Improvement of Final Demand Data Flow at Polestar, Refining Final Demand Management in the Geometry Assurance Process

Abstract

Geometric variations in components, despite modern manufacturing precision, can detract significantly from a vehicle’s functionality and perceived quality. Geometry assurance is crucial for managing these variations by ensuring robustness and high product quality through assigning favorable tolerances. Moreover, efficient data management plays a significant role in geometry assurance, facilitating the necessary exchange of information across various stages of the product realization loop. This thesis focuses on optimizing the data flow and management of final demand data within the geometry assurance process at Polestar, a Swedish automotive company specializing in the production of electric vehicles. This master’s thesis aimed at mapping the current geometry assurance process at Polestar, and by identifying and mitigating inefficiencies, propose improvements for enhancing data quality and process performance. The scope of the study is limited to the geometry assurance process, focusing specifically on the transformation and management of final demand data throughout the distinct phases of the process. Through a literature study, interviews, a pilot study and the mapping of process activities, the current process was studied in detail. The findings reveal significant inefficiencies in the current data flow and process approach. Data flow challenges were predominantly associated with compromised data integrity, as a result of the manual steps involved in data transformation and the propagation of errors due to lack of integration between software tools. Further, the mapping of the current process outlined an approach that often lacks clarity and a solid ground. The proposed enhancements include the implementation of an API that enables automated data transfer, formatting and storing, eliminating errors associated with manual data exchange across departments and stages. It facilitates communication across three different software programs utilized in different phases of the geometry assurance process, ensuring that the demand data is linked. Through this, data flow efficiency is significantly enhanced and data quality is improved. Further, suggestions intended to optimize the overall performance of the process include implementing standardized work routines and optimizing time management practices. The suggested improvements not only address immediate inefficiencies but also lay a foundation for continuous improvement in data management practices within the company.