A Clustering Based Matrix For Selecting Appropriate Quality Tools And Techniques In Industrial Revolution 4.0 - Mohd Isa, Saifuddin (2019)
The purpose of this research was to explore a systematic pattern for selecting quality tools and techniques in industrial revolution 4.0 particularly in smart manufacturing context. This study asked, “What are the appropriate tools and techniques concerning circumstances of quality dimensions and smart manufacturing?” To answer this question, this research developed a diagnostic matrix by developing the outcome matrix for selecting appropriate quality tools and techniques. This matrix is intended to help non-expert users and industrial practitioner to select appropriate sets of quality tools and techniques for solving different quality problems. By conducting an analysis, the researcher uncovered homogeneous patterns of enough quality case studies, which ultimately provided the basis for selecting appropriate groups of quality tools and techniques in different circumstances. Multiple case study and in-depth literature review were employed as the research design approach. Two key data collection methods (qualitative methods) were used: Firstly, primary data from face-to-face interview with Toyo Memory Technology and Intel Malaysia and secondly, secondary data from previous study. Accordingly, this review on the previous study allows the researcher to establish the theoretical framework. This review coupled with the case study analysis led to the identification on the real implementation of quality tools and techniques in the industries. Thus, the researcher gained the information on how the industries select the quality tools and techniques to manage quality performance in the organization and the researcher examined the association and prevalence of different quality tools and techniques and the quality dimensions in context of smart manufacturing component. The study developed the clustering-based matrix of quality tools and techniques for smart manufacturing. After developing and verifying the developed matrix, the researcher discussed their strengths and limitations as well as their roles for selecting the appropriate quality tools and techniques in the context of smart manufacturing industries. The finding of this study is a clustering-based matrix for selecting appropriate quality tools and techniques in smart manufacturing that has been successfully developed. The proposed matrix applies quality management dimensions and smart manufacturing component to facilitate waste elimination, defect reduction and improving productivity in smart manufacturing that can be used as a basis for many future investigations in the field of quality management and industrial revolution 4.0.