Analytics Solution for Product Quality Analysis in Manufacturing

This thesis explores the integration of data analytics and effective data management to enhance product quality analysis in manufacturing. The aim is to develop an analytics solution model using publicly available data, focusing on its impact on manufacturing processes. Key research questions address the strategic use of data analytics in product quality analysis and its effect on manufacturing outcomes. The literature review synthesises influential works in manufacturing, Industry 4.0, predictive analytics, and data mining. The proposed methodology involves building a Python-based analytics solution model using open data repositories for manufacturing data. Expected outcomes include best practices for data quality, an analytics solution model for quality assurance, and insights into the relationship between process conditions and product quality. The thesis structure encompasses an introduction, literature review, methodology, results analysis, discussion, conclusion, and references, with potential appendices containing code snippets.