Development of a Rough-MABAC-DoE-based Metamodel for Supplier Selection in an Iron and Steel Industry

  • Ritwika Chattopadhyay Department of Production Engineering, Jadavpur University, Kolkata, India
  • Partha Protim Das Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, India
  • Shankar Chakraborty Department of Production Engineering, Jadavpur University, Kolkata, India
Keywords: Supplier selection; Rough numbers; MABAC; DoE; Metamodel

Abstract

In the context of supply chain management, supplier selection can be defined as the process by which organizations score and evaluate a range of alternative suppliers to choose the best possible one who can provide superior quality of raw materials at cheaper rate and lesser lead time. It is a decision making process with multiple trade-offs between various conflicting criteria which in turn helps the organizations identify the suitable suppliers that would establish a robust supply chain assisting in maintaining a competitive edge. The main objective of supplier selection is thus focused on reducing purchase risk, maximizing overall value to the organization, and developing closeness and long-term relationships between the suppliers and the organization. In this paper, while selecting the most suitable supplier for gearboxes in an Indian iron and steel industry, assessments of three decision makers on the performance of five candidate suppliers with respect to five evaluation criteria are first aggregated using rough numbers. The definitive distances of those rough numbers are then treated as the inputs to a 25 full-factorial design plan with the corresponding multi-attributive border approximation area comparison (MABAC) scores as the output variables. Finally, a design of experiments (DoE)-based metamodel is formulated to interlink the computed MABAC scores with the considered criteria. The competing suppliers are ranked based on this rough-MABAC-DoE-based metamodel, which also easies out the computational steps when new suppliers are included in the decision making process.

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References

Abdullah, L., Chan, W., & Afshari, A. (2019). Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions. Journal of Industrial Engineering International, 15(2), 271-285. https://doi.org/10.1007/s40092-018-0289-z

Abdulshahed, A., Badi, I., & Blaow, M. (2017). A grey-based decision-making approach to the supplier selection problem in a steelmaking company: A case study in Libya. Grey Systems: Theory and Application, 7(3), 385-396. https://doi.org/10.1108/GS-01-2017-0002

Akcan, S., & Güldeş, M. (2019). Integrated multicriteria decision-making methods to solve supplier selection problem: a case study in a hospital. Journal of Healthcare Engineering, 5614892, https://doi.org/10.1155/2019/5614892

Badi, I., & Ballem, M. (2018). Supplier selection using the rough BWM-MAIRCA model: A case study in pharmaceutical supplying in Libya. Decision Making: Applications in Management and Engineering, 1(2), 16-33. https://doi.org/10.31181/dmame1802016b

Badi, I., Abdulshahed, A. M., & Shetwan, A. (2018). A case study of supplier selection for a steelmaking company in Libya by using the combinative distance-based assessment (CODAS) model. Decision Making: Applications in Management and Engineering, 1(1), 1-12. https://doi.org/10.31181/dmame180101b

Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., & Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research, 89, 337-347. https://doi.org/10.1016/j.cor.2016.02.015

Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051-13069. https://doi.org/10.1016/j.eswa.2012.05.056

Chakraborty, S., Dandge, S.S., & Agarwal, S. (2020). Non-traditional machining processes selection and evaluation: A rough multi-attributive border approximation area comparison approach. Computers & Industrial Engineering, 139, 106201. https://doi.org/10.1016/j.cie.2019.106201

Chattopadhyay, R., Chakraborty, S., & Chakraborty, S. (2020). An integrated D-MARCOS method for supplier selection in an iron and steel industry. Decision Making: Applications in Management and Engineering, 3(2), 49-69. https://doi.org/10.31181/dmame2003049c

Durmić, E., Stević, Ž., Chatterjee, P., Vasiljević, M., & Tomašević, M. (2020). Sustainable supplier selection using combined FUCOM–Rough SAW model. Reports in mechanical engineering, 1(1), 34-43. https://doi.org/10.31181/rme200101034c

Javad, M. O., Darvishi, M., & Javad, A. O. (2020). Green supplier selection for the steel industry using BWM and fuzzy TOPSIS: A case study of Khouzestan steel company. Sustainable Futures, 2, 100012. https://doi.org/10.1016/j.sftr.2020.100012

Kumar, A., Pal, A., Vohra, A., Gupta, S., Manchanda, S., & Dash, M. (2018). Construction of capital procurement decision making model to optimize supplier selection using fuzzy Delphi and AHP-DEMATEL. Benchmarking: An International Journal, 25 (5), 1528-1547. https://doi.org/10.1108/BIJ-01-2017-0005

Li, X., Yu, S., & Chu, J. (2018). Optimal selection of manufacturing services in cloud manufacturing: A novel hybrid MCDM approach based on rough ANP and rough TOPSIS. Journal of Intelligent & Fuzzy Systems, 34(6), 4041-4056. DOI: 10.3233/JIFS-171379

Luzon, B., & El-Sayegh, S. M. (2016). Evaluating supplier selection criteria for oil and gas projects in the UAE using AHP and Delphi. International Journal of Construction Management, 16(2), 175-183. https://doi.org/10.1080/15623599.2016.1146112

Mahmutagić, E., Stević, Ž., Nunić, Z., Chatterjee, P., & Tanackov, I. (2021). An integrated decision-making model for efficiency analysis of the forklifts in warehousing systems. Facta Universitatis, Series: Mechanical Engineering, 19(3), 537-553. 10.22190/FUME210416052M

Matić, B., Jovanović, S., Das, D. K., Zavadskas, E. K., Stević, Ž., Sremac, S., & Marinković, M. (2019). A new hybrid MCDM model: Sustainable supplier selection in a construction company. Symmetry, 11(3), 353. https://doi.org/10.3390/sym11030353

Mukherjee, K. (2017). Supplier Selection: An MCDA-based Approach. Springer, New Delhi.

Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using multi-attributive border approximation area comparison (MABAC). Expert Systems with Applications, 42(6), 3016-3028. https://doi.org/10.1016/j.eswa.2014.11.057

Pamučar, D., Stević, Ž., & Zavadskas, E.K. (2018a). Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Applied Soft Computing, 67, 141-163. https://doi.org/10.1016/j.asoc.2018.02.057

Pamučar, D., Stević, Ž., Sremac, S. (2018b). A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393

Radović, D., Stević, Ž., Pamučar, D., Zavadskas, E. K., Badi, I., Antuchevičiene, J., & Turskis, Z. (2018). Measuring performance in transportation companies in developing countries: a novel rough ARAS model. Symmetry, 10(10), 434. https://doi.org/10.3390/sym10100434

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009

Roy, J., Chatterjee, K., Bandyopadhyay, A., & Kar, S. (2018). Evaluation and selection of medical tourism sites: A rough analytic hierarchy process based multi‐attributive border approximation area comparison approach. Expert Systems, 35(1), e12232. https://doi.org/10.1111/exsy.12232

Saaty, T.L., (1988). What is the Analytic Hierarchy Process? Mathematical Models for Decision Support, 48, 109-121

Shojaei, P., & Bolvardizadeh, A. (2020). Rough MCDM model for green supplier selection in Iran: a case of university construction project. Built Environment Project and Asset Management, 10(3), 437-452. https://doi.org/10.1108/BEPAM-11-2019-0117

Sremac, S., Stević, Ž., Pamučar, D., Arsić, M., & Matić, B. (2018). Evaluation of a third-party logistics (3PL) provider using a rough SWARA–WASPAS model based on a new rough dombi aggregator. Symmetry, 10(8), 305. https://doi.org/10.3390/sym10080305

Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231

Stević, Ž., Pamučar, D., Subotić, M., Antuchevičiene, J., & Zavadskas, E. K. (2018). The location selection for roundabout construction using Rough BWM-Rough WASPAS approach based on a new Rough Hamy aggregator. Sustainability, 10(8), 2817. https://doi.org/10.3390/su10082817

Stević, Ž., Pamučar, D., Kazimieras Zavadskas, E., Ćirović, G., & Prentkovskis, O. (2017). The selection of wagons for the internal transport of a logistics company: A novel approach based on rough BWM and rough SAW methods. Symmetry, 9(11), 264. https://doi.org/10.3390/sym9110264

Stojić, G., Stević, Ž., Antuchevičienė, J., Pamučar, D., & Vasiljević, M. (2018). A novel rough WASPAS approach for supplier selection in a company manufacturing PVC carpentry products. Information, 9(5), 121. https://doi.org/10.3390/info9050121

Verma, R., & Pullman, M.E. (1998). An analysis of the supplier selection process. Omega, 26(6), 739-750. https://doi.org/10.1016/S0305-0483(98)00023-1

Vonderembse, M.A., & Tracey, M. (1999). The impact of supplier selection criteria and supplier involvement on manufacturing performance. Journal of Supply Chain Management, 35(2), 33-39. https://doi.org/10.1111/j.1745-493X.1999.tb00060.x

Wang, C.-N., Viet, V. T., Ho, T. P., Nguyen, V. T., & Nguyen, V. T. (2020). Multi-Criteria Decision Model for the Selection of Suppliers in the Textile Industry. Symmetry, 12(6), 979. https://doi.org/10.3390/sym12060979

Yazdani, M., Chatterjee, P., Zavadskas, E. K., & Zolfani, S. H. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, 142, 3728-3740. https://doi.org/10.1016/j.jclepro.2016.10.095

Zhai, L. Y., Khoo, L. P., & Zhong, Z. W. (2008). A rough set enhanced fuzzy approach to quality function deployment. The International Journal of Advanced Manufacturing Technology, 37(5), 613-624. https://doi.org/10.1007/s00170-007-0989-9

Zhai, L.-Y., Khoo, L.-P., & Zhong, Z.-W. (2009). A rough set based QFD approach to the management of imprecise design information in product development. Advanced Engineering Informatics, 23(2), 222-228. https://doi.org/10.1016/j.aei.2008.10.010

Zimmer, K., Fröhling, M., & Schultmann, F. (2016). Sustainable supplier management - a review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5), 1412-1442. https://doi.org/10.1080/00207543.2015.1079340

Žižović, M., Pamučar, D. (2019). New model for determining criteria weights: Level based weight assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126-137. https://doi.org/10.31181/dmame1902102z

Published
2022-02-18
How to Cite
Chattopadhyay, R., Das, P. P., & Chakraborty, S. (2022). Development of a Rough-MABAC-DoE-based Metamodel for Supplier Selection in an Iron and Steel Industry. Operational Research in Engineering Sciences: Theory and Applications, 5(1), 20-40. https://doi.org/10.31181/oresta190222046c