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Green Outsourcer Selection Model Based on Confidence Interval of PCI for SMT Process

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  • Kuen-Suan Chen

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
    Department of Business Administration, Asia University, Taichung 413305, Taiwan
    Department of Business Administration, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Feng-Chia Li

    (Department of Distribution Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Kuei-Kuei Lai

    (Department of Business Administration, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Jung-Mao Lin

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

Abstract

Taiwan’s electronics industry usually outsources most of its important components for production to enhance market competitiveness and operational flexibility. The quality of all component products is important to ensure the quality of the final product. In electronic assembly, printed circuit boards (PCBs) are key components that carry other electronic components to provide a stable circuit working environment. Surface Mounted Technology (SMT) is the mainstream technology in electronic assembly plants. Obviously, good SMT process quality is relatively important to the final product quality. The process capability index (PCI) is the most widely used process quality evaluation tool in the industry. Therefore, this paper used the PCI representing quality as the green outsourcer selection tool for the SMT process, derived the confidence interval of PCI to develop a quality evaluation model of green outsourcers, and considered the model as the green outsourcer selection model. Meanwhile, this model can be provided to enterprises, outsourcers, or suppliers to evaluate and improve the process quality of components to ensure the quality of components and final products. Since the selection model is based on confidence intervals, it can reduce the risk of misjudgment due to sampling error.

Suggested Citation

  • Kuen-Suan Chen & Feng-Chia Li & Kuei-Kuei Lai & Jung-Mao Lin, 2022. "Green Outsourcer Selection Model Based on Confidence Interval of PCI for SMT Process," Sustainability, MDPI, vol. 14(24), pages 1-12, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16667-:d:1001655
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    References listed on IDEAS

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    1. Muhammad Aslam & G. Srinivasa Rao & Liaquat Ahmad & Chi-Hyuck Jun, 2022. "A new control chart using GINI CPK," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(1), pages 197-211, January.
    2. Kuen-Suan Chen, 2022. "Fuzzy testing of operating performance index based on confidence intervals," Annals of Operations Research, Springer, vol. 311(1), pages 19-33, April.
    3. Bengie Omar Vazquez Reyes & João Carlos Colmenero, 2022. "A fuzzy decision-making methodology for third-party logistics selection in the existence of the company's policy priorities: case of household appliance manufacturer," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 43(2), pages 193-237.
    4. Kuen-Suan Chen & Ming-Chieh Huang & Chun-Min Yu & Hsuan-Yu Chen, 2022. "Quality-Based Supplier Selection Model for Products with Multiple Quality Characteristics," Sustainability, MDPI, vol. 14(14), pages 1-17, July.
    5. Wu, Chien-Wei & Pearn, W.L., 2008. "A variables sampling plan based on Cpmk for product acceptance determination," European Journal of Operational Research, Elsevier, vol. 184(2), pages 549-560, January.
    6. Mou-Yuan Liao, 2015. "Assessing process incapability when collecting data from multiple batches," International Journal of Production Research, Taylor & Francis Journals, vol. 53(7), pages 2041-2054, April.
    7. Thevenin, Simon & Ben-Ammar, Oussama & Brahimi, Nadjib, 2022. "Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1199-1215.
    8. J.N. Roul & K. Maity & S. Kar & M. Maiti, 2015. "Multi-item reliability dependent imperfect production inventory optimal control models with dynamic demand under uncertain resource constraint," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 4993-5016, August.
    9. Wang, Ching-Hsin & Chen, Kuen-Suan, 2020. "New process yield index of asymmetric tolerances for bootstrap method and six sigma approach," International Journal of Production Economics, Elsevier, vol. 219(C), pages 216-223.
    10. Jun Yang & Fanbing Meng & Shuo Huang & Yanhe Cui, 2020. "Process capability analysis for manufacturing processes based on the truncated data from supplier products," International Journal of Production Research, Taylor & Francis Journals, vol. 58(20), pages 6235-6251, October.
    11. Chun-Min Yu & Tsun-Hung Huang & Kuen-Suan Chen & Tsung-Yu Huang, 2022. "Construct Six Sigma DMAIC Improvement Model for Manufacturing Process Quality of Multi-Characteristic Products," Mathematics, MDPI, vol. 10(5), pages 1-13, March.
    12. Wetzstein, Anton & Hartmann, Evi & Benton jr., W.C. & Hohenstein, Nils-Ole, 2016. "A systematic assessment of supplier selection literature – State-of-the-art and future scope," International Journal of Production Economics, Elsevier, vol. 182(C), pages 304-323.
    13. Sardesai, Saskia & Klingebiel, Katja, 2023. "Maintaining viability by rapid supply chain adaptation using a process capability index," Omega, Elsevier, vol. 115(C).
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