Big Data Analysis in Smart Manufacturing: A Review

Nagorny, Kevin; Lima-Monteiro, Pedro; Barata, José; Colombo, Armando Walter

doi:10.4236/ijcns.2017.103003

Cited by 74 publications

(42 citation statements)

References 41 publications

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“…Numerous analytical methods exist, and are covered extensively in the available literature [14], [25]- [27]. Some common methods include counts, statistical methods, classification, regression analyses, decision trees, and artificial intelligence.…”

Section: Calculations Through Analytics (Question 3)mentioning

confidence: 99%

Finding the Four Qualities of Intelligent Industrial Reporting

Botes

Hamer

Jaarsveld

et al. 2019

SAJIE

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Internationally, innovation and technology are driving change through concepts such as 'Industry 4.0'. However, due to various constraints, South Africa is lagging behind in this transformation. Furthermore, local industry generates large amounts of data that could contribute to a positive transformation. Data analytics, in the form of reporting, may therefore present a workable alternative to understand better the intricate nature of real-world operations. This paper identifies four qualities for the practical application of data analytics, with the aim of intelligent reporting. The four qualities are focus area, data availability, analytics, and visualisation. Research on each quality shows that they have various levels. A comprehensive literature review supports these findings. Forty studies are included that were selected through a process of relevant research criteria. A case study is presented to show how the four qualities contribute to the development of intelligent reports as an objective representation of industry performance.

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Section: Calculations Through Analytics (Question 3)mentioning

confidence: 99%

Finding the Four Qualities of Intelligent Industrial Reporting

Botes

Hamer

Jaarsveld

et al. 2019

SAJIE

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show abstract

“…It includes classification, prediction, time series, and analysis [4][5][6][7][8]. To achieve these goals, DM solutions employ a wide variety of enabling techniques and specific mining techniques to both predict and describe interpretable and valuable information [4,5,[8][9][10]. The enabling techniques mainly refer to the methods for data cleaning, data integration, data transformation, and data reduction that can support the implementation of DM in the narrow sense, while specific mining techniques, like regression, support vector machine, and artificial neural network (ANN), are the approaches used to explore useful knowledge from massive data [4,9].…”

Section: Introductionmentioning

confidence: 99%

“…The details of enabling techniques, DM applications for different manufacturing task and different manufacturing industry, patterns in the use of specific mining techniques, application performance, and software used in these applications have been widely studied, and one can refer to [4][5][6][7][8][9][10] for comprehensive review. Electronic product manufacturing industries also exploited several DM methods with the purpose of summarization, clustering, association, classification, prediction, and so on [5,8].…”

Section: Introductionmentioning

confidence: 99%

Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production

Zheng

Kim

et al. 2018

Journal of Electrical and Computer Engineering

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Improving the accuracy of material feeding for printed circuit board (PCB) template orders can reduce the overall cost for factories. In this paper, a data mining approach based on multivariate boxplot, multiple structural change model (MSCM), neighborhood component feature selection (NCFS), and artificial neural networks (ANN) was developed for the prediction of scrap rate and material feeding optimization. Scrap rate related variables were specified and 30,117 samples of the orders were exported from a PCB template production company. Multivariate boxplot was developed for outlier detection. MSCM was employed to explore the structural change of the samples that were finally partitioned into six groups. NCFS and ANN were utilized to select scrap rate related features and construct prediction models for each group of the samples, respectively. Performances of the proposed model were compared to manual feeding, ANN, and the results indicate that the approach exhibits obvious superiority to the other two methods by reducing surplus rate and supplemental feeding rate simultaneously and thereby reduces the comprehensive cost of raw material, production, logistics, inventory, disposal, and delivery tardiness compensation.

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“…Associated to this concept arises the Industry 4.0 concept that intends to change the paradigm of the productive process, in which we have data-driven decisions. To this end, and in order to concretize the Industry 4.0 revolution, the use of Big Data becomes essential [33][34][35].…”

Section: -7-manufacturing Sectormentioning

confidence: 99%

Big Data: Concept, Potentialities and Vulnerabilities

Almeida

2018

ESJ

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The evolution of information systems and the growth in the use of the Internet and social networks has caused an explosion in the amount of available data relevant to the activities of the companies. Therefore, the treatment of these available data is vital to support operational, tactical and strategic decisions. This paper aims to present the concept of big data and the main technologies that support the analysis of large data volumes. The potential of big data is explored considering nine sectors of activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy challenges of big data are discussed. It was possible to conclude that despite the potential for using the big data to grow in the previously identified areas, there are still some challenges that need to be considered and mitigated, namely the privacy of information, the existence of qualified human resources to work with Big Data and the promotion of a data-driven organizational culture.

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Big Data Analysis in Smart Manufacturing: A Review

Cited by 74 publications

References 41 publications

Finding the Four Qualities of Intelligent Industrial Reporting

Finding the Four Qualities of Intelligent Industrial Reporting

Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production

Big Data: Concept, Potentialities and Vulnerabilities

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