Directions for using big data analytics in logistics management
Logistics operations are becoming increasingly complex and require accurate data for effective management. The use of big data in logistics management is a relevant issue due to the growing volume of data and the need to optimize delivery and inventory management processes to meet market demands. Th...
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| Format: | Article |
| Language: | English |
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Simon Kuznets Kharkiv National University of Economics
2024-03-01
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| Series: | Управління розвитком |
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| Online Access: | https://devma.com.ua/journals/t-23-1-2024/napryami-zastosuvannya-big-data-analitiki-v-logistichnomu-menedzhmenti |
| _version_ | 1797205866308435968 |
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| author | D. Aubakirova |
| author_facet | D. Aubakirova |
| author_sort | D. Aubakirova |
| collection | DOAJ |
| description | Logistics operations are becoming increasingly complex and require accurate data for effective management. The use of big data in logistics management is a relevant issue due to the growing volume of data and the need to optimize delivery and inventory management processes to meet market demands. The purpose of the study was to develop ways to optimize the management of big data analysis in logistics. To achieve this goal, the methods of analysis, experimentation, and comparison were used. As a result of the study, strategies for optimizing logistics management of big data analysis were developed and successfully applied. The Python programming language based programme effectively optimizes delivery routes using a clustering algorithm and visualizes the results of this process. Additionally, an informative diagram has been drawn up to illustrate the key stages of the developed strategies. The study also developed and presented a table describing the use of big data analysis methods in various logistics companies. The companies were compared in terms of functionality, data, results, and field of activity. It is established that the use of machine learning methods and optimization of data storage and processing significantly increases the efficiency of logistics operations. The results of this study can be used by logistics companies of any size, as well as enterprises engaged in supply chain management. In addition, the recommendations and strategies developed in this study may be useful for information technology and data analytics professionals involved in the development of software solutions and systems to optimize logistics processes |
| first_indexed | 2024-04-24T08:57:55Z |
| format | Article |
| id | doaj.art-b96ff65d162441b8a442cd318c88e4c4 |
| institution | Directory Open Access Journal |
| issn | 2413-9610 2663-2365 |
| language | English |
| last_indexed | 2024-04-24T08:57:55Z |
| publishDate | 2024-03-01 |
| publisher | Simon Kuznets Kharkiv National University of Economics |
| record_format | Article |
| series | Управління розвитком |
| spelling | doaj.art-b96ff65d162441b8a442cd318c88e4c42024-04-16T06:29:34ZengSimon Kuznets Kharkiv National University of EconomicsУправління розвитком2413-96102663-23652024-03-012312736https://doi.org/10.57111/devt/1.2024.27119Directions for using big data analytics in logistics managementD. AubakirovaLogistics operations are becoming increasingly complex and require accurate data for effective management. The use of big data in logistics management is a relevant issue due to the growing volume of data and the need to optimize delivery and inventory management processes to meet market demands. The purpose of the study was to develop ways to optimize the management of big data analysis in logistics. To achieve this goal, the methods of analysis, experimentation, and comparison were used. As a result of the study, strategies for optimizing logistics management of big data analysis were developed and successfully applied. The Python programming language based programme effectively optimizes delivery routes using a clustering algorithm and visualizes the results of this process. Additionally, an informative diagram has been drawn up to illustrate the key stages of the developed strategies. The study also developed and presented a table describing the use of big data analysis methods in various logistics companies. The companies were compared in terms of functionality, data, results, and field of activity. It is established that the use of machine learning methods and optimization of data storage and processing significantly increases the efficiency of logistics operations. The results of this study can be used by logistics companies of any size, as well as enterprises engaged in supply chain management. In addition, the recommendations and strategies developed in this study may be useful for information technology and data analytics professionals involved in the development of software solutions and systems to optimize logistics processeshttps://devma.com.ua/journals/t-23-1-2024/napryami-zastosuvannya-big-data-analitiki-v-logistichnomu-menedzhmentisupply controluse of volumetric informationways to optimize managementtransport and warehouse organizationbig data analysis |
| spellingShingle | D. Aubakirova Directions for using big data analytics in logistics management Управління розвитком supply control use of volumetric information ways to optimize management transport and warehouse organization big data analysis |
| title | Directions for using big data analytics in logistics management |
| title_full | Directions for using big data analytics in logistics management |
| title_fullStr | Directions for using big data analytics in logistics management |
| title_full_unstemmed | Directions for using big data analytics in logistics management |
| title_short | Directions for using big data analytics in logistics management |
| title_sort | directions for using big data analytics in logistics management |
| topic | supply control use of volumetric information ways to optimize management transport and warehouse organization big data analysis |
| url | https://devma.com.ua/journals/t-23-1-2024/napryami-zastosuvannya-big-data-analitiki-v-logistichnomu-menedzhmenti |
| work_keys_str_mv | AT daubakirova directionsforusingbigdataanalyticsinlogisticsmanagement |