in supply chain and operations management

[1]  Jennifer Blackhurst,et al.  Toward supply chain viability theory: from lessons learned through COVID-19 pandemic to viable ecosystems , 2023, Int. J. Prod. Res..

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

[3]  Yang Liu,et al.  Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window , 2023, Robotics Comput. Integr. Manuf..

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

[5]  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.

[6]  Xiu Hui. Loh,et al.  Beneficiary-centric decision support framework for enhanced resource coordination in humanitarian logistics: A case study from ASEAN , 2022, Transportation Research Part E: Logistics and Transportation Review.

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

[8]  Gökan May,et al.  A literature review and design methodology for digital twins in the era of zero defect manufacturing , 2022, Int. J. Prod. Res..

[9]  D. Ivanov Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis , 2022, Annals of operations research.

[10]  Q. Yan,et al.  Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm , 2022, Comput. Oper. Res..

[11]  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.

[12]  Sachin S. Kamble,et al.  Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework , 2022, Technological Forecasting and Social Change.

[13]  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.

[14]  David A. Wuttke,et al.  Seeing the Bigger Picture? Ramping up Production with the Use of Augmented Reality , 2022, Manuf. Serv. Oper. Manag..

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

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

[17]  Lei Yue,et al.  Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor , 2020, Int. J. Prod. Res..

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

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

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

[21]  Dmitry Ivanov,et al.  Design redundancy in agile and resilient humanitarian supply chains , 2019, Ann. Oper. Res..

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

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

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

[25]  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.

[26]  G. Strobel,et al.  Supply Chains in the Era of Digital Twins – A Review , 2022, Procedia Computer Science.

[27]  J. Moosavi,et al.  Simulation-based assessment of supply chain resilience with consideration of recovery strategies in the COVID-19 pandemic context , 2021, Computers & Industrial Engineering.

[28]  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.

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

[30]  D. Ivanov Exiting the COVID-19 pandemic: after-shock risks and avoidance of disruption tails in supply chains , 2021, Annals of operations research.

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

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

[33]  M. Tiwari,et al.  Impact of COVID-19 on logistics systems and disruptions in food supply chain , 2020, Int. J. Prod. Res..

[34]  Kyu Tae Park,et al.  The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control , 2020, Int. J. Prod. Res..

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

[36]  Judit Monostori,et al.  Mitigation of the ripple effect in supply chains: Balancing the aspects of robustness, complexity and efficiency , 2021 .

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

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

[39]  Dmitry Ivanov,et al.  ‘A blessing in disguise’ or ‘as if it wasn’t hard enough already’: reciprocal and aggravate vulnerabilities in the supply chain , 2020, Int. J. Prod. Res..

[40]  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.

[41]  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..

[42]  D. Ivanov Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case , 2020, Transportation Research Part E: Logistics and Transportation Review.

[43]  Alexandre Dolgui,et al.  Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain† , 2019, Int. J. Prod. Res..

[44]  Stefano Riemma,et al.  Digital Twin Models in Industrial Operations: A Systematic Literature Review , 2020, Procedia Manufacturing.

[45]  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 .

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

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

[48]  Dmitry A. Ivanov,et al.  Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods , 2019, Comput. Ind. Eng..

[49]  D. Ivanov Revealing interfaces of supply chain resilience and sustainability: a simulation study , 2018, Int. J. Prod. Res..

[50]  Wilfried Sihn,et al.  Digital Twin in manufacturing: A categorical literature review and classification , 2018 .

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

[52]  Dmitry Ivanov,et al.  Simulation-based ripple effect modelling in the supply chain , 2017, Int. J. Prod. Res..

[53]  S. Seuring,et al.  Conducting content‐analysis based literature reviews in supply chain management , 2012 .