Focusing quality efforts and improving prioritisation of nonconformities by utilising field return data

Abstract

While there is a plethora of evidence displaying the benefits of both continuous improvement as well as sound improvement project prioritisation, there is limited research in this area in the context of nonconformities arising in the field. Furthermore, many companies still heavily rely on ad-hoc problem-solving and subjective judgements for dealing with nonconformities. This, in combination with the fact that scarce resources pervade all organisations in today’s harsh market environment, sheds a light on the importance of improvement project prioritisation. This thesis thus seeks to bridge this gap and investigate how a manufacturing company can prioritise among nonconformities to assure that the most critical ones are being addressed. This includes investigating what attributes and methods should be utilised and how the outcome should be communicated throughout an organisation. This thesis was performed at a case company and is based on qualitative and quantitative data collected through interviews, focus groups, observations, and a survey at the case company. The findings of this thesis suggest a prioritisation model which can be used to support informed and structured decisions for prioritisation. The proposed model is adopting a weighted multi-attribute approach for evaluation and prioritisation of non-conformities and integrates elements from both Pareto- and FMEA analysis, which aid the realisation of more effective and strategic quality improvements. With this thesis, we hope to illuminate the potential benefits that can be achieved by acknowledging and prioritising nonconformities from field returns while more research on the area is still needed.