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


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.



Internet of Things


Radio frequency identification


Business intelligence


Enterprise management system


Supply chain management


Customer relationship management


Enterprise resource planning


Artificial intelligence


Multi-case study technique


Multi-echelon optimization


Structural equation modelling


Partial least squares-based structural equation modelling


Self-adaptive collaborative control


Human resources


Content delivery network


Universal mobile telecommunications service


Wireless local area network


European telecommunication standards institute


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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).

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