An intelligent strategy map to evaluate improvement projects of auto industry using fuzzy cognitive map and fuzzy slack-based efficiency model

Abstract

Effective selection of improvement projects is a basic step to enhance an organization’s superiority. If improvement projects are not defined and selected properly, the improvement plan is prone to risks. Conventionally, these projects are created using self-assessment processes based on an intelligent strategy map, which is done in terms of functional perspectives of Balanced Scorecard (BSC). However, due to its static behavior, BSC needs to be reviewed under dynamic conditions and based on strategic goals. In this study, to remove this shortcoming, cause, and effect relationships between goals and performance measurements in organizations’ BSC are investigated. This is accomplished by using the Fuzzy Cognitive Map (FCM) method. Having modification capability over time, this FCM method is suitable for grasping changing strategies of organizations and their competitors. Furthermore, based on the BSC, the effectiveness value of each proposed improvement project on the organization’s goals is calculated considering FCM and a hybrid learning algorithm. Then, improvement projects based on the organization’s goals and resource constraints are prioritized using fuzzy Slack-Based Data Envelopment Analysis (SBDEA). Integrating these methods provides a unique decision support system tool for decision makers. Finally, to evaluate the efficiency and accuracy of the proposed approach, a real case study on an automotive-parts supplier is presented along with its results.