Enhancing Car Purchasing Decisions: A Simple Additive Weighting-Based Decision Support System for Multi-criteria Evaluation
Keywords:
Car Purchasing, Decision Support System, Simple Additive Weighting (SAW), Multi-criteria Decision Making, Automotive IndustryAbstract
This research focuses on the development and implementation of a Car Purchasing Decision Support System, employing the Simple Additive Weighting (SAW) method, aimed at aiding consumers in navigating the multifaceted landscape of car purchases. The system utilizes a structured approach to evaluate and rank car models based on multiple criteria, including price, fuel efficiency, safety rating, and design preferences. Through the application of the SAW method, the research delineates a systematic framework for decision-making, offering transparency and clarity in the evaluation process. Findings highlight the effectiveness of the Decision Support System in providing structured guidance to buyers, empowering them with comprehensive information to make informed decisions aligned with their preferences. The study not only presents a hierarchy of suitability among car models but also emphasizes the significance of criteria weights in influencing the rankings. It underscores the system's adaptability, allowing for adjustments in criteria weights to accommodate changing buyer preferences and market dynamics. The implications of this research extend beyond individual decision-making, offering insights for industry stakeholders into consumer preferences and market trends. Recommendations for future improvements advocate for enhanced data integration, user-centric design, and the incorporation of ethical and social factors to further refine these Decision Support Systems.
References
Abraham, H., McAnulty, H., Mehler, B., & Reimer, B. (2017). Case study of today’s automotive dealerships: Introduction and delivery of advanced driver assistance systems. Transportation Research Record, 2660(1), 7–14.
Apak, S., Göğüş, G. G., & Karakadılar, İ. S. (2012). An analytic hierarchy process approach with a novel framework for luxury car selection. Procedia-Social and Behavioral Sciences, 58, 1301–1308.
Barros, R. C., Basgalupp, M. P., De Carvalho, A. C., & Freitas, A. A. (2011). A survey of evolutionary algorithms for decision-tree induction. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(3), 291–312.
Beckert, J. (2011). 5 The Transcending Power of. The Worth of Goods: Valuation and Pricing in the Economy, 106.
Biswas, T. K., & Chaki, S. (2022). Applications of modified simple additive weighting method in manufacturing environment. International Journal of Engineering, 35(4), 830–836.
Bonczek, R. H., Holsapple, C. W., & Whinston, A. B. (2014). Foundations of decision support systems. Academic Press.
Choo, E. U., Schoner, B., & Wedley, W. C. (1999). Interpretation of criteria weights in multicriteria decision making. Computers & Industrial Engineering, 37(3), 527–541.
Coutinho-Rodrigues, J., Simão, A., & Antunes, C. H. (2011). A GIS-based multicriteria spatial decision support system for planning urban infrastructures. Decision Support Systems, 51(3), 720–726.
Elwyn, G., O’Connor, A., Stacey, D., Volk, R., Edwards, A., Coulter, A., Thomson, R., Barratt, A., Barry, M., & Bernstein, S. (2006). Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. Bmj, 333(7565), 417.
Hassanzadeh, S. (2016). A cloud manufacturing based approach to suppliers selection and its implementation and application perspectives. Brunel University London.
Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R. (2018). Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Computing and Applications, 29, 555–564.
Kashid, U. S., Kashid, D. U., & Mehta, S. N. (2019). A review of mathematical multi-criteria decision models with a case study. International Conference on Efficacy of Software Tools for Mathematical Modeling (ICESTMM’19).
Katz, H. (2022). The media handbook: A complete guide to advertising media selection, planning, research, and buying. Routledge.
Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12–32.
Lucas Jr, H., Agarwal, R., Clemons, E. K., El Sawy, O. A., & Weber, B. (2013). Impactful research on transformational information technology: An opportunity to inform new audiences. Mis Quarterly, 371–382.
Odu, G. O. (2019). Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management, 23(8), 1449–1457.
Osheroff, J. A., Teich, J., Levick, D., Saldana, L., Velasco, F., Sittig, D., Rogers, K., & Jenders, R. (2012). Improving outcomes with clinical decision support: an implementer’s guide. Himss Publishing.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.
Saaty, T. L. (2006). Rank from comparisons and from ratings in the analytic hierarchy/network processes. European Journal of Operational Research, 168(2), 557–570.
Salem Khalifa, A. (2004). Customer value: a review of recent literature and an integrative configuration. Management Decision, 42(5), 645–666.
Sperling, D., & Gordon, D. (2009). Two billion cars: driving toward sustainability. Oxford University Press.
Walker, B., Carpenter, S., Anderies, J., Abel, N., Cumming, G., Janssen, M., Lebel, L., Norberg, J., Peterson, G. D., & Pritchard, R. (2002). Resilience management in social-ecological systems: a working hypothesis for a participatory approach. Conservation Ecology, 6(1).
Wilson, D. C. (2009). The paradoxes of transparency: science and the ecosystem approach to fisheries management in Europe. Amsterdam University Press.
Yusop, Z. Bin, Ahmed, K., Shirazi, S. M., & Zardari, N. H. (2015). Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management. Springer.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Dimas Eri Abyakta
This work is licensed under a Creative Commons Attribution 4.0 International License.
