Towards Improved Data Quality Management Tools in Logistics

Abstract

In today’s logistics environment, high-quality data is essential for ensuring efficient processes and sustaining competitiveness.
However, missing, erroneous, or duplicate entries in master data often lead to significant business consequences, such as inefficient supply chains, increased operating costs, and poor decision-making.
Existing data screening, cleaning and scoring (DSCS) tools for detecting data errors and thus measuring data quality are often cumbersome to use and are not tailored to the specific needs of logistical master data.
In this paper, we present design knowledge to guide the development of DSCS tools.
We gathered requirements through dedicated workshops and distilled them into a set of actionable design features.
To evaluate our design features, we implemented them in a software prototype, which was tested in a usability and multi-case study. Our contribution in form of design features equips logistics practitioners with concrete guidance for creating and implementing effective DSCS tools in their organizations.