Operational Data Integration and Visual Analytics for Decision-Making in SMB Fleets
DOI:
https://doi.org/10.71222/jyzsg119Keywords:
operational data integration, visual analytics, fleet management, decision-making, Small and Medium-Sized Businesses (Smbs), telematics, Data-Driven OptimizationAbstract
This research investigates the integration of operational data and visual analytics to enhance decision-making for small and medium-sized business (SMB) fleets. Effective fleet management is critical for SMBs, impacting costs, efficiency, and customer satisfaction. However, many SMBs lack the resources and expertise to fully leverage their operational data. This study explores how data from various sources, including vehicle telematics, maintenance records, fuel consumption, and driver behavior, can be integrated into a unified platform. We then examine how visual analytics techniques, such as interactive dashboards and geospatial visualizations, can be employed to extract actionable insights. The research focuses on developing a practical framework for SMBs to improve fleet performance, reduce operational costs, and enhance customer service through data-driven decision-making. A case study involving a regional delivery fleet is used to demonstrate the effectiveness of the proposed approach. The implementation led to measurable improvements, including an 8% increase in fuel efficiency (miles per gallon), a 15% reduction in unplanned vehicle downtime, and a 20% decrease in hard-braking incidents. This research provides valuable insights and guidance for SMBs seeking to optimize their fleet operations through data integration and visual analytics.References
1. J. Kim, “Visual analytics for operation-level construction monitoring and documentation: State-of-the-art technologies, research challenges, and future directions,” Frontiers in Built Environment, vol. 6, 2020. doi: 10.1111/j.1540-6261.1992.tb04679.x
2. O. H. Olayinka, “Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness,” International Journal of Scientific Research Archive, vol. 4, no. 1, pp. 280–296, 2021. doi: 10.30574/ijsra.2021.4.1.0179
3. A. A. Kharlamov, “The use of visual analytics in decision making in operations and supply chain management: A systematic literature review,” unpublished.
4. A. Böhm, J. Dittrich, N. Mukherjee, I. Pandis, and R. Sen, “Operational analytics data management systems,” Proceedings of the VLDB Endowment (PVLDB), vol. 9, no. 13, pp. 1601–1604, 2016.
5. A. Coscia, A. Suh, R. Chang, and A. Endert, “Preliminary guidelines for combining data integration and visual data analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 10, pp. 6678–6690, 2023. doi: 10.1109/TVCG.2023.3334513
6. D. Stodder, Visual analytics for making smarter decisions faster, Best Practices Report, TDWI Research, 2015.
7. A. Khakpour, R. Colomo-Palacios, and A. Martini, “Visual analytics for decision support: A supply chain perspective,” IEEE Access, vol. 9, pp. 81326–81344, 2021. doi: 10.1109/ACCESS.2021.3085496
8. W. Cui, “Visual analytics: A comprehensive overview,” IEEE Access, vol. 7, pp. 81555–81573, 2019. doi: 10.1109/ACCESS.2019.2923736
9. S. Xiao, Q. Shi, L. Shao, B. Du, Y. Wang, Q. Shen, and W. Zeng, “MetroBUX: A topology-based visual analytics for bus operational uncertainty exploration,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 6, pp. 5525–5538, 2024.
10. K. Nazemi, C. A. Secco, L. B. Sina, U. Eliseeva, E. Correll, and M. Blazevic, “Visual analytics for decision-making,” in Proc. 28th Int. Conf. Information Visualisation (IV), 2024, pp. 150–159.
11. P. C. Hudson and J. A. Rzasa, Knowledge visualizations: A tool to achieve optimized operational decision making and data integration, Ph.D. dissertation, Naval Postgraduate School, Monterey, CA, USA, 2015.
12. S. C. Suh and T. Anthony, Eds., Big Data and Visual Analytics. Cham, Switzerland: Springer International Publishing, 2017.
