Development of dynamic balanced scorecard using case-based reasoning method and adaptive neuro-fuzzy inference system

Abstract

In recent years, selecting the strategies has been recognized as one of the most challenging issues facing senior and strategy managers of companies. In this regard, this article introduced an integrated framework that helps strategy managers to determine the organization's strategy by analyzing the long-term objectives and visions. The proposed methodology is based on developing and enhancing the performance of the balanced scorecard (BSC) by covering its limitations through being combined with system dynamics (SD) simulation, case-based reasoning (CBR) method, and adaptive neuro-fuzzy inference system (ANFIS) model. The SD model is built based on the company's strategy map to predict the future status of the company based on its selected strategies. The CBR method and ANFIS model are also to develop the sensitivity analysis and policy-making stage utilizing learning and human memory application. An Iranian food and beverage company is considered as a real-world case study to demonstrate how the proposed method could work and validate the research methodology's applicability. As the main finding, the model yields appropriate strategies with minor errors to reach the targets defined by managers. According to the proposed dynamic BSC, managers can select their strategies and observe their financial variables' outcomes on the planning horizon. Moreover, they can set future targets for their financial variable to receive strategies for their achievement.