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Business Intelligence Dashboard for Driver Performance in Fleet Management

Published:03 May 2020Publication History

ABSTRACT

Transportation is at the center of logistics as it represents the physical movement of materials between points in a supply chain. The problem involve in the transportation industry is fleet management. Fleet management is the broad topic that involve vehicles maintenance, operation capacity, driver selection and so on. This project focus on performance of the driver in fleet management. Imbalance driver contribution in fleet management decline the productivity of the organization especially in transportation industry. Each driver should be evaluated and analysed on their productivity and contribution towards the organization based on their performance. The aim of this project is to identify the factors influencing driver performance in logistic transportation and provide business intelligence dashboard for visualize driver performance for organization in their decision making. One transportation industry has been selected for the case study relying on business intelligence framework and tools for the development of dashboard. As the finding of this project, a conceptual model representing factors influencing driver performance is proposed. A dashboard was developed to provide business insight and help the organization in decision making based on the conceptual model proposed. The dashboard comprises of four main components which are summary, delivery, driver profile and driver behaviour. The dashboard was evaluated with respondents who involved in fleet management.

References

  1. Gitahi, P. M., & Ogollah, K. 2014. Influence of Fleet Management Practices on Service Delivery to Refugees in United Nations High Commissioner for Refugees Kenya Programme. European Journal of Business Management Vol.2, Issue 1, 2014, 2(1), 1--18.Google ScholarGoogle Scholar
  2. Saghaei, H. (2016). Design and Implementation of a Fleet Management System Using Novel GPS / GLONASS Tracker and Web-Based Software. In Proceedings of 1st International Conference on New Research Achievements in Electrical and Computer Engineering, IEEE, At Tehran, Iran, Volume: 1Google ScholarGoogle Scholar
  3. Pierro, M. J., & Schneider, W. R. (2001). US 6301531 B1. United States. Retrieved from https://patentimages. storage.googleapis.com/e9/b8/72/e435779cd8cbb8/US6301531.pdfGoogle ScholarGoogle Scholar
  4. Chou, P. B.-L., Iyer, B. S., Lai, J., Levas, A., Lieberman, L. I., Lui, T.-K., ... Kim, E. (2001). US 6330499 B1. United States.Google ScholarGoogle Scholar
  5. Molin, H. M., & Au, K. D. (2018). US 9922567 B2. United States.Retrieved from: https://patentimages.storage.googleapis.com/29/d3/92/433b04-3d290f6f/US9922567.pdfGoogle ScholarGoogle Scholar
  6. Benjamin G. Bates. (2000). US006085805A. United States. Retrieved from https://patentimages.storage.googleapis.com/a2/9e/c2/e8b8f2d 1230f96/US6085805.pdfGoogle ScholarGoogle Scholar
  7. Chien, C. F., & Chen, L. F. 2008. Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications. 34,1 (Jan. 2008), 280--290. DOI = https://doi.org/10.1016/j.eswa.2006.09.003Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Vivaldini, M., Pires, S. R. I., & Souza, F. B. De. (2012b). Improving Logistics Services Through The Technology Used in Fleet Management, Journal of Information Systems and Technology Managemnt. 9,3, 541--562. DOI= https://doi.org/10.4301/S1807-17752012000300006Google ScholarGoogle Scholar
  9. Sundar Kumar. 2013. Are you measuring the right metrics to optimize logistics processes? Genpact Intelligent Power by Process, 5.Google ScholarGoogle Scholar
  10. Júnior, J. F., Carvalho, E., Ferreira, B. V., De Souza, C., Suhara, Y., Pentland, A., & Pessin, G. 2017. Driver behavior profiling: An investigation with different smartphone sensors and machine learning. PLoS ONE. 12,4.: e0174959. DOI= https://doi.org/10.1371/journal.pone.0174959Google ScholarGoogle Scholar
  11. Simsekoglu, Ö. 2018. Socio-demographic characteristics, psychological factors and knowledge related to electric car use: A comparison between electric and conventional car drivers. Transport Policy, 72 (June 2017), 180--186. DOI = https://doi.org/10.1016/j.tranpol.2018.03.009Google ScholarGoogle Scholar
  12. Padilla, J. L., Doncel, P., Gugliotta, A., & Castro, C. (2018a). Which drivers are at risk? Factors that determine the profile of the reoffender driver. Accident Analysis and Prevention. 119, 237--247. DOI = https://doi.org/10.1016/j.aap.2018.07.021Google ScholarGoogle ScholarCross RefCross Ref
  13. del Valle, C. H. C. 2019. Cognitive profile of optimistic and pessimistic drivers attending re-education courses. Transportation Research Part F: Traffic Psychology and Behaviour, 65 (Aug. 2019), 598--609, DOI=. https://doi.org/10.1016/j.trf.2018.04.018Google ScholarGoogle Scholar
  14. Hamblin, P. 1987. Lorry driver ' s time habits in work and their involvement in traffic accidents, 30, 9 (May 2007), 1323--133, DOI= https://doi.org/10.1080/00140138708966026Google ScholarGoogle Scholar
  15. Simões, A., Bianchi Piccinini, G. F., Rôla, S., & Ferreira, A. L. 2013. Gender and age-related differences in the perception of in-vehicle mobile phone usage among Portuguese drivers. IET Intelligent Transport Systems, 7, 2, 223--229. DOI = https://doi.org/10.1049/iet-its.2012.0149Google ScholarGoogle ScholarCross RefCross Ref
  16. Vivoli, Roberto, Bergomi, M., Rovesti, S., Bussetti, P., & Guaitoli, G. M. 2006. Biological and behavioral factors affecting driving safety. Journal of Preventive Medicine and Hygiene, 47, 2, 69--73, DOI = https://doi.org/10.1029/2006WR005449Google ScholarGoogle Scholar
  17. Yang, Y., Wong, A., & McDonald, M. 2015. Does gender make a difference to performing in-vehicle tasks? IET Intelligent Transport Systems, 9, 4, 359--365. DOI = https://doi.org/10.1049/iet-its.2013.0117Google ScholarGoogle ScholarCross RefCross Ref
  18. Stahl, P., Donmez, B., & Jamieson, G. A. 2014. Anticipation in driving: The role of experience in the efficacy of pre-event conflict cues. IEEE Transactions on Human- Machine Systems, 44, 5, 603--613, DOI= https://doi.org/10.1109/THMS.2014.2325558Google ScholarGoogle ScholarCross RefCross Ref
  19. Liao, Y., Li, G., & Chen, F. 2017. Context-Adaptive support information for truck drivers: An interview study on its contents priority. In Proceeding of IEEE Intelligent Vehicles Symposium, Proceedings, 1268--1273. DOI = https://doi.org/10.1109/IVS.2017.7995886Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Jin, J., & Deng, Y. 2017. A comparative study on traffic violation level prediction using different models. In Proceedings of 2017 4th International Conference on Transportation Information and Safety, ICTIS 2017 - Proceedings, 1134--1139. DOI = https://doi.org/10.1109/ICTIS.2017.8047913Google ScholarGoogle ScholarCross RefCross Ref
  21. Gayathri, N. & Chandrakala, K.. (2014). Embedded driver assistance system for effective dynamic vehicle routing. In Proceeding of International Conference on Embedded Systems, ICES 2014. 182--187. DOI= 10.1109/EmbeddedSys.2014.6953153Google ScholarGoogle ScholarCross RefCross Ref
  22. Shattell, M., Apostolopoulos, Y., Snmez, S., & Griffin, M. 2010. Occupational stressors and the mental health of truckers. Issues in Mental Health Nursing. 31,9, 561--568 DOI = https://doi.org/10.3109/01612840.2010.488783Google ScholarGoogle ScholarCross RefCross Ref
  23. Vujanović, D., Mijailović, R., Momčilović, V., & Papić, V. 2010. Energy efficiency as a criterion in the vehicle fleet management process. Thermal Science, 14, 4, 865--878. DOI= https://doi.org/10.2298/TSCI090719010VGoogle ScholarGoogle ScholarCross RefCross Ref
  24. Amiama, C., Pereira, J. M., Carpente, L., & Salgado, J. 2015. Spatial decision support system for the route management for milk collection from dairy farms. Transportation Letters, 7,5, 279--288.DOI= https://doi.org/10.1179/1942787515Y.0000000001Google ScholarGoogle ScholarCross RefCross Ref
  25. Andrejić, M., Bojović, N., & Kilibarda, M. 2016. A framework for measuring transport efficiency in distribution centers. Transport Policy, 45, 99--106. DOI= https://doi.org/10.1016/j.tranpol.2015.09.013Google ScholarGoogle ScholarCross RefCross Ref
  26. Factor, R. 2018. An empirical analysis of the characteristics of drivers who are ticketed for traffic offences. Transportation Research Part F: Traffic Psychology and Behaviour, 53, 1--13.DOI= https://doi.org/10.1016/j.trf.2017.12.001Google ScholarGoogle ScholarCross RefCross Ref
  27. Surbakti, H., & Ta'a, A. 2018. Tacit knowledge for business intelligence framework: A part of unstructured data? Journal of Theoretical and Applied Information Technology, 96, 3, 616--625.Google ScholarGoogle Scholar
  28. Fruhling, A., & Lee, S. 2005. Assessing the Reliability, Validity and Adaptability of PSSUQ and Adaptability of PSSUQ. In AMCIS 2005 Proceedings, 2394--2402.Google ScholarGoogle Scholar

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      cover image ACM Other conferences
      IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning
      January 2020
      441 pages
      ISBN:9781450372947
      DOI:10.1145/3377571

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      Publication History

      • Published: 3 May 2020

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