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Sustainable supplier selection for Satrec Vitalait Milk Company in Senegal using the novel grey relational analysis method

Souleymane Diba (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Naiming Xie (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China)

Grey Systems: Theory and Application

ISSN: 2043-9377

Article publication date: 20 June 2019

Issue publication date: 20 June 2019




The purpose of this paper is to evaluate, analyse and select the best suppliers for Satrec Vitalait Milk Company, operating in Senegal, based on criteria obtained from economic, environmental and social dimensions of sustainable supply chain management, through the application of Deng’s grey relational analysis (GRA) model, absolute GRA model (ADGRA) and a novel second synthetic GRA (SSGRA) model, combined with one decision making under the uncertainty-based model, namely, the Hurwicz criteria.


The research adopts a new synthetic GRA model and highlights its reliability on small sample gathered from four senior experts of the company who administered a total number of 28 specialists operating in four departments of the company, through the employment of a self-administered questionnaire designed based on criteria identified from the literature that were refined via a Q-sort model.


The outcomes of the research methodology designated that all the selected five suppliers present a degree of attaining sustainability due to the fact that supplying unprocessed milk does not require the use of polluting methods for stocking and transportation. The undertaken study specifies that all the socio-environmental criteria play a crucial role in shaping the sustainability level of Satrec Vitalait’s suppliers and demonstrates the accuracy of the results obtained through the second synthetic degree of grey relation analysis for ranking the suppliers. Supplier 2 was found to be the best supplier for the company and, as result, a model for other suppliers to mimic.

Research limitations/implications

Future researchers can replicate the GRA-based supply chain model proposed in the current study in different environments especially in the context of green supply chain. Also, in future the SSGRA model, while using the bidirectional ADGRA instead of the conventional ADGRA, should also be tested, especially when the data sequences associated with different supply chain parameters have inconsistent directions. Also, comparative analysis of SSGRA-based results with that of modern statistical methods like structural equation modelling can also be used for future explorations. Furthermore, the current study is built upon the data associated with the Satrec Vitalait Milk Company (Senegal); therefore, the findings should be generalised with caution.


The study can be seen as a first-stepping stone for gauging and selecting the best sustainable supplier for Satrec Vitalait using grey system theory. For purpose of attaining the research goal, the SSGRA was exploited as an innovative experimental approach to estimate relationships between criteria with regard to the sustainability level of the company’s suppliers. Under this scope, relationships between criteria themselves and their goal were depicted by Deng’s degree of GRA and AGRA, respectively. The research is innovative by means of the framework of its methodology and data analysis.



This study is part of the first author’s Master thesis written under the supervision of Professor Naiming Xie. The authors want to thank the Grey Systems Society of Pakistan (GSSP, GreySys Foundation) for their technical guidance and support, and extend their thanks to the managers of Satrec Vitalait, for their contribution to our research and for providing us the necessary information regarding their company.


Diba, S. and Xie, N. (2019), "Sustainable supplier selection for Satrec Vitalait Milk Company in Senegal using the novel grey relational analysis method", Grey Systems: Theory and Application, Vol. 9 No. 3, pp. 262-294.



Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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