Improving Data Management and Quality Control of Non-conformities in Prototype Building

Abstract

Managing nonconformity data and implementing effective quality control measures are important for the success of organizations, and the improvement of product or service quality. This thesis focuses on addressing the challenges associated with data management and quality control of nonconformities within Valmet automotive EV Protofactory Uusikaupunki.

To address this topic comprehensively, the research begins with a theoretical review of data management practices and quality control methodologies relevant to nonconformities. Through a qualitative case study, the study examines the current state of data management and quality control in the case company. This involves evaluating existing processes, identifying requirements, and investigating associated challenges. Key stakeholders are interviewed using a semi-structured approach to gain valuable insights into their perspectives, experiences, and expectations regarding data management and quality control of nonconformities which provide with important information about the strengths and weaknesses of current practices and serve as a guide for potential improvements.

The research findings emphasize the importance of having a well-structured data management process specifically designed to handle nonconformities, This means that organizations need to establish a systematic approach for data collection, storage, analysis, and reporting. Moreover, the study proposes the implementation of robust quality control measures to effectively address nonconformities and minimize their occurrence.