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Visualization of Vehicle Usage Based on Position Data for Root-Cause Analysis: A Case Study in Scania CV AB
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media, Human-Computer Interaction.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Root cause analysis (RCA) is a process in Scania carried out to understand the root cause of vehicle breakdowns. It is commonly done by studying vehicle warranty claims and failure reports, identifying patterns that are correlated to the breakdowns, and then analyzing the root cause based on those findings. Vehicle usage is believed to be one of the factors that may contribute towards the breakdowns, but the data on vehicle usage is not commonly utilized in RCA.

This thesis investigates a way to help RCA process by introducing a dataset of vehicle usage based on position data gathered in project FUMA (Fleet telematics big data analytics for vehicle Usage Modeling and Analysis). A user-centered design process of a visualization tool which presents FUMA data for people working in RCA process was carried out. Interviews were conducted to gain insights about the RCA process and generate design ideas. PACT framework was used to organize the ideas, and Use Cases were developed to project a conceptual scenario. A low fidelity prototype was developed as design artifact for the visualization, and a formative test was done to validate the design and gather feedback for future prototyping iterations.

In each design phase, more insights about how visualization of vehicle usage should be used in RCA were obtained. Based on this study, the prototype design showed a promising start in visualizing vehicle usage for RCA purpose. Improvement on data presentation, however, still needs to be addressed to reach the level of practicality required in RCA.

Abstract [sv]

Root cause analysis (RCA) är en process på Scania som används för att förstå rotorsaken till fordons behov av reparation.Oftast studeras fordonets försäkringsrapporter och felrapporter, för att identifiera och analysera mönster som motsvarar de olika behoven för reparation. Fordonsanvändningen tros vara en av de faktorer som bidrar till reparationsbehoven, men data angående detta används sällan i RCA.

Denna rapport undersöker hur RCA-processen kan dra nytta av positionsdata som samlats in i projekt FUMA (Fleet telematics big data analytics for vehicle Usage Modeling and Analysis). En användarcentrerad designmetodik har använts för att ta fram ett visualiseringsverktyg som presenterar FUMA-data för personer som deltar i RCA-processen. Intervjuer har genomförts för att samla insikter om RCA-processen och för att generera designidéer. PACT-ramverket användes sedan för att organisera idéerna, och användningssituationer togs fram för att skapa ett konceptuellt scenario. En low-fidelity prototyp togs fraför personer som deltar i RCA-processenm som en designartefakt för visualiseringen och ett utvecklande test genomfördes för att validera designen och samla in feedback för framtida iterationer av prototyping.

Under varje design-fas, samlades mer insikter om hur visualiseringen av fordonsanvändning skulle kunna användas för RCA in. Baserat på detta, visade design-prototypen en lovande start för visualisering av fordonsanvändning i RCA. Förbättringar på hur data presenteras måste dock genomföras, så att rätt genomförbarhet för RCA uppnås.

Place, publisher, year, edition, pages
2018. , p. 51
Keywords [en]
HCI, UX, visualization, Scania, design, interaction
National Category
Human Aspects of ICT Interaction Technologies
Identifiers
URN: urn:nbn:se:uu:diva-355909OAI: oai:DiVA.org:uu-355909DiVA, id: diva2:1231571
External cooperation
Scania CV AB
Educational program
Master Programme in Human-Computer Interaction
Presentation
2018-06-04, Ekonomikum, B105, Uppsala, 11:15 (English)
Supervisors
Examiners
Available from: 2018-07-23 Created: 2018-07-08 Last updated: 2018-07-23Bibliographically approved

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