ITECH 1100 Report: Data Mining and Disruption in Automotive Sector

This report explores the transformative impact of data mining on the automotive industry, focusing on areas such as design, manufacturing, usage management, and marketing planning. It details how data mining facilitates predictive analytics, optimizes processes, and improves decision-making. The report further examines the influence of data mining on market strategies, legal compliance, architectural integrations (like AI for fuel management), and ethical considerations. It contrasts the pre- and post-implementation scenarios, highlighting improvements in marketing effectiveness, data-driven decision-making, and overall industry outcomes. The document includes process model diagrams illustrating the changes brought about by data mining implementation. Desklib provides access to this and other solved assignments to aid students in their studies.

Running Head: IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
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1IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
Table of Contents
Part 1..........................................................................................................................................2
Part 2..........................................................................................................................................2
Part 3..........................................................................................................................................3
Part 4..........................................................................................................................................3
Process model diagram..............................................................................................................5

2IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
Part 1
Data mining is a platform that ensures a transformation in the process in the
functioning of the automotive industry. The transformation in the terms of analytics. With the
assistance of the data mining process predictive analytics can be transformed into optimizing
analytics. Hence betterment in target achievement and quality control area gets benefitted.
Drawing conclusion with the assistance of the data that is collected via the platform of the
data mining process ensures that higher accuracy is achieved. As per data mining
terminologies knowledge extraction process is dependent on the fundamental set of ideas
from which the models can be derived from (Alkahtani et al, 2019). The algorithms present in
the models are the main driving force that makes the entire functioning happen. Designing
errors have been proving to be costly and hence the main issue that is present is that the
efficiency in the process decreases. This decrease in the efficiency in the process includes
defects in sketch level production system. in case issue is present in the sketch level of the
designing process, it is expected that the entire process will be performed with higher risk. As
per data mining in the section of analytics this issue can be mitigated.
Part 2
3 processes by which data mining will incorporate eruption are as follows: -
 Designing and manufacturing will be the most highly affected section after the
introduction of the data mining process. With the assistance of the processing of the
data mining the main benefit that tracking of changes that are required in the design
phase can be easily estimated and design quality gets better. Hence manufacturing
part gets better.
 Another department that will be highly benefitted due to the incorporation of the data
mining is usage and management. The main issue that is faced in the automotive

3IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
industry includes fleet management and fuel management. Data mining will be
helping the users to perform better understanding of the requirements important for
the maintenance of the automotive. Data mining will be improving the overall
efficiency and axe off the maintenance cost.
 Marketing planning is one of the major aspects that will be benefited by the
introduction of the data mining process. With the assistance of the data mining
process, the chief aspect that is provided includes better statistics collection can be
made. This ensures that the data that are collected are precise and hence better
management of the marketing process can be made. With the assistance of the
statistics gained with the assistance of the data mining the competitors present in the
market can be estimated and hence strategies can be developed in a better manner.
Part 3
Market: Since the introduction of data mining the entire process of marketing have
been exploding. The main reason is that the data that are generated via the data mining
process helps in providing better sympathetic of the scheme that will be having a better
considerate of the market details. This ensures that the market details can be used by the
automotive industry to launch their products and compete with the other industries.
Law: With the assistance of data mining another advantage that will be received
includes the fact that the issues that were faced by the other brands can be known. With the
assistance of understanding the laws that are related to the functional; process, abiding by the
law gets easier.
Architecture: as per the understanding of when fuel refilling is to be performed via
data mining includes usage of AI as well. An integration in between the AI and data mining
helps functions in a manner that with the assistance of the data mining process, data is

4IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
generated and with the assistance of AI decision making regarding the quantity of fuel
present and when it is to be refilled is performed.
Norm: data mining has been suffering due to ethics related problems lately. The
reason of this suffering is that the data that are collected are somewhat not always with the
permission of the clients. In case of application of the data mining in the automotive, live
tracking can be done that also without the concern of the client himself.
Part 4
Before implementation
Events
Marketing was a major concern in the automotive industry. Issues used to arise
regarding poor marketing due to lack of data have been harming the industry. This insists that
there have been incidents that people in the automotive industry failed to recognize the needs
and wants of the common people (Özköse, Arı & Gencer 2015).
Activities
The sole activity that was present includes manual checking of and survey conductance. This
has created a lot of chaos in the past as one had to deal with several people in a time. Despite
this aspect the survey conducted can provide data only of about a particular group of people
who participated. Hence data were not enough for performing proper marketing.
Decision points
The decisions that were made were manual. No assistance was received. This has
been a major issue in this case marketing in the automotive industry and hence performance
that also because of the decision making aspect were in a declining state (Hofmann, Neukart
& Bäck 2017). Hence it can be stated that decision making was inefficient. In cases sometime

5IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
automotive industry did not have a clue on which they were to make improvement on as
proper data sets were not present.
Actors
The mangers of the automotive industries are the actors in this case. Not only the
drivers but also the owners are the actors as well.
Outcomes
The outcome of this aspect is never positive. The outcomes are seldom inefficient.
After Implementation
Events: Marketing of automotive is the sector that has been benefitted to its fullest
after the implementation of the data mining technology. Decision making in the automotive
industry have been getting better regarding the marketing strategies. Due to the process of
integration of data mining with AI this decision making process has become easier (Palade,
Nicolaescu & Kifor 2016).
Activities: The activities that are performed includes data gathering as per the process
of data mining. The data that are collected are analyzed via the platform of AI. This analysis
of the data with the assistance of AI, helps in understanding of the needs and wants of the
clients (Gröger et al, 2016).
Decision points: Decision points are the major advantage that is received after the
implementation of the data mining technology (Yamaguchi et al, 2016). As per the
implementation of the data mining technology automaton in the decision making of the
marketing strategies can be bettered (Luckow et al, 2015). As the needs and demands of the
people are well understood developing of marketing strategy gets easier. This will affect the
automotive industry in a positive manner.

6IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
Actors: The management committees of the automotive industry have been the actors
in this case.
Outcome: Positive outcome is expected after implementation of the above process.
Process model diagram
Without data mining
Figure 1: Process model diagram
(Created by author)

7IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
Data mining implemented
Figure 2: Process model diagram
(Created by author)

8IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
Transcription
Hi, hope you are having a nice day.
This video will be providing us a brief understanding of the technology of data mining and
the advantage that it will be providing to the automotive industry. With the introduction of
the technology of data mining the main aspect that will be present includes the fact that better
processing of data can be made and hence decision making will be befitted. This also
includes the fact that better understanding of the project can be made and decision making
can be performed. This decision making process will enhance better business understanding
as per the management of the entire system. This is one of the main aspect that has been
providing better understanding of the marketing process of the automotive that implements
the technology of data mining. This process enhances better functional purpose of the
automotive company. This has been providing better consistency in the functional part of the
automotive industry helping them reach the clients and understanding their needs. This
proper understanding of their need helps in better strategy development and hence this
strategy development enhances the functional process of business management. This is one of
the main reason that implementation of the business of automotive industry will get better
with time.

9IMPACT OF DATA MINING IN AUTOMATIVE INDUSTRY
References
Yamaguchi, T., Kaga, T., Donzé, A., & Seshia, S. A. (2016, October). Combining
requirement mining, software model checking and simulation-based verification for
industrial automotive systems. In 2016 Formal Methods in Computer-Aided Design
(FMCAD) (pp. 201-204). IEEE.
Hofmann, M., Neukart, F., & Bäck, T. (2017). Artificial intelligence and data science in the
automotive industry. arXiv preprint arXiv:1709.01989.
Alkahtani, M., Choudhary, A., De, A., & Harding, J. A. (2019). A decision support system
based on ontology and data mining to improve design using warranty
data. Computers & Industrial Engineering, 128, 1027-1039.
Gröger, C., Kassner, L., Hoos, E., Königsberger, J., Kiefer, C., Silcher, S. and Mitschang, B.,
2016, April. The Data-driven Factory. In Proceedings of the 18th International
Conference on Enterprise Information Systems (pp. 40-52). SCITEPRESS-Science
and Technology Publications, Lda.
Luckow, A., Kennedy, K., Manhardt, F., Djerekarov, E., Vorster, B., & Apon, A. (2015,
October). Automotive big data: Applications, workloads and infrastructures. In 2015
IEEE International Conference on Big Data (Big Data) (pp. 1201-1210). IEEE.
Özköse, H., Arı, E. S., & Gencer, C. (2015). Yesterday, today and tomorrow of big
data. Procedia-Social and Behavioral Sciences, 195, 1042-1050.
Palade, H. C., Nicolaescu, S. S., & Kifor, C. V. (2016). Model of Handling Big Data and
Knowledge Management in Automotive Industry. In Managing Innovation and
Diversity in Knowledge Society Through Turbulent Time: Proceedings of the
MakeLearn and TIIM Joint International Conference (pp. 731-740).