Metrika članka

  • citati u SCindeksu: 0
  • citati u CrossRef-u:0
  • citati u Google Scholaru:[=>]
  • posete u poslednjih 30 dana:8
  • preuzimanja u poslednjih 30 dana:5
članak: 7 od 16  
Back povratak na rezultate
Ekonomski horizonti
2017, vol. 19, br. 3, str. 193-209
jezik rada: srpski, engleski
vrsta rada: pregledni članak
doi:10.5937/ekonhor1703193T

Creative Commons License 4.0
Model za upravljanje lancem snabdevanja zasnovan na intervalnim fazi brojevima tipa-2 i TOPSIS metodi
Univerzitet u Kragujevcu, Fakultet inženjerskih nauka

e-adresa: galovic@kg.ac.rs

Sažetak

Poboljšanje performansi lanca snabdevanja rezultira povećavanjem efektivnosti poslovanja preduzeća integrisanih u lanac snabdevanja i celog lanca snabdevana. Rešenje problema unapređivanja performansi lanca snabdevanja može se dobiti merenjem i poboljšanjem odnosnih performansi, što predstavlja osnovnu svrhu ovog istraživanja. Relativnu važnost performansi i vrednosti njihovih ključnih indikatora procenjuju donosioci odluka. Njihove procene su opisane lingvističkim variablama, koje su modelirane intervalnim fazi brojevima tipa-2. Relativna važnost performansi je zadata pomoću fazi matrice relativne važnosti svakog para performansi. Ponderi performansi su izračunati pomoću metode sopstvenog vektora. Vrednosti performansi su računate primenom operatora fazi srednje vrednosti. Rang preduzeća sa respektovanjem svih razmatranih performansi i njihova težina su određeni konvencionalnom TOPSIS metodom. Rangiranje preduzeća koja su integrisana u lanac snabevanja može da se označi kao glavni rezultat istraživanja. Na osnovu dobijenog ranga mogu da se preduzmu odgovarajuće mere za poboljšanje performansi onih preduzeća koja su najlošije ocenjena shodno posmatranim performansama. Predloženi model je testiran na primeru lanca snabdevanja automobilske industrije u Centralnoj Srbiji.

Ključne reči

performanse lanca snabdevanja; intervalni fazi brojevi tipa-2; fazi AHP; TOPSIS; menadžment mere

Reference

Anitha, J. (2014) Determinants of employee engagement and their impact on employee performance. International Journal of Productivity and Performance Management, 63(3): 308-323
Baas, S.M., Kwakernaak, H. (1977) Rating and ranking of multiple-aspect alternatives using fuzzy sets. Automatica, 13(1): 47-58
Beamon, B.M. (1999) Measuring supply chain performance. International Journal of Operations & Production Management, 19(3): 275-292
Behery, M., Jabeen, F., Parakandi, M. (2014) Adopting a contemporary performance management system. International Journal of Productivity and Performance Management, 63(1): 22-43
Bezuidenhout, C.N., Bodhanya, S., Sanjika, T., Sibomana, M., Boote, G.L.N. (2012) Network-analysis approaches to deal with causal complexity in a supply network. International Journal of Production Research, 50(7): 1840-1849
Bolstorff, P., Rosenbaum, R. (2003) Supply chain excellence. New York: American Management Association
Castillo, O., Melin, P. (2012) Recent advances in interval Type-2 fuzzy systems. New York, NY: Springer Science & Business Media, Vol. 1
Chang, D. (1996) Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3): 649-655
Chen, S., Lee, L. (2010) Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Systems with Applications, 37(4): 2790-2798
Cheng, C., Chen, T., Chen, Y. (2014) An analysis of the structural complexity of supply chain networks. Applied Mathematical Modelling, 38(9-10): 2328-2344
Cocca, P., Alberti, M. (2010) A framework to assess performance measurement systems in SMEs. International Journal of Productivity and Performance Management, 59(2): 186-200
Dubois, D., Prade, H. (1980) Fuzzy sets and systems. New York: Academic Press
Feili, H.R., Farahani, N.V., Vesaghi, N. (2011) Integration of fuzzy analytic hierarchy process (FAHP) with balance score card (BSC) in order to evaluate the performance of information technology in industry. Journal of Mathematics and Computer Science, 2(2): 271-283
Felfel, H., Ayadi, O., Masmoudi, F. (2016) Multi-objective stochastic multi-site supply chain planning under demand uncertainty considering downside risk. Computers & Industrial Engineering, 102: 268-279
Frohlich, M.T., Westbrook, R. (2002) Demand chain management in manufacturing and services: web-based integration, drivers and performance. Journal of Operations Management, 20(6): 729-745
Hakimollahi, M., Naini, S.J., Bagherpour, M., Jafari, S., Shahmoradi, A. (2012) Balanced scorecard with fuzzy inference as a performance measurement in an automotive manufacturing line. International Journal of Automotive Engineering, 2(4): 276-283
Kahraman, C., Öztayşi, B., Uçal, S.İ., Turanoğlu, E. (2014) Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowledge-Based Systems, 59: 48-57
Kaplan, S.R., Norton, P.D. (2008) The execution premium: Linking strategy to operations for competitive advantages. Boston, MA: Harvard Business School Publishing Corporation
Kuei, C.H., Madu, C.N. (2001) Identifying critical success factors for supply chain quality management (SCQM). Asia Pacific Management Review, 6(4): 409-423
Kupers, R. (2001) What organizational leaders should know about the new science of complexity. Complexity, 6(1): 14-19
Lootsma, F.A. (1997) Fuzzy Logic for Planning and Decision Making. Boston, MA: Springer Nature
Maiti, T., Giri, B.C. (2017) Two-period pricing and decision strategies in a two-echelon supply chain under price-dependent demand. Applied Mathematical Modelling, 42: 655-674
Mendel, J.M., Liu, F. (2007) Super-Exponential Convergence of the Karnik-Mendel Algorithms for Computing the Centroid of an Interval Type-2 Fuzzy Set. IEEE Transactions on Fuzzy Systems, 15(2): 309-320
Merigó, J.M., Casanovas, M. (2008) Using fuzzy numbers in heavy aggregation operators. International Journal of Information Technology, 4(3): 177-182
Neely, A., Gregory, M., Platts, K. (1995) Performance measurement system design. International Journal of Operations & Production Management, 15(4): 80-116
Nestic, S., Djordjevic, A., Puskaric, H., Djordjevic, M.Z., Tadic, D., Stefanovic, M. (2015) The evaluation and improvement of process quality by using the fuzzy sets theory and genetic algorithm approach. Journal of Intelligent & Fuzzy Systems, 29(5): 2017-2028
Pal, P., Kumar, B. (2008) '16T': toward a dynamic vendor evaluation model in integrated SCM processes. Supply Chain Management: An International Journal, 13(6): 391-397
Pathak, S.D., Day, J.M., Nair, A., Sawaya, W.J., Kristal, M. M. (2007) Complexity and Adaptivity in Supply Networks: Building Supply Network Theory Using a Complex Adaptive Systems Perspective*. Decision Sciences, 38(4): 547-580
Pawlak, Z. (1998) Rough set theory and its applications to data analysis. Cybernetics and Systems, 29(7): 661-688
Presutti, W.D. (2003) Supply management and e-procurement: creating value added in the supply chain. Industrial Marketing Management, 32(3): 219-226
Ramesh, V., Kodali, R. (2012) A decision framework for maximising lean manufacturing performance. International Journal of Production Research, 50(8): 2234-2251
Saaty, T.L. (1990) How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1): 9-26
Sadikoglu, E., Zehir, C. (2010) Investigating the effects of innovation and employee performance on the relationship between total quality management practices and firm performance: An empirical study of Turkish firms. International Journal of Production Economics, 127(1): 13-26
Saranga, H., Moser, R. (2010) Performance evaluation of purchasing and supply management using value chain DEA approach. European Journal of Operational Research, 207(1): 197-205
Shih, H., Shyur, H., Lee, E. S. (2007) An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7-8): 801-813
Tadic, D., Aleksic, A., Mimovic, P., Puskaric, H., Misita, M. (2016) A model for evaluation of customer satisfaction with banking service quality in an uncertain environment. Total Quality Management & Business Excellence, str. 1-20
Tadić, D., Đorđević, A., Erić, M., Stefanović, M., Nestić, S. (2017) Two-step model for performance evaluation and improvement of New Service Development process based on fuzzy logics and genetic algorithm. Journal of Intelligent & Fuzzy Systems, 33(6): 3959-3970
Wong, C.Y., Boon-itt, S. (2008) The influence of institutional norms and environmental uncertainty on supply chain integration in the Thai automotive industry. International Journal of Production Economics, 115(2): 400-410
Xu, L., Li, Y., Govindan, K., Xu, X. (2015) Consumer returns policies with endogenous deadline and supply chain coordination. European Journal of Operational Research, 242(1): 88-99
Yoon, K., Hwang, C.L. (1981) Multiple attribute decision making: Methods and applications. Berlin, BRD: Springer-Verlag Berlin An, doi:10.1007/978-3-642-48318-9
Zadeh, L.A. (1975) The concept of a linguistic variable and its application to approximate reasoning-II. Information Sciences, 8(4): 301-357
Zhai, L., Khoo, L., Zhong, Z. (2009) Design concept evaluation in product development using rough sets and grey relation analysis. Expert Systems with Applications, 36(3): 7072-7079
Zhang, Z., Zhang, S. (2017) Comments on 'A note on 'A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets''. Applied Mathematical Modelling, 41: 702-710
Zimmermann, H. (2001) Fuzzy sets theory: And its applications. Amsterdam, Netherlands: Springer-Verlag, doi: 10.1007/978-94-010-0646-0