A novel hesitant-fuzzy-based group decision approach for outsourcing risk. (1st December 2021) - Yazdani, Morteza; Mohammed, Ahmed; Bai, Chunguang; Labib, Ashraf
Highlights: A decision analysis model for outsourcing risk measurement is proposed. We combined failure mode effect analysis and combined compromise solution tools. The decision-making method is implemented in a chemical industry. Our computation outcomes have been validated by sensitivity analysis. Hesitant fuzzy approach enhances the degree of accuracy. Abstract: Outsourcing is recognized as a strategic instrument for companies to move towards diversified operation advantages and efficient global market. However, outsourcing agreements may fail due to insufficient risk consideration and evaluation. This requires an effective risk evaluation approach that necessitates an entire comprehension of the system, its requirements and dimensions. This paper proposes a novel fuzzy group multiple-criteria decision-making approach through integrating triangular fuzzy hesitant sets (TFHS), Failure Mode and Effect Analysis (FMEA) and combined compromise solution (CoCoSo) algorithm. This approach (F-FMEA-CoCoSo, henceforth) analyzes and evaluates the possible risks level of alternative outsourcing providers. It hereby provides measurable information for managers according to the linguistic opinions of industrial experts. Utilization of hesitant fuzzy variables allows decision making participants to state their opinion more precisely. A case study of an Iranian chemical company is used to exemplify the applicability and suitability of the proposed F-FMEA-CoCoSo approach. The model reflects an analytical approach while experts are experiencing considerable uncertainty in risky conditions of outsourcing operations. CoCoSo contains an integrated approach of compromise solutions and its utilization enables investigators to assure results reliability which is proven via the sensitivity analysis. Through this study, we found that risk evaluation of outsourcing providers must consider four key-factors: multi-experts, multi-criteria, multi-uncertainties and measurability.