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OR and analytics for digital, resilient, and sustainable manufacturing 4.0

Author

Listed:
  • Tsan-Ming Choi

    (National Taiwan University)

  • Alexandre Dolgui

    (IMT Atlantique)

  • Dmitry Ivanov

    (Berlin School of Economics and Law)

  • Erwin Pesch

    (University of Siegen
    HHL Leipzig Graduate School of Management)

Abstract

No abstract is available for this item.

Suggested Citation

  • Tsan-Ming Choi & Alexandre Dolgui & Dmitry Ivanov & Erwin Pesch, 2022. "OR and analytics for digital, resilient, and sustainable manufacturing 4.0," Annals of Operations Research, Springer, vol. 310(1), pages 1-6, March.
    • Handle: RePEc:spr:annopr:v:310:y:2022:i:1:d:10.1007_s10479-022-04536-3
    DOI: 10.1007/s10479-022-04536-3
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    References listed on IDEAS

    as
    1. Arbabian, Mohammad E. & Wagner, Michael R., 2020. "The impact of 3D printing on manufacturer–retailer supply chains," European Journal of Operational Research, Elsevier, vol. 285(2), pages 538-552.
    2. Tsan‐Ming Choi & Subodha Kumar & Xiaohang Yue & Hau‐Ling Chan, 2022. "Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 9-31, January.
    3. Choi, Tsan-Ming, 2019. "Blockchain-technology-supported platforms for diamond authentication and certification in luxury supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 17-29.
    4. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    5. Dmitry Ivanov & Christopher S. Tang & Alexandre Dolgui & Daria Battini & Ajay Das, 2021. "Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 2055-2078, April.
    6. Alexandre Dolgui & Dmitry Ivanov & Semyon Potryasaev & Boris Sokolov & Marina Ivanova & Frank Werner, 2020. "Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 58(7), pages 2184-2199, April.
    7. Giuseppe Fragapane & Dmitry Ivanov & Mirco Peron & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics," Annals of Operations Research, Springer, vol. 308(1), pages 125-143, January.
    8. Gah-Yi Ban & Cynthia Rudin, 2019. "The Big Data Newsvendor: Practical Insights from Machine Learning," Operations Research, INFORMS, vol. 67(1), pages 90-108, January.
    9. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    10. Burgos, Diana & Ivanov, Dmitry, 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, Elsevier, vol. 152(C).
    11. Li, Han & Gupta, Ashish & Zhang, Jie & Flor, Nick, 2020. "Who will use augmented reality? An integrated approach based on text analytics and field survey," European Journal of Operational Research, Elsevier, vol. 281(3), pages 502-516.
    12. Alexandre Dolgui & Dmitry Ivanov, 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, Taylor & Francis Journals, vol. 60(2), pages 442-451, January.
    13. Rahul Rai & Manoj Kumar Tiwari & Dmitry Ivanov & Alexandre Dolgui, 2021. "Machine learning in manufacturing and industry 4.0 applications," International Journal of Production Research, Taylor & Francis Journals, vol. 59(16), pages 4773-4778, August.
    14. Xiaotong Sun & Wei Xu & Hongxun Jiang & Qili Wang, 2021. "A deep multitask learning approach for air quality prediction," Annals of Operations Research, Springer, vol. 303(1), pages 51-79, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Mohammadi, Mehrdad & Dehghan, Milad & Pirayesh, Amir & Dolgui, Alexandre, 2022. "Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic," Omega, Elsevier, vol. 113(C).
    2. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    3. Mohammed, Ahmed & Lopes de Sousa Jabbour, Ana Beatriz & Koh, Lenny & Hubbard, Nicolas & Chiappetta Jabbour, Charbel Jose & Al Ahmed, Teejan, 2022. "The sourcing decision-making process in the era of digitalization: A new quantitative methodology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    4. Li, Guo & Li, Xiaochuan & Zheng, Hong, 2023. "Discount preannouncement in the digital supply chain era," International Journal of Production Economics, Elsevier, vol. 258(C).
    5. Lopes de Sousa Jabbour, Ana Beatriz & Chiappetta Jabbour, Charbel Jose & Choi, Tsan-Ming & Latan, Hengky, 2022. "‘Better together’: Evidence on the joint adoption of circular economy and industry 4.0 technologies," International Journal of Production Economics, Elsevier, vol. 252(C).

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