Big Data Analysis Proposal for Manufacturing Firm

Database Systems Journal, 2013

The aim of this article is to show the importance of Big Data and its growing influence on companies. It also shows what kind of big data is currently generated and how much big data is estimated to be generated. We can also see how much are the companies willing to invest in big data and how much are they currently gaining from their big data. There are also shown some major influences that big data has over one major segment in the industry (manufacturing) and the challenges that appear.

Proceedings of the Winter Simulation Conference 2014, 2014

Big data analytics is at the brink of changing the landscape in NXP Semiconductors Back End manufacturing operations. Numerous IT tools, implemented over the last decade, collect gigabytes of data daily, though the potential value of this data still remains to be explored. In this paper, the software tool called Heads Up is presented. Heads Up intelligently scans, filters, and explores the data with use of simulation. The software provides real-time relevant information, which is of high value in daily, as well as long term, production management. The software tool has been introduced at the NXP high volume manufacturing plant GuangDong China, where it is about to shift the paradigm on manufacturing operations. 1 INTRODUCTION "Knowledge provides power in many manufacturing contexts, enabling and facilitating the preservation of valuable heritage, new learning, solving intricate problems, creating core competencies and initiating new situations for both individuals and organizations now and in the future" (Choudhary, Harding, and Lin 2007). This quote captures the strategy set out by the NXP semiconductors Industrial Engineering and Technology Center (ITEC). In recent years numerous IT tools were implemented, which collect huge amounts of data at production facilities. This data is extensively analyzed during new product introductions or trouble shooting. However, the value potential of this data in steering every day production operations is yet to be exploited. This paper presents a Greenfield project, which is the joint effort of NXP ITEC and the Manufacturing Networks research group of the Technical University Eindhoven, is presented. The goal of this project is to test and integrate the use of data mining tools in every day production operations, which is in line with the grand challenges in manufacturing (Fowler and Rose 2004). APG, NXPs Assembly Plant in GuangDong China, was selected as the pilot site. The APG factory produces millions of products per day, on a few thousand pieces of equipment, and it collects over 26 Gigabytes of data per day, which is equivalent to 100 paper Oxford dictionaries. Manually, scanning and filtering this data is too labour intensive. This has so far prohibited the use of this data in day-today decision making. Due to the shear scale of APG operations it is difficult to get a grasp on "what is happening" inside the factory. Also due to economic migrations and China's industrial development policy, factories in China have a high people turn over, which limits opportunities of knowledge build-up and retention. This gives rise to consider new, more suitable production system concepts, like quantitative analysis tools to measure production efficiency and to point out hot spots. Moreover, these tools should facilitate day-today operational decision making, following rational thinking patterns. Based on smart metrics, these tools must give clues to basic operational questions like:

Now-a-days we rarely observe any company or any industry who don't have any database. Industries with huge amounts of data are finding it difficult to manage. They all are in search of some technology which can make their work easy and fast. The primary purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data over local data and how they differ with each other. This paper surveys different hardware platforms available for big data and local data and assesses the advantages and drawbacks of each of these platforms. INTRODUCTION This is an era of Big Data. Big Data is making radical changes in traditional data analysis platforms. To perform any kind of analysis on such huge and complex data, scaling up the hardware platforms becomes imminent and choosing the right hardware/software platforms becomes very important. In this research we are showing how big data has been improvising over the local databases and other technologies. Present day, big data is making a huge turnaround in technological world and so to manage and access data there must be some kind of linking between big data and local data which is not done yet. This whole study is based on the relations between big data and local data and how big data is better than any other databases. In our study we are overcoming the problem of connectivity between big data and local data. The linking will be done by connecting to the server which will help both the databases to share their data. This article is structured as follows: Section 2 consist of the purpose behind writing this paper and studying the corresponding topics, Section 3 present the background details about the study, Section 4 include the comparison of between different types of data and databases, Section 5 resides the conclusion laid from the study. PURPOSE The purpose behind this research is to show how big data is ruling the databases of the industrial giants, to put light on its different applications, how much feasible it is over local data or relational database. Big data today is even in the form of cluster also referred by HBase which works over Hadoop Distributed File System (HDFS), now here the data is stored in the form computational cluster, as Hbase is an unstructured database so if in the case of cloud disaster the data will be lost thus, the paper even suggests how this can be overcome. Paper provides a comparative study between big data and small data so that a user can easily establish one for motive.

Big Data is a crucial component not only to support business decisions, but to gain competitive advantage in an increasingly globalized world, where those who have more information are one step ahead, in this concept companies like technology companies like Facebook, Google and traditional manufacturing companies are making high investments in Big Data and Advanced Analytics with the challenge of translating this huge volume of data. This research effort aims to map the challenges that traditional Manufacturing Industry companies must take advantage of the potential of Big Data and Advanced Analysis to generate real value for the Business through a case study of a global technology company. We found that despite the professionals working on technological development, they understand that the generation of value and the lack of strong evidence of how much Big Data is adding to the current scenario of the Manufacturing Industry was one of the biggest motivations behind this research, which after the case study carried out gave a clear understanding that the manufacturing industry is not prepared to make the most of this new era of Analytics. Another important fact revealed by the surveys is the common concern about the need to invest in improving the competence of the resource in any organization to explore the Big Data environment.

2020

Digitization and data-driven manufacturing process is needed for today's industry. The term Industry 4.0 stands for today industrial digitization which is defined as a new level of organization and control over the entire value chain of the life cycle of products; it is geared towards increasingly individualized customer's high-quality expectations. However, due to the increase in the number of connected devices and the variety of data, it has become difficult to store and analyze data with conventional systems. The motivation of this paper is to provide an overview of the understanding of the big data pipeline, providing a real-time on-premise data acquisition, data compression, data storage and processing with Apache Kafka and Apache Spark implementation on Apache Ha-doop cluster, and identifying the challenges and issues occurring with implementation the Farplas manufacturing company, which is one of the biggest Tier 1 automotive supplier in Turkey, to study the new trend...

Zenodo (CERN European Organization for Nuclear Research), 2023

This paper provides insight into the concept of big data and big data analytic platforms like Spark and Storm to handle a variety of data coming from various sources like the Internet of Things (IoT) which could pose a challenge in production in the industries. The paper discusses the concept of Industry 4.0 and shows how big data in Industry 4.0 can lead to the growth of the economy and the promotion of business in the industry. The paper presents some challenges faced by big data and proffers solutions to these challenges. Hadoop technology is suggested as one of the methods to address some of the challenges of big data. The components of Hadoop technology such as Hadoop distributed file system (HDES), Map reduce, HBASE, HCatalog, Pig and so on, are discussed alongside their functions. The methodology followed to achieve these objectives stated above is a systematic review of current works of literature and related works.

International Journal of Advanced Computer Science and Applications, 2019

The importance of data analysis in companies grows every day, with a global market that generates large amounts of transactions. Industry 4.0 is one of the technological trends, which is a set of diverse technologies whose objective is the digitalization and technological connectivity of the entire value chain of organizations. Data analysis and decision making in real time have a positive impact on efficiency. One of the technologies that support this concept is big data, which can support companies to use and manage large volumes of data as support in decision making. In this research project, the computational environment of Apache Hadoop software has been analyzed to create a technological strategy that supports companies in creating a roadmap to know and implement big data technology; as a result, a computer laboratory for big data has been created at the Autonomous University of Coahuila, Mexico to support medium-sized manufacturing companies in their data analysis strategy for decision making.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Big data can bring "great value" to our lives in almost every aspect. Technologically, Big Data brings changes in our lives as it enables the full integration and analysis of diverse and heterogeneous data to help us make decisions. Today, with Big Data technology, thousands of data from seemingly unrelated domains can help make important decisions. This is the power of Big Data. Application Areas Health, Well-being, Policy Making and Public Opinion, Smart Cities and More Efficient Society and Robotic Interaction, New Online Education Model: Model student-teacher geometry Foundations in Big Data Analytics Research: Develop and research fundamental theories, algorithms, techniques, methodologies, technologies to solve problems effectively and efficiently to allow the application of Big Data problems. Scientists in the field and big data researchers. This Paper Presentation talks about Big Data analysis and its application in different industrial domains under the following sub topic: Introduction: A brief overview of Big Data analysis and its significant application in different industry and in the field of research. Case study: Discussion of a case study of Big Data together with IoT provides the infrastructure for collecting the data for different domain application of smart cities as important application goal. Work flow: Showcases simplified version of overall work flow of the case study. Conclusion: Discussion of concluded outcome and breakthrough of data analytics.

Proceedings of the 8th International Conference on Data Science, Technology and Applications, 2019

Due to the emergence of the fourth industrial revolution, manufacturing business all over the world is changing dramatically; it needs enhanced efficiency, competency and productivity. More and more manufacturing machines are equipped with sensors and the sensors produce huge volume of data. Most of the companies do neither realize the value of data nor how to capitalize the data. The companies lack techniques and tools to collect, store, process and analyze the data. The objective of this paper is to propose data analytic techniques to analyze manufacturing data. The analytic techniques will provide both descriptive and predictive analysis. In addition, data from the company's ERP system is integrated in the analysis. The proposed techniques will help the companies to improve operational efficiency and achieve competitive benefits.

IOP Conference Series: Materials Science and Engineering, 2019

The aim of the paper is establishing a link between Big Data connected with Industry 4.0. We are developing a modern economy which is based on supply chains and logistics. Within each logistics are IT solutions. Some of the world's solutions are compiled in this paper and guidelines are set in the development of such solutions. Industry development 4.0, and in particular the development of Big data, gives us the possibility to introduce new ways of the industry. In the development of industry, maximizing production and reducing costs is extremely important. Real-time data and advanced algorithms give us the possibility to obtain real-time operational industry. Big data and their analysis are of great use in the industry because they improve the accuracy of the decision-making process. We must organize a structure that can design, manage and develop projects based on IoT and Big Data / Analytics technologies. In the industry, good sensor technology provides access to information. The paper has a focus on the managing the database with Big data in the Industry 4.0.