The number of items having perishable attributes is increasing as science and technology improve and rising customer expectations, affecting a wide range of enterprises. Because of its unique characteristics, such as short life cycle goods, variable demand, limited forecasting, a higher level of impulsive buying, high market competitiveness, and global sourcing, this study focuses on the rapid changes in fashions in the garment industry and the risks that apparel industry owners must face. For examining the behavior and links of the fast-fashion clothing business, a systematic process with supply chain stages is recommended. The Bayesian Belief Network approach is used to analyze the risks associated with all of these goods' supply chain processes and determine the expected value of losses and their likelihood. For successful strategic decision-making and decision-making, many firm scenarios have been designed. The prioritized levels among the challenges that the apparel industry would confront when implementing the entire SCM are shown in this study using recognized mathematical techniques. Risk variables are prioritized using the Bayesian Belief Network (BBN), which uses conditional probability criteria to rank them.