Business Analytics and Data Mining Techniques for Manufacturing Industry

This report analyzes the business analytics and data mining techniques used in the manufacturing industry for decision making. It explores the applications, opportunities, and challenges associated with these techniques.

University
Semester
TECH7406- Business Intelligence and Data Ware
housing
Research Report
Individual Report – 2 Manufacturing Industry
Student ID
Student Name
Submission Date
Table of Contents
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Introduction...........................................................................................................................................3
Business Analytics and Data mining Techniques for Manufacturing Industry......................................3
Applications of the Manufacturing Industry..........................................................................................4
Business Opportunities for Manufacturing Industry..............................................................................5
Challenges.............................................................................................................................................5
Conclusion.............................................................................................................................................6
References.............................................................................................................................................6
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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.
In this project focus on the following aspects such as,
 Business analytics and data mining techniques
 Applications of the manufacturing industry based on business analytics
and data mining technique
 Generate the new opportunities for manufacturing industry
 Finally, analysis the any challenges that associated with the application of
business analytics and data mining techniques for manufacturing industry.
These are will be analyzed and discussed in detail.
Business Analytics and Data mining Techniques for Manufacturing
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. And regarding the business
intelligence approaches in manufacturing there mainly has two types such as pre - packaged
dash board application and custom business intelligence application which concentrate on the
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spread sheet based online analytical processing. Manufacturing industry in Business analytics
and data mining process is illustrated as below.
In manufacturing industry, data mining is used for extracting a huge amount of data
knowledge for optimization purposes. To show significant requirement to research related to
universal data integration and the concept of data storage for data mining in this industry for
generating 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 Manufacturing 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. 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
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operation in manufacturing process and it ultimately leading to improve the manufacturing
process (Tan, 2012).
Business Opportunities for Manufacturing Industry
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
These opportunities are used to require the new view of the information as a vital asset in the
manufacturing industry. It also requires the various view of the operation of manufacturing
and most cases are new competency (A generic data analytics system for manufacturing
production, 2018).
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
 Keeping products relevant
Conclusion
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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. This report is focus on the following aspects like,
 The data mining techniques and Business analytics.
 Applications of the manufacturing industry based on data mining
techniques and Business analytics.
 Generate new opportunities for manufacturing industry
 Finally, analysis the any challenges that associated with the application of
business analytics and data mining techniques for manufacturing industry.
These are analyzed and discussed in detail.
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.
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