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Statistical Methods to Control and Predict Quality Performance of Spare Part Operations

Göteborg : Chalmers tekniska högskola, 2018. 65 s. Master thesis. E - Department of Technology Management and Economics, Chalmers University of Technology, Göteborg, Sweden; E2018:062, 2018.
[Examensarbete på avancerad nivå]

With the increasing competition in market, automotive companies are constantly seeking opportunities to obtain competitive advantage. Aftermarket services offer big potential to do this. In order to unveil the potential and turn it into customer satisfaction and profits, the companies need to focus on the quality performance of their operations. One of the ways to achieve better quality performance passes through the better use of statistical methods. At Volvo Group, continuous improvement is a part of the company identity. With the desire of continuous improvement, the purpose of this thesis is set as to explore statistical methods to predict and control spare parts distribution centre operations. By doing so, the delivery fulfilment errors can be hindered to occur before the shipment of the parts and quality performance indicator that measures customer satisfaction can be improved. With this purpose, a suitable theoretical framework was created and the literature review was conducted to better understand the previous research and applications in this field. Synchronously, the operations in Volvo spare parts distribution centres were investigated through observation and interviews. Moreover, the available historical data was unveiled for using it with statistical methods. The analysis was done by exploring applicable statistical methods by considering two different levels of the organization, global and site. As conclusion, it is recognized that the available data has high potential for benefiting various statistical methods in order to provide better insights from the operations and show directions to make improved steering decisions based on facts.

Nyckelord: statistical methods, control charts, predictive modelling, quality performance

Publikationen registrerades 2018-06-08. Den ändrades senast 2018-09-04

CPL ID: 255063

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