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

Journal of Combinatorial Optimization volume 45, Article number: 57 (2023) Cite this article

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%.

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Data availability

All data generated or analysed during this study are included in the manuscript.

Code availability

Not applicable.

Abbreviations

IoT:

Internet of Things

RFID:

Radio frequency identification

BI:

Business intelligence

EMS:

Enterprise management system

SCM:

Supply chain management

CRM:

Customer relationship management

ERP:

Enterprise resource planning

AI:

Artificial intelligence

MCS:

Multi-case study technique

MEO:

Multi-echelon optimization

SEM:

Structural equation modelling

PLS-SEM:

Partial least squares-based structural equation modelling

SCC:

Self-adaptive collaborative control

HR:

Human resources

CDN:

Content delivery network

UMTS:

Universal mobile telecommunications service

WLAN:

Wireless local area network

ETSI:

European telecommunication standards institute

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Funding

No funds, grants were received by any of the authors.

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Authors and Affiliations

  1. Department of Computer Science, Al-Turath University College, Baghdad, Iraq

    Mustafa Musa Jaber

  2. Department of Computer Science, Dijlah University College, Baghdad, 10021, Iraq

    Sura Khalil Abd

  3. Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Najaf, 10023, Iraq

    Mustafa Qahtan Alsudani & Mohammed Hasan Ali

  4. College of Technical Engineering, The Islamic University, Najaf, Iraq

    Ahmed Alkhayyat

  5. Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq

    Z. H. Kareem

  6. Computer Technical Engineering, Mazaya University College, Thi Qar, Iraq

    Ahmed Rashid Mohhan

  7. Medical Instrumentation Techniques Engineering Department, Al-Farahidi University, Baghdad, Iraq

    Mustafa Musa Jaber

Authors
  1. Mustafa Qahtan Alsudani
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  2. Mustafa Musa Jaber
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  3. Mohammed Hasan Ali
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  4. Sura Khalil Abd
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  5. Ahmed Alkhayyat
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  7. Ahmed Rashid Mohhan
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All Author are contributed to the design and methodology of this study, the assessment of the outcomes and the writing of the manuscript.

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Correspondence to Mustafa Musa Jaber.

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Alsudani, M.Q., Jaber, M.M., Ali, M.H. et al. Smart logistics with IoT-based enterprise management system using global manufacturing. J Comb Optim 45, 57 (2023). https://doi.org/10.1007/s10878-022-00977-5

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  • DOI: https://doi.org/10.1007/s10878-022-00977-5

Keywords

  • Logistics
  • Industry
  • IoT
  • Warehouse
  • Customer
  • Global manufacturing
  • Management