The Impact of Visualizing Operational Deviations on Quality - A Case Study at a Manufacturing Company
Examensarbete för masterexamen
Quality and operations management (MPQOM), MSc
Andersson Baumgartner, Matthias
In today’s fierce market conditions and within an industry heavy influenced by quality- and lean management, continuous improvement is almost a necessity for success. In addition, the ever-increasing digitalization presents both challenges and opportunities for organizations worldwide. This thesis departs from these opportunities by investigating how the visualization of operational deviations may impact the quality for a global truck manufacturer. The overall purpose of the thesis was to explore the potential impact that visualization may have on quality, as well as to create a framework for how data collection and visualization should be structured. To answer this, the methodology guiding this thesis is that of an inductive, qualitative, case-study. The study is structured in two parts, one creating a theoretical linkage between how visualization impacts quality and complementing this with the study’s findings, the other part is developing a framework for the data collection process and visualization. In total, 18 interviews were conducted with both managers and team leaders, which acted as input for the theoretical linkage and as a foundation for the developed frameworks. This thesis is underpinned by theory that cover areas such as quality management, data quality, data-driven decision-making, and data visualization. The results from this case study show that Plant Y has a rigorous emphasis on both data collection and visualization of quality deviations, however, both these processes have significant improvement opportunities where digitalization efforts and restructuring of information systems can prove beneficial. Concludingly, visualization of operational deficiencies has the potential to increase quality at a manufacturing company. To increase the possibility of beneficial results, the data collection methods and procedures should be structured in a way to enable data analysis, and visualizations must be easily understood in order for it to guide decision-making.