INTELLIGENT DECISION SUPPORT SYSTEM FOR SELECTION OF QUALITY TOOLS AND TECHNIQUES VIA CATEGORIZATION INPUT FUNCTION OUTPUT DEFINITION MODEL

Abstract

Vast numbers of Quality Tools and Techniques (QTT) for problem-solving and improvement activities give a significant challenge to determine the suitable QTT. The chosen QTT affect the effectiveness of improvement activity and misused of QTT can slow down the improvement process and flawed the conclusion. Hence, the use of QTT is found as one of the critical success factors in the quality improvement. The overall purpose of this study was to develop the web-based intelligent decision support system for selection of quality tools and techniques (QTT) based on the CIFOD model. This study presents a CIFOD model that was developed from the attributes of QTT's characteristic. In this study, 97 attributes of QTT's characteristic were classified into 5 main groups and 16 sub-groups. CIFOD is the acronym of those 5 main groups namely Categorization, Input, Function, Output dan Definition. Interdependence multivariate analysis was adopted to analyze the attributes and the CF A through AMOS shows the classification of the attributes into CIFOD model was fit to the data sample. 45 common QTTs were selected to map with the attributes that obtained from expert opinion and the study found that the QTT were able to define through the attributes. The web-based IDSS namely eCIFOD was produced by translating the QTT mapping into the SQL Union and SQL Inner Join. PHP scripts were developed to run the system and uploaded to online after the Black Box Testing show good result. A single case with a single unit of the case study research strategy was used to conduct the UAT. The result shows the eCIFOD able to list out 11 QTis that needed by the improvement team and the level of user acceptance level for the eCIFOD exhibit the positive feedback. Finally, the finding of this study provided contributions to the theoretical implication and the managerial implication. In the theoretical implication, the findings contribute to the literature of QTT's body of knowledge in term of various viewpoints on the attributes of QTT's characteristic to define the QTT and the development of QTT classification namely CIFOD model. For the managerial implication, the findings contribute to the decision support of QTT selection for practical application of problem­solving and quality improvement activities.