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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Muhammad Syafrudin & Norma Latif Fitriyani & Donglai Li & Ganjar Alfian & Jongtae Rhee & Yong-Shin Kang, 2017. "An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability," Sustainability, MDPI, vol. 9(11), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:2139-:d:119631
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    References listed on IDEAS

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    1. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    2. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    3. Gunasekaran, Angappa & Spalanzani, Alain, 2012. "Sustainability of manufacturing and services: Investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 140(1), pages 35-47.
    4. Marc A. Rosen & Hossam A. Kishawy, 2012. "Sustainable Manufacturing and Design: Concepts, Practices and Needs," Sustainability, MDPI, vol. 4(2), pages 1-21, January.
    5. Jeffrey A. Roberts & Il-Horn Hann & Sandra A. Slaughter, 2006. "Understanding the Motivations, Participation, and Performance of Open Source Software Developers: A Longitudinal Study of the Apache Projects," Management Science, INFORMS, vol. 52(7), pages 984-999, July.
    6. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 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, Elsevier, vol. 154(C), pages 72-80.
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    Cited by:

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    2. Yufan Wang & Haili Zhang, 2020. "Achieving Sustainable New Product Development by Implementing Big Data-Embedded New Product Development Process," Sustainability, MDPI, vol. 12(11), pages 1-20, June.
    3. Eduardo Viciana & Alfredo Alcayde & Francisco G. Montoya & Raul Baños & Francisco M. Arrabal-Campos & Antonio Zapata-Sierra & Francisco Manzano-Agugliaro, 2018. "OpenZmeter: An Efficient Low-Cost Energy Smart Meter and Power Quality Analyzer," Sustainability, MDPI, vol. 10(11), pages 1-13, November.
    4. José María Portalo & Isaías González & Antonio José Calderón, 2021. "Monitoring System for Tracking a PV Generator in an Experimental Smart Microgrid: An Open-Source Solution," Sustainability, MDPI, vol. 13(15), pages 1-23, July.

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