An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

Muhammad Syafrudin, Norma Latif Fitriyani, Donglai Li, Ganjar Alfian, Jongtae Rhee and Yong-Shin Kang
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Muhammad Syafrudin: Department of Industrial and Systems Engineering, Dongguk University, Seoul 100-715, Korea
Norma Latif Fitriyani: Department of Industrial and Systems Engineering, Dongguk University, Seoul 100-715, Korea
Donglai Li: Department of Industrial and Systems Engineering, Dongguk University, Seoul 100-715, Korea
Ganjar Alfian: u-SCM Research Center, Nano Information Technology Academy, Dongguk University, Seoul 100-715, Korea
Jongtae Rhee: Department of Industrial and Systems Engineering, Dongguk University, Seoul 100-715, Korea
Yong-Shin Kang: Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea

Sustainability, 2017, vol. 9, issue 11, 1-18

Abstract: Currently, the manufacturing industry is experiencing a data-driven revolution. There are multiple processes in the manufacturing industry and will eventually generate a large amount of data. Collecting, analyzing and storing a large amount of data are one of key elements of the smart manufacturing industry. To ensure that all processes within the manufacturing industry are functioning smoothly, the big data processing is needed. Thus, in this study an open source-based real-time data processing (OSRDP) architecture framework was proposed. OSRDP architecture framework consists of several open sources technologies, including Apache Kafka, Apache Storm and NoSQL MongoDB that are effective and cost efficient for real-time data processing. Several experiments and impact analysis for manufacturing sustainability are provided. The results showed that the proposed system is capable of processing a massive sensor data efficiently when the number of sensors data and devices increases. In addition, the data mining based on Random Forest is presented to predict the quality of products given the sensor data as the input. The Random Forest successfully classifies the defect and non-defect products, and generates high accuracy compared to other data mining algorithms. This study is expected to support the management in their decision-making for product quality inspection and support manufacturing sustainability.

Keywords: manufacturing; big data; real-time processing; Kafka; storm; MongoDB (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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