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Design and application of Internet of things-based warehouse management system for smart logistics
Open accessAbstract
Warehouse operations need to change due to the increasing complexity and variety of customer orders. The demand for real-time data and contextual information is requried because of the highly customised orders, which tend to be of small batch size but with high variety. Since the orders frequently change according to customer requirements, the synchronisation of purchase orders to support production to ensure on-time order fulfilment is of high importance. However, the inefficient and inaccurate order picking process has adverse effects on the order fulfilment. The objective of this paper is to propose an Internet of things (IoT)-based warehouse management system with an advanced data analytical approach using computational intelligence techniques to enable smart logistics for Industry 4.0. Based on the data collected from a case company, the proposed IoT-based WMS shows that the warehouse productivity, picking accuracy and efficiency can be improved and it is robust to order variability.
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“Smart transportation enabled by AIoT covers traffic participants (e.g., smart internet of vehicles [256]), traffic infrastructures [257], and industry applications (e.g., smart connected logistics [258]).”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [258]
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Hasan, M. K., Ma, J., Habib, A. K. M. A., Mamun, A. A., Ghazal, T. M., & Saeed, R. A. (2022) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr56254.2022.9995768 · Cited by 14
“Authors have proposed a low-traffic, high-traffic workflow product portfolio warehouse management system [6].”
BackgroundEmerging Technologies of IoT Usage in Global Logistics
Shah, S. R., Rutherford, R., & Menon, S. (2020) · International Conference on Computation, Automation and Knowledge Management (ICCAKM) · doi:10.1109/iccakm46823.2020.9051530 · Cited by 14
Optimization of the Intelligent Asset Management System Based on WSN and RFID Technology
Chen, Y., & Guang-gui, C. (2022) · Journal of Sensors · doi:10.1155/2022/3436530 · Cited by 14
“fused sensor nodes and RFID tags and divided them into two types of tags, active, and passive and solved the synergistic working mechanism of the two as a key problem [16].”
BackgroundArduino IOT Based Inventory Management System Using Load Cell and NodeMCU
Mansor, M. N., Talib, N. A. A., Saidi, S. A., Mustafa, W. A., & Zamri, N. F. (2023) · Journal of Advanced Research in Applied Sciences and Engineering Technology · doi:10.37934/araset.32.3.1225 · Cited by 14
Li, K. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3370375 · Cited by 13
“Digital Object Identifier 10.1109/JIOT.2024.3370375 chemical logistics, IoT-based green logistics, IoT-based agricultural, food, drink, and cold logistics [8], IoT-based emergency logistics, IoT-based military logistics, IoT-based postal logistics, IoT-based railway logistics, etc.”
BackgroundInfluentialRawat, U., Kumar, A., & Anbanandam, R. (2024) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-024-00421-z · Cited by 13
Lojistik Yönetiminin Dijital Dönüşümü: Akıllı Lojistik Üzerine Sistematik Literatür Haritalaması
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A forecasting analytics model for assessing forecast error in e-fulfilment performance
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“In addition, this research could be integrated with numerous Smart X studies, including smart logistics, smart cities and smart streets [62–64], beyond smart factory fields.”
Yang, J. X., Li, L. D., & Rasul, M. G. (2021) · International Journal of Machine Learning and Computing · doi:10.18178/ijmlc.2021.11.3.1042 · Cited by 13
“The classical neural networks for object detection, classification and counting have demonstrated promising improvements, and this provides a direction for future researchers to explore a better model at the receiving stage in warehouse management [22], [45], [57], [72], [83], [87].”
BackgroundSimulation of in-house logistics operations for manufacturing
Coelho, F., Macedo, R. S., Relvas, S., & Barbosa‐Póvoa, A. P. (2022) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2022.2027521 · Cited by 13
Development of an IoT-based Inventory Management System for Retail Stores
Saillaja, V., Menaka, M., Kumaravel, V., & Machap, K. (2023) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss57650.2023.10169810 · Cited by 12
Sustainability Impact of Automated Warehouses in Industry 4.0 scenario
Cosma, A. M. I., Zangara, G., Silvestri, L., & Filice, L. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.02.044 · Cited by 12
Warehouse Management Problem and a KPI Approach: a Case Study
Marziali, M., Rossit, D. A., & Toncovich, A. (2021) · Management and Production Engineering Review · doi:10.24425/mper.2021.138530 · Cited by 12
Transforming Hong Kong's warehousing industry with a novel business model: A game-theory analysis
Yang, C., Lan, S., Lin, T.-Y., Wang, L., Zhuang, Z., & Huang, G. Q. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102073 · Cited by 12
Federated digital twins platform for smart city logistics: A knowledge-driven approach
Liu, Y., Pan, S., & Ballot, É. (2025) · International Journal of Production Economics · doi:10.1016/j.ijpe.2025.109772 · Cited by 11
Zhang, J., Lv, Y., Li, Y., & Liu, J. (2022) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/euc57774.2022.00018 · Cited by 11
“In recent years, with the rapid development of intelligent manufacturing technology, how to improve the intelligence and informatization of production workshops has become a concern for manufacturing enterprises [1].”
BackgroundSelf-citationAI-Powered Supply Chain Optimization: Enhancing Demand Forecasting and Logistics
Yarlagadda, K. C. (2025) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2025.7.4.92 · Cited by 11
“As documented in Expert Systems with Applications, intelligent forecasting systems now employ sophisticated algorithms capable of detecting complex patterns within multidimensional data [4].”
InfluentialPerformance evaluation of logistics enterprises based on non-radial and non-angle network SBM model
Cui, H. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189492 · Cited by 11
“The literature [23] used delivery time, delivery safety, delivery convenience and inventory capacity to evaluate logistics performance.”
Odeyinka, O. F., & Omoegun, O. G. (2023) · Industrial engineering and management. · doi:10.5772/intechopen.113147 · Cited by 11
Metadata
- Authors
- Lee, C. K. M., Lv, Y., Ng, K. K. H., Ho, W., & Choy, K. L.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Wuhan University of Technology (CN)
- The University of Melbourne (AU)
- Corresponding author
- Lv, Yaqiong
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2017-10-27
- Volume
- 56
- Issue
- 8
- Pages
- 2753-2768
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W2766877481
- Cited by
- 431
- References
- 32
- Citations by year
- 2026: 21 · 2025: 47 · 2024: 48 · 2023: 88 · 2022: 62 · 2021: 69 · 2020: 59 · 2019: 29 · 2018: 8
- FWCI
- 28.78
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- International Journal of Production Research (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- Funders
- Collabroative Reserach (H-ZDAR)
- Collaborative Research (H-ZDAR)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Advanced Manufacturing and Logistics Optimization
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- Internet of Things, warehouse management systems, smart logistics, order picking, order fulfillment, order variability, Industry 4.0, real-time analytics, computational intelligence, warehouse productivity
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