A Study of Anticipatory Failure Determination (AFD) based on Scenario Analysis Methods

Abstract

Theory of Inventive Problem Solving (TRIZ) is a methodological algorithm that helps solve complex problems by generating original ideas. The Anticipatory Failure Determination (AFD) method is one of the problem-solving methodologies within TRIZ. It is a creative way to solve problems when you cannot find the cause of a problem or observe the situation in which it occurs. This study aimed to investigate the effectiveness of the Anticipatory Failure Determination (AFD) method, which is one of the problem solving methodologies within the Theory of Inventive Problem Solving (TRIZ). TRIZ is a methodological algorithm designed to address complex problems by stimulating the generation of original ideas. It involves a shift in thinking: instead of looking for the cause of a problem (i.e., "why does it happen?"), it looks for ideas to solve the problem (i.e., "how can I make it happen?") by formulating a hypothesis and testing the hypothesis. In doing so, they explore and leverage resources that exist within or adjacent to the system to support the failure hypothesis. A resource is anything that is used to fulfill the ideal final result. In this study, scenario analysis, a methodology synonymous with foresight, is integrated into the AFD framework to enhance problem-solving capabilities. By incorporating scenario analysis, the formulation of failure hypotheses crucial to the AFD process is facilitated. Additionally, a case study demonstrating the application of AFD augmented by scenario analysis is presented, showcasing its efficacy in resolving complex problems. This integration aims to proactively mitigate future risks and prevent potential damages. Through this research, a comprehensive understanding of how scenario analysis enriches the AFD methodology is provided, empowering practitioners to pre-emptively address emerging challenges. Ultimately, the goal is to fortify problem-solving capacities and foster resilience in the face of evolving complexities.