The suppliers’ selection process through Extended Fuzzy Cognitive Maps and the Technique for Order of Preference by Similarity to Ideal Solution

Authors

  • Giovanni Mazzuto UNIVPM - Dipartimento di Ingegneria Industriale e Scienze Matematiche Università Politecnica delle Marche, Italia Italy
  • Leonardo Postacchini UNIVPM - Dipartimento di Ingegneria Industriale e Scienze Matematiche Università Politecnica delle Marche, Italia Italy
  • Filippo Emanuele Ciarapica
  • Maurizio Bevilacqua UNIVPM - Dipartimento di Ingegneria Industriale e Scienze Matematiche Università Politecnica delle Marche, Italia Italy

Keywords:

Decision support systems, Extended fuzzy cognitive map, Supplier selection process, TOPSIS, Scenario analysis

Abstract

In recent times, supplier selection has become one of the most important and crucial activities for companies. In this study, using the extended fuzzy cognitive maps (E-FCM) and technique for order of preference by similarity to ideal solution (TOPSIS), a decision-making support system is realised to assist managers in this activity. E-FCM expresses a causal relationship among criteria, computing linguistic variables to describe a complex situation. The proposed system allows managers to conduct an a priori evaluation regarding supplier suitability, according to both company and market requirements. A panel of experts was formed, according to their expertise areas, to cover the entire problem domain and model it. The problem was investigated in terms of the factors identified by the experts, such as costs, delivery quality, organisational capability, supplier flexibility, service quality and supplied product quality. These factors were analysed using the TOPSIS approach to rank the suppliers, and the use of TOPSIS allows for discrimination of the E-FCM. This decision-making support system was applied to a real case scenario to test its functionality; in particular, an Italian shoes and accessories company. The TOPSIS ideal solutions were defined from two different points of view: based on the standard TOPSIS procedure and on specifics fixed by the company managers. The two approaches resulted in considerably different outcomes, highlighting the need to consider concepts related to company expectations in the E-FCM.

Author Biographies

  • Giovanni Mazzuto, UNIVPM - Dipartimento di Ingegneria Industriale e Scienze Matematiche Università Politecnica delle Marche, Italia Italy

    Giovanni Mazzuto, Ph. D. in Industrial Plants at the Polytechnical University of Marche, graduated in 2010 in the engineering of the industrial automation at the University of Ancona. His research activity mainly deals with an environmental analysis of process plants and maintenance management, analysis of the behavior of the supply chain, project management and product development. He is the author of several papers that have been published on international journals (International Journal of Production Research, Journal of Loss Prevention in the Process Industries, International Journal of Business Performance and Supply Chain Modelling, International Journal for RF Technologies: Research and Applications) and conference proceedings. Email: g.mazzuto@staff.univpm.it

  • Leonardo Postacchini, UNIVPM - Dipartimento di Ingegneria Industriale e Scienze Matematiche Università Politecnica delle Marche, Italia Italy

    Leonardo Postacchini is a Ph.D. in Sustainable Energy and Technology. He obtained his bachelor's degree in Logistics and Production Engineering (summa cum laude), and then his master's degree in Management Engineering (summa cum laude) both at Università Politecnica delle Marche (Ancona, Italy). He worked as project manager for photovoltaic plants and as a process engineer for a wastewater treatment plant. In 2016 he started collaborating with the Università Politecnica delle Marche as a postdoctoral researcher, developing life cycle sustainability researches (Life Cycle Costing, Environmental Life Cycle Assessment and Social Life Cycle Assessment) on industrial and manufacturing processes and wastewater treatment technologies. Email: l.postacchini@staff.univpm.it

  • Filippo Emanuele Ciarapica

    Filippo Emanuele Ciarapica, Associate Professor in Industrial Plants at Free University of Bolzano/Bozen, graduated with distinction in 1999 in mechanical engineering at the University of Ancona. In 2003 he has got Ph. D. in Energy Management at the University of Ancona. From 2002 he has been giving courses of “Industrial Logistic” and “Industrial Facility Management” at the Polytechnical University of Marche, Italy. He is author of several papers (more than 60) that have been published on national and international proceedings and journals (Safety Science, International journal of loss prevention in process industry, Quality & Reliability Engineering International, International Journal of Quality & Reliability Management, International Journal of Production Research, International Journal of Sustainable Engineering, Business Process Management Journal,…). His research topics mainly focus on industrial plant design, development of risk assessment methodologies, strategies for the integration of management standard (ISO 9000-Vision 2000, ISO 14000 and OHSAS 18001) and production systems, development of soft computing techniques. Email: f.ciarapica@univpm.it

  • Maurizio Bevilacqua, UNIVPM - Dipartimento di Ingegneria Industriale e Scienze Matematiche Università Politecnica delle Marche, Italia Italy

    Maurizio Bevilacqua, a full professor in Industrial Plants at the Polytechnical University of Marche, graduated with distinction in 1986 in mechanical engineering at the University of Ancona. His research activity mainly deals with multiphase flow transport and separation analysis, environmental analysis of process plants and maintenance management. He is author of several papers (more than 100) that have been published on national and international journals (SPE Production Engineering, Reliability Engineering & System Safety, Quality & Reliability Engineering International, International Journal of Quality & Reliability Management, International Journal of Logistics, International Journal of Operations & Production Management, The Journal of Enterprise Information Management, Technology Law and Insurance, Journal of Cleaner Production, …) and conference proceedings. Email: m.bevilacqua@univpm.it

References

• Aggarwal, M. (2017). Adaptive linguistic weighted aggregation operators for multi-criteria decision making. Applied Soft Computing, Vol. 58, p. 690-699.

• Araz, C., Ozfirat, P.M., Ozkarahan, I. (2007). An integrated multicriteria decision-making methodology for outsourcing management. Computers & Operations Research, Vol. 34, 3738-3756.

• Arend, R. (2006). Implications for Including Shared Strategic Control in Multi-party Relationship Models. European Management Journal, Vol. 24 (1), p. 38-48.

• Atkinson, R., Crawford, L., Ward, S. (2006). Fundamental uncertainties in projects and the scope of project management. International Journal of Project Management, Vol. 24, p. 687-698.

• Axelrod, R. (2015). Structure of decision: The cognitive maps of political elites. Princeton university press.

• Babu, A., Suresh, N. (1996). Project management with time, cost, and quality considerations. European Journal of Operational Research, Vol. 88, p. 320-327.

• Bakeshlou, E. A., Khamseh, A. A., Asl, M. A. G., Sadeghi, J., and Abbaszadeh, M. (2017). Evaluating a green supplier selection problem using a hybrid MODM algorithm. Journal of Intelligent Manufacturing, Vol. 28(4), p. 913-927.

• Baykasoğlu, A., & Gölcük, İ. (2015). Development of a novel multiple-attribute decision making model via fuzzy cognitive maps and hierarchical fuzzy TOPSIS. Information Sciences, Vol. 301, p. 75-98.

• Bevilacqua, M., Ciarapica, F. E., and Mazzuto, G. (2018). Fuzzy cognitive maps for adverse drug event risk management. Safety science, Vol. 102, p. 194-210.

• Bevilacqua, M., Ciarapica, F. E., D'Ettorre, D., Mazzuto, G., & Paciarotti, C. (2014). Total quality control through value stream mapping: a case study of small medium enterprises. International Journal of Integrated Supply Management, Vol. 9(1-2), p. 94-109.

• Bevilacqua, M., Ciarapica, F. E., Mazzuto, G., and Paciarotti, C. (2013). Application of fuzzy cognitive maps to drug administration risk management. IFAC Proceedings Volumes, Vol. 46 (7), p. 438-443.

• Bevilacqua, M., Ciarapica, F.E. and Mazzuto, G. (2012). Analysis of injury events with fuzzy cognitive maps. Journal of Loss Prevention in the Process Industries, Vol. 25(4), p. 677-685.

• Bevilacqua, M., Ciarapica, F.E. and Mazzuto, G. (2016). A Fuzzy Cognitive Maps Tool for Developing a RBI&M Model. Quality and Reliability Engineering International, Vol. 32(2), p. 373-390.

• Bruno, G., Esposito, E., Genovese, A., Passaro, R. (2012). AHP-based approaches for supplier evaluation: Problems and perspectives. Journal of Purchasing and Supply Management, Vol. 18, p. 159-172.

• Bueno, S., Salmeron, J.L. (2009). Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications, Vol. 36 (3), p. 5221-5229.

• Büyüközkan, G., & Göçer, F. (2017). Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Applied Soft Computing, Vol. 52, p. 1222-1238.

• Chan, F.T., and Kumar, N. (2007). Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega, Vol. 35(4), p. 417-431.

• Chen, W., and Zou, Y. (2017). An integrated method for supplier selection from the perspective of risk aversion. Applied Soft Computing, Vol. 54, p. 449-455.

• Clayton, M.J. (1997). Delphi: a technique to harness expert opinion for critical decision‐making tasks in education. Educational Psychology Vol. 17, p. 373-386.

• Cole, J.R., and Persichitte, K.A. (2000). Fuzzy cognitive mapping: Applications in education. International Journal of Intelligent Systems, Vol. 15, p. 1–25.

• Crook, T.R., Combs, J.G. (2007). Sources and consequences of bargaining power in supply chains. Journal of Operations Management Vol. 25, p. 546-555.

• Dawes, J.G. (2008). Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5 Point, 7 Point and 10 Point Scales. International Journal of Market Research, Vol. 50(1), p. 61-104.

• De Boer, L., Labro, E., Morlacchi, P. (2001). A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management, Vol. 7(2), p. 75-89.

• Dey, P.K., Bhattacharya, A., Ho, W., and Clegg, B. (2015). Strategic supplier performance evaluation: A case-based action research of a UK manufacturing organisation. International Journal of Production Economics, Vol. 166, p. 192-214.

• Garavelli, A.C. (2003). Flexibility configurations for the supply chain management. International Journal of Production Economics, Vol. 85, p. 141-153.

• García-Cascales, M.S., Lamata, M.T. (2012). On rank reversal and TOPSIS method. Mathematical and Computer Modelling, Vol. 56(5–6), p. 123-132.

• Ghodsypour, S.H., O'Brien, C. (1998). A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International Journal of Production Economics, Vol. 56, p. 199-212.

• Görener, A., Ayvaz, B., Kuşakcı, A. O., & Altınok, E. (2017). A hybrid type-2 fuzzy based supplier performance evaluation methodology: The Turkish Airlines technic case. Applied Soft Computing, Vol. 56, p. 436-445.

• Gupta, H., and Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, Vol. 152, p. 242-258.

• Hajek, P., and Prochazka, O. (2016). Interval-valued fuzzy cognitive maps for supporting business decisions. In Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on (p. 531-536). IEEE.

• Hajek, P., and Prochazka, O. (2017). Interval-Valued Intuitionistic Fuzzy Cognitive Maps for Supplier Selection. In International Conference on Intelligent Decision Technologies (p. 207-217). Springer, Cham.

• Håkansson, H., Wootz, B. (1975). Supplier selection in an international environment: An experimental study. Journal of Marketing Research, p. 46-51.

• Johnsen, T.E. (2009). Supplier involvement in new product development and innovation: Taking stock and looking to the future. Journal of Purchasing and Supply Management, Vol. 15, p. 187-197.

• Kandasamy, W.V., and Smarandache, F. (2003). Fuzzy cognitive maps and neutrosophic cognitive maps. Infinite Study.

• Kar, M. B., Chatterjee, K., & Kar, S. (2014). A network-TOPSIS based fuzzy decision support system for supplier selection in risky supply chain. In Computational Sciences and Optimization (CSO), 2014 Seventh International Joint Conference on (p. 288-293). IEEE.

• Klingebiel, R., Rammer, C. (2014). Resource allocation strategy for innovation portfolio management. Strategic Management Journal, Vol. 35, p. 246-268.

• Kosko, B., 1986. Fuzzy cognitive maps. International journal of man-machine studies, Vol. 24(1), p. 65-75.

• Lasch, R., Janker, C.G. (2005). Supplier selection and controlling using multivariate analysis. International journal of Physical Distribution & Logistics Management, Vol. 35(6), p. 409-425.

• Lin, R.H. (2009). Potential use of FP-growth algorithm for identifying competitive suppliers in SCM. Journal of the Operational Research Society, Vol. 60(8), p. 1135-1141.

• Liu, T., Deng, Y., and Chan, F. (2018). Evidential supplier selection based on DEMATEL and game theory. International Journal of Fuzzy Systems, Vol. 20(4), p. 1321-1333.

• Luzzini, D., Caniato, F., Spina, G. (2014). Designing vendor evaluation systems: An empirical analysis. Journal of Purchasing and Supply Management, Vol. 20(2), p. 113-129.

• Meredith, J.R., Mantel Jr, S.J. (2011). Project management: a managerial approach. John Wiley & Sons.

• Okoli, C., Pawlowski, S.D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & Management, Vol. 42(1), p. 15-29.

• Omurca, S.I. (2013). An intelligent supplier evaluation, selection and development system. Applied Soft Computing, Vol. 13(1), p. 690-697.

• Özesmi, U. and Özesmi, S. L. (2004). Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological modelling, Vol. 176(1), p. 43-64.

• Pedrycz, W., Ekel, P., and Parreiras, R. (2011). Fuzzy multicriteria decision-making: models, methods and applications. John Wiley & Sons.

• Platje, A., Seidel, H., Wadman, S. (1994). Project and portfolio planning cycle: project-based management for the multiproject challenge. International Journal of Project Management, Vol. 12(2), p. 100-106.

• Primo, M.A., Amundson, S.D. (2002). An exploratory study of the effects of supplier relationships on new product development outcomes. Journal of Operations Management, Vol. 20(1), p. 33-52.

• Qin, J., Liu, X., and Pedrycz, W. (2017). An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. European Journal of Operational Research, Vol. 258(2), p. 626-638.

• Rao, C., Xiao, X., Goh, M., Zheng, J., Wen, J. (2017). Compound mechanism design of supplier selection based on multi-attribute auction and risk management of supply chain. Computers & Industrial Engineering, Vol. 105, p. 63-75.

• Salmeron, J. L., Vidal, R., & Mena, A. (2012). Ranking fuzzy cognitive map based scenarios with TOPSIS. Expert Systems with Applications, Vol. 39(3), p. 2443-2450.

• Sjoerdsma, M., van Weele, A.J. (2015). Managing supplier relationships in a new product development context. Journal of Purchasing and Supply Management, Vol. 21(3), p. 192-203.

• Stevenson, W.J., Sum, C.C. (2002). Operations management. McGraw-Hill/Irwin New York, NY.

• Stylios, C.D., Georgopoulos, V.C., Groumpos, P.P. (1997). Introducing the theory of fuzzy cognitive maps in distributed systems, Intelligent Control, 1997. Proceedings of the 1997 IEEE International Symposium on, Istanbul, Turkey, p. 55-60.

• Stylios, C.D., Groumpos, P.P. (1999). A soft computing approach for modelling the supervisor of manufacturing systems. Journal of Intelligent & Robotic Systems, Vol. 26(3-4), p. 389-403.

• Stylios, C.D., Groumpos, P.P. (2004). Modeling complex systems using fuzzy cognitive maps. IEEE Transactions on Systems Man and Cybernetics Part a-Systems and Humans, Vol. 34(1), p. 155-162.

• Tang, C., Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks. International Journal of Production Economics, Vol. 116(1), p. 12-27.

• Ustun, O.,Demirtas, E.A.(2008).An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection. Omega, Vol. 36(4), p. 509-521.

• Van der Rhee, B., Verma, R., Plaschka, G. (2009). Understanding trade-offs in the supplier selection process: The role of flexibility, delivery, and value-added services/support. International Journal of Production Economics, Vol. 120(1), p. 30-41.

• Wagner, S.M., Hoegl, M. (2006). Involving suppliers in product development: Insights from R&D directors and project managers. Industrial Marketing Management, Vol. 35(8), p. 936-943.

• Wu, C., Barnes, D. (2010). Formulating partner selection criteria for agile supply chains: A Dempster–Shafer belief acceptability optimisation approach. International Journal of Production Economics, Vol. 125(2), p. 284-293.

• Xiao, Z., Chen, W., and Li, L. (2012). An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation. Applied Mathematical Modelling, Vol. 36(4), p. 1444-1454.

• Zhong, L., and Yao, L. (2017). An ELECTRE I-based multi-criteria group decision making method with interval type-2 fuzzy numbers and its application to supplier selection. Applied Soft Computing, Vol. 57, p. 556-576.

Downloads

Published

2022-05-20

Issue

Vol. 7 No. 3 (2019): The Journal of Modern Project Management

Section

Articles-JMPM

License

Copyright (c) 2022 Giovanni Mazzuto, Leonardo Postacchini, Filippo Emanuele Ciarapica, Maurizio Bevilacqua

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

The suppliers’ selection process through Extended Fuzzy Cognitive Maps and the Technique for Order of Preference by Similarity to Ideal Solution. (2022). The Journal of Modern Project Management, 7(3). https://journalmodernpm.com/manuscript/index.php/jmpm/article/view/JMPM02102

Similar Articles

1-10 of 372

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)