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Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics

Author

Listed:
  • Ahmed Zainul Abideen

    (Institute of Business Excellence, Universiti Teknologi MARA, Shah Alam 40450, Malaysia)

  • Veera Pandiyan Kaliani Sundram

    (Institute of Business Excellence, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
    Faculty of Business and Management, Universiti Teknologi MARA, Selangor Branch, Puncak Alam 42300, Malaysia)

  • Jaafar Pyeman

    (Institute of Business Excellence, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
    Faculty of Business and Management, Universiti Teknologi MARA, Selangor Branch, Puncak Alam 42300, Malaysia)

  • Abdul Kadir Othman

    (Institute of Business Excellence, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
    Faculty of Business and Management, Universiti Teknologi MARA, Selangor Branch, Puncak Alam 42300, Malaysia)

  • Shahryar Sorooshian

    (Department of Business Administration, University of Gothenburg, 41124 Gothenburg, Sweden
    Prime School of Logistics, Saito University College, Selangor 46200, Malaysia)

Abstract

Background : As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. Methods : Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. Results : The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. Conclusion : A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.

Suggested Citation

  • Ahmed Zainul Abideen & Veera Pandiyan Kaliani Sundram & Jaafar Pyeman & Abdul Kadir Othman & Shahryar Sorooshian, 2021. "Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics," Logistics, MDPI, vol. 5(4), pages 1-22, November.
    • Handle: RePEc:gam:jlogis:v:5:y:2021:i:4:p:84-:d:689244
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    References listed on IDEAS

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    Cited by:

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    2. Malena Zielske & Tobias Held & Athanasios Kourouklis, 2022. "A Framework on the Use of Agile Methods in Logistics Startups," Logistics, MDPI, vol. 6(1), pages 1-21, February.
    3. Jiamuyan Xie, 2022. "Information Sharing in a Supply Chain with Asymmetric Competing Retailers," Sustainability, MDPI, vol. 14(19), pages 1-21, October.
    4. Ekaterina V. Orlova, 2023. "Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods," Mathematics, MDPI, vol. 11(18), pages 1-22, September.
    5. Kabadurmus, Ozgur & Kayikci, Yaşanur & Demir, Sercan & Koc, Basar, 2023. "A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    6. Weng Siew Lam & Weng Hoe Lam & Pei Fun Lee, 2023. "A Bibliometric Analysis of Digital Twin in the Supply Chain," Mathematics, MDPI, vol. 11(15), pages 1-24, July.

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