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RETRACTED ARTICLE: Smart logistics with IoT-based enterprise management system using global manufacturing

Author

Listed:
  • Mustafa Qahtan Alsudani

    (Imam Ja’afar Al-Sadiq University)

  • Mustafa Musa Jaber

    (Al-Turath University College
    Al-Farahidi University)

  • Mohammed Hasan Ali

    (Imam Ja’afar Al-Sadiq University)

  • Sura Khalil Abd

    (Dijlah University College)

  • Ahmed Alkhayyat

    (The Islamic University)

  • Z. H. Kareem

    (Al-Mustaqbal University College)

  • Ahmed Rashid Mohhan

    (Mazaya University College)

Abstract

Smart logistics will encourage replacing manual systems with the Internet of Things (IoT) or automated handling equipment taking care of repetitive tasks in the enterprise management system. Opportunities to address the issues arise from the development of smart logistics. When used with other quantitative analytic tools and techniques, today’s IoT may generate vast amounts of data and reveal intricate correlations between the many transactions represented by that data. Smart logistics can benefit from the inclusion of these features. The complication and variety of consumer orders necessitate a change in warehouse operations. There is a need for real-time data and contextual data on highly tailored orders' large diversity and small batch sizes. To achieve on-time order fulfilment, the synchronization of purchase orders to support production is critical to the frequent changes in customer needs. Order fulfilment suffers as a result of inefficient and erroneous order selection. Computational intelligence techniques are used in the research to provide an advanced data analysis methodology for Industry 4.0’s smart logistics through global manufacturing. Advanced data analysis methods for Industry 4.0’s smart logistics are developed using computational intelligence approaches. However, IoT-SL can increase logistics productivity, picking accuracy, and efficiency based on data obtained from a case firm and is resilient to order unpredictability. Smart contracts, logistics planners, and asset condition monitoring are included in the paper's smart logistics system. A prototype solution is implemented to demonstrate responsibility, traceability, and obligation for asset management across the supply chain by multiple stakeholders participating in a logistics scenario. It is important to look at how IoT technologies are being used in the smart logistics industry from transportation, storage, loading/unloading, carrying, distributed processing and information transfer, thereby achieving real-time monitoring, increased logistics productivity, logistics management, increased delivery of goods and efficiency of 98.3%.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jcomop:v:45:y:2023:i:2:d:10.1007_s10878-022-00977-5
    DOI: 10.1007/s10878-022-00977-5
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    References listed on IDEAS

    as
    1. Emil Jatib Khatib & Raquel Barco, 2021. "Optimization of 5G Networks for Smart Logistics," Energies, MDPI, vol. 14(6), pages 1-19, March.
    2. Monios, Jason & Bergqvist, Rickard, 2020. "Logistics and the networked society: A conceptual framework for smart network business models using electric autonomous vehicles (EAVs)," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    3. Zhiheng Zhao & Mengdi Zhang & Gangyan Xu & Dengyin Zhang & George Q. Huang, 2020. "Logistics sustainability practices: an IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles," International Journal of Production Research, Taylor & Francis Journals, vol. 58(24), pages 7490-7506, December.
    4. 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.
    5. Yaqiong Lv & Shangjia Xiang & Tianyi Zhu & Shuzhu Zhang, 2020. "Data-Driven Design and Optimization for Smart Logistics Parks: Towards the Sustainable Development of the Steel Industry," Sustainability, MDPI, vol. 12(17), pages 1-12, August.
    6. Pan, Xiongfeng & Li, Mengna & Wang, Mengyang & Zong, Tianjiao & Song, Malin, 2020. "The effects of a Smart Logistics policy on carbon emissions in China: A difference-in-differences analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
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    Cited by:

    1. Muhammad Saleem Sumbal & Waqas Ahmed & Huzeifa Shahzeb & Felix Chan, 2023. "Sustainable Technology Strategies for Transportation and Logistics Challenges: An Implementation Feasibility Study," Sustainability, MDPI, vol. 15(21), pages 1-19, October.

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