Internet of things in logistics systems: recent literature review and research perspectives

Authors

DOI:

https://doi.org/10.5585/exactaep.2021.15999

Keywords:

Logistics systems, Industry 4.0, Internet of Things (IoT), Systematic literature review.

Abstract

This paper aims to present perspectives for the application of IoT technologies in logistics systems covering theoretical and practical aspects of the research area, in addition to providing an updated bibliographic portfolio of studies that relate to the theme. A Systematic Literature Review was carried out in order to identify the main characteristics of the research area and to group the theoretical studies and the practical perspectives analyzed. As results, the bibliometric analysis evidenced the growth of the research area and the most important scientific journals that publish content related to IoT-based platforms in logistics contexts. In content analysis, the perspectives are grouped into: (i) conceptual propositions and requirements, (ii) new methods and models to support decision making, (iii) development of technology-based approaches and (iv) empirical studies. In conclusion, is presented the description of directions for future perspectives, both from the scientific point of view and from the practical point of view.

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Author Biographies

Icaro Romolo Sousa Agostino, Universidade Federal de Santa Catarina

Doutorando em Engenharia de Produção pela Universidade Federal de Santa Catarina - UFSC (2019 - atual). Mestre em Engenharia de Produção pela Universidade Federal de Santa Maria - UFSM (2017 - 2019). Engenheiro de Produção pela Universidade Ceuma (2012 - 2017). Tem experiência na área de Engenharia de Produção, com ênfase em Previsão Aplicada, Modelagem Estatística, Simulação de Eventos Discretos e Métodos Quantitativos para tomada de Decisão.

Charles Ristow, Universidade Federal de Santa Catarina

Mestre em Engenharia de Produção pela Universidade Federal de Santa Catarina (2018-2020). Possui graduação em Bacharelado em Ciências da Computação pela Fundação Universidade Regional de Blumenau (2007) e MBA em Gerenciamentos de Projetos pelo Instituto Nacional de Pós-Graduação (2014). Atualmente é Analista de Sistemas na Senior Sistemas e Professor na Escola Superior de Cerveja e Malte.

Carlos Manuel Taboada Rodriguez, Universidade Federal de Santa Catarina

Doutor em Engenharia Economica pela Technische Unversitat Dresden (1985) e graduado em Engenharia Industrial pela Universidad de La Habana (1970). Atua como Professor Titular no Departamento de Engenharia de Produção e Sistemas da Universidade Federal de Santa Catarina (UFSC), onde leciona disciplinas de Logística e Supply Chain Management nos cursos de Graduação e Pós-graduação (doutorado e mestrado). Tem mais de 47 anos de experiência em docência, atuando também como professor visitante de várias universidades em Países latino-americanos e Europeus. É Coordenador Geral do Laboratório de Desempenho Logístico (LDL), do Grupo de Estudos Logísticos (Gelog) da UFSC e do Programa Catarinense de Logística Empresarial (PROCALOG). Seus temas de pesquisa envolvem: Maturidade Logística, Produção e Avaliação de Desempenho Logístico

References

Accorsi, R., Cholette, S., Manzini, R., & Tufano, A. (12 de 2018). A hierarchical data architecture for sustainable food supply chain management and planning. Journal of Cleaner Production, 203, 1039-1054. https://doi.org/10.1016/j.jclepro.2018.08.275.

Aria, M., & Cuccurullo, C. (11 de 2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11, 959-975. https://doi.org/doi:10.1016/j.joi.2017.08.007.

Aryal, A., Liao, Y., Nattuthurai, P., & Li, B. (12 de 2018). The emerging big data analytics and IoT in supply chain management: a systematic review. Supply Chain Management: An International Journal. https://doi.org/10.1108/scm-03-2018-0149.

Avventuroso, G., Silvestri, M., & Frazzon, E. M. (2018). Additive Manufacturing Plant for Large Scale Production of Medical Devices: A Simulation Study. IFAC-PapersOnLine, 51, 1442-1447. https://doi.org/10.1016/j.ifacol.2018.08.312.

Chandra, A. A., & Lee, S. R. (10 de 2014). A Method of WSN and Sensor Cloud System to Monitor Cold Chain Logistics as Part of the IoT Technology. International Journal of Multimedia and Ubiquitous Engineering, 9, 145-152. https://doi.org/10.14257/ijmue.2014.9.10.15.

Chen, S.-L., Chen, Y.-Y., & Hsu, C. (3 de 2014). A New Approach to Integrate Internet-of-Things and Software-as-a-Service Model for Logistic Systems: A Case Study. Sensors, 14, 6144-6164. https://doi.org/10.3390/s140406144.

Chen, Y., Zhao, S., & Zhai, Y. (2014). Construction of intelligent logistics system by RFID of internet of things based on cloud computing. Journal of Chemical and Pharmaceutical Research, 6, 1676-1679.

Cho, S., & Kim, J. (3 de 2017). Smart Logistics Model on Internet of Things Environment. Advanced Science Letters, 23, 1599-1602. https://doi.org/10.1166/asl.2017.8604.

Chuang, C.-H., Lee, D.-H., Chang, W.-J., Weng, W.-C., Shaikh, M. O., & Huang, C.-L. (4 de 2017). Real-Time Monitoring via Patch-Type Piezoelectric Force Sensors for Internet of Things Based Logistics. IEEE Sensors Journal, 17, 2498-2506. https://doi.org/10.1109/jsen.2017.2665653.

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (1 de 2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5, 146-166. https://doi.org/10.1016/j.joi.2010.10.002.

Frazzon, E. M., Kück, M., & Freitag, M. (2018). Data-driven production control for complex and dynamic manufacturing systems. CIRP Annals, 67, 515-518. https://doi.org/10.1016/j.cirp.2018.04.033.

Fruchterman, T. M., & Reingold, E. M. (11 de 1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21, 1129-1164. https://doi.org/10.1002/spe.4380211102.

Guerrero-Bote, V. P., & Moya-Anegón, F. (10 de 2012). A further step forward in measuring journals' scientific prestige: The SJR2 indicator. Journal of Informetrics, 6, 674-688. https://doi.org/10.1016/j.joi.2012.07.001.

Guo, Y., & Qu, J. (8 de 2015). Study on Intelligent Logistics Management Information System Based on IOT and Cloud Computation in Big Data Era. The Open Cybernetics & Systemics Journal, 9, 934-941. https://doi.org/10.2174/1874110x01509010934.

Guo, Z., Zhang, Y., Zhao, X., & Song, X. (3 de 2017). A Timed Colored Petri Net Simulation-Based Self-Adaptive Collaboration Method for Production-Logistics Systems. Applied Sciences, 7, 235. https://doi.org/10.3390/app7030235.

Haq, A., & Kannan, G. (2006). Effect of forecasting on the multi-echelon distribution inventory supply chain cost using neural network, genetic algorithm and particle swarm optimisation. International Journal of Services Operations and Informatics, 1, 1-22. https://doi.org/10.1504/IJSOI.2006.010186.

He, W., & Chu, X. (2 de 2014). A Novel Navigation Information Management System for Food Maritime Logistics Based on Internet of Things. Advance Journal of Food Science and Technology, 6, 280-283. https://doi.org/10.19026/ajfst.6.25.

Heger, J., Grundstein, S., & Freitag, M. (11 de 2017). Online-scheduling using past and real-time data. An assessment by discrete event simulation using exponential smoothing. CIRP Journal of Manufacturing Science and Technology, 19, 158-163. https://doi.org/10.1016/j.cirpj.2017.07.003.

Hopkins, J., & Hawking, P. (5 de 2018). Big Data Analytics and IoT in logistics: a case study. The International Journal of Logistics Management, 29, 575-591. https://doi.org/10.1108/ijlm-05-2017-0109.

Hu, M., Huang, F., Hou, H., Chen, Y., & Bulysheva, L. (4 de 2016). Customized logistics service and online shoppers' satisfaction: an empirical study. (P. S. Professor Pan Wang, Ed.) Internet Research, 26, 484-497. https://doi.org/10.1108/intr-11-2014-0295.

Kang, Y.-S., Park, I.-H., & Youm, S. (12 de 2016). Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains. Sensors, 16, 2126. https://doi.org/10.3390/s16122126.

Kim, J.-S., Lee, H.-J., & Oh, R.-D. (5 de 2015). Smart Integrated Multiple Tracking System development for IOT based Target-oriented Logistics Location and Resource Service. International Journal of Smart Home, 9, 195-204. https://doi.org/10.14257/ijsh.2015.9.5.19.

Kong, X. T., Fang, J., Luo, H., & Huang, G. Q. (6 de 2015). Cloud-enabled real-time platform for adaptive planning and control in auction logistics center. Computers & Industrial Engineering, 84, 79-90. https://doi.org/10.1016/j.cie.2014.11.005.

Lee, C. K., Lv, Y., Ng, K. K., Ho, W., & Choy, K. L. (10 de 2017). Design and application of Internet of things-based warehouse management system for smart logistics. International Journal of Production Research, 56, 2753-2768. https://doi.org/10.1080/00207543.2017.1394592.

Lee, J., Bagheri, B., & Kao, H.-A. (1 de 2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23. https://doi.org/10.1016/j.mfglet.2014.12.001.

Lee, Y. H., Jung, J. W., Eum, S. C., Park, S. M., & Nam, H. K. (2006). Production quantity allocation for order fulfilment in the supply chain: a neural network based approach. Production Planning and Control, 17, 378-389. https://doi.org/10.1080/09537280600621909.

Leusin, M., Frazzon, E., Maldonado, M. U., Kück, M., & Freitag, M. (11 de 2018). Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era. Technologies, 6, 107. https://doi.org/10.3390/technologies6040107.

Li, Y. N., Peng, Y. L., Zhang, L., Wei, J. F., & Li, D. (12 de 2015). Quality monitoring traceability platform of agriculture products cold chain logistics based on the internet of things. Chemical Engineering Transactions, 46, 517-522. https://doi.org/10.3303/CET1546087.

Lima-Junior, F. R., & Carpinetti, L. C. (6 de 2019). Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks. International Journal of Production Economics, 212, 19-38. https://doi.org/10.1016/j.ijpe.2019.02.001.

Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (8 de 2018). Machine learning for internet of things data analysis: a survey. Digital Communications and Networks, 4, 161-175. https://doi.org/10.1016/j.dcan.2017.10.002.

Maslarić, M., Nikoličić, S., & Mirčetić, D. (11 de 2016). Logistics Response to the Industry 4.0: the Physical Internet. Open Engineering, 6. https://doi.org/10.1515/eng-2016-0073.

Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer o., Schuh G., Sihn W., Ueda, K. (2016). Cyber-physical systems in manufacturing. CIRP Annals, 65, 621-641. https://doi.org/10.1016/j.cirp.2016.06.005.

Papert, M., Rimpler, P., & Pflaum, A. (10 de 2016). Enhancing supply chain visibility in a pharmaceutical supply chain. International Journal of Physical Distribution & Logistics Management, 46, 859-884. https://doi.org/10.1108/ijpdlm-06-2016-0151.

Peres, F. A., & Fogliatto, F. S. (1 de 2018). Variable selection methods in multivariate statistical process control: A systematic literature review. Computers & Industrial Engineering, 115, 603-619. https://doi.org/10.1016/j.cie.2017.12.006.

Qiu, X., Luo, H., Xu, G., Zhong, R., & Huang, G. Q. (1 de 2015). Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP). International Journal of Production Economics, 159, 4-15. https://doi.org/10.1016/j.ijpe.2014.09.001.

Qu, T., Lei, S. P., Wang, Z. Z., Nie, D. X., Chen, X., & Huang, G. Q. (5 de 2016). IoT-based real-time production logistics synchronization system under smart cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 84, 147-164. https://doi.org/10.1007/s00170-015-7220-1.

Qu, T., Thürer, M., Wang, J., Wang, Z., Fu, H., Li, C., & Huang, G. Q. (4 de 2017). System dynamics analysis for an Internet-of-Things-enabled production logistics system. International Journal of Production Research, 55, 2622-2649. https://doi.org/10.1080/00207543.2016.1173738.

R Core Team. (2018). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Fonte: https://www.R-project.org/.

Ray, P. P. (12 de 2016). A survey of IoT cloud platforms. Future Computing and Informatics Journal, 1, 35-46. https://doi.org/10.1016/j.fcij.2017.02.001.

Silva, N., Ferreira, L. M., Silva, C., Magalhães, V., & Neto, P. (2017). Improving Supply Chain Visibility With Artificial Neural Networks. Procedia Manufacturing, 11, 2083-2090. https://doi.org/10.1016/j.promfg.2017.07.329.

Tao, F., Qi, Q., Liu, A., & Kusiak, A. (7 de 2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169. https://doi.org/10.1016/j.jmsy.2018.01.006.

Thoben, K.-D., Wiesner, S., & Wuest, T. (2017). “Industrie 4.0” and smart manufacturing-a review of research issues and application examples. International Journal of Automation Technology, 11, 4-16. https://doi.org/10.20965/ijat.2017.p0004.

Thürer, M., Pan, Y. H., Qu, T., Luo, H., Li, C. D., & Huang, G. Q. (12 de 2016). Internet of Things (IoT) driven kanban system for reverse logistics: solid waste collection. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-016-1278-y.

Trappey, A. J., Trappey, C. V., Fan, C.-Y., Hsu, A. P., Li, X.-K., & Lee, I. J. (9 de 2017). IoT patent roadmap for smart logistic service provision in the context of Industry 4.0. Journal of the Chinese Institute of Engineers, 40, 593-602. https://doi.org/10.1080/02533839.2017.1362325.

Tu, M. (2 de 2018). An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management. The International Journal of Logistics Management, 29, 131-151. https://doi.org/10.1108/ijlm-11-2016-0274.

Tu, M., Lim, M. K., & Yang, M.-F. (2 de 2018). IoT-based production logistics and supply chain system – Part 1. Industrial Management & Data Systems, 118, 65-95. https://doi.org/10.1108/imds-11-2016-0503.

Tu, M., Lim, M. K., & Yang, M.-F. (2 de 2018). IoT-based production logistics and supply chain system – Part 2. Industrial Management & Data Systems, 118, 96-125. https://doi.org/10.1108/imds-11-2016-0504.

Verdouw, C. N., Robbemond, R. M., Verwaart, T., Wolfert, J., & Beulens, A. J. (8 de 2015). A reference architecture for IoT-based logistic information systems in agri-food supply chains. Enterprise Information Systems, 12, 755-779. https://doi.org/10.1080/17517575.2015.1072643.

Wang, L., Törngren, M., & Onori, M. (10 de 2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517-527. https://doi.org/10.1016/j.jmsy.2015.04.008.

Yan, R. (5 de 2017). Optimization approach for increasing revenue of perishable product supply chain with the Internet of Things. Industrial Management & Data Systems, 117, 729-741. https://doi.org/10.1108/imds-07-2016-0297.

Yang, R., Li, B., & Hu, Y. (5 de 2016). An Experimental Study for Intelligent Logistics: A Middleware Approach. Chinese Journal of Electronics, 25, 561-569. https://doi.org/10.1049/cje.2016.05.024.

Zhang, Y., Guo, Z., Lv, J., & Liu, Y. (9 de 2018). A Framework for Smart Production-Logistics Systems Based on CPS and Industrial IoT. IEEE Transactions on Industrial Informatics, 14, 4019-4032. https://doi.org/10.1109/tii.2018.2845683.

Zhang, Y., Zhao, L., & Qian, C. (10 de 2017). Modeling of an IoT-enabled supply chain for perishable food with two-echelon supply hubs. Industrial Management & Data Systems, 117, 1890-1905. https://doi.org/10.1108/imds-10-2016-0456.

Zhong, R. Y., Lan, S., Xu, C., Dai, Q., & Huang, G. Q. (8 de 2015). Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing. The International Journal of Advanced Manufacturing Technology, 84, 5-16. https://doi.org/10.1007/s00170-015-7702-1.

Zou, Z., Chen, Q., Uysal, I., & Zheng, L. (5 de 2014). Radio frequency identification enabled wireless sensing for intelligent food logistics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372, 20130313-20130313. https://doi.org/10.1098/rsta.2013.0313.

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Published

2021-06-10

How to Cite

Agostino, I. R. S., Ristow, C., & Rodriguez, C. M. T. (2021). Internet of things in logistics systems: recent literature review and research perspectives. Exacta, 19(2), 251–275. https://doi.org/10.5585/exactaep.2021.15999

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Vol. 19 No. 2 (2021): (abr./jun.)

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