A MULTI-CRITERIA DECISION-MAKING APPROACH TO TRANSPORT AND LOGISTICS BASED ON BUSINESS INTELLIGENCE
DOI:
https://doi.org/10.58213/vidhyayana.v8i5.684Keywords:
MCDM- Multi-Criteria Decision making, Transport, Logistics, Business Intelligence, TrendsAbstract
In an ever-evolving world, transport and logistics businesses are facing a variety of challenges. From changing customer expectations to competition from large e-commerce players, the industry is being pushed to its limits. The current study can make revolutionize the industry which has the great potentiality for business with BI. Fortunately, business intelligence can give transport and logistics companies the necessary insights they need to thrive. In this article, we have explored how business intelligence (BI) can help transport and logistics companies stay competitive in today's market. Study objectives include identifying ways to streamline transportation and logistics operations, reduce costs, and enhance overall efficiency. The study evaluates the BI technologies available, as well as how they can improve operations, reduce costs, and increase customer satisfaction. Business intelligence is a tool that has become invaluable in many industries, and transport and logistics are no exception. Business intelligence allows companies to collect data on their operations, analyze it, and gain insights into their operations in order to make better decisions. This study will explore how BI can be used in the industry to optimize operations, reduce costs, and improve customer service. The study findings indicate that the Business intelligence tools give transport and logistics companies the ability to see their operations in a new light, understand their customers better, and make informed decisions that drive to succeed. A data quality issue and high implementation costs are two major factors that pose challenges when using business intelligence (BI) tools, since accurate and reliable data is essential for generating insightful insights and making informed decisions.
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Author Biography
Abin George, Sr. Technical Officer (1)
References
Giannopoulos, G. A. (2009). Towards a European ITS for freight transport and logistics: Results of current EU funded research and prospects for the future. European Transport Research Review, 1(4). https://doi.org/10.1007/s12544-009-0022-5
Grabińska, A., & Ziora, L. (2019). The Application of Business Intelligence Systems in Logistics. Review of Selected Practical Examples. System Safety: Human - Technical Facility - Environment, 1(1). https://doi.org/10.2478/czoto-2019-0130
Huie, C. P. (2016). Perceptions of business intelligence professionals about factors related to business intelligence input in decision making. International Journal of Business Analytics, 3(3). https://doi.org/10.4018/IJBAN.2016070101
Iliashenko, V., Ilin, I., Schuur, P., Gerrits, B., Kalyazina, S., & Esser, M. (2022). Business Intelligence Systems Application in Transport and Logistics Companies. Lecture Notes in Networks and Systems, 246. https://doi.org/10.1007/978-3-030-81619-3_52
Iyer, L. S. (2021). AI enabled applications towards intelligent transportation. Transportation Engineering, 5. https://doi.org/10.1016/j.treng.2021.100083
Jin, D. H., & Kim, H. J. (2018). Integrated understanding of big data, big data analysis, and business intelligence: A case study of logistics. Sustainability (Switzerland), 10(10). https://doi.org/10.3390/su10103778
Klumpp, M. (2018). Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements. International Journal of Logistics Research and Applications, 21(3). https://doi.org/10.1080/13675567.2017.1384451
Konovalenko, I., & Ludwig, A. (2019). Event processing in supply chain management – The status quo and research outlook. In Computers in Industry (Vol. 105). https://doi.org/10.1016/j.compind.2018.12.009
Kumar, A., & Kalse, A. (2022). Usage and adoption of artificial intelligence in SMEs. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.01.595
Liu, C., Zhou, Y., Cen, Y., & Lin, D. (2019). (Smart CPS) Integrated application in intelligent production and logistics management: technical architectures concepts and business model analyses for the customised facial masks manufacturing. International Journal of Computer Integrated Manufacturing, 32(4–5). https://doi.org/10.1080/0951192X.2019.1599434
Liu, T. K., Lin, S. S., & Hsueh, P. W. (2021). Optimal design for transport and logistics of steel mill by-product based on double-layer genetic algorithms. Journal of Low Frequency Noise Vibration and Active Control, 40(1). https://doi.org/10.1177/1461348419872368
Liu, T., Shen, A., Hu, X., Tong, G., & Gu, W. (2017). The application of collaborative business intelligence technology in the hospital spd logistics management model. Iranian Journal of Public Health, 46(6).
Nwaubani, J. (2011). Title: Business Intelligence and Logistics. In OLYMPUS INTERNATIONAL CONFERENCE ON SUPPLY CHAINS.
Pagano, P., Antonelli, S., & Tardo, A. (2022). C-Ports: A proposal for a comprehensive standardization and implementation plan of digital services offered by the “Port of the Future.” In Computers in Industry (Vol. 134). https://doi.org/10.1016/j.compind.2021.103556
Permala, A., Kotovirta, V., Huovila, H., Maijala, P., Salmela, H., & Hiljanen, H. (2007). Logistics - Increasing intelligence in supply chains. VTT Publications, 635.
