Collaborative Decision Support Systems (CDSSs) have been increasingly used to organize collaborative transport networks to develop sustainable freight transport. CDSSs involve two main components: a collaborative planning algorithm for matching transport orders to trucking capacities and an interactive front-end (e.g., websites and Email systems) for dispatching freight matches and communication among collaborators. The literature has mostly focused on developing and testing advanced algorithms using historical data. However, these studies test only one component and ignore the front-end that greatly affects the CDSS performance in daily practice. Overall, the literature lacks studies that evaluate and improve the CDSSs based on the feedback of end-users with a pilot test. Though poor data availability and quality are well-known issues in the logistics industry, no previous studies have discussed how to deal with the data issues in the real applications of CDSSs. To bridge these gaps, this study reports our experiences with testing an early version of a CDSS for automated freight matching in Denmark. The test results revealed some issues related to the ease of the CDSS usage and validity of the identified matches. A methodology is proposed to analyze the test results and to inspire ideas for improvement. The analyses showed that low data quality (e.g., missing values) is a significant barrier to developing effective front-end and valid matching. Due to the low data quality, automated matching can be more effective if carriers set their matching preferences through access to the CDSS. Besides, the front-end Email system should be developed in a way that reduces the number of emails, enables snap judgment, and visualizes the match details. Finally, some improvement suggestions are proposed and evaluated.
- Data quality
- Decision support systems