In English

Balancing Powertrain Attributes Using Object-Process Methodology

MARTIN ŠIMÁK
Göteborg : Chalmers tekniska högskola, 2017. 60 s. Examensarbete - Institutionen för bygg- och miljöteknik, Chalmers tekniska högskola; BOMX02-17-89, 2017.
[Examensarbete på avancerad nivå]

This researched studies a specific way of capturing, storing, sharing and accessing highly technical knowledge in a cross-functional context. The first objective of the study is to conclude on capturing highly technical knowledge in a system engineering architecture, the Object-Process Methodology. The second objective is to evaluate accessing and managing the highly technical knowledge with Object-Process Methodology in a cross-functional context. The theory review consists three related works, 3-T framework, Knowledge Query and Request framework, and Knowledge Recommendation framework. The theory serves as base for evaluating representing and sharing the highly technical knowledge with the simplified modeling architecture. The research uses inductive approach. The data collection process involved unstructured interviewing of 15 participants, more than 3 months of observation method within the Volvo Car Corporation social setting, literature review and documents examination. Highly technical knowledge can be represented using Object-Process Methodology. Constructing physical system diagrams allows transformation of real vehicle system into the modeling environment in form of objects, processes and relationships between them. Processes representing systems functions (such as noise or vibration), are connected with relationships to objects’ states, and explaining their behaviour in the system. Application of Object-Process Methodology for accessing the knowledge eliminates multiple processes of knowledge management frameworks review in the theory section. Powertrain attributes can be balanced more efficiently using Object-Process Methodology. Due to the system agility, the project management practice process in the vehicle development process will be empowered.

Nyckelord: Knowledge Management, Knowledge Recommendation, Knowledge Query, Object-Process Methodology, Powertrain Attributes, System Engineering.



Publikationen registrerades 2017-12-04. Den ändrades senast 2017-12-04

CPL ID: 253517

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