Production data analysis system using novel process capability indices-based circular economy
| Pages | 1655-1668 |
| DOI | https://doi.org/10.1108/IMDS-03-2019-0166 |
| Date | 09 September 2019 |
| Published date | 09 September 2019 |
| Author | Kuo-Ping Lin,Chun-Min Yu,Kuen-Suan Chen |
| Subject Matter | Information & knowledge management |
Production data analysis system
using novel process capability
indices-based circular economy
Kuo-Ping Lin
Asia University, Taichung, Taiwan and
China Medical University, Taichung, Taiwan
Chun-Min Yu
National Chin-Yi University of Technology, Taichung, Taiwan, and
Kuen-Suan Chen
National Chin-Yi University of Technology, Taichung, Taiwan and
Asia University, Taichung, Taiwan
Abstract
Purpose –The purpose of this paper is to establish mechanism s for process improveme nt so that
production efficienc y and product quality can be expected, and crea te a sustainable development in terms
of circular economy.
Design/methodology/approach –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.
Findings –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).
Originality/value –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.
Keywords Internet of Things (IoT), Intelligent manufacturing, I-cloud, Process capability indices,
Production data analysis
Paper type Research paper
1. Introduction
Companies must take into account both their own growth and environmental protection;
many studies have consequently been conducted on green energy and the concept of a
circular economy (see Guo et al., 2017; Li et al., 2018; Lin et al., 2016; Wang, 2016; Wang
et al., 2018; Wu et al., 2010). Chen, Wang and Tan (2019) pointed out that sustainable
production can be achieved by the enhancement of product quality, which has the added
benefit of increasing product value and industry competitiveness. Increasing process
quality lowers scrap and rework rates, lengthens product life span, and reduces product
maintenance, which can in turn reduce environmental pollution. As technologies
associated with the Internet of Things (IoT) have gradually matured, the measurement
and collection of production data have continued to advance, enabling the collection of big
production data. Effective data analysis and application can enhance manufacturing
Industrial Management & Data
Systems
Vol. 119 No. 8, 2019
pp. 1655-1668
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-03-2019-0166
Received 23 March 2019
Revised 27 May 2019
Accepted 25 June 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1655
Production
data analysis
system
To continue reading
Request your trial