A Hybrid Model for Supply Chain Risk Management Based on Five-dimensional Sustainability Approach in Telecommunication Industry

Document Type : Original Article

Authors

Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

Abstract

Sustainability of supply chain risk management is one of the main competitive advantages of every organization for long-standing. There are several models in the research literature to manage sustainability risks of the supply chain. Considering that critical risks have the highest impact and have the largest share of risk management resources, they need to be identified using special techniques to make risk management more accurate and more reliable. In this paper, a new three-phase model is presented to supply chain sustainability risks management. This model includes the failure mode and effects analysis phase for identifying and assessing all risks and classification them, fuzzy VIKOR phase for ranking critical risks, and management phase to deal with critical risks. The categorization of risks was conducted according to a new five-dimensional approach to sustainable progress, including environmental, economic, social, technical, and organizational aspects on various sectors of the supply chain. The telecommunication industry of Iran is considered to show the model performance. The results indicated that consideration of the fuzzy VIKOR phase is necessary in order to accurately assess critical risks because of the priority of critical risks is not correctly identified through Failure mode and effects analysis due to the shortcomings of this method and cause errors. It was also found that the technical risks initiated by the organization are the most dangerous risk that threatens the sustainability of the supply chain.

Keywords

Main Subjects


  1. H., Goodarzian Urimi. M., Shokoohi Rad. A., “Risk Assessment of Gasoline Storage Unit of National Iranian Oil Product Distribution Company using PHAST Software”, International Journal of Engineering, Transactions A: Basics Vol. 34, No. 04, (2021), 763-768, doi: 10.5829/IJE.2021.34.04A.02.
  2. F., Hosseini-Nasab. H., Fakhrzad. M. B., “A Multi-objective Sustainable Medicine Supply Chain Network Design using a Novel Hybrid Multi-Objective Metaheuristic Algorithm”, International Journal of Engineering, Transactions A: Basics Vol. 33, No. 10, (2020) 1986-1995, doi: 10.5829/IJE.2020.33.10A.17.
  3. F. Omondi. B., “Leveraging Digital Approaches for Transparency in Sustainable Supply Chains: A Conceptual Paper”, Sustainability, Vol. 12, No. 12, (2020) 6129, doi: 10.3390/su12156129.
  4. S. K., Kumar. P., Barua. M. K., “Prioritizing the responses to manage risks in green supply chain: An Indian plastic manufacturer perspective” Sustainable Production and Consumption, Vol. 01, No, 03, (2015) 67-86, doi: 10.1016/j.spc.2015.05.002.
  5. J. R., Boyer. S. L., “Developing a consensus definition of supply chain management: a qualitative study”, International Journal of Physical Distribution and Logistics Management, Vol. 39, No. 8, (2009) 690-711, doi: 10.1108/09600030910996323.
  6. A., Sharma. P. C., “Social sustainability in supply chain decisions: Indian manufacturers. Environment”, development and Sustainability, Vol. 20, No. 4, (2018) 1707-1721, doi: 10.1007/s10668-017-9961-5.
  7. S., Yoopetch. C., Lai, P., “Mapping the Knowledge Base of Sustainable Supply Chain Management: A Bibliometric Literature Review”, Sustainability, Vol. 12, No. 18, (2020) 7348, doi: 10.3390/su12187348.
  8. Akbari-Kasgari. M., Khademi-Zare. H., Fakhrzad. M.B., M. Hajiaghaei-Keshteli. M., Honarvar. M., “A Closed-loop Supply Chain Network Design Problem in Copper Industry”, International Journal of Engineering, Transactions A: Basics 33, No. 10, (2020) 2008-2015, doi: 10.5829/IJE.2020.33.10A.19.
  9. S. A., “Assessing the rigor of case study research in supply chain management”, Supply Chain Management: An International Journal, Vol. 13, No. 2, (2008) 128-137, doi: 10.1108/13598540810860967.
  10. M., Papadopoulos. T., “Supply chain sustainability: A risk management approach”, International Journal of Production Economics, Vol. 171, No. 4, (2016) 455-470, doi: 10.1016/j.ijpe.2015.06.032.
  11. Z., Sarkis. J., “Investigating the relationship of sustainable supply chain management with corporate financial performance”, International Journal of Productivity and Performance Management, Vol. 62, No. 8, (2013) 871-888, doi: 10.1108/IJPPM-03-2013-0033.
  12. S., Maihami. R, “Optimizing the sustainable decisions in a multi-echelon closed-loop supply chain of the manufacturing/remanufacturing products with a competitive environment. Environment”, Development and Sustainability, Vol. 22, No 1, (2019) 1-27, doi: 10.1007/s10668-019-00491-5.
  13. H., Ray. S., Wang. Y.,” Special Issue of Production and Operations Management: Global Supply Chain Risk Management”, Production and Operations Management, Vol. 20, No. 3, (2011) 489-489, doi: 10.1111/j.1937-5956.2011.01242. x.
  14. O., Matsukawa. H., Nakashima. K., “Supply chain risk management”, International Journal of Production Economics, Vol. 139, No.1, (2012) 1-2, doi: 10.1016/j.ijpe.2012.06.015.
  15. G., Tabasi. S., Jami. M., Danesh Narooei. D., “Evaluation of the impacting factors on sustainable mining development, using the Grey-DEMATEL approach”, International Journal of Engineering, Transactions A: Basics Vol. 32, No. 10, (2019) 1497-1505, doi: 10.5829/IJE.2019.32.10A.20.
  16. W. R., “The sustainability handbook: The complete management guide to achieving social, economic, and environment”, Routledge, London, (2008).
  17. D. R., “Corporate survival: The critical importance of sustainability risk management”, Iuniverse Inc, (2005).
  18. Business for social responsibility (BSR), “Perspectives on information management in sustainable supply chains”, Available at http://www.bsr.org/reports/BSR_Info-Management-Supply-Chains1.pdf, (2007).
  19. W. K., Nalluri. V., Ma. S., Lin. M., Lin. C. T., “An Exploration of the Critical Risk Factors in Sustainable Telecom Services: An Analysis of Indian Telecom Industries”, Sustainability, Vol. 13, No. 2, (2021) 445, doi: 10.3390/su13020445.
  20. R. D., “Value and risk”, Journal of Banking and Finance, Vol. 26, No. 2, (2002) 297-301, doi: 10.1016/S0378-4266(01)00223-0.
  21. Ravi Sankar. N., Prabhu. B.S., “Modified approach for prioritization of failures in a system failure mode and effects analysis”, International Journal of Quality & Reliability Management, Vol. 18, No. 3, (2001) 324-336, doi: 10.1108/02656710110383737.
  22. R. K., Kumar. D., Kumar. P., “Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling”, International Journal of Quality and Reliability Management, Vol. 22, No. 9, (2005) 986-1004, doi: 10.1108/02656710510625248.
  23. K. H., Cheng. C. H., “A risk assessment methodology using intuitionistic fuzzy set in FMEA”, International Journal of Systems Science, Vol. 41, No. 12, (2010) 1457-1471, doi: 10.1080/00207720903353633.
  24. K. S., Wang. Y. M., Poon. G. K. K., Yang. J. B., “Failure mode and effects analysis by data envelopment analysis”, Decision Support Systems, Vol. 48, No. 1, (2009) 246-256, doi: 10.1016/j.dss.2009.08.005.
  25. Hadi-Vencheh. A., Aghajan. M., “Failure mode and effects analysis: A fuzzy group MCDM approach”, Journal of Soft Computing and Applications, Vol. 1, No. 14, (2013), doi: 10.5899/2013/jsca-00016.
  26. H. C., You. J. X., You. X. Y., Shan. M. M., “A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method”, Applied Soft Computing, Vol. 28, No. C, (2015) 579-588, doi: 10.1016/j.asoc.2014.11.036.
  27. F., Ishizaka. A., Gamberini. R., Rimini. B., Messori. M., “FlowSort-GDSS–A novel group multi-criteria decision support system for sorting problems with application to FMEA”, Expert Systems with Applications, Vol. 42, No. 17, (2015) 6342-6349, doi: 10.1016/j.eswa.2015.04.028.
  28. W., Ming. X., Wu. Z., Zhu. B., “A rough TOPSIS approach for failure mode and effects analysis in uncertain environments”, Quality and Reliability Engineering International, Vol. 30, No. 4, (2014) 473-486, doi: doi.org/10.1002/qre.1500.
  29. S. M., Razmi. J., Ghorbani. M., “Classify purchasing items based on risk and profitability attributes; using MCDM and FMEA techniques”, Research Journal of International Studies, Vol. 1, No. 21, (2011) 80-85, doi: 10.13140/2.1.3457.0882.
  30. B., Salimi. M., Charkhchian. M.,” A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process”, The International Journal of Advanced Manufacturing Technology, Vol. 77, No. 1, (2015) 357-368, doi: 10.1007/s00170-014-6466-3.
  31. A., Mousavi. S. F., Yazdankhah. A., “Group decision making process for supplier selection with VIKOR under fuzzy environment”, Expert Systems with Applications, Vol. 37, No. 1, (2010) 24-30, doi: 10.1016/j.eswa.2009.04.063.
  32. Yücenur. G. N., Demirel. N. Ç., “Group decision making process for insurance company selection problem with extended VIKOR method under fuzzy environment”, Expert Systems with Applications, Vol. 39, No. 3, (2012) 3702-3707, doi: 10.1016/j.eswa.2011.09.065.
  33. R. J., Vinodh. S., “Application of fuzzy VIKOR and environmental impact analysis for material selection of an automotive component”, Materials and Design, Vol.37, (2012) 478-486, doi: 10.1016/j.matdes.2012.01.022.
  34. C. H., Wang. F. K., Tzeng. G. H., “The best vendor selection for conducting the recycled material based on a hybrid MCDM model combining DANP with VIKOR”, Resources conservation and recycling, Vol. 66, (2012) 95-111, doi: 10.1016/j.resconrec.2012.02.009.
  35. H. Y., Chen. J. K., Chen. I. S., Zhuo. H. H., “Ranking universities based on performance evaluation by a hybrid MCDM model”, Measurement, Vol. 45, No. 5, 856-880, doi: 10.1016/j.measurement.2012.02.009.
  36. X. Y., You. J. X., Liu. H. C., Zhen. L., “Group multi-criteria supplier selection using an extended VIKOR method with interval 2-tuple linguistic information”, Expert Systems with Applications, Vol. 42, No. 4, (2015) 1906-1916, doi: 10.1016/j.eswa.2014.10.004.
  37. M., Mavi, R. K., Santos-Arteaga. F. J., Doust. E. R., “An extended VIKOR method using stochastic data and subjective judgments”, Computers & Industrial Engineering, Vol. 97, (2016) 240-247, doi: 10.1016/j.cie.2016.05.013. ‏
  38. J. Y., Yuan, F. F., Wan. S. P.,"Extended VIKOR method for multiple criteria decision-making with linguistic hesitant fuzzy information”, Computers & Industrial Engineering, Vol. 112, (2017) 305-319, doi: 10.1016/j.cie.2017.07.025.
  39. H. C., Liu. L., Lin. Q. L., “Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology”, IEEE Transactions on Reliability, Vol. 62, No. 1, (2013) 23-36, doi: 10.1109/TR.2013.2241251.
  40. H. C., You. J. X., Ding. X. F., Su. Q., “Improving risk evaluation in FMEA with a hybrid multiple criteria decision-making method”, International Journal of Quality and Reliability Management, Vol. 32, No. 7, (2015) 763-782, doi: 10.1108/IJQRM-10-2013-0169.
  41. S. H. R., Hashemi. S. S., Mohammadi. Y., Zavadskas. E. K., “Fuzzy belief structure based VIKOR method: an application for ranking delay causes of Tehran metro system by FMEA criteria”, Transport, Vol. 31, No. 1, (2016) 108-118, doi: 10.3846/16484142.2016.1133454.
  42. L. E., Liu. H. C., Quan. M. Y., “Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments”, Computers & Industrial Engineering, Vol. 102, (2016) 175-185, doi: 10.1016/j.cie.2016.11.003.
  43. H. C., You. J. X., You. X. Y., “Evaluating the risk of healthcare failure modes using interval 2-tuple hybrid weighted distance measure”, Computers & Industrial Engineering, Vol. 78, (2014) 249-258, doi: 10.1016/j.cie.2014.07.018.
  44. Safari H., Faraji, Z., Majidian. S., “Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR”, Journal of Intelligent Manufacturing, Vol. 27, No. 2, (2016) 475-486, doi: 10.1007/s10845-014-0880-0.
  45. K. S., Chan. A., Yang, J. B., “Development of a fuzzy FMEA based product design system”, The International Journal of Advanced Manufacturing Technology, Vol. 36, No. 7, (2008) 633-649, 10.1007/s00170-006-0898-3.
  46. M. J., Salimi. A., Yousefi. S., “Identifying and managing failures in stone processing industry using cost-based FMEA”, The International Journal of Advanced Manufacturing Technology, Vol. 88, No. 9, (2017) 3329-3342, doi: 10.1007/s00170-016-9019-0.
  47. K. S., Wang. Y. M., Poon. G. K. K., Yang. J. B., “Failure mode and effects analysis using a group-based evidential reasoning approach”, Computers and Operations Research, Vol. 36, No. 6, (2009) 1768-1779, doi: 10.1016/j.cor.2008.05.002.
  48. E., Sandborn. P., Humphrey. D., “Assessing the value of a lead-free solder control plan using cost-based FMEA”, Microelectronics Reliability, Vol. 55, No. 6, (2015) 969-979, doi: 10.1016/j.microrel.2015.02.022.
  49. Ekmekçio─člu. M., Can Kutlu. A., “A fuzzy hybrid approach for fuzzy process FMEA: An application to a spindle manufacturing process”, International Journal of Computational Intelligence Systems, Vol. 5, No. 4, (2012) 611-626, doi: 10.1080/18756891.2012.718104.
  50. F., Rahmani. D., “Sustainability risk management in the supply chain of telecommunication companies: A case study”, Journal of Cleaner Production, (2018), 203, 53-67, doi: 10.1016/j.jclepro.2018.08.174.