An Analysis of the Effectiveness of a Multi-Disciplinary Decision Support System on System-Level Decision Making

• Main Author: Seletos, Troy Mario
• Format: Others
• Published: BYU ScholarsArchive 2016
• Subjects: decision support system; product design generator; System DSS; multi-disciplinary decision making; engineering systems; Mechanical Engineering
• Online Access: https://scholarsarchive.byu.edu/etd/5844; https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6843&context=etd

Decisions Support Systems (DSSs) are used to enhance decision maker speed and effectiveness. However, without a view of an entire system, any decision may have unanticipated effects such as sub-optimal outcomes. The purpose of this research is to show that with a system-level analysis, more informed decisions can be made that take into account a larger system or greater number of dimensions or objectives. This research also explores the benefits of using a DSS over analysis of unprocessed data and the effectiveness of integrating a product design generator (PDG) with a business DSS, creating a system DSS, where system-level effects can be analyzed. These are connected using software which allows them to be interactive, and dynamically updating. After this DSS was developed a variation was also made and decision makers evaluated these tools to identify how they performed in comparison to each other. In one variation, aspects of the tool were split up, guiding the decision maker through the analysis while the other did not. Using survey questions and recording decision makers' actions, it was found that decision makers are significantly faster and came to better conclusions when using the DSS over unprocessed data. However, it was also seen that the difference between the two variants of the System DSS tests was insignificant. This suggests that the limits in potential interactions in the one variant of a system DSS did not substantially reduce the ability of a decision maker to explore and make good design decisions. Overall this research showed that having a system-level tool is better than the unprocessed data, and that more extreme differences in a DSS are required for improved comparisons to establish which visualizations and elements are most effective in a System DSS. Future effort should be made to completely isolate different portions of the System DSS and see how well users are able to make decisions with it compared to the full system analysis.