IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i9p3760-d354392.html
   My bibliography  Save this article

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Author

Listed:
  • Manuel Woschank

    (Chair of Industrial Logistics, Montanuniversitaet Leoben, 8700 Leoben, Austria)

  • Erwin Rauch

    (Industrial Engineering and Automation (IEA), Faculty of Science and Technology, Free University of Bozen-Bolzano, 39100 Bolzano, Italy)

  • Helmut Zsifkovits

    (Chair of Industrial Logistics, Montanuniversitaet Leoben, 8700 Leoben, Austria)

Abstract

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

Suggested Citation

  • Manuel Woschank & Erwin Rauch & Helmut Zsifkovits, 2020. "A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics," Sustainability, MDPI, vol. 12(9), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3760-:d:354392
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/9/3760/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/9/3760/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Raffaele Cioffi & Marta Travaglioni & Giuseppina Piscitelli & Antonella Petrillo & Fabio De Felice, 2020. "Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions," Sustainability, MDPI, vol. 12(2), pages 1-26, January.
    2. Luan Thanh Le & Gunwoo Lee & Keun-Sik Park & Hwayoung Kim, 2020. "Neural network-based fuel consumption estimation for container ships in Korea," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 615-632, July.
    3. Christian F. Durach & Joakim Kembro & Andreas Wieland, 2017. "A New Paradigm for Systematic Literature Reviews in Supply Chain Management," Journal of Supply Chain Management, Institute for Supply Management, vol. 53(4), pages 67-85, October.
    4. Giusti, Riccardo & Manerba, Daniele & Bruno, Giorgio & Tadei, Roberto, 2019. "Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 92-110.
    5. Nader Karballaeezadeh & Farah Zaremotekhases & Shahaboddin Shamshirband & Amir Mosavi & Narjes Nabipour & Peter Csiba & Annamária R. Várkonyi-Kóczy, 2020. "Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems," Energies, MDPI, vol. 13(7), pages 1-22, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhencheng Fan & Zheng Yan & Shiping Wen, 2023. "Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    2. Meir Russ, 2021. "Knowledge Management for Sustainable Development in the Era of Continuously Accelerating Technological Revolutions: A Framework and Models," Sustainability, MDPI, vol. 13(6), pages 1-32, March.
    3. Ahmed Zainul Abideen & Jaafar Pyeman & Veera Pandiyan Kaliani Sundram & Ming-Lang Tseng & Shahryar Sorooshian, 2021. "Leveraging Capabilities of Technology into a Circular Supply Chain to Build Circular Business Models: A State-of-the-Art Systematic Review," Sustainability, MDPI, vol. 13(16), pages 1-26, August.
    4. Mustafa Qahtan Alsudani & Mustafa Musa Jaber & Mohammed Hasan Ali & Sura Khalil Abd & Ahmed Alkhayyat & Z. H. Kareem & Ahmed Rashid Mohhan, 2023. "RETRACTED ARTICLE: Smart logistics with IoT-based enterprise management system using global manufacturing," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-31, March.
    5. Farajpour, Farnoush & Hassanzadeh, Alireza & Elahi, Shaban & Ghazanfari, Mehdi, 2022. "Digital supply chain blueprint via a systematic literature review," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Beatriz Acero & Maria Jesus Saenz & Davide Luzzini, 2022. "Introducing synchromodality: One missing link between transportation and supply chain management," Journal of Supply Chain Management, Institute for Supply Management, vol. 58(1), pages 51-64, January.
    2. Riccardo Giusti & Daniele Manerba & Roberto Tadei, 2021. "Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    3. Johannes Rentschler & Ralf Elbert & Felix Weber, 2022. "Promoting Sustainability through Synchromodal Transportation: A Systematic Literature Review and Future Fields of Research," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    4. Matteo Acquarone & Claudio Maino & Daniela Misul & Ezio Spessa & Antonio Mastropietro & Luca Sorrentino & Enrico Busto, 2023. "Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control," Energies, MDPI, vol. 16(6), pages 1-22, March.
    5. Aditi S. Saha & Rakesh D. Raut & Vinay Surendra Yadav & Abhijit Majumdar, 2022. "Blockchain Changing the Outlook of the Sustainable Food Supply Chain to Achieve Net Zero?," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
    6. Pani, Agnivesh & Mishra, Sabya & Sahu, Prasanta, 2022. "Developing multi-vehicle freight trip generation models quantifying the relationship between logistics outsourcing and insourcing decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    7. Pirrone, Lorenzo & Meyer, Dennis, 2021. "Development of a Procurement-4.0-PMS using the Balanced Scorecard," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 691-721, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    8. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    9. Robert Suurmond & Finn Wynstra & Jan Dul, 2020. "Unraveling the Dimensions of Supplier Involvement and their Effects on NPD Performance: A Meta‐Analysis," Journal of Supply Chain Management, Institute for Supply Management, vol. 56(3), pages 26-46, July.
    10. Thomas Johnsen & Marie-Anne Le Dain & Nadine Kiratli & Holger Schiele, 2022. "Editorial: Purchasing and innovation: Past, present and future of the field of research," Post-Print hal-03761525, HAL.
    11. Nassim Mrabti & Nadia Hamani & Laurent Delahoche, 2022. "A Comprehensive Literature Review on Sustainable Horizontal Collaboration," Sustainability, MDPI, vol. 14(18), pages 1-38, September.
    12. Tessmann, R. & Elbert, R., 2022. "Multi sided platforms in competitive B2B networks with varying governmental influence – a taxonomy of Port and Cargo Community System business models," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 132320, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Michael Sony & Jiju Antony & Guilherme L. Tortorella, 2023. "Critical Success Factors for Successful Implementation of Healthcare 4.0: A Literature Review and Future Research Agenda," IJERPH, MDPI, vol. 20(5), pages 1-22, March.
    14. Alberto Rodrigo González & Nicoletta González-Cancelas & Beatriz Molina Serrano & Alberto Camarero Orive, 2020. "Preparation of a Smart Port Indicator and Calculation of a Ranking for the Spanish Port System," Logistics, MDPI, vol. 4(2), pages 1-22, May.
    15. Maksymilian Mądziel, 2023. "Liquified Petroleum Gas-Fuelled Vehicle CO 2 Emission Modelling Based on Portable Emission Measurement System, On-Board Diagnostics Data, and Gradient-Boosting Machine Learning," Energies, MDPI, vol. 16(6), pages 1-15, March.
    16. Chen, Xi & Wong, Tse Chiu, 2021. "Application of social media data in supply chain management : A systematic review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 499-523, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    17. Fazi, Stefano & Fransoo, Jan C. & Van Woensel, Tom & Dong, Jing-Xin, 2020. "A variant of the split vehicle routing problem with simultaneous deliveries and pickups for inland container shipping in dry-port based systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    18. Muhammad Umar Farooq & Amjad Hussain & Tariq Masood & Muhammad Salman Habib, 2021. "Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19," Sustainability, MDPI, vol. 13(5), pages 1-33, February.
    19. Diéssica Oliveira-Dias & José Moyano-Fuentes & Juan Manuel Maqueira-Marín, 2022. "Understanding the relationships between information technology and lean and agile supply chain strategies: a systematic literature review," Annals of Operations Research, Springer, vol. 312(2), pages 973-1005, May.
    20. Sara Rogerson & Vendela Santén & Uni Sallnäs, 2021. "The Influence of Power and Trust on the Initiation and Duration of Modal Shift Solutions," Sustainability, MDPI, vol. 13(7), pages 1-19, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3760-:d:354392. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.