Guidance for goal achievement in knowledge-intensive processes using intuitionistic fuzzy sets

Abstract

Throughout the execution of a knowledge-intensive process (KiP), knowledge workers need to make critical decisions such as skipping a task or canceling a process instance. These decisions significantly impact the efficiency and effectiveness of KiP execution and should, therefore, be made in a well-informed manner. When historical data, such as event logs, is available, it can be leveraged to support knowledge workers in making these decisions. However, KiPs often lack useful historical data, as each KiP instance is unique and hardly repeatable. To address this issue, this paper proposes the novel concept of potential goal achievement, i.e., the extent to which a goal can be achieved at the end of the process, considering the collected (but incomplete) data, to support knowledge workers in efficiently executing KiPs. An approach based on Intuitionistic Fuzzy Sets (IFSs) is introduced to calculate the potential goal achievement without relying on historical data. The use of potential goal achievement in supporting knowledge workers’ decisions is demonstrated, and the effectiveness of the approach is evaluated through simulations. The results demonstrate that modeling and calculating potential goal achievement support knowledge workers in achieving goals more efficiently.