ITECH7406: Business Intelligence and Data Mining Research Report

This report, prepared for the ITECH7406 Business Intelligence and Data Warehousing course, analyzes business analytics and data mining techniques within the manufacturing industry. It explores how these techniques optimize manufacturing processes, improve product quality, and enhance profitability. The report reviews the applications of business intelligence and data mining, highlighting their role in decision-making. It covers data mining's use in extracting knowledge for optimization, the application of various algorithms, and the identification of data patterns. The report also addresses business opportunities like manufacturing intelligence and predictive analytics while acknowledging challenges such as balancing maintenance with throughput and managing an aging workforce. In conclusion, the report successfully examines the transformative impact of business intelligence and data mining in the manufacturing sector.

TECH7406- Business Intelligence
and Data Warehousing
Research Report
Individual Report – 2 Manufacturing Industry

Introduction
Main objective of this project is to prepare the report to analysis the business
analytics and data mining techniques to the selected domains, here we are chosen the
manufacturing industry and, also this report is used to review the applications of
business intelligence analytics and data mining in manufacturing industry for decision
making contents. This project is used to enable user to understand the how business
intelligence analytics and data mining technique revolutionize business today.

Business Analytics and Data mining
Techniques for Banking Industry
 The business analytics and data mining technique in manufacturing process is used to optimize
the manufacturing process to improved the products quality, reliability, energy efficiency and also
provide the better profit margins for an organization.
 These are used to solving the today manufacturing challenges and to gain the competitive
advantage among other benefits. The high competitive pressure in the manufacturing industry is
created the effective, efficient and continuously improved the manufacturing process which is
critical success factor in manufacturing industry.
 So, we use the pattern based and indication based manufacturing process optimization data
mining approaches which is used to implements the optimization of the manufacturing process.
This is emphasizes the potential using of the comprehensive analytics to improve the business
activities.

Business Analytics and Data mining
Techniques for Banking Industry
The manufacturing industry in Business analytics and data mining process is
illustrated as below.

Business Analytics and Data mining
Techniques for Banking Industry
 The data mining in manufacturing industry is used to extract the large amounts
of data knowledge for optimization purposes.
 To shows the significant need for research on universal data integration and data
storage concepts for the data mining in manufacturing industry to generate the
versatile pre - configured and truly process the centric data mining applications
which are used to adapt to heterogeneous manufacturing environments and
various branches.
 It mainly focuses on the quality and failure analysis in manufacturing industry
(Pilon, 2019).

Applications of the Banking Industry
 The applications of the data mining in manufacturing processes are use the
various business intelligence and data mining algorithms which are used to
extract the useful information and creating the knowledge.
 The techniques of business intelligence and data mining can be used to identify
the data patterns and correlation between the various process variables in
manufacturing process which is used to classify and discover the new
relationships between the variables and thus extract the previously unknown
process knowledge.

Applications of the Banking Industry
 These two techniques are used to recognize the benefits of manufacturing technology
and it is used effectively in various areas such as medicine, banking, insurance,
marketing and more.
 These two techniques are unlocking the useful knowledge from the data bases and these
are used to improve the process operation, energy consumption and product quality.
 These techniques are used to provide the more and fast convenient interpretation which
can assist the engineers and operators in the decision making regarding the plant
operation in manufacturing process and it ultimately leading to improve the
manufacturing process (Tan, 2012).

Business Opportunities
The data mining and business analytics techniques are add the new business value
and generated the new business opportunities with the manufacturing industry such as
 Manufacturing Intelligence
 Predictive Analytics
 Production Quality Management
 Manufacturing Network Monitoring
 Manufacturing visualization and visibility
 Integrated scheduling and operations execution

Challenges
The challenges that associated with the application of the business analytics and
data mining technique for manufacturing industry is represented as below (Wang, 2013).
 Balancing maintenance with throughput
 Environmental concerns
 Health care costs
 Aging work force
 Rising quality of offshore manufacturing
 Increasing Automation
 Smarter Customer base

Conclusion
This project was successfully prepared the report which is effectively
analyses the business analytics and data mining techniques to the manufacturing
industry and, also this report is also effectively review the applications of business
intelligence analytics and data mining in manufacturing industry for decision
making contents. This project is used to enable user to understand the how business
intelligence analytics and data mining technique revolutionize business today.

References
 A generic data analytics system for manufacturing production. (2018). Big Data Mining and
Analytics, 1(2), pp.160-171.
 Pilon, A. (2019). 10 Major Challenges Facing Your Small Manufacturing Business - Small
Business Trends. [online] Small Business Trends. Available at:
https://smallbiztrends.com/2018/05/small-manufacturing-challenges.html [Accessed 6 May 2019].
 Tan, J. (2012). Data Mining Technology in Manufacturing Research. Advanced Materials
Research, 616-618, pp.2034-2037.
 Wang, K. (2013). Towards zero-defect manufacturing (ZDM)—a data mining approach. Advances
in Manufacturing, 1(1), pp.62-74.