Evaluation of Performance of Chicken Meat Suppliers Using Fuzzy-MCDM Method (Case Study: Arak City-Iran)
الموضوعات : فصلنامه ریاضیAsma Etebari 1 , Rahmat Arab 2 , Mohammad Amirkhan 3
1 - Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran.
2 - Department of Industrial Engineering, Gorgan Faculty of Engineering, Golestan University, Gorgan, Iran
3 - Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran.
الکلمات المفتاحية: Fuzzy sets, Performance evaluation, Performance management, Chicken meat suppliers,
ملخص المقالة :
Performance management encompasses important activities aimed at fostering effective and efficient behaviors within an organization. Performance evaluation, as one of the tools of performance management, can serve as a robust foundation for decision-making concerning personnel affairs, such as promotions, transfers, demotions, dismissals, and salary increases or decreases. Performance evaluation indicators for chicken meat suppliers in the city of Arak were identified. To collect opinions of chicken meat store managers (24 stores), instruments such as questionnaires were employed. Fuzzy Likert Scale (FLS) was utilized to convert verbal data obtained from the questionnaires. Following the validation of the questionnaire, fuzzy set theory and fuzzy decision-making techniques were used to rank the studied suppliers (six suppliers). Data analysis in MATLAB determined that Supplier 6 (Dorsa Chicken Company) exhibited the best performance, while Supplier 2 (Fakhrar Company) demonstrated the poorest performance.
[1] A. Alem Tabriz, E. Roghanian and F. Mojibian, A new combining model for ranking generalized fuzzy numbers, International Journal of Industrial Engineering & Production Management, 1 (23) (2012) 129–137.
[2] H. Ali, J. Zhang and M. Shoaib, An integrated methodology for sustainable supplier selection and order allocation to tackle global supply chain uncertainties, International Journal of Industrial Engineering: Theory, Applications and Practice, 30 (4) (2023) 1016–1041.
[3] A. Azizi, D. Aikhuele and F. Souleman, A fuzzy TOPSIS model to rank automotive supplier, Procedia Manufacturing, 2 (2015) 159–164.
[4] A. Bakhtiari Tavana, M. Rabieh, M. S. Pishvaee and M. Esmaeili, A stochastic mathematical programming approach to resilient supplier selection and order allocation problem: A case study of Iran Khodro supply chain, Scientia Iranica, 30 (5) (2023) 1796–1821.
[5] G. E. Battess and T. J. Coelli, A model for technical inefficiency effects in a stochastic frontier production function for panel data, Empirical Economics, 20 (1995) 325–332.
[6] D. Caprio, A. Ebrahimnejad, H. Alrezaamiri and F. Santos-Arteaga, A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights, Alexandria Engineering Journal, 61 (5) (2022) 3403–3415.
[7] T. Y. Chen, Comparative analysis of SAW and TOPSIS based on interval-valued fuzzy sets: Discussions on score functions and weight constraints, Expert Systems with Applications, 39 (2012) 1848–1861.
[8] V. Chiesa, F. Frattini, V. Lazzarotti and R. Manzini, Designing a performance measurement system for the research activities: A reference framework and an empirical study, Journal of Engineering and Technology Management, 25 (3) (2008) 213–226.
[9] S. Y. Chou, Y. H. Chang and C. Y. Shen, A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes, European Journal of Operational Research, 189 (1) (2008) 132–145.
[10] D. Closs, C. Spier and N. Meachman, Sustainability to support end to end value chains: The role of supply chain management, Academy of Marketing Science, 1 (39) (2011) 116–101.
[11] L. C. Courrol and R. E. Samad, Determination of chicken meat contamination by porphyrin fluorescence, Journal of Luminescence, 199 (2018) 67–70.
[12] L. J. Cronbach, Coefficient alpha and the internal structure of tests, Psychometrika, 16 (1951) 297–334.
[13] F. B. Custódio, M. Vasconcelos-Neto, K. Theodoro, R. C. Chisté, and M. B. Gloria, Assessment of the quality of refrigerated and frozen pork by multivariate exploratory techniques, Meat Science, 139 (2018) 7–14.
[14] A. Dalvand, H. Abbasianjahromi, M. Ravanshadnia and E. Zeighami, Designing a mamdani fuzzy inference expert system for evaluating human resources in the iranian construction industry, Interdisciplinary Journal of Management Studies, 15 (4) (2022) 851–873.
[15] S. Davis and T. Albright, An investigation of the Effect of balanced scorecard implementation on financial performance, Management Accounting Research, 15 (2) (2004) 135–153.
[16] M. H. Dehghani Sadrabadi, F. Sabouhi, A. Bozorgi-Amiri and M. Sheikhalishahi, A robust-stochastic data envelopment analysis model for supplier performance evaluation of the telecommunication industry under uncertainty, RAIRO - Operations Research, 57 (1) (2023) 263–290.
[17] O. Engin and M. İşler, An efficient parallel greedy algorithm for fuzzy hybrid flow shop scheduling with setup time and lot size: A case study in apparel process, Journal of Fuzzy Extension and Applications, 3 (3) (2022) 249–262.
[18] B. Erdebilli, İ. Yilmaz and T. Aksoy, An interval-valued pythagorean fuzzy AHP and COPRAS hybrid methods for the supplier selection problem, International Journal of Computational Intelligence Systems, 16 (2023) 18–26.
[19] R. Eslamipoor and A. Nobari, A reliable and sustainable design of supply chain in healthcare under uncertainty regarding environmental impacts, Journal of Applied Research on Industrial Engineering, 10 (2) (2023) 256–272.
[20] C. Evecen and A. Beşkese, A performance appraisal model proposal for blue collar employee in a university, 13th International Research/Expert Conference: Trends in the Development of Machinery and Associated Technology, Hammamet, Tunisia, (2009) 269–272.
[21] M. Fallah, Efficiency, effectiveness and productivity of personnel’s health in petrochemical companies, Journal of Applied Research on Industrial Engineering, 7 (3) (2020) 280–286.
[22] M. R. Fathi, A. Khosravi, S. Razi Moheb Saraj and A. Behrooz, Futures studies of gas industry in Iran based on fuzzy MCDM methods and critical uncertainty approach, Fuzzy Optimization and Modeling Journal, 4 (3) (2023) 72–95.
[23] H. Fazlollahtabar and S. Ebadi, Skill training supply chain performance evaluation using network data envelopment analysis, International Journal of Industrial Engineering & Production Research, 34 (1) (2023) 1–14.
[24] N. Gerami Seresht and A. R. Fayek, Computational method for fuzzy arithmetic operations on triangular fuzzy numbers by extension principle, International Journal of Approximate Reasoning, 106 (2019) 172–193.
[25] N. Gerami Seresht and A. R. Fayek, Fuzzy arithmetic operations: Theory and applications in construction engineering and management, Fuzzy Hybrid Computing in Construction Engineering and Management, (2018) 111–147.
[26] A. M. Ghalayini, J. S. Noble and T. J. Crowe, An integrated dynamic performance measurement system for improving manufacturing competitiveness, International Journal of Production Economics, 48 (3) (1997) 207–225.
[27] R. Gibbons and K. Murphy, Relative performance evaluation for chief executive officers, ILR Review, 43 (3) (1990) 30–51.
[28] K. Govindan, Sustainable consumption and production in the food supply chain: A conceptual framework, International Journal of Production Economics, 195 (2018) 419–431.
[29] D. P. Green, Sustainable food supply chains, Journal of Aquatic Food Product Technology, 19 (2) (2010) 55–56.
[30] T. M. Guerreiro, D. N. De Oliveira, C. F. O. Rodrigues Melo, E. Oliveira Lima and R. R. Catharino, Migration from plastic packaging into meat, Food Research International, 109 (2018) 320–324.
[31] A. Haerian Ardekani, H. Koosha and F. Mirsaeedi, Application and comparison of simple additive weighting method, fuzzy analytic hierarchy process and support vector machine in identifying the internal and external factors in SWOT’s analysis, Strategic Management Researches, 22 (60) (2016) 37–62.
[32] M. Hassanpour, Evaluation of Iranian automotive industries, International Journal of Research in Industrial Engineering, 8 (2) (2019) 115–139.
[33] H. Çiçek and H. Turan, The effect of sustainable supply chain management on company performance mediated by competitive advantage, International Journal of Industrial Engineering: Theory, Applications and Practice, 30 (4) (2023) 1052–1077.
[34] R. Islam and S. M. Rasad, Employee performance evaluation by the AHP: A case study, Asia Pacific Management Review, 11(3) (2006) 163–176.
[35] H. Izadi and S. Amiri, Evaluating the efficiency of Iranian movie theaters’ halls, International Journal of Data Envelopment Analysis, 10 (2) (2022) 39–50.
[36] H. Jafari and M. Ehsanifar, Using interval arithmetic for providing a MADM approach, Journal of Fuzzy Extension and Applications, 1 (1) (2020) 60–68.
[37] H. Jafari and M. Ehsanifar, Approach to implementing health and environmental safety system in construction projects using fuzzy logic, International Journal of Innovation in Engineering, 2 (4) (2022) 27–40.
[38] H. Jafari, M. Faraji and R. Farsi, Evaluation of performance of different units of water and wastewater company using DEA, International Journal of Applied Operational Research, 9 (3) (2019) 21–27.
[39] H. Jafari and A. Sheykhan, Integrating developed evolutionary algorithm and Taguchi method for solving fuzzy facility’s layout problem, Fuzzy Optimization and Modeling Journal, 2 (3) (2021) 24–35.
[40] M. Khadem, A. Toloie Eshlaghy and K. Fathi hafshejani, Improving the performance of adaptive neural fuzzy inference system (ANFIS) using a new meta-heuristic algorithm, International Journal of Mathematical Modelling & Computations, 12 (4) (2022) 299–312.
[41] B. Liu and Y. K. Liu, Expected value of fuzzy variable and fuzzy expected value models, IEEE Transactions on Fuzzy Systems, 10 (4) (2002) 445–450.
[42] G. Liu, The impact of supply chain relationship on food quality, Procedia Computer Science, 131 (2018) 860–865.
[43] F. Majidi and M. Khademi, Enhancing recruitment efficiency: Leveraging fuzzy logic optimization for effective skill management in human resources, Fuzzy Optimization and Modeling Journal, 4 (1) (2023) 15–25.
[44] L. Manha Peres, S. Barbon, E. Mayumi Fuzyi, A. P. Barbon, D. F. Barbin, P. T. Maeda Saito, N. Andreo and A. Maria Bridi, Fuzzy approach for classification of pork into quality grades: coping with unclassifiable samples, Computers and Electronics in Agriculture, 150 (2018) 455–464.
[45] A. Manian, M. R. Fathi, M. Karimi Zarchi and A. Omidian, Performance evaluating of IT department using a modified fuzzy TOPSIS and BSC methodology (case study: Tehran province gas company), Journal of Management Research, 3 (2) (2011) 1–18.
[46] N. Milkias, Chicken meat production, consumption and constraints in Ethiopia, Food Science and Quality Management, 54 (2016) 1–12.
[47] A. Mohaghar, M. R. Fathi and A. H. Jafarzadeh, A supplier selection method using AR-DEA and fuzzy VIKOR, International Journal of Industrial Engineering: Theory, Applications and Practice, 20 (5) (2013) 387–400.
[48] A. D. Neely, Defining performance measurement: adding to the debate, Perspectives on Performance, 4 (2) (2005) 14–15.
[49] A. Noruzy, V. M. Dalfard, B. Azhdari, S. Nazari-Shirkouhi and A. Rezazadeh, Relations between transformational leadership, organizational learning, knowledge management, organizational innovation, and organizational performance: an empirical investigation of manufacturing firms, The International Journal of Advanced Manufacturing Technology, 64 (5) (2013) 1073–1085.
[50] H. Omrani, Z., Oveysi, A., Emrouznejad and T. Teplova, A mixed-integer network DEA with shared inputs and undesirable outputs for performance evaluation: Efficiency measurement of bank branches, Journal of the Operational Research Society, 74 (4) (2023) 1150–1165.
[51] P. Pitchipoo, P. Venkumar, S. Rajakarunakaran and R. Ragavan, Decision model for supplier evaluation and selection in process industry: A hybrid DEA approach, International Journal of Industrial Engineering: Theory, Applications and Practice, 25 (2) (2018) 186–199.
[52] J. Pourmahmoud and D. Norouzi, Provide a new model for evaluating and ranking DMUs with ordinal data, International Journal of Industrial Mathematics, 14 (3) (2022) 271–277.
[53] E. Roszkowska and D. Kacprzak, The fuzzy SAW and fuzzy TOPSIS procedures based on ordered fuzzy numbers, Information science, 369 (2016) 564–584.
[54] H. Saleh, F. hosseinzadeh, M. Rostamy and M. Shafiee, Performance evaluation and specifying of Return to scale in network DEA, Journal of Advanced Mathematical Modeling, 10 (2) (2020) 309–340.
[55] K. Salehi, A. Mehrabian, H. Amoozad Khalili and M. Navabakhsh, Analysis of specific states in nonparametric decision-making methods, International Journal of Industrial Engineering: Theory, Applications and Practice, 29 (2) (2022) 192–205.
[56] U. Sekaran, Research Methods in Management. Institute for Management and Planning Education and Research, (2009) 1–11.
[57] J. Simmons, Employee significance within stakeholder-accountable performance management systems, The TQM Journal, 20 (5) (2008) 463–475.
[58] N. Soleimanpoor Tamam, A. Arianfar, V. Hakimzadeh and B. Emadzadeh, Production of gelatin nanoparticles by solvent dissolution method for use as food-grade, Iranian Food Science and Technology Research Journal, 19 (4) (2023) 463–476.
[59] A. Sorourkhah, Coping uncertainty in the supplier selection problem using a scenario-based approach and distance measure on type-2 intuitionistic fuzzy sets, Fuzzy Optimization and Modeling Journal, 3 (4) (2022) 1–8.
[60] B. Taşkan and B. Karatop, Development of the field of organizational performance during the industry 4.0 period, International Journal of Research in Industrial Engineering, 11 (2) (2022)134–154.
[61] A. J. Timothy and R. F. Gerald, Social context of performance evaluation decisions, The Academy of Management Journal, 36 (1) (1993) 80–105.
[62] D. Wu, Supplier selection: A hybrid model using DEA, decision tree and neural network, Expert Systems with Applications, 36 (2009) 9105–9112.
[63] N. Yakovleva, J. Sarkis and T. Sloan, Sustainable benchmarking of supply chains: the case of the food industry, International Journal of Production Research, 5 (50) (2011) 1297–1317.
[64] M. Yarahmadi, M. Mirhoseini, M. Komasi and M. Ehsanifar, The factors affecting human resources productivity in urban construction projects: A Comparison of Relative Importance Index and Fuzzy Logic Methods, Fuzzy Optimization and Modeling Journal, 4 (3) (2023) 45–71.