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Home All issues Volume 11 (2024) Manufacturing Rev., 11 (2024) 8 References
Open Access
Review
Issue
Manufacturing Rev.
Volume 11, 2024
Article Number 8
Number of page(s) 17
DOI https://doi.org/10.1051/mfreview/2024006
Published online 09 April 2024
  1. F. Farajpour, A. Hassanzadeh, S. Elahi, M. Ghazanfari, Digital supply chain blueprint via a systematic literature review, Technolog. Forecast. Social Change 184 (2022) 121976 [CrossRef] [Google Scholar]
  2. B. Brown, J. Sikes, P. Willmott, Bullish on Digital: McKinsey Global Survey Results, Insights Publ. (2013). http://www.mckinsey.com/businessfunctions/digital-mckinsey/our-insights/bullish-on-digital-mckinseyglobal-survey-results ( accessed 15 October 2016) [Google Scholar]
  3. A. Gunasekaran, T. Papadopoulos, R. Dubey, S.F. Wamba, S.J. Childe, B. Hazen, Gartner, inc. and/or its affiliates, Supply Chain Technologies and Digital Transformation (2023). Available at: Https://Www.Gartner.Com/En/Supply-Chain/Topics/Supply-Chain-Digital-Transformation [Google Scholar]
  4. K. Belova, Digital Transformation in Supply Chain: Key Trends, Technologies, and Use Cases (2022). https://Pixelplex.Io/Blog/What-Is-Digital-Transformation-in-Supply-Chain. [Google Scholar]
  5. A.G. Frank, L.S. Dalenogare, N.F. Ayala, Industry 4.0 technologies: implementation patterns in manufacturing companies, Int. J. Product. Econ. 210 (2019) 15–26 [CrossRef] [Google Scholar]
  6. J. Barata, The fourth industrial revolution of supply chains: a tertiary study, J. Eng. Technol. Manag. 60 (2021) 101624 [CrossRef] [Google Scholar]
  7. O. Bongomin, A. Yemane, B. Kembabazi, C. Malanda, M. Chikonkolo Mwape, N. Mpofu Sheron, D. Tigalana, Industry 4.0 disruption and its neologisms in major industrial sectors: a state of the art, J. Eng. 2020 (2020) 1–45 [Google Scholar]
  8. B. Meindl, N.F. Ayala, J. Mendonça, A.G. Frank, The four smarts of Industry 4.0: evolution of ten years of research and future perspectives, Technolog. Forecast. Social Change 168 (2021) 120784 [CrossRef] [Google Scholar]
  9. S. Raj, A. Sharma, Supply Chain Management in the Cloud (2014). Available at https://www.accenture.com/tr-en/insight-supply-chain-management-cloud [Google Scholar]
  10. G. Hanifan, A. Sharma, C. Newberry, The digital supply network: a new paradigm for supply chain management, Accent. Glob. Manag. Consult (2014) 1–8 [Google Scholar]
  11. S. Schrauf, P. Berttram, Industry 4.0: how digitization makes the supply chain more efficient, agile, and customer-focused, Strateg. Technol (2016) 1–32 [Google Scholar]
  12. Z. Li, G. Liu, L. Liu, X. Lai, G. Xu, IoT-based tracking and tracing platform for prepackaged food supply chain, Ind. Manag. Data Syst. 117 (2017) 1906–1916 [CrossRef] [Google Scholar]
  13. W.C. Tan, M.S. Sidhu, Review of RFID and IoT integration in supply chain management Oper. Res. Perspect. 9 (2022) [Google Scholar]
  14. M. Brinch, Understanding the value of big data in supply chain management and its business processes: towards a conceptual framework, Int. J. Oper. Product. Manag. 38 (2018) 1589–1614 [CrossRef] [Google Scholar]
  15. I. Taboada, H. Shee, Understanding 5G technology for future supply chain management, Int. J. Logist. Res. Appl. 24 (2020) 1–15 [Google Scholar]
  16. B. Musigmann, H. von der Gracht, E. Hartmann, Blockchain technology in logistics and supply chain management—a bibliometric literature review from 2016 to January 2020, IEEE Trans. Eng. Manag. 67 (2020) 988–1007 [CrossRef] [Google Scholar]
  17. M. Ben-Daya, E. Hassini, Z. Bahroun, Internet of things and supply chain management: a literature review, Int. J. Product. Res. 57 (2019) 4719–4742 [CrossRef] [Google Scholar]
  18. D. Tranfield, D. Denyer, P. Smart, Towards a methodology for developing evidence-informed management knowledge by means of systematic review, Br. J. Manag. 14 (2003) 207–222 [CrossRef] [Google Scholar]
  19. L. Ruiz-Garcia, L. Lunadei, The role of RFID in agriculture: applications, limitations and challenges, Comput. Electr. Agric. 79 (2011) 42–50 [CrossRef] [Google Scholar]
  20. C.N. Verdouw, A.J.M. Beulens, J.G.A.J. van der Vorst, Virtualisation of floricultural supply chains: a review from an internet of things perspective, Comput. Electr. Agric. 99 (2013) 160–175 [CrossRef] [Google Scholar]
  21. Fragkiadakis, An IoT-based platform for supply chain monitoring, in 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) (2021) [Google Scholar]
  22. E. Manavalan, K. Jayakrishna, A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements, Comput. Ind. Eng. 127 (2019) 925–953 [CrossRef] [Google Scholar]
  23. A. Tzounis, N. Katsoulas, T. Bartzanas, C. Kittas, Internet of Things in agriculture, recent advances and future challenges, Biosyst. Eng. 164 (2017) 31–48 [CrossRef] [Google Scholar]
  24. B. Yan, X.H. Wu, B. Ye, Y.W. Zhang, Three-level supply chain coordination of fresh agricultural products in the Internet of Things, Ind. Manag. Data Syst. (2017) 117 (9), 1842-1865 [Google Scholar]
  25. L. Njomane, A. Telukdarie, Impact of COVID-19 food supply chain: comparing the use of IoT in three South African supermarkets, Technol. Soc. 71 (2022) 102051 [CrossRef] [Google Scholar]
  26. S. Jangirala, A.K. Das, A.V. Vasilakos, Designing secure lightweight blockchain-enabled RFID-based authentication protocol for supply chains in 5G mobile edge computing environment, IEEE Trans. Ind. Inf. 16 (2019) 7081–7093 [Google Scholar]
  27. R.Y. Zhong, G.Q. Huang, S. Lan, Q.Y. Dai, X. Chen, T. Zhang, A big data approach for logistics trajectory discovery from RFID-enabled production data, Int. J. Product. Econ. 165 (2015) 260–272 [CrossRef] [Google Scholar]
  28. H.S. Heese, Inventory record inaccuracy, double marginalization, and RFID adoption, Prod. Oper. Manag. 16 (2007) 542–553 [CrossRef] [Google Scholar]
  29. H.S. Heese, Inventory record inaccuracy, RFID technology adoption and supply chain coordination, in: T.M. Choi, T. Cheng (Eds.), Supply Chain Coordination under Uncertainty. International Handbooks on Information Systems Springer, Berlin, Heidelberg (2011) [Google Scholar]
  30. Q. Dai, R. Zhong, G.Q. Huang, T. Qu, T. Zhang, T.Y. Luo, Radio frequency identification-enabled real-time manufacturing execution system: a case study in an automotive part manufacturer, Int. J. Comput. Integr. Manufactur. 25 (2012) 51–65 [CrossRef] [Google Scholar]
  31. R.Y. Zhong, G.Q. Huang, S. Lan, Q.Y. Dai, T. Zhang, C. Xu, A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing, Adv. Eng. Inf. 29 (2015) 799–812 [CrossRef] [Google Scholar]
  32. S.F. Wamba, A.T. Chatfield, A contingency model for creating value from RFID supply chain network projects in logistics and manufacturing environments, Eur. J. Inf. Syst. 18 (2009) 615–636 [CrossRef] [Google Scholar]
  33. K. Sari, Exploring the impacts of radio frequency identification (RFID) technology on supply chain performance, Eur. J. Oper. Res. 207 (2010) 174–183 [CrossRef] [Google Scholar]
  34. G. Ferrer, S.K. Heath, N. Dew, An RFID application in large job shop remanufacturing operations, Int. J. Product. Econ. 133 (2011) 612–621 [CrossRef] [Google Scholar]
  35. O. Urbano, A. Perles, C. Pedraza, S. Rubio-Arraez, M.L. Castelló, M.D. Ortola, R. Mercado, Cost-effective implementation of a temperature traceability system based on smart RFID tags and IoT services, Sensors 20 (2020) 1163 [CrossRef] [PubMed] [Google Scholar]
  36. A. Shrivastava, S.J. Suji Prasad, A.R. Yeruva, P. Mani, P. Nagpal, A. Chaturvedi, IoT based RFID attendance monitoring system of students using Arduino ESP8266 & Adafruition defined area, Cybern. Syst (2023) 1–12 . DOI: 10.1080/01969722.2023.2166243 [Google Scholar]
  37. S. Xie, F. Zhang, R. Cheng, Security enhanced RFID authentication protocols for healthcare environment, Wireless Pers. Commun. 117 (2020) 1–16 [Google Scholar]
  38. R.G. Richey, T.R. Morgan, K. Lindsey-Hall, F.G. Adams, A global exploration of Big Data in the supply chain, Int. J. Phys. Distrib. Logist. Manag. 46 (2016) 710–739 [CrossRef] [Google Scholar]
  39. B.T. Hazen, C.A. Boone, J.D. Ezell, L.A. Jones-Farmer, Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications, Int. J. Prod. Econ. 154 (2014) 72–80 [CrossRef] [Google Scholar]
  40. M. Giannakis, M. Louis, A multi-agent based system with big data processing for enhanced supply chain agility, J. Enterprise Inform. Manag. 29 (2016) 706–727 [CrossRef] [Google Scholar]
  41. M. Giannakis, M. Louis, A multi-agent based system with big data processing for enhanced supply chain agility, J. Enterprise Inf. Manag. 29 (2016) 706–727 [CrossRef] [Google Scholar]
  42. N.P. Singh, S. Singh, Building supply chain risk resilience: Role of big data analytics in supply chain disruption mitigation, Benchmarking 26 (2019) 2318–2342 [CrossRef] [Google Scholar]
  43. I.A.T. Hashem, I. Yaqoob, N.B. Anuar, S. Mokhtar, A. Gani, S.U. Khan, The rise of “big data” on cloud computing: review and open research issues, Inf. Syst. 47 (2015) 98–115 [CrossRef] [Google Scholar]
  44. H. Kaur, S.P. Singh, Heuristic modeling for sustainable procurement and logistics in a supply chain using big data, Comput. Oper. Res. 98 (2018) 301–321 [CrossRef] [MathSciNet] [Google Scholar]
  45. J. Mageto, Big data analytics in sustainable supply chain management: a focus on manufacturing supply chains, Sustainability 13 (2021) 7101 [CrossRef] [Google Scholar]
  46. T.M. Choi, Y. Chen, Circular supply chain management with large scale group decision making in the big data era: the macro-micro model, Technol. Forecast. Social Change 169 (2021) 120791 [CrossRef] [Google Scholar]
  47. R. Alshawabkeh, H. AL-Awamleh, M.S Alkhawaldeh, R. Kanaan, S. Al-Hawary, A. Mohammad, R. Alkhawalda, The mediating role of supply chain management on the relationship between big data and supply chain performance using SCOR model, Uncertain Supply Chain Manag. 10 (2022) 729–736 [CrossRef] [Google Scholar]
  48. N.K. Dev, R. Shankar, R. Gupta, J. Dong, Multicriteria evaluation of real-time key performance indicators of supply chain with consideration of big data architecture, Comput. Ind. Eng. 128 (2019) 1076–1087 [CrossRef] [Google Scholar]
  49. S. Bag, L.C. Wood, L. Xu, P. Dhamija, Y. Kayikci, Big data analytics as an operational excellence approach to enhance sustainable supply chain performance, Resour. Conserv. Recycl. 153 (2020) 104559 [CrossRef] [Google Scholar]
  50. R. Cole, M. Stevenson, J. Aitken, Blockchain technology: implications for operations and supply chain management, Supply Chain Manag. 24 (2019) 469–483 [CrossRef] [Google Scholar]
  51. L. Kehoe, N. O'connell, D. Andrzejewski, K. Gindner, D. Dalal, When two chains combine: supply chain meets blockchain (2017). Available at https://www2.deloitte.com/content/dam/Deloitte/pt/Documents/blockchainsupplychain/IE_C_TL_Supplychain_meets_blockchain_pdf. [Google Scholar]
  52. S. Laaper, J. Fitzgerald, E. Quasney, W. Yeh, M. Basir, Using blockchain to drive supply chain innovation, in Digit. Supply Chain Manag. Logist. Proc. Hambg. Int. Conf. Logist (2017) Vol. 1, p. 2013 [Google Scholar]
  53. Y. Shibuya, V. Babich, Multi-tier supply chain financing with blockchain (2021). Available at SSRN, 3787044. [Google Scholar]
  54. S.A. Raza, A systematic literature review of RFID in supply chain management, J. Enterprise Inf. Manag. 35 (2022) 617–649 [CrossRef] [Google Scholar]
  55. A. Khanna, S. Jain, A. Burgio, V. Bolshev, V. Panchenko, Blockchain-enabled supply chain platform for indian dairy industry: safety and traceability, Foods 11 (2022) 2716 [CrossRef] [PubMed] [Google Scholar]
  56. M. Nakasumi, Information sharing for supply chain management based on block chain technology, in 2017 IEEE 19th conference on business informatics (CBI), IEEE (2017) Vol. 1, pp. 140–149 [Google Scholar]
  57. P. Michelman, Seeing beyond the blockchain hype, MIT Sloan Manag. Rev. 58 (2017) 17 [Google Scholar]
  58. S.E. Chang, Y.C. Chen, M.F. Lu, Supply chain re-engineering using blockchain technology: a case of smart contract based tracking process, Technolog. Forecast. Social Change 144 (2019) 1–11 [CrossRef] [Google Scholar]
  59. I.A. Omar, R. Jayaraman, M.S. Debe, K. Salah, I. Yaqoob, M. Omar, Automating procurement contracts in the healthcare supply chain using blockchain smart contracts, IEEE Access 9 (2021) 37397–37409 [CrossRef] [Google Scholar]
  60. B. Haque, R. Hasan, O.M. Zihad, SmartOil: blockchain and smart contract‐based oil supply chain management, IET Blockchain 1 (2021) 95–104 [CrossRef] [Google Scholar]
  61. S. Mendonça, B. Damásio, L.C. de Freitas, L. Oliveira, M. Cichy, A. Nicita, The rise of 5G technologies and systems: a quantitative analysis of knowledge production, Telecommunications Policy 46 (2022) 102327 [CrossRef] [Google Scholar]
  62. M. Agiwal, N. Saxena, A. Roy, Towards connected living: 5G enabled internet of things (IoT), IETE Tech Rev. 36 (2019) 1–13 [Google Scholar]
  63. S.K. Rao, R. Prasad, Impact of 5G technologies on industry 4.0, Wirel. Pers. Commun. 100 (2018) 145–159 [CrossRef] [Google Scholar]
  64. 5GPPP, 5G and Factories of the Future (2015). Available at https://5g-ppp.eu/wp-content/uploads/2014/02/5G-PPP-White-Paperon-Factories-of-the-Future-Vertical-Sector.pdf [Google Scholar]
  65. L. Wood, Global 5G Market Report 2019-2025-Market Is Expected to Reach $277 Billion by 2025 at a CAGR of 111% − ResearchAndMarkets.Com (2019, April 10) [Google Scholar]
  66. S. Rommer, P. Hedman, M. Olsson, L. Frid, S. Sultana, C. Mulligan, 5G Core Networks. Academic Press (2020). https://doi.org/B9780081030097000016 [Google Scholar]
  67. A. Dolgui, D. Ivanov, 5G in digital supply chain and operations management: fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything, Int. J. Product. Res. 60 (2022) 442–451 [CrossRef] [Google Scholar]
  68. J.F. Cheng, Y. Yang, X.F. Zou, Y. Zuo, 5G in manufacturing: a literature review and future research, Int. J. Adv. Manufactur. Technol. (2022) Available from: https://doi.org/10.1007/s00170-022-08990-y. [Google Scholar]
  69. A. Lagorio, C. Cimini, R. Pinto, S. Cavalieri, 5G in logistics 4.0: potential applications and challenges, Proc. Comput. Sci. 217 (2023) 650–659 [CrossRef] [Google Scholar]
  70. E.J. Khatib, R. Barco, Optimization of 5G networks for smart logistics, Energies 14 (2021) 1758 [CrossRef] [Google Scholar]
  71. C. Küpper, J. Rösch, H. Winkler, Empirical findings for the usage of 5G as a basis for real time locating systems (RTLS) in the automotive industry, Proc. CIRP 107 (2022) 1287–1292 [CrossRef] [Google Scholar]
  72. M. Siddiqi, H. Yu, J. Joung, 5G ultra-reliable low-latency communication implementation challenges and operational issues with IoT devices, Electronics 8 (2019) 981 [CrossRef] [Google Scholar]
  73. P. Trakadas, N. Nomikos, E.T. Michailidis, T. Zahariadis, F.M. Facca, D. Breitgand, S. Rizou, X. Masip, P. Gkonis, Hybrid clouds for data-intensive, 5G-enabled IoT applications: an overview, Key Issues Relevant Arch, Sensors 19 (2019) 3591 [Google Scholar]
  74. W. Gao, Y. Zhang, D. Ramanujan, K. Ramani, Y. Chen, C.B. Williams, C.C.L. Wang, Y.C. Shin, S. Zhang, M. Gebler, A.J.S. Uiterkamp, C. Visser, A global sustainability perspective on 3D printing technologies, Energy Policy 74 (2014) 158–167 [CrossRef] [Google Scholar]
  75. G.R. Janssen, I.J. Blankers, E.A. Moolenburgh, A.L. Posthumus, TNO: The impact of 3-D printing on supply chain management ( 2014). Accessed December 14, 2019: https://pdfs.semanticscholar.org/1882/0f3c5985cf2ddccf0e9a1ff69c5124d3c1c1.pdf?_ga=2.162100219.651246241.1586378106-1013887197.1586378106 [Google Scholar]
  76. J. Holmström, J. Partanen, J. Tuomi, M. Walter, Rapid manufacturing in the spare parts supply chain: alternative approaches to capacity deployment, J. Manufactur. Technol. Manag. 21 (2010) 687–697 [CrossRef] [Google Scholar]
  77. A. Aimar, A. Palermo, B. Innocenti, The role of 3D printing in medical applications: a state of the art, J. Health Eng. 2019 (2019) 5340616 [CrossRef] [Google Scholar]
  78. Y. Bozkurt, E. Karayel, 3D printing technology; methods, biomedical applications, future opportunities and trends, J. Mater. Res. Technol. 14 (2021) 1430–1450 [CrossRef] [Google Scholar]
  79. S.H. Huang, P. Liu, A. Mokasdar, L. Hou, Additive manufacturing and its societal impact: a literature review, Int. J. Adv. Manufactur. Technol. 67 (2013) 1191–1203 [CrossRef] [Google Scholar]
  80. Y. Xiong, H. Lu, G.D. Li, S.M. Xia, Z.X. Wang, Y.F. Xu, Game changer or threat: the impact of 3D printing on the logistics supplier circular supply chain, Ind. Market. Manag. 106 (2022) 461–475 [CrossRef] [Google Scholar]
  81. L. Agnusdei, A. Del Prete, Additive manufacturing for sustainability: a systematic literature review, Sustain. Fut. 4 (2022) 100098 [CrossRef] [Google Scholar]
  82. M.V. Shree, V. Dhinakaran, V. Rajkumar, P.B. Ram, M.D. Vijayakumar, T. Sathish, Effect of 3D printing on supply chain management, Mater. Today: Proc. 21 (2020) 958–963 [CrossRef] [Google Scholar]
  83. A. Beltagui, S. Gold, N. Kunz, G. Reiner, Rethinking operations and supply chain management in light of the 3D printing revolution, Int. J. Prod. Econ. 255 (2023) 108677 [CrossRef] [Google Scholar]
  84. P. Kulkarni, A. Kumar, G. Chate, P. Dandannavar, Elements of additive manufacturing technology adoption in small- and medium-sized companies, Innov. Manag. Rev. 18 (2021) 400–416 [CrossRef] [Google Scholar]
  85. G.S. Walsh, J. Przychodzen, W. Przychodzen, Supporting the SME commercialization process: the case of 3D printing platforms, Small Enterprise Res. 24 (2017) 257–273 [CrossRef] [Google Scholar]
  86. A. Thomas, U. Mishra, A sustainable circular economic supply chain system with waste minimization using 3D printing and emissions reduction in plastic reforming industry, J. Clean. Prod. 345 (2022) 131128 [CrossRef] [Google Scholar]
  87. J. Sun, W. Zhou, D. Huang, J.Y. Fuh, G.S. Hong, An overview of 3D printing technologies for food fabrication, Food Bioprocess Technol 8 (2015) 1605–1615 [CrossRef] [Google Scholar]
  88. M. Mehrpouya, A. Dehghanghadikolaei, B. Fotovvati, A. Vosooghnia, S.S. Emamian, A. Gisario, The potential of additive manufacturing in the smart factory industrial 4.0: a review, Appl. Sci. 9 (2019) 3865 [CrossRef] [Google Scholar]
  89. H.K. Chan, J. Griffin, J.J. Lim, F. Zeng, A.S.F. Chiu, The impact of 3D printing technology on the supply chain: manufacturing and legal perspectives, Int. J. Prod. Econ. 205 (2018) 156–162 [CrossRef] [Google Scholar]
  90. F. Tao, Q. Qi, L. Wang, A.Y.C. Nee, Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison, Engineering 5 (2019) 653–661 [CrossRef] [Google Scholar]
  91. DHL. Digital Twins in Logistics: A DHL Perspective on the Impact of Digital Twins on the Logistics Industry 2019. DHL: San Francisco, CA, USA, 2019, Available at; [Google Scholar]
  92. L. Wang, T. Deng, Z.-J.M. Shen, H. Hu, Y. Qi, Digital twin-driven smart supply chain, Front. Eng. Manag. 9 (2022) 56–70 [CrossRef] [Google Scholar]
  93. J. Cohen, A coefficient of agreement for nominal scales, Educ. Psycholog. Measur. 20 (1960) 37–46 [CrossRef] [Google Scholar]
  94. A. Sharma, P. Zanotti, L.P. Musunur, Drive through robotics: Robotic automation for last mile distribution of food and essentials during pandemics, IEEE Access 8 (2020) 127190–127219 [CrossRef] [Google Scholar]
  95. T. Binsfeld, B. Gerlach, Quantifying the benefits of digital supply chain twins—a simulation study in organic food supply chains, Logistics 6 (2022) 46 [CrossRef] [Google Scholar]
  96. J. Spindler, T. Kec, T. Ley, Lead-time and risk reduction assessment of a sterile drug product manufacturing line using simulation, Comput. Chem. Eng. 152 (2021) 107401 [CrossRef] [Google Scholar]
  97. K.T. Park, Y.H. Son, S.D. Noh, The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control, Int. J. Product. Res. 59 (2021) 5721–5742 [CrossRef] [Google Scholar]
  98. B. Gerlach, S. Zarnitz, B. Nitsche, F. Straube, Digital supply chain twins-conceptual clarification, use cases and benefits, Logistics 5 (2021) 86 [CrossRef] [Google Scholar]
  99. C. Cimino, E. Negri, L. Fumagalli, Review of digital twin applications in manufacturing, Comput. Ind. 113 (2019) 103130 [CrossRef] [Google Scholar]
  100. G. Lugaresi, Z. Jemai, E. Sahin, Digital Twins for Supply Chains: Current Outlook and Future Challenges (2023) Proceedings/Recueil Des Communications Année 2023 hal-04137290. [Google Scholar]
  101. D. Bechtsis, N. Tsolakis, D. Vlachos, J.S. Srai, Intelligent autonomous vehicles in digital supply chains: a framework for integrating innovations towards sustainable value networks, J. Cleaner Prod. 181 (2018) 60–71 [CrossRef] [Google Scholar]
  102. A.K. Tyagi, S.U. Aswathy, Autonomous intelligent vehicles (AIV): research statements, open issues, challenges and road for future, Int. J. Intell. Networks 2 (2021) 83–102 [Google Scholar]
  103. IBM, How does blockchain work (2018). Available at: www.ibm.com/blockchain/what-is-blockchain (accessed 5 November 2018) [Google Scholar]
  104. N. Bahnes, S. Relvas, H. Haffaf, Cooperation between intelligent autonomous vehicles to enhance container terminal operations, J. Innov. Digit. Ecosyst. 3 (2016) 22–29 [CrossRef] [Google Scholar]
  105. D. Bechtsis, N. Tsolakis, D. Vlachos, E. Iakovou, Sustainable supply chain management in the digitalisation era: the impact of automated guided vehicles, J. Cleaner Prod. 142 (2017) 3970–3984 [CrossRef] [Google Scholar]
  106. Reiss, B. Pitts, Objects may be closer than they appear: how autonomous vehicles will bring value in a truly intelligent supply chain, Logist. Manag. 60 (2021) 18 [Google Scholar]
  107. D. Tsolakis, D. Bechtsis, J.S. Srai, Intelligent autonomous vehicles in digital supply chains: from conceptualisation, to simulation modelling, to real-world operations, Bus. Process Manag. J. 25 (2019) 414–437 [CrossRef] [Google Scholar]
  108. K. Sanogo, A.M. Benhafssa, M. Sahnoun, B. Bettayeb, M. Abderrahim, A. Bekrar, A multi-agent system simulation based approach for collision avoidance in integrated job-shop scheduling problem with transportation tasks, J. Manuf. Syst. 68 (2023) 209–226 [CrossRef] [Google Scholar]
  109. T. Walter, C. Zhenzhen Wang, O. Guillaud, E. Cotte, A. Pasquer, O. Vinet, G. Poncet, T. Ponchon, J.C. Saurin, Management of desmoid tumours: a large national database of familial adenomatous patients shows a link to colectomy modalities and low efficacy of medical treatments, United Eur. Gastroenterol. J. 5 (2017) 735–741 [CrossRef] [Google Scholar]
  110. M.C. Cronin, M.A. Awasthi, M.A. Conway, D. O'Riordan, J. Walsh, Design and development of a material handling system for an autonomous intelligent vehicle for flexible manufacturing, Proc. Manufactur. 51 (2020) 493–500 [CrossRef] [Google Scholar]
  111. C.R. Carter, D.S. Rogers, A framework of sustainable supply chain management: moving toward new theory, Int. J. Phys. Distrib. Logist. Manag. 38 (2008) 360–387 [CrossRef] [Google Scholar]
  112. L. Chen, S. Teng, B. Li, X. Na, Y. Li, Z. Li, J. Wang, D. Cao, N. Zheng, F.-Y. Wang, Milestones in autonomous driving and intelligent vehicles—Part II: Perception and planning, IEEE Trans Syst. Man Cybern 53 (2023) 1–15 [Google Scholar]
  113. P. Kittipanya-Ngam, K.H. Tan, A framework for food supply chain digitalization: lessons from Thailand, Prod. Plann. Control 31 (2020) 158–172 [CrossRef] [Google Scholar]
  114. P.J. Sun, Privacy protection and data security in cloud computing: a survey, challenges, and solutions, IEEE Access 7 (2019) 147420–147452 [Google Scholar]
  115. J.P.B. Rodrigues, Â. Liberal, S.A. Petropoulos, I.C.F.R. Ferreira, M.B.P.P. Oliveira, Â. Fernandes, L. Barros, Agri-food surplus, waste and loss as sustainable biobased ingredients: a review, Molecules 27 (2022) 5200 [CrossRef] [Google Scholar]
  116. FAO, Global Food Losses and Food Waste-Extent, Causes and Prevention. SAVE FOOD: An Initiative on Food Loss and Waste Reduction (2022). Available online: https://www.fao.org/3/i2697e/i2697e.pdf [Google Scholar]
  117. A.M. Ross, D.H. Rhodes, D.E. Hastings, Defining changeability: reconciling flexibility, adaptability, scalability, modifiability, and robustness for maintaining system lifecycle value, Syst. Eng. 11 (2008) 246–262 [CrossRef] [Google Scholar]
  118. P. Soto-Acosta, Navigating uncertainty: post-pandemic issues on digital transformation, Inf. Syst. Manag. (2023) https://doi.org/10.1080/10580530.2023.2274531 [Google Scholar]
  119. S. Akter, Big data and predictive analytics for supply chain and organizational performance, J. Bus. Res. 70 (2017) 308–317 [CrossRef] [Google Scholar]
  120. H.L. Lee, V. Padmanabhan, S. Whang, The bullwhip effect in supply chains Sloan Management Review, 38, 93–102 (1997) [Google Scholar]
  121. Y. Zhan, K.H. Tan, An analytic infrastructure for harvesting big data to enhance supply chain performance, Eur. J. Oper. Res. 281 (2020) 559–574 [CrossRef] [Google Scholar]
  122. J. Ahmad, A. Garg, G. Mustafa, A.A. Mohammed, M.Z. Ahmad, 3D printing technology as a promising tool to design nanomedicine-based solid dosage forms: contemporary research and future scope, Pharmaceutics 15 (2023) 1448 [CrossRef] [Google Scholar]
  123. S. Sun, X. Wang, Y. Zhang, Sustainable traceability in the food supply chain: the impact of consumer willingness to pay, Sustainability 9 (2017) 999 [Google Scholar]
  124. M. Ahn, C. Huang, P.C. Huang, X. Zhong, J. Himmelreich, K. Desouza, R. Knepper, Cyber-physical innovations: cyber-infrastructure for research, cyber-physical architecture for real-time applications, autonomous vehicle (AV) governance and AI artifacts for public value, in: J. Lee, G.V. Pereira, S. Hwang (Eds.), Proceedings of the 22nd Annual International Conference on Digital Government Research: Digital Innovations for Public Values: Inclusive Collaboration and Community, DGO 2021 (pp.590–592). Article 3463721 (ACM International Conference Proceeding Series). Association for Computing Machinery (2021) [Google Scholar]
  125. W. Kim, S. Kim, J. Jeong, H. Kim, H. Lee, B.D. Youn, Digital twin approach for on-load tap changers using data-driven dynamic model updating and optimization-based operating condition estimation, Mech. Syst. Signal Process. 181 (2022) 109471 [CrossRef] [Google Scholar]

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