Abstract
Information systems are very crucial in today’s organizations, and hence the selection of the right system has become a very critical decision. As time has progressed, with new issues affecting the supply chain and the performance metrics being continually rewritten, the responsibility of the information systems has increased manifold. Nowadays, information systems are expected to perform a number of functions such as information security, big data handling, green supply chain and risk management and thus the basic problem of system selection is now more complex. Also, adding to the complexity is the fact that these new issues are interdependent and most of the times influence other issues in a variety of direct or indirect ways. This study addresses this problem by proposing a new model for information system selection by incorporating the latest trends in the supply chain. It also proposes an integrated methodology, to solve such a problem where interdependence between criteria exists. The advantages of this methodology over other existing techniques are delinking the evaluation of interdependent criteria weights from performance evaluation, flexibility of inputs, ability to handle vagueness and uncertainty in judgements. The methodology is illustrated using a numerical example.
Similar content being viewed by others
References
Ashton K (2009) That ‘internet of things’ thing”. RFiD J 22(7):97–114
Barua A, Konana P, Whinston AB, Yin F (2004) An empirical investigation of net-enabled business value. MIS Q 28(4):585–620
Bernroider EWN, Stix V (2006) Profile distance method—a multi attribute decision making approach for information system investments. Decis Support Syst 42(2):988–998
Bhagwat R, Sharma MK (2009) An application of the integrated AHP-PGP model for performance measurement of supply chain management. Prod Plan Control 20(8):678–690
Boudreau MC, Chen AJ, Huber M (2007) Green IS: building sustainable business practices. Information systems. Global Text Project, Athens, pp 1–15
Chan HK, Chan FTS (2009) Effect of information sharing in supply chains with flexibility. Int J Prod Res 47(1):213–232
Chen C (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114(1):1–9
Choy KL, Lee WB, Lo V (2004) An enterprise collaborative management system: a case study of supplier relationship management. J Enterp Inf Manag 17(3):191–207
Deeter-Schmelz D, Bizarri A, Graham R, Howdyshell C (2001) Business-to-business online purchasing: suppliers’ impact on buyers’ adoption and usage intent. J Suppl Chain Manag 37(1):4–10
Delen D, Oztekin A, Tomak L (2012) An analytic approach to better understanding and management of coronary surgeries. Decis Support Syst 52(3):698–705
Devaraj S, Krajewski L, Wei JC (2007) Impact of eBusiness technologies on operational performance: the role of production information integration in the supply chain. J Oper Manag 25(6):1199–1216
Dubey R, Gunasekaran A, Ali SS (2015) Exploring the relationship between leadership, operational practices, institutional pressures and environmental performance: a framework for green supply chain. Int J Prod Econ 160:120–132
Dutta A, McCrohan K (2002) Management’s role in information security in a cyber economy. Calif Manag Rev 45(1):67–87
Fahimnia B, Sarkis J, Davarzani H (2015) Green supply chain management: a review and bibliometric analysis. Int J Prod Econ 162:101–114
Fekri R, Aliahmadi A, Fathian M (2008) Identifying the cause and effect factors of agile NPD process with fuzzy DEMATEL method: the case of Iranian companies. J Intell Manuf 20(4):637–648
Fuchs C (2008) The implications of new information and communication technologies for sustainability. Environ Dev Sust 10(3):291–309
Ganesh M, Raghunathan S, Rajendran C (2014) The value of information sharing in a multi-product, multi-level supply chain: impact of product substitution, demand correlation, and partial information sharing. Decis Support Syst 58:79–94
Giannakis M, Louis M (2016) A multi-agent based system with big data processing for enhanced supply chain agility. J Enterp Inf Manag 29(5):706–727
Gorodetsky V, Karsaev O, Konyushiy V, Samoylov V (2012) Transportation logistics services from cloud. In: Proceedings of the 3rd international conferences on web intelligence and intelligent agent technology (WI-IAT), Macau, China: IEEE/WIC/ACM, pp 215–219
Handfield RB, Nichols LE (1999) Introduction to supply chain management. Prentice-Hall, New Delhi
Heckmann I, Comes T, Nickel S (2015) A critical review on supply chain risk–definition, measure and modeling. Omega 52:119–132
Hong KK, Kim YG (2002) The critical success factors for ERP implementation: an organizational fit perspective. Inf Manag 40(1):25–40
Hsu CC, Kannan VR, Tan KC, Leong GK (2008) Information sharing, buyer–supplier relationships and firm performance: a multi-region analysis. Int J Phys Distrib Logist Manag 38(4):296–310
Huang CD, Behara RS, Goo J (2014) Optimal information security investment in a healthcare information exchange: an economic analysis. Decis Support Syst 61:1–11
Huong Tran TT, Childerhouse P, Deakins E (2016) Supply chain information sharing: challenges and risk mitigation strategies. J Manuf Technol Manag 27(8):1102–1126
Karsak EE, Ozogul CO (2009) An integrated decision making approach for ERP system selection. Expert Syst Appl 36(1):660–667
Kulp SC, Lee HL, Ofek E (2004) Manufacturer benefits from information integration with retail customers. Manag Sci 50(4):431–444
Kusi-Sarpong S, Bai C, Sarkis J, Wang X (2015) Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology. Resour Policy 46:86–100
Kutlu AC, Ekmekcioglu M (2012) Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst Appl 39(1):61–67
Lee JW, Kim SH (2001) An integrated approach for interdependent information system project selection. Int J Project Manag 19(2):111–128
Liao X, Li Y, Lu B (2007) A model for selecting an ERP system based on linguistic information processing. Inf Syst 32(7):1005–1017
Maditinos D, Chatzoudes D, Tsairidis C (2011) Factors affecting ERP system implementation effectiveness. J Enterp Inf Manag 25(1):60–78
Newman LH (2014) Target’s heating and refrigeration company gave hackers the key to customer data. Slate, Online Magazine. Accessed 24 Feb 2014
Park K, Min H, Min S (2016) Inter-relationship among risk taking propensity, supply chain security practices, and supply chain disruption occurrence. J Purch Supply Manag 22(2):120–130
Pereira JV (2009) The new supply chain’s frontier: information management. Int J Inf Manag 29(5):372–379
Ptak CA (2000) ERP tools, techniques, and applications for integrating the supply chain. St Lucie Press, New York
Qazi A, Quigley J, Dickson A, Ekici ŞÖ (2016) Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies. Eur J Oper Res. doi:10.1016/j.ejor.2016.10.023
Richey RG Jr, Morgan TR, Lindsey-Hall K, Adams FG (2016) A global exploration of big data in the supply chain. Int J Phys Distrib Logist Manag 46(8):710–739
Samvedi A, Jain V, Chan FTS (2011) An integrated approach to machine tool selection using fuzzy AHP and grey relational analysis. Int J Prod Res 50(12):3211–3221
Samvedi A, Jain V (2013) A study on the interactions between supply chain risk management criteria using fuzzy DEMATEL method. Int J Oper Res 18(3):255–271
Sangaiah AK, Gao XZ, Ramachandran M, Zheng X (2015) A fuzzy DEMATEL approach based on intuitionistic fuzzy information for evaluating knowledge transfer effectiveness in GSD projects. Int J Innov Comput Appl 6(3–4):203–215
Sangaiah AK, Subramaniam PR, Zheng X (2015) A combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factors. Neural Comput Appl. doi:10.1007/s00521-014-1771-1
Sangaiah AK, Gopal J, Basu A, Subramaniam PR (2015) An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome. Neural Comput Appl. doi:10.1007/s00521-015-2040-7
Santhanam R, Kyparisis GJ (1996) A decision model for interdependent information system project selection. Eur J Oper Res 89(2):380–399
Schmitt AJ, Sun SA, Snyder LV, Shen ZJM (2015) Centralization versus decentralization: risk pooling, risk diversification, and supply chain disruptions. Omega 52:201–212
Shafiu I, Wang WYC, Singh H (2016) Information security compliance behaviour of supply chain stakeholders: influences and differences. Int J Inf Syst Supply Chain Manag 9(1):1–16
Steel E, Angwin J (2010) On the Web’s cutting edge, anonymity in name only. Wall Street J. http://www.wsj.com/articles/SB10001424052748703294904575385532109190198
Swaminathan S (2012) The effects of big data on the logistics industry. Profit Oracle. http://www.oracle.com/us/corporate/profit/archives/opinion/021512-sswaminathan-1523937.html
Tan KH, Zhan Y, Ji G, Ye F, Chang C (2015) Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. Int J Prod Econ 165:223–233
Tang O, Nurmaya Musa S (2011) Identifying risk issues and research advancements in supply chain risk management. Int J Prod Econ 133(1):25–34
Teltumbde A (2000) A framework of evaluating ERP projects. Int J Prod Res 38(17):4507–4520
Van Donk DP, Van Doorne R (2016) The impact of the customer order decoupling point on type and level of supply chain integration. Int J Prod Res 54(9):2572–2584
Waller MA, Fawcett SE (2013) Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J Bus Logist 34(2):77–84
Wang G, Gunasekaran A, Ngai EW, Papadopoulos T (2016) Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int J Prod Econ 176:98–110
Wang P (2010) Chasing the hottest IT: effects of information technology fashion on organizations. MIS Q 34(1):63–85
Wang TY, Shaw CF, Chen YL (2000) Machine selection in flexible manufacturing cell: a fuzzy multiple attribute decision making approach. Int J Prod Res 38(9):2079–2097
Watson R, Boudreau M, Chen A (2010) Information systems and environmentally sustainable development: energy informatics and new directions for the IS community. MIS Q 34(1):23–38
Watson R, Boudreau M, Chen A, Sepúlveda HH (2011) Green projects: an information drives analysis of four cases. J Strateg Inf Syst 20(1):55–62
Wei CC, Wang MJJ (2004) A comprehensive framework for selecting an ERP system. Int J Project Manag 22(2):161–169
Wei CC, Chien CF, Wang MJJ (2005) An AHP based approach to ERP system selection. Int J Prod Econ 96(1):47–62
Wiengarten F, Humphreys P, Gimenez C, McIvor R (2016) Risk, risk management practices, and the success of supply chain integration. Int J Prod Econ 171:361–370
Wu KJ, Liao CJ, Tseng ML, Chiu AS (2015) Exploring decisive factors in green supply chain practices under uncertainty. Int J Prod Econ 159:147–157
Wu WW, Lee YT (2007) Developing global manager’s competencies using the fuzzy DEMATEL method. Expert Syst Appl 32(2):499–507
Young M, Pollard D (2012) What businesses can learn from big data and high performance analytics in the manufacturing industry,” big data insight group. Accessed 22 July 2014
Xu T, Nassar S (2016) Supply chain information security: emerging challenges in the telecommunications industry. In: Masys AJ (ed) Exploring the security landscape: non-traditional security challenges, pp 195–230. Springer. doi:10.1007/978-3-319-27914-5_10
Acknowledgements
The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 15201414) and a grant from The Natural Science Foundation of China (Grant No. 71471158). The authors also would like to thank The Hong Kong Polytechnic University Research Committee for financial and technical support.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Samvedi, A., Jain, V., Chan, F.T.S. et al. Information system selection for a supply chain based on current trends: the BRIGS approach. Neural Comput & Applic 30, 1619–1633 (2018). https://doi.org/10.1007/s00521-016-2776-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-016-2776-8