Applying machine learning to key performance indicators

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/250254
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Type: Examensarbete för masterexamen
Master Thesis
Title: Applying machine learning to key performance indicators
Authors: Thorström, Marcus
Abstract: Background Making predictions on Key Performance Indicators (KPI) requires statistical knowledge, and knowledge about the underlying entity. This means that a measurement designer needs to do manual work to define and deploy the KPIs. As the use of machine learning has become increasingly popular, computing power has become cheaper and more accessible, we can replace manual assessments with automated algorithms. Using the predictive power of machine learning to predict KPIs is a natural step in this direction. Objective This thesis investigates three different KPIs in two different domains; it explores how to apply machine learning in predictions of these KPIs. The KPIs are defect inflow and defect backlog of a single product at Ericsson AB and the status level of parameters used in car projects at Volvo Car Corporation (VCC). Method The method is divided into six research cycles where all three KPIs are investigated using different aspects and methods. The two main methods used is a linear regression approach and a rolling time frame. The linear regression method is applied two times to different aspects of the status KPI at VCC. The rolling time frame is applied to all three KPIs investigated in the remaining four research cycles. Result The result shows a relative error of 12% when applying the linear regression approach to the status KPI from VCC and 24% when predicting each status level by itself using the linear regression approach. The rolling time frame showed that the best prediction for predicting one week ahead is to use the previous value as this gives an error of 1%, both when predicting the average status and each status level by itself.The defect inflow predictions showed an error of 19% when applying a KNN algorithm to the rolling time frame.The defect backlog yielded an error of below 1% when using the previous value as a prediction. Conclusion The inflow predictions was the only predictions that proved better then previous attempts in literature, but as this was only applied on a single product in a single company this is not generalizable. It does provide a new way of predicting the defect inow not previously seen before. The result from the linear prediction at VCC reviled a way of working which was desirable for the organization as the linear reporting was an ideal goal to strive for.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/250254
Collection:Examensarbeten för masterexamen // Master Theses



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