Relevant bibliographies by topics / Big data in logistics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Big data in logistics'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Big data in logistics"

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Lekić, Matea, Kristijan Rogić, Adrienn Boldizsár, Máté Zöldy, and Ádám Török. "Big Data in Logistics." Periodica Polytechnica Transportation Engineering 49, no. 1 (2019): 60–65. http://dx.doi.org/10.3311/pptr.14589.

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With certainty, we can say that we are in the process of a new big revolution that has its name, Big Data. Though the term was devised by scientists from the area such as astronomy and genomics, Big Data is everywhere. They are both a resource and a tool whose main task is to provide information. However, as far as it can help us better understand the world around us, depending on how they are managed and who controls them, they can take us in some other direction. Although the figures that bind to Big Data can seem enormous at this time, we must be aware that the amount of what we can collect
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Vagapov, Shamil N., and Valentina M. Repnikova. "USING BIG DATA TO OPTIMIZE LOGISTIC OPERATIONS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 3/13, no. 156 (2025): 228–39. https://doi.org/10.36871/ek.up.p.r.2025.03.13.026.

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The article explores theoretical, methodological, and practical aspects of applying Big Data technologies to optimize enterprises’ logistic operations in the context of digital transformation. The relevance of the study is driven by the rapid development of Industry 4.0, the expansion of digitalization, and the growing need to improve the efficiency of transport and logistics processes through the integration of next-generation analytical tools. The role of Big Data as a key resource is substantiated, enabling the development of predictive management models, route optimization, demand forecast
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Gao, Fei, and Qilan Zhao. "Big Data Based Logistics Data Mining Platform: Architecture and Implementation." International Journal of Interdisciplinary Telecommunications and Networking 6, no. 4 (2014): 24–34. http://dx.doi.org/10.4018/ijitn.2014100103.

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With the development of intelligent logistics, enormous amount of logistics data are be-coming one of the sources of big data. Building the logistics information platform with big data mining and analysis capabilities to make full use of the huge logistics data is the inexorable trend for intelligent logistics. This paper studied the characteristics of the logistics big data, then, a big data based logistics data mining platform is designed and implemented by utilizing big data processing and storage techniques. The architecture and functions of the platform will be described in detail. This p
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Li, Zhuoyang. "Big Data Management: Empowering Sustainable Logistics with Data-Driven Operation Optimization." Advances in Economics, Management and Political Sciences 54, no. 1 (2023): 64–68. http://dx.doi.org/10.54254/2754-1169/54/20230878.

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Numerous disciplines, including logistics, have experienced a paradigm shift since the advent of big data. As the backbone of many industries, logistics has been significantly impacted by the expansion of big data. The purpose of this paper is to investigate the far-reaching effects of big data technology on the development of sustainable logistics, with a particular emphasis on the role of big data in various parts of the logistics process. Through a literature review and case analysis, this paper focuses on summarizing the application of big data in all aspects of logistics, and selecting an
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M, Mohamed Ali V. "Make logistics smarter: using big data." Journal of Management and Science 6, no. 3 (2016): 326–32. http://dx.doi.org/10.26524/jms.2016.34.

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Information and Communication Technology (ICT) has been providing effective and efficient path for the enhancement of each every business. ICT are now a days generating a rate that doubles the data every two years. In this VUCA (Volatility, Uncertainty, Complexity and Ambiguity) world, organizations are competing with each other for effective usage of logistics performance.
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Chen, Yitong. "Logistics Development in Laizhou under Big Data." Advances in Economics, Management and Political Sciences 77, no. 1 (2024): 251–56. http://dx.doi.org/10.54254/2754-1169/77/20241729.

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The extensive utilization of big data in logistics management is increasingly prevalent due to economic development and advancements in Internet technology. This research examines the present state and optimization method of big data utilization in logistics management, using Laizhou City as a case study. By examining the progress and current challenges of logistics in Laizhou City, this study proposes optimization solutions including the implementation of a big data platform, enhancement of logistics and transportation routes, allocation of resources, and risk management. As an example, we ex
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Ye, Yufan. "Intelligent Logistics under Artificial Intelligence and Big Data." Advances in Economics, Management and Political Sciences 62, no. 1 (2023): 277–82. http://dx.doi.org/10.54254/2754-1169/62/20231360.

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With the development of science and technology, high-tech technologies such as artificial intelligence and big data are gradually being applied to daily life. Digitalization has become a new driving force for the transformation and upgrading of the logistics industry, and there are many problems in the current logistics market. The logistics industry has developed rapidly, but the problems of low inventory management and transportation efficiency have not been effectively solved. Research in the field of logistics should not be limited to certain aspects, but should take a holistic approach. I
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Chitta, Shyamsunder. "Leveraging Big Data for Third Party Logistics." Asian Journal of Research in Business Economics and Management 7, no. 1 (2017): 1. http://dx.doi.org/10.5958/2249-7307.2017.00001.9.

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Shaikh, Dr Sumaira. "Logistics Management System using Big Data Analytics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–13. http://dx.doi.org/10.55041/ijsrem27750.

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Nowadays, the e-commerce sector has emerged as a significant player in the current financial landscape. It offers a wide range of advantages for global customers, and given the increasing demand for various services and products, there is a pressing need for the development of efficient Web applications. This e-commerce application serves as the bridge between small businesses and their clientele. The application harnesses the capabilities of various technologies, including MongoDB for data storage, Node.js and Express.js for a fast and efficient backend, and HTML CSS for constructing a user-f
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Chen, Zixuan, and Zishan Liao. "Impact of Data Technology on Logistics Industry Business Performance." Highlights in Business, Economics and Management 16 (August 2, 2023): 282–87. http://dx.doi.org/10.54097/hbem.v16i.10571.

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In the big data era, more and more people elect to conduct their business online, and logistics firms are expanding in tandem with the quick growth of e-commerce platforms. However, since the outbreak of the epidemic in 2020, it has dealt a heavy blow to the whole world. Despite the economic downturn and difficult development in this particular period, it has also brought challenges and opportunities for logistics companies. The study finds that the effectiveness of logistics companies in managing their warehouses can be improved through the use of AI-driven data analytics and machine learning
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Dissertations / Theses on the topic "Big data in logistics"

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Zubar, Tymofiy, Тимофій Андрійович Зубар, Olena Volovyk, and Олена Іванівна Воловик. "Big data in logistics: last mile application." Thesis, National Aviation University, 2021. https://er.nau.edu.ua/handle/NAU/50494.

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1. 3PL Survey - https://www.3plstudy.com/ 2. 5 Examples of How Big Data in Logistics Can Transform The Supply Chain - https://www.datapine.com/blog/how-big-data-logistics-transform-supply-chain<br>Big data is revolutionizing many business areas, including logistics and business processes in it. The complexity and dynamics of logistics, coupled with the reliance on many movable parts, can cause bottlenecks at any point in the supply chain, making big data application a vital element of effectiveness in logistical processes design and management. For example, big data logistics can be used to o
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Baitalmal, Mohammad Hamza. "A Grounded Theory Model of the Relationship between Big Data and an Analytics Driven Supply Chain Competitive Strategy." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404511/.

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The technology for storing and using big data is evolving rapidly and those that can keep pace are likely to garner additional competitive advantages. One approach to uncovering existing practice in a manner that provides insights for building theory is the use of grounded theory. The current research employs qualitative research following a grounded theory approach to explore gap in understanding the relationship between big data (BD) and the supply chain (SC). In this study eight constructs emerged: Organizational and environmental factors, big data and supply chain analytics, alignment, dat
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ADAMASHVILI, NINO. "Big data analytics tools for improving the decision-making process in agrifood supply chain." Doctoral thesis, Università di Foggia, 2021. https://hdl.handle.net/11369/425167.

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Introduzione: Nell'interesse di garantire una sicurezza alimentare a lungo termine di fronte a circostanze mutevoli, è necessario comprendere e considerare gli aspetti ambientali, sociali ed economici del processo di produzione. Inoltre, a causa della globalizzazione, sono stati sollevati i problemi delle lunghe filiere agroalimentari, l'asimmetria informativa, la contraffazione, la difficoltà di tracciare e rintracciare l'origine dei prodotti e le numerose questioni correlate quali il benessere dei consumatori e i costi sanitari. Le tecnologie emergenti guidano verso il raggiungimento di nuov
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Baitalmal, Mohammad Hamza. "A Grounded Theory Model of the Relationship between Big Data and an Analytics-Driven Supply Chain Competitive Strategy." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc1404511/.

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The technology for storing and using big data is evolving rapidly and those that can keep pace are likely to garner additional competitive advantages. One approach to uncovering existing practice in a manner that provides insights for building theory is the use of grounded theory. The current research employs qualitative research following a grounded theory approach to explore gap in understanding the relationship between big data (BD) and the supply chain (SC). In this study eight constructs emerged: Organizational and environmental factors, big data and supply chain analytics, alignment, dat
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Fu, Shuting. "Bayesian Logistic Regression Model with Integrated Multivariate Normal Approximation for Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/451.

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The analysis of big data is of great interest today, and this comes with challenges of improving precision and efficiency in estimation and prediction. We study binary data with covariates from numerous small areas, where direct estimation is not reliable, and there is a need to borrow strength from the ensemble. This is generally done using Bayesian logistic regression, but because there are numerous small areas, the exact computation for the logistic regression model becomes challenging. Therefore, we develop an integrated multivariate normal approximation (IMNA) method for binary data with
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陳煜民 and Yuk-man Brian Chan. "Strategy for information management in re-engineering the logistics business." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B3126637X.

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Chen, Zhilin. "Bayesian Analysis of Binary Sales Data for Several Industries." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/596.

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The analysis of big data is now very popular. Big data may be very important for companies, societies or even human beings if we can take full advantage of them. Data scientists defined big data with four Vs: volume, velocity, variety and veracity. In a short, the data have large volume, grow with high velocity, represent with numerous varieties and must have high quality. Here we analyze data from many sources (varieties). In small area estimation, the term ``big data' refers to numerous areas. We want to analyze binary for a large number of small areas. Then standard Markov Chain Monte Carlo
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Fang, Yuan, and 方媛. "A cost-based model for optimising the construction logisticsschedules." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46080351.

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Stiebe, Michael. "#sustainabletransport : A FAIR Cross-Platform Social Media Analysis Approach to Sociotechnical Sustainable Transport Research." Thesis, Linnéuniversitetet, Institutionen för kulturvetenskaper (KV), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105759.

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The paper reports findings from a FAIR principles-based research project dedicated to investigating how cross-field research between the DH and Sociotechnical Sustainable Transport Research could help to enhance the holistic understanding of sociotechnical low-carbon transport transitions.  Using the hashtag search queries #sustainabletransport and #sustainablemobility, 33,121 Tweets (2013-2021) and 8,089 Instagram images including captions (2017/2018-2021) were mined using Python scripts. Quantitative text and sentiment analyses were applied to the Tweets and image captions. Additionally, an
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Aarflot, Markus, and Pontus Jangstam. "Future Logistical Services from Connected Vehicles : A Case Study at Scania CV AB." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64013.

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The road based transportation operations are growing rapidly, but the current infrastructure cannot sustain the entire growth. At the same time vehicle utilisation and fill rates are low. Improved efficiency of the operations is a necessary way forward for road based transportation. Parallel to this, heavy vehicle producers are currently improving the efficiency with services accompanying the product that are focused on the driver and the vehicle performance. However, the data from connected vehicles required for these services only entail a small amount of the operational data generated by co
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Books on the topic "Big data in logistics"

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Mei, Hong, Weiguo Zhang, Wenfei Fan, et al., eds. Big Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0705-9.

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Liao, Xiangke, Wei Zhao, Enhong Chen, et al., eds. Big Data. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9709-8.

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Xu, Zongben, Xinbo Gao, Qiguang Miao, Yunquan Zhang, and Jiajun Bu, eds. Big Data. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2922-7.

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King, Stefanie. Big Data. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06586-7.

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Fasel, Daniel, and Andreas Meier, eds. Big Data. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-11589-0.

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Mohanty, Hrushikesha, Prachet Bhuyan, and Deepak Chenthati, eds. Big Data. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2494-5.

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Jin, Hai, Xuemin Lin, Xueqi Cheng, Xuanhua Shi, Nong Xiao, and Yihua Huang, eds. Big Data. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1899-7.

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König, Christian, Jette Schröder, and Erich Wiegand, eds. Big Data. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20083-1.

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Gottlob, Georg, Giovanni Grasso, Dan Olteanu, and Christian Schallhart, eds. Big Data. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39467-6.

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Chen, Min, Shiwen Mao, Yin Zhang, and Victor C. M. Leung. Big Data. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06245-7.

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Book chapters on the topic "Big data in logistics"

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Nadiia, Reznik, Fesun Artem, Gergi Denys, et al. "Green Logistics as a Sustainable Development Concept of Logistics Systems in a Circular Economy." In Studies in Big Data. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-75095-3_9.

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Brouer, Berit Dangaard, Christian Vad Karsten, and David Pisinger. "Big Data Optimization in Maritime Logistics." In Studies in Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30265-2_14.

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Sigmund, Jan. "Advanced Analytics and Big Data in Supply Chain Planning." In Disrupting Logistics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61093-7_10.

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Betti, Quentin, Benoit Montreuil, Raphaël Khoury, and Sylvain Hallé. "Smart Contracts-Enabled Simulation for Hyperconnected Logistics." In Studies in Big Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38677-1_6.

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Singh, Jagdeep, C. Shilpa Rao, Shivoham Singh, and Niraj Gupta. "Optimizing Supply Chains Through Innovations in Logistics Management." In Studies in Big Data. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80656-8_30.

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Sharma, Rohit, and Anjali Shishodia. "Blockchain Technology Enablers in Physical Distribution and Logistics Management." In Studies in Big Data. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87304-2_14.

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Francois, Pierre. "SaaS and Big Data Solutions in the Area of Logistics Related Services." In Disrupting Logistics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61093-7_11.

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Meng, Lei, Zhonglin Ye, Haixing Zhao, Yanlin Yang, and Fuxiang Ma. "Hypernetwork Model Based on Logistic Regression." In Big Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0705-9_14.

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Kretzschmar, Johannes, Kai Gebhardt, Christoph Theiß, and Volkmar Schau. "Range Prediction Models for E-Vehicles in Urban Freight Logistics Based on Machine Learning." In Data Mining and Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_17.

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Shi, Peishen, Puyu Wang, and Hai Zhang. "Distributed Logistic Regression for Separated Massive Data." In Big Data. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1899-7_20.

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Conference papers on the topic "Big data in logistics"

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Tang, Ke. "Application of Big Data Analysis Model in Intelligent Logistics." In 2024 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC). IEEE, 2024. https://doi.org/10.1109/iiotbdsc64371.2024.00038.

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Chen, Yuzheng. "Intelligent Logistics Monitoring System Based on IoT and Big Data." In 2025 2nd International Conference on Smart City and Information System (ICSCIS). IEEE, 2025. https://doi.org/10.1109/icscis65391.2025.11069567.

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Li, Xiaoming. "Logistics Distribution Path Optimization Analysis Platform based on Big Data Algorithm." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10690926.

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Provatas, Nikodimos, Evdokia Kassela, Nikolaos Chalvantzis, et al. "SELIS BDA: Big Data Analytics for the Logistics Domain." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378421.

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Wen, Rong, Wenjing Yan, and Allan N. Zhang. "Weighted clustering of spatial pattern for optimal logistics hub deployment." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7841050.

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Korth, Benjamin, Christian Schwede, and Markus Zajac. "Simulation-ready digital twin for realtime management of logistics systems." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622160.

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Wen, Rong, and Wenjing Yan. "Urban Dynamic Logistics Pattern Mining with 3D Convolutional Neural Network." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622166.

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Moldagulova, Aiman, Ryskhan Satybaldiyeva, and Abu Kuandykov. "Application of Big Data in Logistics." In ICEMIS'20: The 6th International Conference on Engineering & MIS 2020. ACM, 2020. http://dx.doi.org/10.1145/3410352.3410785.

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Ben Ayed, Abdelkarim, Mohamed Ben Halima, and Adel M. Alimi. "Big data analytics for logistics and transportation." In 2015 4th International Conference on Advanced Logistics and Transport (ICALT). IEEE, 2015. http://dx.doi.org/10.1109/icadlt.2015.7136630.

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Li, Yizhi. "Intelligent Logistics System Based on Big Data." In 2020 International Conference on Robots & Intelligent System (ICRIS). IEEE, 2020. http://dx.doi.org/10.1109/icris52159.2020.00081.

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Reports on the topic "Big data in logistics"

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Gropman, Alan. The BIG 'L' American Logistics in World War II. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada421840.

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Zwitter, Andrej J., and Amelia Hadfield. Governing Big Data. Librello, 2014. http://dx.doi.org/10.12924/pag2014.02010001.

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Sutterfield, Jon M. Eighth Air Force Bombing 20-25 February 1944: How Logistics Enabled Big Week" To Be "Big"". Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada425032.

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Gawade, Rushikesh. Big data and big dollars are changing cricket. Edited by Chris Bartlett. Monash University, 2023. http://dx.doi.org/10.54377/783a-d051.

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Gildea, Timothy R. Big Data health Physics. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1603973.

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Goldstein, Itay, Chester Spatt, and Mao Ye. Big Data in Finance. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w28615.

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Big data en salud digital. Chair Alberto Urueña López and José María San Segundo Encinar. ONTSI : Fundación Vodafone España, 2017. http://dx.doi.org/10.30923/5896-8.

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Alewijn, M. Big data - Banana origin determination. Wageningen Food Safety Research, 2020. http://dx.doi.org/10.18174/516096.

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Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. Big Data Analytics Test Bed. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada589903.

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Farboodi, Maryam, Roxana Mihet, Thomas Philippon, and Laura Veldkamp. Big Data and Firm Dynamics. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w25515.

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