This paper aims to improve the ABC analysis method used for inventory management by applying the Pythagorean Fuzzy TODIM approach. ABC analysis is one the well-known and widely used inventory classification techniques which divides inventory items into three categories according to their importance and value. However, the traditional ABC analysis does not consider the imprecision and vagueness of real-world inventory data, which can lead to inaccurate results and poor inventory management decisions. The proposed approach enhances the traditional ABC analysis by incorporating fuzzy numbers to be considered in real-world inventory data. The improved ABC analysis helps companies to optimize inventory levels, reduce costs, improve customer service, and increase overall operational efficiency. To check for the reliability and effectiveness of the developed model under different scenarios sensitivity analysis is conducted. Additionally, the comparative analysis among other existing models further demonstrates the model's accuracy. The model prepared shows that the Pythagorean Fuzzy TODIM approach is superior to the conventional ABC analysis in terms of reliability and dealing with the uncertain inventory data. Overall, this paper provides a novel and effective approach to inventory management and offers valuable insights for practitioners and researchers in the field.

One of the primary concerns on performance measurement is to know how much a particular project cost. However, using traditional method on project-based products often leads to inappropriate results. In this paper, we re-examine this issue by comparing the cost of a power station construction project using ABC versus traditional method. The results of survey show that ABC method is capable of providing better estimates for overhead costs compared with traditional method. In other words, ABC method helps reduce some of the unnecessary overhead cost items and increase on some other cost components. This helps increase the relative efficiency of the system by reducing total cost of project.