I en globaliserad och högt konkurrensutsatt värld så behöver företag och organisationer ständigt arbeta för att bli bättre och mer effektiva. Ett av de mest erkända förbättringsprogrammen är Six sigma som med grund i data arbetar mot mindre variation och bättre resultat i processer. Hur fungerar detta i miljöer där personalfrågor och människan är i centrum? Hur kan HR funktionen i ett tillverkningsbolag använda sig av Six sigma metodik i arbetet mot en lägre personalomsättning? Hög personalomsättning är något som i många fall är problematiskt, då det hindrar en kontinuerlig utveckling i arbetet och kräver stora utbildningskostnader. Finns det sätt att förstå personalomsättning med nya digitaliserade data, och hur kan kvalitetsverktyg användas för att minska personalomsättningen? Studien undersöker ett urval av de mest använda Six sigma verktygen och hur dessa kan användas i arbete med hög personalomsättning bland nyanställd personal. Med en metod som utgår från DMAIC modellen så bedöms utvalda kvalitetsverktyg. Parallellt så redovisas orsaker och lärdomar kring personalomsättning inom industrin. Genom intervjuer och datainsamling med organisationens chefer så framkommer indikationer på vilka parametrar som driver personalomsättningen. Studiens resultat visar på att det fungerar väl att applicera Six sigma verktyg på HR problem, även om det finns vissa begränsningar i användandet av statistiska verktyg. Resultatet mynnar ut i en sammanställning av de mest effektiva verktygen för detta problemområde och ett förslag påmodell som chefer och rekryterare kan använda i förbättringsarbeten / In a global market with high competition the need for companies to improve and streamline their processes increase. One of the most well-known improvement programs is Six sigma, that has its background in data analyses to improve variance in a process. How does it work in an HR environment where most problems are human and behavior based? How can an HR department in a manufacturing company use common Six sigma methodology in its work towards lower employee turnover? High employee turnover is problematic for many companies, and it affects the continuity of the organization and result in costly education. Is there a way to understand employee turnover with new digital accessible data, and how can you use quality tools to improve the area? The study explores a choice of the most used six sigma tools and sees how these can be used in the work against high employee turnover specifically for new employees. The method is based on the DMAIC model and rate the chosen quality tools along with reflections to learn and understand what drives employee turnover in this industry. Through interviews and datacollection with the managers of the organizations some indicators appear that could show us the causes of the turnover. The result shows that six sigma tools are effective in problem solving even in HR related problems, even if it does show some restrictions compared to regular production problem types.The study delivers a list of the most effective tools and a model for recruiters and managers to use when working with future HR problems.

Text in English / Staff turnover affects employees, employers and their clients. When counsellors leave a particular employment context, relationships have to be modified or terminated, and in some instances, clients have to start counselling afresh. This study focused on testing whether self-esteem and burnout can be predictors of voluntary turnover. Relating the three variables of self-esteem, employee burnout, and employee turnover intentions among counsellors reveals a dearth in literature and research. This study utilised qualitative and quantitative data. A sample of 200 counsellors received questionnaires to collect quantitative data and 162 questionnaires were analysed. The Intentions-To-Stay Questionnaire by Roodt (2004) measured turnover intention, the Maslach Burnout Inventory (Maslach & Jackson, 1981) measured Burnout and the Rosenberg Self-esteem scale (Rosenberg, 1965) measured self-esteem. Qualitative data collection utilised the focus group interview. 23 participants for one group interview were selected using Convenience sampling. Significant relationship was found between self-esteem and age, gender, marital status, duration of work, academic qualification and job status. Results indicated that the emotional exhaustion subscale has statistically significant relationships with age, academic qualification, marital status and employment status. The depersonalization subscale indicated a statistically significant relationship with age, gender, marital status and employment status. The personal accomplishment subscale indicated statistically significant relationship with gender, academic qualification, marital status, duration of employment and employment status. A statistically significant relationship was found between turnover intention and age, marital status, highest academic qualification of participant and employment status. Further, a statistically significant relationship was found between turnover intention and burnout but not between turnover intention and self-esteem. This analysis confirmed burnout as a predictor variable and self-esteem as not. Study findings revealed three categories of reasons influencing turnover among counsellors; namely diversification reasons, growth and development reasons, and remunerative reasons. Counsellor narratives of burnout revealed three major themes namely: exhaustion, work settings, characteristics, and sources of stress. Workplace and institutional interventions were categorized into two namely developmental and normative. An integrated model of clinical supervision for responding to burnout and turnover intention was presented as part of the discussion. / Psychology / D.Phil. (Psychology)