Quality Management of Billing‐Relevant Data in Logistics and Supply Chains: A Case Study

Abstract

As the trend toward the digitization of complex business processes continues, the relevance of data quality for corporate success has increased. Especially, in multistep processes where data are created, modified, and transferred between different systems and departments, ensuring high data quality through continuous improvement is a competitive advantage. The interdependencies within multistep processes make troubleshooting more difficult and complex, as is typically the case in supply chains and logistics. At present, research on improving the data quality in complex process chains is relatively limited compared to the vast body of literature in operations research. Therefore, this exploratory study begins with a literature review on the measurement and monitoring of data quality in logistics and supply chains. Based on the findings from literature and the identified total data quality management model, a case study was conducted. As the first measuring approach, a survey was distributed to 148 employees in the central logistics department of a multinational automobile manufacturer to analyze the quality of billing‐relevant data in vehicle logistics. Although both subjective and objective approaches for measuring data quality have been described in the literature, automated techniques for continuous assessment of data quality have only increased in popularity in recent years. There is still potential for further research in the fields of process‐oriented measurement and monitoring that consider the interdependencies between systems and departments involved in multistage logistics processes. In the logistics and supply chain literature, the most common dimensions of data quality that can be measured automatically were accuracy, completeness, consistency, and timeliness. Consistency and accuracy were also found critical in the reference case, which could potentially be the result of unsatisfactory system interfaces, data quality checks, and system landscape. The statements related to the data quality checks, the system landscape, and the understandability dimension were rated quite differently by the different departments. The survey helped identify weaknesses that should be further investigated and improved in the future to ensure continuous process operation and profitability.