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 analysis of supply chain related graduate programmes in Europe

This is the post-print version of the Article. The official published version of the Article can be accessed from the links below. Copyright @ 2013 Emerald Group Publishing Limited.Purpose – Motivated by a lack of studies in graduate level supply chain education, this research aims to explore trends in supply chain-related graduate programmes in Europe and to propose a framework for designing such programmes. Design/methodology/approach – The authors determine “knowledge” and “skills” areas applicable to supply chain management (SCM) education and analyse supply chain-related graduate programmes published by the European Logistics Association in 2004. They revisit the same programmes in 2011 to determine the recent situation and the trends. The authors use cluster analysis to reveal the similarities and differences among these programmes. Findings – The authors find two distinct clusters: focused and diversified. Focused programmes offer modules in knowledge and skills areas apart from SCM at a negligible level and place more emphasis on SCM in 2011 when compared to 2004. Diversified programmes show a similar increase in the emphasis on SCM with more variety in the knowledge and skills areas. Research limitations/implications – The authors' findings are based on SCM programmes delivered in Europe and over two discrete time periods. Future research should seek to extend this analysis to other continents with larger samples and incorporate the industry perspective to determine the potential gap between what programmes offer and what industry requires. Practical implications – SCM-related graduate programmes continue to redefine themselves. Clustering predominantly serves the universities in re-assessing and re-engineering their programmes, helps prospective graduates in their selection process and assists managers in their recruitment practices. Originality/value – This paper establishes a baseline for assessing SCM-related graduate programmes with respect to the knowledge and skills they offer and introduces a framework that may serve as a starting point for the design and positioning of such programmes

Intuitionistic fuzzy edas method: an application to solid waste disposal site selection

Evaluation based on Distance from Average Solution (EDAS) is a new multicriteria decision making (MCDM) method, which is based on the distances of alternatives from the average scores of attributes. Classical EDAS has been already extended by using ordinary fuzzy sets in case of vague and incomplete data. In this paper, we propose an interval-valued intuitionistic fuzzy EDAS method, which is based on the data belonging to membership, nonmembership, and hesitance degrees. A sensitivity analysis is also given to show how robust decisions are obtained through the proposed intuitionistic fuzzy EDAS. The proposed intuitionistic fuzzy EDAS method is applied to the evaluation of solid waste disposal site selection alternatives. The comparative and sensitivity analyses are also included

A fuzzy multi attribute decision framework with integration of QFD and grey relational analysis

International audienceObjective : This paper proposes a multi attribute decision support model in a supply chain in order to solve complex decision problems. The paper provides a platform to ease decision process through the integration of quality function deployment (QFD) and grey relational analysis (GRA) in demonstrating main supply chain drivers under fuzzy environment. Methodology : The proposed method is important because of several points: First of all, in a supply chain system, evaluation factors are not really independent and must be addressed in relation to the external factors such as customer requirements. Hence, we have applied QFD tool. Second, due to the constant uncertainty in the supply chain environment, fuzziness among the factors has to be considered. So, an interval valued fuzzy model was implemented. Third, to examine the proposed decision system in reality, it was applied in Risk and Uncertain Conditions for Agriculture Production Systems (RUC-APS) project. Contribution : An integrated version of QFD and GRA is presented. It is assumed that QFD can act to measure optimal solutions based on the distance to ideal solutions. In an interval-valued fuzzy environment the enormous volume of computation by Euclidean distance doesn't allow decision makers to obtain the results easily. This drawback is addressed using gray relational analysis. The gray relational coefficient is integrated to the fuzzy QFD to measure the distance of potential solutions from ideal solutions. This integration facilitates decision making process in further problems once big data are available. Results : To obtain the importance degrees of logistic indicators in the supply chain, expert team considered the environmental, social & cultural, and economic factors as external dimension of the QFD. The other dimension of QFD includes supply chain drivers such as quality, environmental management system, supply chain flexibility, corporate social responsibility, transportation service condition, and financial stability. The decision model is solved and the ranking of indicators is achieved. A sensitivity analysis helps to test and check the performance of the decision model

Intuitionistic fuzzy EDAS method: An application to solid waste disposal site selection

Evaluation based on Distance from Average Solution (EDAS) is a new multicriteria decision making (MCDM) method, which is based on the distances of alternatives from the average scores of attributes. Classical EDAS has been already extended by using ordinary fuzzy sets in case of vague and incomplete data. In this paper, we propose an interval-valued intuitionistic fuzzy EDAS method, which is based on the data belonging to membership, nonmembership, and hesitance degrees. A sensitivity analysis is also given to show how robust decisions are obtained through the proposed intuitionistic fuzzy EDAS. The proposed intuitionistic fuzzy EDAS method is applied to the evaluation of solid waste disposal site selection alternatives. The comparative and sensitivity analyses are also included.Sin financiación1.068 JCR (2017) Q4, 199/241 Environmental StudiesUE