Analysis of Integrating a Production Design Generator with a Business Decision Support System for System-Level Decision Making

Troy Seletos,John Salmon

doi:10.3390/systems5010014

Abstract

Decision support systems (DSSs) are used to enhance decision making speed and effectiveness. However, without a view of the entire system, any decision may have unanticipated effects, such as sub-optimal outcomes. This paper explores the benefits of applying a DSS over the analysis of unprocessed data and the effectiveness of integrating a product design generator (PDG) with a business DSS where system-level effects can be analyzed. 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 that were used for testing was insignificant. Overall, this research shows that having a system-level tool is better than the unprocessed data and that large differences in a DSS are required for improvement between them.

Highlights

Decision support systems (DSSs) are software tools that are widely used in industry and are created to assist individuals in making decisions
In the realm of product development, a field more closely related to the research performed in this study, researchers experimented with how decision makers implement new products effectively [9] and discussed creating automated product designs through product design generators (PDGs) [10,11]
The multi-objective system DSS is composed of a product design generator and a business DSS allowing optimization of the business inputs, both of which are defined

Summary

Introduction

Decision support systems (DSSs) are software tools that are widely used in industry and are created to assist individuals in making decisions. Some recent studies have investigated the enabling technology from DSSs for improving data analysis in multi-objective spaces Studies explore topics such as the emotional process of decision making [5], performing dynamic analysis on temporal (time-dependent) data [6], ‘situational awareness’ to better understand the performance of different applications [7] and optimization models for business planning [8]. Research is ongoing into how DSSs increase effectiveness and help with decision making by focusing the DSS on useful information [12] These efforts have resulted in the development of improved systems geared toward many different industries or needs, such as ambulance dispatch or flood warnings in California [13,14]. DSSs facilitate data fusion to support better decision making when time for additional data analysis is unavailable

Methods

Results

Conclusion