Data Mining's Role in Shaping the Automotive Industry: A Report

This report examines the implementation and impact of data mining within the automotive industry, highlighting its role in enhancing decision-making processes and strategic planning. The analysis covers key areas such as architecture development, marketing strategies, and fuel management, contrasting the inefficiencies of manual data collection with the efficiencies gained through data mining platforms. The report showcases how data mining enables more accurate data analysis and improves the accuracy of decisions. The report also includes references to relevant research papers that support the application of data mining in the automotive industry, including studies on artificial neural networks, big data systems, and predictive maintenance. The document emphasizes the potential for improved outcomes and provides a comprehensive overview of the benefits of data mining in the automotive sector.

INTRODUCTION OF
DATA MINING IN
AUTOMOTIVE INDUSTRY

INTRODUCTION
Introduction of the data mining process ensures that the automotive
industry gets highly benefitted with its data analysis and hence decision
making. This ensures that the data collected via the platform of data
mining are highly accurate and hence decision made are also accurate.

DATA MINING DISRUPTION
The disruptions are as follows: -
• Better decision making of the architecture.
• Better marketing strategy deployment
• Better fuel management

CHOSEN CRITERIA
• Usage of data mining in the process of the architecture development of
the system are the major aspects that ensures that proper
management of the construction system is performed as per the data
that are gained via the platform of data mining and hence providing a
better understanding of the client requirement.

BEFORE IMPLEMENTATION
• Manual data collection is to be made via survey
• Decisions are made via data collected with manually collected data
base.
• Not very efficient decision point
• Predicted outcome

AFTER IMPLEMENTATION
• Data collected via data mining platform
• Decisions are made with the help of data mining
• Efficient decision points present
• Improvisation expected

REFERENCES
• Al-Mubayyed, O. M., Abu-Nasser, B. S., & Abu-Naser, S. S. (2019). Predicting Overall Car Performance Using Artificial Neural Network.
• Santos, M. Y., e Sá, J. O., Andrade, C., Lima, F. V., Costa, E., Costa, C., ... & Galvão, J. (2017). A big data system supporting bosch braga
industry 4.0 strategy. International Journal of Information Management, 37(6), 750-760.
• Yan, J., Meng, Y., Lu, L., & Li, L. (2017). Industrial big data in an industry 4.0 environment: Challenges, schemes, and applications for
predictive maintenance. IEEE Access, 5, 23484-23491.
• Cheng, G. J., Liu, L. T., Qiang, X. J., & Liu, Y. (2016, June). Industry 4.0 development and application of intelligent manufacturing. In 2016
international conference on information system and artificial intelligence (ISAI) (pp. 407-410). IEEE.
• Purr, S., Meinhardt, J., Lipp, A., Werner, A., Ostermair, M., & Glück, B. (2015). Stamping plant 4.0–basics for the application of data mining
methods in manufacturing car body parts. In Key Engineering Materials (Vol. 639, pp. 21-30). Trans Tech Publications.
• Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
• Kašćelan, V., Kašćelan, L., & Novović Burić, M. (2016). A nonparametric data mining approach for risk prediction in car insurance: a case
study from the Montenegrin market. Economic research-Ekonomska istraživanja, 29(1), 545-558.
• van der Vegte, W. F. (2016, August). Taking Advantage of Data Generated by Products: Trends, Opportunities and Challenges. In ASME 2016
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. V01BT02A025-
V01BT02A025). American Society of Mechanical Engineers.