Supplier selection in Telecom supply chain management: a Fuzzy-Rasch based COPRAS-G method
In the past decade, global competition are forcing firms to increase their level of outsourcing for raw or semi-finished products and building long term relationship with their supply chain partners. The objective is to present a wide-ranging decision making technique for ranking supplier alternatives in view of the effect of selected criteria. A proposed method is developed aiming the usage of Fuzzy-Rasch model applying five point Likert scale for criteria weight and Grey based COmplex PRoportional ASsessment (COPRAS-G) method for evaluating and ranking the potential alternatives, as per criteria. The applicability of the induced methodology for supplier selection problem in all environments is shown through a case study in telecommunication sector. A sensitivity analysis is performed based on changing weight patterns of criteria to show the stability in ranking result of the proposed approach. Further, a comparative analysis between the ranking results of proposed method done with existing grey multi-attribute decision-making methods viz. VIKOR-G, ARAS-G and TOPSIS-G using spearman’s correlation coefficient for checking the reliability of the ranking result.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Aghdaie, M. H.; Hashemkhani Zolfani, S.; Zavadskas, E. K. 2013b. Decision making in machine tool selection: an integrated approach with SWARA and COPRAS-G methods, Inzinerine Ekonomika – Engineering Economics 24(1): 5–17.
Badri Ahmadi, H.; Hashemi Petrudi, S.; Wang, X. 2016. Integrating sustainability into supplier selection with analytical hierarchy process and improved grey relational analysis: a case of telecom industry, The international Journal of Advanced Manufacturing Technology 90(9–12): 1–15.
Beikkhakhian, Y.; Javanmardi, M.; Karbasian, M.; Khayambashi, B. 2015. The application of ISM model in evaluating agile supplier selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods, Expert systems with Applications 42: 6224–6236. https://doi.org/10.1016/j.eswa.2015.02.035
Belevdere, S.; Morton, N. 2010. Applying the Rasch analysis in health care is increasing and is applied for variable reasons in mobility instruments, Journal of Clinical Epidemiology 63(12): 1287–1297. https://doi.org/10.1016/j.jclinepi.2010.02.012
Buyukozkan, G.; Cifci, G. 2012. A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers, Expert Systems with Applications 39(3): 3000–3011.
Chatterjee, K.; Kar, S. 2013a. A hybrid MCDM approach for selection of financial institution in supply chain risk management, in IEEE International conference on Fuzzy Systems (FUZZ IEEE) 2013, 08 October, Hyderabad, India, 1–7.
Chatterjee, K.; Kar, S. 2013b. An Induced Fuzzy Rasch-VIKOR model for Warehouse Location evaluation under Risky Supply chain, in Proceeding of 5th International Conference on Pattern Recognition and Machine Intelligence (PREMI 2013), Kolkata, India, 10–14 December. Lecture notes in computer science 8251: 714–719.
Chatterjee, K.; Kar, S. 2016. Multi-criteria analysis of supply chain risk management using interval valued fuzzy TOPSIS, OPSEARCH 53(3): 474–499. https://doi.org/10.1007/s12597-015-0241-6
Chatterjee, P.; Chatterjee, R. 2012. Supplier evaluation in manufacturing environment using compromise ranking method using grey interval numbers, International Journal of Industrial Engineering Computations 3: 393–402. https://doi.org/10.5267/j.ijiec.2011.12.007
Chen, M.; Tzeng, G. 2004. Combining grey relation and TOPSIS concepts for selecting an expatriate host country, Mathematical and computer modelling 40: 1473–1490. https://doi.org/10.1016/j.mcm.2005.01.006
Deng, J. 1998. Introduction to grey system theory, The Journal of Grey Theory 1(1): 1–24.
Dou, Y.; Zhu, Q.; Sarkis, J. 2014. Evaluating green supplier development programs with a grey analytical network process-based methodology, European Journal of Operational Research 233(1): 420–431. https://doi.org/10.1016/j.ejor.2013.03.004
Dong, Q.; Cooper, O. 2016. An orders-of-magnitude AHP supply chain risk assessment framework, International Journal of Production Economics 182: 144–156. https://doi.org/10.1016/j.ijpe.2016.08.021
Ecer, F. 2014. A hybrid banking websites quality evaluation model using AHP and COPRAS-G: a Turkey case, Technological and Economic Development of Economy 20(4): 758–782. https://doi.org/10.3846/20294913.2014.915596
Felice, F.; Deldoost, M.; Faizollahi, M.; Petrillo, A. 2015. Performance measurement model for supplier selection based on AHP, International Journal of Engineering Business Management 7(17): 1–13. https://doi.org/10.5772/61702
Freeman, J.; Chen, T. 2015. Green supplier selection using an AHP-Entropy-TOPSIS framework, Supply chain management 20(3): 327–340. https://doi.org/10.1108/SCM-04-2014-0142
Golmohammadi, D.; Parast, M. 2012. Developing a grey-based decision-making model for supplier selection, International Journal of Production Economics 137: 191–200. https://doi.org/10.1016/j.ijpe.2012.01.025
Hashemi, S.; Karimi, A.; Tavana, M. 2015. An integrated green supplier selection approach with analytic network process and improved Grey relational analysis, International Journal of Production Economics 159: 178–191. https://doi.org/10.1016/j.ijpe.2014.09.027
Hashemkhani Zolfani, S.; Chen, I.; Rezaeiniya, N.; Tamošaitienė, J. 2012. A hybrid MCDM model encompassing AHP and COPRAS-G methods for selecting company supplier in Iran, Technological and Economic Development of Economy 18(3): 529–543. https://doi.org/10.3846/20294913.2012.709472
Ho, H.; Chiang, H.; Pham, D.; Fann, W.; Nagai, M. 2016. A learning outcomes assessment analysis based on the mathematical modeling of Rasch GSP curve, GSM and MSM, Studies in Engineering and Technology 3(1): 109–123. https://doi.org/10.11114/set.v3i1.1766
Huang, J.; Peng, K. 2011. Using Rasch model and GRA to assess international tourist hotel industry performance, Journal of Grey system 23(3): 299–310.
Huang, J.; Peng, K. 2012. Fuzzy Rasch model in TOPSIS: a new approach for generating fuzzy numbers to assess the competitiveness of the tourism industries in Asian countries, Tourism Management 33: 456–465. https://doi.org/10.1016/j.tourman.2011.05.006
India Risk Survey 2014 (IRS). 2014. Report-FICCI 2014 [online], [cited 06 May 2016]. Available from Internet: http://ficci.in/SEDocument/20276/India-Risk-Survey-2014.pdf
India Risk Survey 2015 (IRS). 2015. Report-FICCI 2015 [online], [cited 06 May 2016]. Available from Internet: http://ficci.in/SEDocument/20328/India-Risk-Survey-2015.pdf
India Risk Survey 2016 (IRS). 2016. Report-FICCI 2016 [online], [cited 06 May 2016]. Available from Internet: http://ficci.in/SEDocument/20348/India-Risk-Survey-2016.pdf
Kar, S.; Chatterjee, K. 2014. Supplier selection under ranking interval type-2 fuzzy sets, in S. Satapathy, B. Biswal, S. Udgata, J. Mandal (Eds.). Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, Volume 327 of the series Advances in Intelligent Systems and computing, 9–17. https://doi.org/10.1007/978-3-319-11933-5_2
Keshavarz Ghorabaee, M.; Amiri, M.; Sadaghiani, J.; Goodarzi, G. 2014. Multiple criteria group decision making for supplier selection based on CORPAS method with interval type-2 fuzzy sets, International Journal of Advanced Manufacturing Technology 75(5): 1115–1130. https://doi.org/10.1007/s00170-014-6142-7
Keshavarz Ghorabaee, M.; Zavadskas, E.; Turskis, A. 2016a. Extended EDAS method for fuzzy multicriteria decision-making: an application to supplier selection, International journal of computers communications & control 11(3): 358–371. https://doi.org/10.15837/ijccc.2016.3.2557
Keshavarz Ghorabaee, M.; Zavadskas, E.; Turskis, Z.; Antucheviciene, J. 2016b. A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making, Economic computation and economic cybernetics studies and research/Academy of Economic studies 50(3): 25–44 [online], [cited 18 April 2016]. Available from Internet: http://www.ecocyb.ase.ro
Li, S.; Murat, A.; Huang, W. 2009. Selection of contract suppliers under price and demand uncertainty in a dynamic market, European Journal of Operational Research 198(3): 830–847. https://doi.org/10.1016/j.ejor.2008.09.038
Liou, J.; Tamošaitienė, J.; Zavadskas, E.; Tzeng, G. 2015. New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management, International Journal of Production Research 54(1): 114–134. https://doi.org/10.1080/00207543.2015.1010747
Meena, P.; Sarmah, S. 2016. Supplier selection and demand allocation under supply disruption risks, The International Journal of Advanced Manufacturing Technology 83(1): 265–274. https://doi.org/10.1007/s00170-015-7520-5
Mehrjerdi, Y. 2014. Strategic system selection with linguistic preferences and grey information using MCDM, Applied Soft Computing 18: 323–337. https://doi.org/10.1016/j.asoc.2013.09.013
Nguyen, H.; Dawal, S.; Nukman, Y.; Aoyama, H. 2014. A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes, Expert Systems with Applications 41: 3078–3090. https://doi.org/10.1016/j.eswa.2013.10.039
Nurnadiah, Z.; Lazim, A. 2012. A new weight of interval type-2 fuzzy Rasch model, Applied Mathematical Sciences 6(75): 3705–3722.
Ogundile, O. 2013. Fraud analysis in Nigeria’s mobile telecommunication industry, International journal of scientific and research publications 3(2): 1–4 [online], [cited 15 April 2016]. Available from Internet: www.ijsrp.org
Onut, S.; Kara, S.; Isik, E. 2009. Long-term supplier selection using a combined fuzzy MCDM approach: a case study for a telecommunication company, Expert Systems with Applications 36(2): 3887–3895. https://doi.org/10.1016/j.eswa.2008.02.045
Oztaysi, B. 2014. A decision model for information technology selection using AHP integrated TOPSIS-Grey: the case of content management systems, Knowledge-Based Systems 70-C: 44–54. https://doi.org/10.1016/j.knosys.2014.02.010
Pancholi, N.; Bhatt, M. 2016. Multicriteria FMECA based decision-making for aluminium wire process rolling mill through COPRAS-G, Journal of Quality and Reliability Engineering vol. 2016, Article ID 8421916. 8 p.
Paul, S. 2015. Supplier selection for managing supply risk in supply chain: a fuzzy approach, The Inter-national Journal of Advanced Manufacturing Technology 79(1): 657–664. https://doi.org/10.1007/s00170-015-6867-y
Peng, K.; Huang, J.; Wu, W. 2013. Rasch model in data envelopment analysis: application in the international tourist hotel industry, Journal of Operational Research Society 64: 938–944. https://doi.org/10.1057/jors.2012.97
Pramod, V.; Banwet, D. 2010. Analytic network process analysis of an Indian Telecommunication service supply chain: a case study, Service Science 2(4): 281–293. https://doi.org/10.1287/serv.2.4.281
Pramod, V.; Banwet, D.; Sharma, P. 2016. Understanding the barriers of service supply chain management: an exploratory case study from Indian telecom industry, OPSEARCH 53(2): 358–374. https://doi.org/10.1007/s12597-015-0234-5
Reza, M.; Moghadam, S.; Afsar, A.; Sohrabi, B. 2008. Inventory Lot-sizing with supplier selection using hybrid intelligent algorithm, Applied soft computing 8: 1523–1529. https://doi.org/10.1016/j.asoc.2007.11.001
Roshandel, J.; Nargesi, S.; Shirkouhi, L. 2013. Evaluating and selecting the supplier in detergent production industry using hierarchical fuzzy TOPSIS, Applied Mathematical Modelling 37: 10170–10181. https://doi.org/10.1016/j.apm.2013.05.043
Shemshadi, A.; Shirazi, H.; Toreihi, M.; Tarokh, M. 2011. A fuzzy VIKOR method for Supplier selection based on entropy measure for objective weighting, Expert Systems with Applications 38(10): 12160–12167. https://doi.org/10.1016/j.eswa.2011.03.027
Tan, K.; Wong, W.; Chung, L. 2016. Information and knowledge leakage in supply chain, Information Systems Frontiers 18(3): 621–638. https://doi.org/10.1007/s10796-015-9553-6
Tavana, M.; Momeni, E.; Rezainiya, N.; Mirhedayatian, S.; Rezaeiniya, H. 2013. A novel hybrid social media platform selection model using fuzzy ANP and COPRAS-G, Expert Systems with Applications 40: 5694–5702. https://doi.org/10.1016/j.eswa.2013.05.015
Turskis, Z., Zavadskas, E. 2010. A novel method for multiple criteria analysis: Grey additive ratio analysis (ARAS-G) method, INFORMATICA 21(4): 597–610.
Viswanadham, N.; Samvedi, A. 2013. Supplier selection based on supply chain ecosystem, performance and risk criteria, International Journal of Production Research 51(21): 6484–6498. https://doi.org/10.1080/00207543.2013.825056
Yu, S.; Wu, B. 2009. Fuzzy item response model: a new approach to generate membership function to score psychological measurement, Quality and Quantity 43: 381–390. https://doi.org/10.1007/s11135-007-9114-2
Zavadskas, E.; Kaklauskas, A.; Sarka, V. 1994. The new method for multi-criteria complex proportional assessment of projects, Technological and Economic Development of Economy 1(3): 131–139.
Zavadskas, E.; Kaklauskas, A.; Turskis, Z.; Tamošaitienė, J. 2008. Selection of the effective dwelling house walls by applying attributes values determined at intervals, Journal of Civil Engineering and Management 14(2): 85–93. https://doi.org/10.3846/1392-3730.2008.14.3
Zavadskas, E.; Turskis, Z.; Kaklauskas, A.; Tamošaitienė, J. 2009a. Selection of the effective dwelling house walls by applying attributes values determined at intervals, Journal of Civil Engineering and Management 14(2): 85–93. https://doi.org/10.3846/1392-3730.2008.14.3
Zavadskas, E.; Kaklauskas, A.; Turskis, Z.; Tamošaitienė, J. 2009b. Multi-Attribute decision-making model by applying Grey numbers, INFORMATICA 20(2): 305–320.
Zhang, J.; Wu, D.; Olsen, D. 2005. The method of grey related analysis to multiple attribute decision-making problems with interval numbers, Mathematical and Computer Modelling 42: 991–998. https://doi.org/10.1016/j.mcm.2005.03.003