@conference {Khaldi2017,
	title = {Prediction of supplier performance: A novel DEA-ANFIS based approach},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {The focus of this paper is on investigating the feasibility of using ANFIS combined with DEA for supplier{\textquoteright}s post-evaluation. The proposed framework aims at modeling performance measurement, and forecasting of a selected hospital{\textquoteright}s drug suppliers. Even though it is broadly employed as a benchmarking tool to evaluate DMUs efficiency, DEA can hardly be used to predict the performance of unseen DMUs. For this reason, ANFIS model has been integrated to DEA due to its nonlinear mapping, strong generalization capabilities and pattern prediction functionalities. DEA based BCC model is used to evaluate the efficiency scores of a set of suppliers, then ANFIS intervenes to learn DEA patterns and to forecast the performance of new suppliers. The results of this research highlight the prediction power of the proposed model in a new scope. They present it as an efficient benchmarking tool and a promising decision support system applied at the operational level. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090416},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028459962\&doi=10.1145\%2f3090354.3090416\&partnerID=40\&md5=da01d9720d77f4831205623526bfc9d9},
	author = {Khaldi, R. and Chiheb, R. and El Afia, A. and Akaaboune, A. and Faizi, R.}
}
