On the development and implications of a new probabilistic model in financial and supply chain management

Abstract

The critical nature of probability distributions in modeling real-world phenomena cannot be underestimated, especially in important fields like financial and supply chain management. These areas often grapple with uncertainty and variability, making it essential to accurately represent data through probability distributions for effective decision-making and risk assessment. Emphasizing the vital role of probability distributions in financial and supply chain management, we unveil a new statistical model called the sine cosine very flexible Weibull (SCVF-Weibull) distribution. We undertake a thorough investigation of the mathematical properties linked to the SCVF-Weibull distribution. Moreover, we describe the methodology for parameter estimation and showcase simulation studies that explore a range of parameter value combinations. In addition, we examine two real-world data sets sourced from the fields of financial and supply chain management. Utilizing these data sets, we conduct a comparative analysis of the SCVF-Weibull distribution against other widely recognized distributions commonly employed in data analysis within these domains. Through the application of four statistical evaluation methods, we empirically demonstrate that the SCVF-Weibull distribution outperforms the rival distributions in the analysis of data sets related to the financial and supply chain management.