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Impact analysis of characteristics in product development: Change in product property with respect to component generations
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Scania has developed a unique modular product system which is an important successfactor, creating exibility and lies at the heart of their business model. R&Duse product and vehicle product properties to describe the product key factors. Theseproduct properties are both used during the development of new features and products,and also utilized by the project oce to estimate the total contribution of a project.Scania want to develop a new method to understand and be able to track and comparethe projects eect over time and also predict future vehicle improvements.

In this thesis, we investigate how to quantify the impact on vehicle product propertiesand predict component improvements, based on data sources that have not beenutilized for these purposes before. The impact objective is ultimately to increase the understandingof the development process of heavy vehicles and the aim for this projectwas to provide statistical methods that can be used for investigative and predictivepurposes. First, with analysis of variance we statistically veried and quantied differencesin a product property between comparable vehicle populations with respectto component generations. Then, Random Forest and Articial Neural Networks wereimplemented to predict future eect on product property with respect to componentimprovements. We could see a dierence of approximately 10 % between the comparablecomponents of interest, which was more than the expected dierence. Theexpectations are based on performance measurements from a test environment. Theimplemented Random Forest model was not able to predict future eect based on theseperformance measures. Articial Neural Networks was able to capture structures fromthe test environment and its predictive performance and reliability was, under the givencircumstances, relatively good.

Place, publisher, year, edition, pages
2017. , 63 p.
Keyword [en]
Statistics, big data
National Category
Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-136911OAI: oai:DiVA.org:umu-136911DiVA: diva2:1114759
External cooperation
Scania CV AB
Educational program
Master of Science in Engineering and Management
Presentation
2017-06-01, MA156, Umeå Universitet, Umeå, 21:08 (English)
Supervisors
Examiners
Available from: 2017-06-26 Created: 2017-06-25 Last updated: 2017-06-26Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf