Enhancing Manufacturing Efficiency: A Big Data Analysis Report

This report delves into the transformative impact of big data within the manufacturing sector. It explores how big data analytics enhances operational efficiency, product quality, and yield, while also facilitating the integration of IT services and the advancement of Industrie 4.0. The report highlights ten revolutionary ways big data can reshape manufacturing, including improved product demand forecasting, plant performance monitoring, and supplier performance prediction. A case study on semiconductor manufacturing illustrates practical applications. The report concludes by emphasizing big data's role in optimizing processes, driving strategic service improvements, and enabling data-driven decision-making for manufacturers. The report also provides insights into the use of advanced analytics across the Six Sigma DMAIC framework, measuring traceability and compliance, and facilitating profitable product customization.

Running head: BIG DATA IN MANUFACTURING
Big Data in Manufacturing
Name of the Student
Name of the University
Author’s note

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Table of Contents
Abstract......................................................................................................................................1
Introduction................................................................................................................................1
1. Augmentation of the efficiency, quality and yield of biopharmaceutical production,
Advancement of the integration of IT services, operational systems and converting the
vision of Industrie 4.0 into a reality.......................................................................................1
2. Showcase of product demand and production, understanding plant performance across
multiple metrics and catering service and support to customers............................................2
3. Integration of advanced analytics across the Six Sigma DMAIC......................................2
4. Greater visibility accuracy in predicting supplier performance on time............................2
5. Measurement of traceability and compliance to the machine level...................................2
6. Selling off most profitable customized configurations of products that enhances
profitable services..................................................................................................................3
7. Break down of compliance systems and quality management...........................................3
8. Daily production impacts on financial performance..........................................................3
9. Strategic service and a contribution to customers’ goals...................................................3
10. Increasing the accuracy, quality and yield of biopharmaceutical production..................4
Case Study: Big Data Analytics for Smart Manufacturing- Case Studies in Semiconductor
Manufacturing............................................................................................................................4
Conclusion..................................................................................................................................5
References..................................................................................................................................6
Appendices.................................................................................................................................8

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Abstract
This report is all about big data and its positive effect, the organizations irrespective
of shape and size can gain the maximum benefits. This can help the organizations to cater the
quality service to the clients or customers. The big data can prove to be fruitful to the
organizations so they can provide them with advanced solutions. A case study has been
discussed in terms of semiconductor manufacturing. All these have been discussed in the
report in a simple elaborate manner.
Introduction
The big data analysis can enhance the business operations irrespective of industry and
the industry size. The impact of big data is enhancing day by day and the industries like IT
industry, the biopharmaceutical industry is gaining the profit.
The report will highlight the big data effects, will showcase how the managers can
handle the business operations with much ease, ten revolutionary ways by which the big data
can bring in the revolution will be detailed along with the case study of the semiconductor
industry.
1. Advancement of the incorporation of IT services, operational systems and the
Industrie 4.0 technology
The Industrie 4.0 is a technology adopted by the Government of Germany and they
have a plan to fully automatize the factories of Germany. Big data is utilized to optimize the
production schedules and this production is based on the supplier, clients, cost constraints and
the availability of machines (Lee, Kao & Yang, 2014). The German suppliers and the
German manufacturers are planning to use the Industrie 4.0 for the embellishment of their
company. Big data analysis can increase the productivity; can ease the manufacturing
procedures as all the manufacturing units will work digitally.

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2. Showcase the demand of the product, the productivity and overview of plant
performance on multiple metrics and catering service and support to customers
The finding from the latest survey of LNS Research and MESA International
demonstrates that the big data has the capability to provide the quality service that is capable
to benefits the manufacturing industry.
3. Integration of advanced analytics across the Six Sigma DMAIC
DMAIC driven improvement program has the capability to furnish the manufacturing
industry aspects from all sides (Lee et al., 2013). The program highlights the production
workflow, can facilitate the customers to great extent.
4. Greater visibility accuracy in detecting the performance of the suppliers timely
The big data analysis techniques can help to get an overall on sight of all the business
procedures, the employees can get an overview of the workflow so that can help them to keep
track of every bit of data flow (Hazen et al., 2014). The big data analysis can help them to
maintain the product quality, can help to manage the accuracy in delivery of the products, and
can help to accomplish the goal or complete the products within the stipulated time.
5. Measurement of traceability and compliance to the machine level
Due to use of the sensors, the operation management team can get a general overview
of all the operations currently being undertaken by the enterprise (Jagadish et al., 2014). The
advanced analytics can enhance the quality; can enhance the workflow of the production
center.
6. Selling off most profitable customized configurations of products that enhances
profitable services
For the complex manufacturers, the well-customized product orders can cater them
the enhanced production process (Dubey et al., 2016). With the aid of advanced analysis of

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the big data the manufacturers can sell their products with the minimum impact to the
prevalent production schedules to the shop floor level, machine scheduling and the staffing.
7. Break down of compliance systems and quality management
With the aid of big data, the manufacturers can get more strategies to advance their
business process can enhance the quality of the project or the product they deal with (Zhong
et al., 2017). The managers and the management team can get rid of the soiled quality
management; they can even get rid of the compliance systems.
8. Daily production impacts on financial performance
Big data together with the advanced analytics can unite and facilitates the productivity
of the enterprise (Tao et al., 2017). The management team can detect the machine level if
they get to know the factory floor plan is executing with full efficiency or not, based on that
they can take the best strategic decision to scale the business activities of their enterprise.
9. Strategic service and a contribution to customers’ goals
The manufacturers can detect the customers’ shopping behaviors or the shopping
patterns, with the aid of big data, the managers can keep track of the wish list of the
employees, all these aspects can help the enterprises to learn the latest market trends and the
customers’ demands and in this way the big data can facilitate both the customers and the
organizations (Noh & Park, 2014).
10. Augmentation of the efficiency, productivity of biopharmaceutical production
The IT services can facilitate the biopharmaceutical production and in this way, the
purity of the ingredients can greatly flourish. The IT aspects and big data mining make the
entire production simple and easy and thus the employees of the enterprise can increase the
productivity of the products, even they can analyses the parameters well (Venkatesh,
Delgado, & Patel, 2017). Based on the analysis the vaccine’s yield can get increased by 50%.

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The heavy expenses can cut down due to the advent of the IT services and the big data
mining.
Case Study: Case Study based on Big Data Analytics to enhance Smart Manufacturing
and its role in Semiconductor Manufacturing
Smart Manufacturing (SM) is the term basically used to enhance the business
operations via linking physical capabilities along with the cyber capabilities and integration
of various aspects of the system. SM with the aid of big data can embellish the business
operations to the next level (Chen & Zhang, 2017). The enhancement is not limited to a
specific industry but it covers all the industries and semiconductor manufacturing is one of
them. The semiconductor industry is portrayed in the report by the following parameters that
are by precise processing prerequisites, highly complicated procedures and the problems
associated with manufacturing the data quality (Yin & Kaynak, 2015). The analytical solution
deployment showcases that the SME is needed to identify the robust solutions. By
incorporating SME the faults within can be detected and possible solutions can be suggested
to mitigate and the faults occurrence. Various me3thods involving the data quality
improvement along with SME incorporation can enhance and facilitate and ease the
productivity and these vital components of the analytics roadmap for SM (Bi & Cochran,
2017). Thus Smart Manufacturing can embellish semiconductor manufacturing procedures to
a greater extent.
Conclusion
It can be concluded from the above discourse that the big data analysis can enhance
every sector of the business organizations, can solve all the faulty issues occurring within, it
can facilitate the biopharmaceutical industry as well as the chemical industry in
semiconductor manufacturing; the Smart Manufacturing concepts have been highlighted in

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the report to support the big data benefits. The big data can aids the organizations can keep
track of the customers' data, their wish list can be well traced, thus the shopping behavior and
the shopping patterns can be analyzed, thus the enterprise can learn the customers' demands
and the current market scenario. The big data mining can help in accessing the data really
fast with ease and simplicity and big data can help to fully automatize the system of the
enterprises. All these aspects of big data in manufacturing have been highlighted and have
been explained in details.

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References
Bi, Z., & Cochran, D. (2014). Big data analytics with applications. Journal of Management
Analytics, 1(4), 249-265.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and
technologies: A survey on Big Data. Information Sciences, 275, 314-347.
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The
impact of big data on world-class sustainable manufacturing. The International
Journal of Advanced Manufacturing Technology, 84(1-4), 631-645.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data
science, predictive analytics, and big data in supply chain management: An
introduction to the problem and suggestions for research and
applications. International Journal of Production Economics, 154, 72-80.
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan,
R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of
the ACM, 57(7), 86-94.
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry
4.0 and big data environment. Procedia Cirp, 16, 3-8.
Lee, J., Lapira, E., Bagheri, B., & Kao, H. A. (2013). Recent advances and trends in
predictive manufacturing systems in big data environment. Manufacturing
Letters, 1(1), 38-41.
Noh, K. S., & Park, S. (2014). An exploratory study on application plan of big data to
manufacturing execution system. Journal of Digital Convergence, 12(1), 305-311.

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Tao, F., Zhang, L., Nee, A. Y. C., & Pickl, S. W. (2016). Editorial for the special issue on big
data and cloud technology for manufacturing.
Venkatesh, M., Delgado, C., & Patel, P. (2017). Mitigating Supply Chain Risk for
Sustainability Using Big Data Knowledge: Evidence from the Manufacturing Supply
Chain. World Academy of Science, Engineering and Technology, International
Journal of Environmental and Ecological Engineering, 4(5).
Yin, S., & Kaynak, O. (2015). Big data for modern industry: challenges and trends [point of
view]. Proceedings of the IEEE, 103(2), 143-146.
Zhong, R. Y., Lan, S., Xu, C., Dai, Q., & Huang, G. Q. (2016). Visualization of RFID-
enabled shopfloor logistics Big Data in Cloud Manufacturing. The International
Journal of Advanced Manufacturing Technology, 84(1-4), 5-16.

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Appendices
The below graph will showcase how the big data and the advanced analytics can
flourish the value chains by detecting the core determinants that can affect the product
performance, this graph also showcases the continuous improvement plan procedures as well.
Fig 1: Big data analysis graph