Selection of Sustainable Supplier for Medical Centers with Data Envelopment Analysis (DEA) & Multi-Attributed Utility Theory (MAUT) Approaches

Document Type : Research Paper

Authors

1 Department of industrial engineering, Iran University of science and technology, Tehran, Iran

2 School of Industrial Engineering, Damghan University, Damghan, Iran

Abstract

Background and Objectives: The selection of the sustainable supplier is important for any industry. Medical centers are not an exception in this case, and selecting the best sustainable supplier is a major step towards increasing their productivity. This paper, using the Data Envelopment Analysis and then using Multi-Attributed Utility Theory as a backup approach to fix errors, attempts to introduce the most important criteria and sub criteria for selecting the best sustainable supplier of medical equipment among domestic and foreign suppliers.
Methods: After reviewing the previous papers, the 13 most important sub-criteria are extracted based on the 3 social, environmental and economic criteria. At first a Data Envelopment Analysis (DEA) model is used to find the initial ranking for sustainable suppliers. Then the Multi-Attributed Utility Theory (MAUT) is employed as a secondary and backup approach to find the utility function. The data obtained are first examined in DEA and then by MAUT and the results are presented in the form of figures, tables and analytical results in the relevant section.
Findings: Based on the 13 sub-criteria introduced at the end of the two-stage ranking, supplier C is selected as the best sustainable supplier.
Conclusion: For initial ranking, DEA is a good method, but to find the utility function of the ranking, the use of MAUT is an effective method with a high degree of proximity.

Keywords

References

1.             Tadić, D., M. Stefanović, and A. Aleksić, The evaluation and ranking of medical device suppliers by using fuzzy topsis methodology. Journal of Intelligent & Fuzzy Systems, 2014. 27(4): p. 2091-2101.
2.             Galankashi, M.R., et al., Supplier selection for electrical manufacturing companies based on different supply chain strategies. Electrical Engineering, 2013.
3.             Grennan, M. and A. Swanson, Transparency and negotiated prices: The value of information in hospital-supplier bargaining. 2016, National Bureau of Economic Research.
4.             Galankashi, M.R., et al., Assessment of supply chain strategies and analysis on the performance of companies deployed strategy using activity based approach. Sains Humanika, 2013. 64(2).
5.             Memari, A., et al. Scenario-based simulation in production-distribution network under demand uncertainty using ARENA. in 2012 7th International Conference on Computing and Convergence Technology (ICCCT). 2012. IEEE.
6.             Goh, M., Z. Shuya, and R. de Souza. Operational Framework for Healthcare Supplier Selection Under a Fuzzy Multi-Criteria Environment. in 23rd International Symposium on Logistics (ISL 2018) Big Data Enabled Supply Chain Innovations. 2018.
7.             Dargi, A., et al., Supplier selection: A fuzzy-ANP approach. Procedia Computer Science, 2014. 31: p. 691-700.
8.             Bevilacqua, M., F. Ciarapica, and G. Giacchetta, A fuzzy-QFD approach to supplier selection. Journal of Purchasing and Supply Management, 2006. 12(1): p. 14-27.
9.             Chan, F.T., et al., Global supplier selection: a fuzzy-AHP approach. International Journal of production research, 2008. 46(14): p. 3825-3857.
10.          De Boer, L., E. Labro, and P. Morlacchi, A review of methods supporting supplier selection. European journal of purchasing & supply management, 2001. 7(2): p. 75-89.
11.          Sedady, F. and M.A. Beheshtinia, A novel MCDM model for prioritizing the renewable power plants’ construction. Management of Environmental Quality: An International Journal, 2019. 30(2): p. 383-399.
12.          Beil, D.R., Supplier selection. Wiley encyclopedia of operations research and management science, 2010.
13.          Frej, E.A., et al., A multicriteria decision model for supplier selection in a food industry based on FITradeoff method. Mathematical Problems in Engineering, 2017. 2017.
14.          Govindan, K., et al., Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 2015. 98: p. 66-83.
15.          Zimmer, K., M. Fröhling, and F. Schultmann, Sustainable supplier management–a review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 2016. 54(5): p. 1412-1442.
16.          Torkzad, A. and M.A. Beheshtinia, Evaluating and prioritizing hospital service quality. International journal of health care quality assurance, 2019. 32(2): p. 332-346.
17.          Ho, W., X. Xu, and P.K. Dey, Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of operational research, 2010. 202(1): p. 16-24.
18.          Lupo, T., A fuzzy framework to evaluate service quality in the healthcare industry: An empirical case of public hospital service evaluation in Sicily. Applied Soft Computing, 2016. 40: p. 468-478.
19.          Singh, A. and A. Prasher, Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application. Total Quality Management & Business Excellence, 2019. 30(3-4): p. 284-300.
20.          Samani, M.R.G. and S.-M. Hosseini-Motlagh, An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 2018: p. 1-50.
21.          Asadi, R., F. Semnani, and P. Shadpour, Factors Influencing Prioritization of Hospital Services for Outsourcing: A Fuzzy Analytic Hierarchy Process Ranking Model. International Journal of Hospital Research, 2017. 6(2): p. 97-103.
22.          Asadi, R. and P. Shadpour, Designing the Hybrid Model of Balanced Scorecard and Analysis of Hierarchical Process for Evaluation of the Outsourced Services Suppliers in Supply Chain of Teaching Hospital. International Journal of Hospital Research, 2017. 6(4): p. 49-62.
23.          Bhattacharya, A., J. Geraghty, and P. Young, Supplier selection paradigm: An integrated hierarchical QFD methodology under multiple-criteria environment. Applied Soft Computing, 2010. 10(4): p. 1013-1027.
24.          Gold, S. and A. Awasthi, Sustainable global supplier selection extended towards sustainability risks from (1+ n) th tier suppliers using fuzzy AHP based approach. Ifac-Papersonline, 2015. 48(3): p. 966-971.
25.          Beheshtinia, M. and V. Nemati-Abozar, A Novel Hybrid Fuzzy Multi-Criteria Decision-Making Model for Supplier Selection Problem (A Case Study in Advertising industry). Journal of Industrial and Systems Engineering, 2017. 9(4): p. 65-79.
26.          Beheshtinia, M.A. and M. Ahangareian Abhari, A New Hybrid Decision Making Method for Selecting Roller Concrete Road Pavement Technology Transfer Method. International Journal of Transportation Engineering, 2018. 5(3): p. 229-242.
27.          Sanayei, A., S.F. Mousavi, and A. Yazdankhah, Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Systems with Applications, 2010. 37(1): p. 24-30.
28.          Chang, T.-H., Fuzzy VIKOR method: a case study of the hospital service evaluation in Taiwan. Information Sciences, 2014. 271: p. 196-212.
29.          Hamid, M., et al., Operating room scheduling by considering the decision-making styles of surgical team members: a comprehensive approach. Computers & Operations Research, 2019. 108: p. 166-181.
30.          Gul, M., et al., Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis. European Journal of Industrial Engineering, 2016. 10(2): p. 196-223.
31.          Beheshtinia, M.A. and S. Omidi, A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes, 2017. 46(8): p. 1386-1407.
32.          Charnes, A., W.W. Cooper, and E. Rhodes, A Data Envelopment Analysis Approach to Evaluation of the Program Follow through Experiment in US Public School Education. 1978, Carnegie-Mellon Univ Pittsburgh Pa Management Sciences Research Group.
33.          Braglia, M. and A. Petroni, A quality assurance-oriented methodology for handling trade-offs in supplier selection. International Journal of Physical Distribution & Logistics Management, 2000. 30(2): p. 96-112.
34.          Shafaghat, T., et al., Efficiency Determination of Hospitals of Shiraz University of Medical Sciences using Simple and Super Efficiency DEA Models. International Journal of Hospital Research, 2017. 6(3): p. 55-68.
35.          Al-Refaie, A., et al., Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simulation Modelling Practice and Theory, 2014. 41: p. 59-72.
36.          Tseng, F.-M., Y.-J. Chiu, and J.-S. Chen, Measuring business performance in the high-tech manufacturing industry: A case study of Taiwan's large-sized TFT-LCD panel companies. Omega, 2009. 37(3): p. 686-697.
37.          Puri, J. and S.P. Yadav, A fuzzy DEA model with undesirable fuzzy outputs and its application to the banking sector in India. Expert Systems with Applications, 2014. 41(14): p. 6419-6432.
38.          Fallahpour, A., et al., An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Neural Computing and Applications, 2016. 27(3): p. 707-725.
39.          Hasan, M.A., R. Shankar, and J. Sarkis, Supplier selection in an agile manufacturing environment using data envelopment analysis and analytical network process. International Journal of Logistics Systems and Management, 2008. 4(5): p. 523-550.
40.          Kailiponi, P., Analyzing evacuation decisions using multi-attribute utility theory (MAUT). Procedia Engineering, 2010. 3: p. 163-174.
41.          Shyur, H.-J. and H.-S. Shih, A hybrid MCDM model for strategic vendor selection. Mathematical and computer modelling, 2006. 44(7-8): p. 749-761.
42.          Ghayebloo, S., et al., Developing a bi-objective model of the closed-loop supply chain network with green supplier selection and disassembly of products: the impact of parts reliability and product greenness on the recovery network. Journal of Manufacturing Systems, 2015. 36: p. 76-86.
43.          Andersen, P. and N.C. Petersen, A procedure for ranking efficient units in data envelopment analysis. Management science, 1993. 39(10): p. 1261-1264.
44.          Sueyoshi, T., DEA non-parametric ranking test and index measurement: slack-adjusted DEA and an application to Japanese agriculture cooperatives. Omega, 1999. 27(3): p. 315-326.
45.          Thrall, R.M., Duality, classification and slacks in DEA. Annals of Operations Research, 1996. 66(2): p. 109-138.
46.          Zhu, J., Robustness of the efficient DMUs in data envelopment analysis. European Journal of operational research, 1996. 90(3): p. 451-460.
47.          Dula, J.H. and B. Hickman, Effects of excluding the column being scored from the DEA envelopment LP technology matrix. Journal of the Operational Research Society, 1997. 48(10): p. 1001-1012.
48.          Seiford, L.M. and J. Zhu, Infeasibility of super-efficiency data envelopment analysis models. INFOR: Information Systems and Operational Research, 1999. 37(2): p. 174-187.
49.          Mehrabian, S., M.R. Alirezaee, and G.R. Jahanshahloo, A complete efficiency ranking of decision making units in data envelopment analysis. Computational optimization and applications, 1999. 14(2): p. 261-266.
50.          Hashimoto, A., A ranked voting system using a DEA/AR exclusion model: A note. European Journal of Operational Research, 1997. 97(3): p. 600-604.
51.          Jacquet-Lagreze, E. and J. Siskos, Assessing a set of additive utility functions for multicriteria decision-making, the UTA method. European journal of operational research, 1982. 10(2): p. 151-164.
International Journal of Hospital Research
Volume 7, Issue 1
February 2018
Pages 82-96

Files

Share

How to cite

Statistics