Implementing fuzzy rank function model for a new supply chain risk management

• Published in: The Journal of supercomputing Vol. 73; no. 8; pp. 3586 - 3602
• Main Authors: Mostafaeipour, Ali, Qolipour, Mojtaba, Eslami, Hamidreza
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2017; Springer Nature B.V
• Subjects: Accuracy; Business competition; Compilers; Computer Science; Disasters; Interpreters; Natural disasters; Organizations; Politics; Processor Architectures; Programming Languages; Risk analysis; Risk assessment; Risk management; Stability; Suppliers; Supply chains; Uncertainty; Supply chain; Rank function; Fuzzy; Risk management
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-017-1960-7

Intensive business competition has encouraged enterprises to focus more on their core activities to outsource the management of supply chain. Issues such as political crises, demand fluctuations, strategy variations, financial instability and natural disasters are the factors that can contribute to emergence of uncertainty and risk in the supply chain. Effective management of such risks is essential for reducing the vulnerability of the supply chain. Supply risk management is the most basic concept of the supply chain risk management; therefore, this study examines this subject by developing a mathematical model consisting of a set of equations to quantify typical risk such as delayed delivery, substandard quality, natural disasters and financial risk of the supplier. This model is designed to select and allocate orders to suppliers to minimize the supply risk imposed by the mentioned risk factors. A fuzzy-order function is implemented to turn this crisp model into a fuzzy supply chain risk assessment model. The defuzzified solutions of the fuzzy model show significant improvements over the result of crisp model. Uncertainty in supply chain and intense competitiveness between organizations and managers create different challenges. To effectively manage these challenges, new management approaches are used to strengthen and improve the effectiveness of organizations. The results show that the fuzzy model yields better results compared to the deterministic model. Moreover, it is demonstrated that the ability of the model for activities risk control is improved. The work presented addresses the same challenging problems that others have tried to address, but the methodology presented in this paper is more efficient in terms of execution performance and accuracy.