Weight & volume data Mapping the requirements of weight and volume master data for logistic processes

Abstract

This thesis explores the requirements and challenges related to weight and volume master data within Volvo Cars Parts Supply and Logistics (PS&L), focusing on its role in supporting efficient logistics operations. Today, the organization experiences discrepancies between weight data provided by Research and Development and op- erational measurements within PS&L, leading to a lack of trust in the accuracy of the source data. Through a structured gap analysis, based on a mapping of how weight data is established and later verified within PS&L, the study examines internal stakeholders, their current challenges and future needs, as well as how weight data is aggregated along the PS&L logistics chain. This analysis investigates how data quality affects logistics processes and what part-level attributes are required to support operations such as picking, packing, and volume optimization. The findings reveal both technical causes, such as manual data entry and lack of validation, and organizational causes, including low data maturity, lack of cross- departmental understanding into part structures, and lack of comprehensive stan- dard for validation. Strengthening master data quality is therefore essential to rebuild trust, increase efficiency, reduce logistics costs and emissions, and enable realistic modeling in applications such as three-dimensional bin packing.