Unveiling key drivers of supply chain sustainability in the telecom sector: An information systems perspective

Document Type : Research Paper

Author

Faculty of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

The telecommunications industry plays a crucial role in global connectivity and communication, and as sustainability concerns become increasingly important, it is imperative for telecom companies to prioritize sustainable practices throughout their supply chains. Information systems have emerged as powerful tools for enhancing supply chain sustainability in this industry. This study utilizes the VIKOR method to analyze the factors that significantly impact information systems on supply chain sustainability in the telecom sector. The analysis demonstrates that real-time data visibility and inventory management are the most influential factors in improving supply chain sustainability in the telecom industry. These factors enable companies to effectively track and optimize their inventory levels, minimize waste, and enhance resource utilization.

Keywords

[1] P. Ahi and C. Searcy, A comparative literature analysis of definitions for green and sustainable supply chain management, J. Clean. Prod. 52 (2013), 329–341.
[2] C. Bai and J. Sarkis, A supply chain transparency and sustainability technology appraisal model for blockchain technology, Int. J. Prod. Res. 58 (2020), no. 7, 2142–2162.
[3] M. Basheer, M. Siam, A. Awn, and S. Hassan, Exploring the role of TQM and supply chain practices for firm supply performance in the presence of information technology capabilities and supply chain technology adoption: A case of textile firms in Pakistan, Uncertain Supply Chain Manag. 7 (2019), no. 2, 275–288.
[4] C.R. Carter and D.S. Rogers, A framework of sustainable supply chain management: Moving toward new theory, Int. J. Phys. Distrib. Logistics Manag. 38 (2008), no. 5, 360–387.
[5] I.J. Chen and A. Paulraj, Understanding supply chain management: Critical research and a theoretical framework, Int. J. Prod. Res. 42 (2004), no. 1, 131–163.
[6] S. Chopra and P. Meindl, Supply Chain Management: Strategy, Planning, and Operation, Pearson Prentice Hall, 2007.
[7] M.J. Eskandari, M. Miri, M. Shirani and H.S. Sangchouli, Sustainable supply chain management and logistics, Manag. Admin. Sci. Rev. 3 (2014), no. 4, 665–667.
[8] K. Ferdows and A. De Meyer, Lasting improvements in manufacturing performance: in search of a new theory, J. Oper. Manag. 9 (1990), no. 2, 168–184.
[9] A. Gunasekaran, N. Subramanian, and T. Papadopoulos, Information technology for competitive advantage within logistics and supply chains: A review, Transport. Res. Part E: Logistics Transport. Rev. 99 (2017), 14–33.
[10] E. Hassini, C. Surti, and C. Searcy, A literature review and a case study of sustainable supply chains with a focus on metrics, Int. J. Prod. Econ. 140 (2012), no. 1, 69–82.
[11] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi and S. Razipour GhalehJough, Analytical hierarchy process (AHP) in guzzy environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[12] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi and S. Razipour GhalehJough, The criteria importance through inter-criteria correlation (CRITIC) in uncertainty environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[13] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi and S. Razipour GhalehJough, Foundations of decision, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[14] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi and S. Razipour GhalehJough, The multi-objective optimization ratio analysis (MOORA) in uncertainty environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[15] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi and S. Razipour GhalehJough, The measuring attractiveness by a categorical based evaluation technique (MACBETH) in uncertainty environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[16] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi, and S. Razipour GhalehJough, Weight determination methods in fuzzy environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[17] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi, and S. Razipour GhalehJough, Technique for order preferences by similarity to ideal solutions (TOPSIS) in uncertainty environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Computational Intell. Springer, Cham. 1121 (2023).
[18] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi, and S. Razipour GhalehJough, Fuzzy introductory concepts, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[19] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi, and S. Razipour GhalehJough, Simple additive weighting (SAW) method in fuzzy environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[20] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi, and S. Razipour GhalehJough, Elimination choice translating reality (ELECTRE) in uncertainty environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[21] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi, and S. Razipour GhalehJough, Multi attributive border approximation area comparison (MABAC) in uncertainty environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[22] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi, and S. Razipour GhalehJough, The complex proportional assessment (COPRAS) in uncertainty environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Comput. Intell. Springer, Cham. 1121 (2023).
[23] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi, and S. Razipour GhalehJough, VIKOR method in uncertainty environment, In: Fuzzy decision analysis: multi attribute decision making approach, Stud. Computational Intell. Springer, Cham. 1121 (2023).
[24] F. Hosseinzadeh Lotfi, T. Allahviranloo, W. Pedrycz, M. Shahriari, H. Sharafi, and S. Razipour GhalehJough, Non-compensatory methods in uncertainty environment, Fuzzy decision analysis: multi attribute decision making approach, Stud. Computational Intell. Springer, Cham. 1121 (2023).
[25] P.H. Ketikidis, S.C.L. Koh, N. Dimitriadis, A. Gunasekaran, and M. Kehajova, The use of information systems for logistics and supply chain management in South East Europe: Current status and future direction, Omega 36 (2008), no. 4, 592–599.
[26] P. Kumar, Information technology: Roles, advantages and disadvantages, Int. J. Adv. Res. Comput. Sci. Software Eng. 4 (2014), no. 6, 1020–1024.
[27] C. Li, Y. Xu, and H. Li, An empirical study of dynamic customer relationship management, J. Retail. Consum. Serv. 12 (2005), no. 6, 431–441.
[28] H. Mahmoudi, M. Sharifi, M.R. Shahriari, and M.A. Shafiee, Solving a reverse logistic model for multilevel supply chain using genetic algorithm, Int. J. Ind. Math. 12 (2020), no. 2, 177–188.
[29] C.L. Martins and M.V. Pato, Supply chain sustainability: A tertiary literature review, J. Clean. Prod. 225 (2019), 995–1016.
[30] V. Mohagheghi, S.M. Mousavi, B. Vahdani, and M.R. Shahriari, R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach, Neural Comput. Appl. 28 (2017), 3869–3888.
[31] R.K. Mudgal, R. Shankar, P. Talib, and T. Raj, Modelling the barriers of green supply chain practices: an Indian perspective, Int. J. Logistics Syst. Manag. 7 (2010), no. 1, 81–107.
[32] M.A. Nayebi, M. Sharifi, M.R. Shahriari, and O. Zarabadipour, Fuzzy-chance constrained multi-objective programming applications for inventory control model, Appl. Math. Sci. 6 (2012), no. 5, 209–228.
[33] A. Resolution, RES/70/1. Transforming our world: the 2030 agenda for sustainable development, Seventieth United Nations General Assembly, New York, Vol. 25, 2015, pp. 86–97.
[34] J. Sarkis, Q. Zhu and K.-H. Lai, An organizational theoretic review of green supply chain management literature, Int. J. Prod. Econ. 130 (2011), no. 1, 1–15.
[35] S. Seuring, J. Sarkis, M. Muller, and P. Rao, Sustainability and supply chain management–an introduction to the special issue, J. Clean. Prod. 16 (2008), no. 15, 1545–1551.
[36] M. Shahriari, Malmquist productivity index for two-stage structures and its applications in bank branches, Int. J. Ind. Math. 3 (2011), no. 4, 325–335.
[37] M. Shahriari, Ranking network-structured decision-making units and its application in bank branches, Int. J. Ind. Math. 5 (2013), no. 4, 397–402.
[38] M.R. Shahriari, Soft computing based on a modified MCDM approach under intuitionistic fuzzy sets, Iran. J. Fuzzy Syst. 14 (2017), no. 1, 23–41.
[39] M.R. Shahriari, Enterprise resource planning selection based on a preference voting and data envelopment analysis, Int. J. Data Env. Anal. 9 (2021), no. 3, 55–64.
[40] M. Shahriari, A. Hajiha and S. Dehghan, A behavioral loyalty model of portable computers’ users, IEEE Int. Conf. Ind. Engin. Engin. Manag., IEEE, 2014, pp. 259–263.
[41] M.R. Shahriari and N. Pilevari, Agile supplier selection in sanitation supply chain using fuzzy VIKOR method, J. Optim. Industr. Eng. 10 (2016), no. 21, 19–28.
[42] M.R. Shahriari, N. Pilevari, and Z. Gholami, The effect of information systems on the supply chain sustainability using DEMATEL method, Commun. Adv. Comput. Sci. Appl.1 (2016), 47–56.
[43] A.A. Shaikh and H. Karjaluoto, Making the most of information technology and systems usage: A literature review, framework and future research agenda, Comput. Human Behav. 49 (2015), 541–566.
[44] K.T. Shibin, R. Dubey, A. Gunasekaran, B. Hazen, D. Roubaud, S. Gupta, and C. Foropon, Examining sustainable supply chain management of SMEs using resource based view and institutional theory, Annal. Oper. Res. 290 (2020), 301–326.
[45] D. Simchi-Levi, P. Kaminsky, and E. Simchi-Levi, Designing and Managing the Supply Chain: Concepts, Strategies, and Cases, New York, McGraw-Hill, 1999.
[46] S.A. Yawar and S. Seuring, Management of social issues in supply chains: A literature review exploring social issues, actions, and performance outcomes, J. Bus. Ethics 141 (2017), no.3, 621–643.
[47] A. Zaretalab, V. Hajipour, M. Sharifi and M.R. Shahriari, A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies, Comput. Ind. Eng. 80 (2015), 33–44.
[48] H. Zeraati, L. Rajabion, H. Molavi, and N.J. Navimipour, A model for examining the effect of knowledge sharing and new IT-based technologies on the success of the supply chain management systems, Kybernetes 49 (2020), no. 2, 229–251.
[49] M. Zorzini, L.C. Hendry, F.A. Huq, and M. Stevenson, Socially responsible sourcing, Reviewing the literature and its use of theory, Int. J. Oper. Prod. Manag. 35 (2015), no. 1, 60–109.
Volume 16, Issue 2
February 2025
Pages 37-49
  • Receive Date: 11 February 2024
  • Revise Date: 20 April 2024
  • Accept Date: 26 April 2024