A novel fuzzy MCDM model for inventory management in order to increase business efficiency
Appropriate implementation and organization of logistics activities greatly contributes to the creation of a better business environment in companies. This is reflected in increased business efficiency, cost rationalization, increased productivity and better overall quality. In order for a company to achieve sustainability of its business and its competitiveness, the link between the marketing logistics system and other logistics subsystems is particularly evident. Thereby, it is necessary to lead proactive management with a focus on key resources. In this paper, two novel integrated models in fuzzy form have been created. The first model includes the integration of the fuzzy Full Consistency Method (fuzzy FUCOM) and the fuzzy Evaluation based on Distance from Average Solution (EDAS) method for sorting 78 products regarding the following four criteria: quantity, unit price, annual procurement costs and demand. The second model involves the integration of the fuzzy FUCOM method and ABC analysis for the purpose of inventory sorting considering different significance of criteria. A range of values has been formed for each product category within the fuzzy FUCOM and fuzzy EDAS models, on the basis of which their sorting has been performed. The advantages and verification of the developed integrated fuzzy models have been performed through comparison with former traditional approaches. It has been determined based on an extensive sensitivity analysis that the developed models have better performance compared to the existing ones.
First published online 09 March 2021
This work is licensed under a Creative Commons Attribution 4.0 International License.
Anthony, P., Behnoee, B., Hassanpour, M., & Pamucar, D. (2019). Financial performance evaluation of seven Indian chemical companies. Decision Making: Applications in Management and Engineering, 2(2), 81–99. https://doi.org/10.31181/dmame1902021a
Arikan, F., & Citak, S. (2017). Multiple criteria inventory classification in an electronics firm. International Journal of Information Technology & Decision Making, 16(02), 315–331. https://doi.org/10.1142/S0219622017500018
Božić, V., & Aćimović, S. (2014). Marketing logistics. Faculty of Economics, Belgrade.
Cherif, H., & Ladhari, T. (2016, November). A new hybrid multi-criteria ABC inventory classification model based on differential evolution and Topsis. In International Conference on Hybrid Intelligent Systems (pp. 78–87). Springer. https://doi.org/10.1007/978-3-319-52941-7_9
Chu, C. W., Liang, G. S., & Liao, C. T. (2008). Controlling inventory by combining ABC analysis and fuzzy classification. Computers & Industrial Engineering, 55(4), 841–851. https://doi.org/10.1016/j.cie.2008.03.006
Douissa, M. R., & Jabeur, K. (2016, January). A new model for multi-criteria ABC inventory classification: PROAFTN method. Procedia Computer Science, 96, 550–559. https://doi.org/10.1016/j.procs.2016.08.233
Douissa, M. R., & Jabeur, K. (2020). A non-compensatory classification approach for multi-criteria ABC analysis. Soft Computing, 24(13), 9525–9556. https://doi.org/10.1007/s00500-019-04462-w
Eraslan, E., & İÇ, Y. T. (2020). An improved decision support system for ABC inventory classification. Evolving Systems, 11, 683–696. https://doi.org/10.1007/s12530-019-09276-7
Erceg, Ž., Starčević, V., Pamučar, D., Mitrović, G., Stević, Ž., & Žikić, S. (2019). A new model for stock management in order to rationalize costs: ABC-FUCOM-interval rough CoCoSo model. Symmetry, 11(12), 1527. https://doi.org/10.3390/sym11121527
Flores, B. E., & Whybark, D. C. (1986). Multiple criteria ABC analysis. International Journal of Operations & Production Management, 6(3), 38–46. https://doi.org/10.1108/eb054765
Hanafi, R., Mardin, F., Asmal, S., Setiawan, I., & Wijaya, S. (2019, October). Toward a green inventory controlling using the ABC classification analysis: A case of motorcycle spares parts shop. In IOP Conference Series: Earth and Environmental Science (Vol. 343, No. 1, p. 012012). IOP Publishing. https://doi.org/10.1088/1755-1315/343/1/012012
Ishizaka, A., & Gordon, M. (2017). MACBETHSort: a multiple criteria decision aid procedure for sorting strategic products. Journal of the Operational Research Society, 68(1), 53–61. https://doi.org/10.1057/s41274-016-0002-9
Ishizaka, A., Lolli, F., Balugani, E., Cavallieri, R., & Gamberini, R. (2018). DEASort: Assigning items with data envelopment analysis in ABC classes. International Journal of Production Economics, 199, 7–15. https://doi.org/10.1016/j.ijpe.2018.02.007
Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599–613. https://doi.org/10.1016/j.cie.2016.06.004
Keller, K. L., & Kotler, P. (2006). Marketing management. Data status.
Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Turskis, Z. (2016). Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection. International Journal of Computers Communications & Control, 11(3), 358–371. https://doi.org/10.15837/ijccc.2016.3.2557
Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
Kheybari, S., Naji, S. A., Rezaie, F. M., & Salehpour, R. (2019). ABC classification according to Pareto’s principle: a hybrid methodology. Opsearch, 56(2), 539–562. https://doi.org/10.1007/s12597-019-00365-4
Kubasakova, I., Poliakova, B., & Kubanova, J. (2015). ABC analysis in the manufacturing company. In Applied mechanics and materials (Vol. 803, pp. 33–39). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/AMM.803.33
Liu, J., Liao, X., Zhao, W., & Yang, N. (2016). A classification approach based on the outranking model for multiple criteria ABC analysis. Omega, 61, 19–34. https://doi.org/10.1016/j.omega.2015.07.004
Mallick, B., Das, S., & Sarkar, B. (2019). Application of the modified similarity-based method for multicriteria inventory classification. Decision Science Letters, 8(4), 445–470. https://doi.org/10.5267/j.dsl.2019.5.001
Oliveira, F., & Vaz, C. B. (2017). Spare parts inventory management using quantitative and qualitative classification. In Engineering Systems and Networks (pp. 233–241). Springer. https://doi.org/10.1007/978-3-319-45748-2_25
Pamučar, D., & Ecer, F. (2020). Prioritizing the weights of the evaluation criteria under fuzziness: the fuzzy Full Consistency Method–FUCOM-F. Facta Universitatis, Series: Mechanical Engineering, 18(3), 419–437. https://doi.org/10.22190/FUME200602034P
Pamučar, D., Deveci, M., Canıtez, F., & Božanić, D. (2020). A fuzzy Full Consistency Method-DombiBonferroni model for prioritizing transportation demand management measures. Applied Soft Computing, 87, 105952. https://doi.org/10.1016/j.asoc.2019.105952
Stević, Ž., Stjepanović, Ž., Božičković, Z., Das, D. K., & Stanujkić, D. (2018a). Assessment of conditions for implementing information technology in a warehouse system: A novel fuzzy piprecia method. Symmetry, 10(11), 586. https://doi.org/10.3390/sym10110586
Stević, Ž., Vasiljević, M., Zavadskas, E. K., Sremac, S., & Turskis, Z. (2018b). Selection of carpenter manufacturer using fuzzy EDAS method. Engineering Economics, 29(3), 281–290. https://doi.org/10.5755/j01.ee.29.3.16818
ten Hompel, M., & Schmidt, T. (2008). Warehouse management. Springer. https://doi.org/10.1007/978-3-540-74876-2
van den Berg, J. P., & Zijm, W. H. (1999). Models for warehouse management: Classification and examples. International Journal of Production Economics, 59(1–3), 519–528. https://doi.org/10.1016/S0925-5273(98)00114-5