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    ASIA unversity > 管理學院 > 經營管理學系  > 期刊論文 >  Item 310904400/112722

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/112722

    Title: Production data analysis system using novel process capability indices-based circular economy
    Authors: 林國平;陳坤盛
    Contributors: 經營管理學系
    Date: 2019-09
    Issue Date: 2020-08-21 15:50:26 (UTC+8)
    Publisher: 亞洲大學
    Abstract: Purpose
    The purpose of this paper is to establish mechanisms for process improvement so that production efficiency and product quality can be expected, and create a sustainable development in terms of circular economy.

    The authors obtain a critical value from statistical hypothesis testing, and thereby construct a process capability indices chart, which both lowers the chance of quality level misjudgment caused by sampling error and provides reference for the processes improvement in poor quality levels. The authors used the bottom bracket of bicycles as an example to demonstrate the model and methods proposed in this study.

    This approach enables us to plot multiple quality characteristics, despite varying attributes and specifications, onto the same process capability analysis chart. And it therefore increases accuracy and precision to reduce rework and scrap rates (reduce), increase product availability, reduce maintenance frequency and increase reuse (reuse), increase the recycle rates of components (recycle) and lengthen service life, which will delay recovery time (recovery).

    Parts manufacturers in the industry chain can upload their production data to the cloud platform. The quality control center of the bicycle manufacturer can utilized the production data analysis model to identify critical-to-quality characteristics. The platform also offers reference for improvement and adds the improvement achievements and experience to its knowledge management to provide the entire industry chain. Feedback is also given to the R&D department of the bicycle manufacturer as reference for more robust product designs, more reasonable tolerance designs, and selection criteria for better parts suppliers, thereby forming an intelligent manufacturing loop system.
    Appears in Collections:[經營管理學系 ] 期刊論文

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