Enabling Predictive Analysis in the Cloud-Based Quality Analyser: A Case Study in the Guitar Industry

Abstract

The cloud-based quality analyser (CQA) is a conceptual framework that has been proposed to perform quality analysis in manufacturing by reducing the dependency to the human quality engineer with respect to faster and more accurate information. The manufacturing industry is currently experiencing significant growth due to increased digitization and automation. The problem arises when facing large volumes of data that need to be processed quickly, leading to a decrease in prediction accuracy. This research aims to develop a predictive analysis module to be implemented in the CQA that was able to perform data preparation, model building, and evaluation. By employing the waterfall methodology, this study developed and implemented the descriptive analysis module in the CQA environment. To assess the module's effectiveness, a case study was carried out in the context of guitar manufacturing. The outcomes indicated that the module performed effectively in developing a quality prediction model using historical data. Additionally, the user acceptance test affirmed the module's acceptability among users. However, to fully gauge the benefits of implementing this module, further case studies across various industries are necessary.