A fuzzy rule based inference system for early debt collection

Nowadays, unpaid invoices and unpaid credits are becoming more and more common. Large amounts of data regarding these debts are collected and stored by debt collection agencies. Early debt collection processes aim at collecting payments from creditors or debtors before the legal procedure starts. In order to be successful and be able to collect maximum debts, collection agencies need to use their human resources efficiently and communicate with the customers via the most convenient channel that leads to minimum costs. However, achieving these goals need processing, analyzing and evaluating customer data and inferring the right actions instantaneously. In this study, fuzzy inference based intelligent systems are used to empower early debt collection processes using the principles of data science. In the paper, an early debt collection system composed of three different Fuzzy Inference Systems (FIS), one for credit debts, one for credit card debts, and one for invoices, is developed. These systems use different inputs such as amount of loan, wealth of debtor, part history of debtor, amount of other debts, active customer since, credit limit, and criticality to determine the output possibility of repaying the debt. This output is later used to determine the most convenient communication channel and communication activity profile

An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM