Efficiency evaluation of two-stage supply chains with loss aversion using the DEA-based cooperative approach. (English) Zbl 1540.90018
Summary: Most supply chains in reality are composed of multiple enterprises which are independent individuals in terms of organizational structures and operating modes. From the perspective of performance evaluation, optimizing each enterprise’s efficiency independently will probably result in undesired inventory of intermediate products in the supply chain. Taking the two-stage supply chain where the outputs from the upstream supplier are taken as the inputs for the downstream manufacturer as an example, this paper introduces enterprises’ relative dominance into allocating the responsibility of inventory management between two decentralized enterprises. To motivate these enterprises to adopt the allocation strategy, this paper takes the loss aversion of enterprises into account to reveal the utility subjectively perceived by each decentralized enterprise insisting on independence instead of cooperation. Subsequently, a DEA-based cooperative model is developed to readjust the production processes of two decentralized enterprises. We find that adjusting the relevant parameters is able to explore the relationship between the proposed model and the non-cooperative model and the relationship between the proposed model and the centralized model. Finally, a practical example of coordinating sustainable supply chains is applied to illustrate the effectiveness of the proposed model.
MSC:
| 90B05 | Inventory, storage, reservoirs |
| 90B50 | Management decision making, including multiple objectives |
| 90C08 | Special problems of linear programming (transportation, multi-index, data envelopment analysis, etc.) |
Keywords:
two-stage supply chain; data envelopment analysis; relative dominance of enterprises; allocation of responsibility for inventory management; loss aversionReferences:
| [1] | Abdellaoui, M, Bleichrodt, H and Paraschiv, C (2007). Loss aversion under prospect theory: A parameter-free measurement. Management Science, 53(10), 1659-1674. |
| [2] | An, QX, Chen, HX, Xiong, BB, Wu, J and Liang, L (2017). Target intermediate products setting in a two-stage system with fairness concern. Omega, 73, 49-59. |
| [3] | An, QX, Yan, H, Wu, J and Liang, L (2016). Internal resource waste and centralization degree in two-stage systems: An efficiency analysis. Omega, 61, 89-99. |
| [4] | Charnes, A and Cooper, WW (1962). Programming with linear fractional functional. Naval Research Logistics, 9(3-4), 181-185. · Zbl 0127.36901 |
| [5] | Charnes, A, Cooper, WW and Rhodes, E (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. · Zbl 0416.90080 |
| [6] | Chen, X, Hao, G and Li, L (2014). Channel coordination with a loss-averse retailer and option contracts. International Journal of Production Economics, 150, 52-57. |
| [7] | Chen, Y, Li, YJ, Liang, L, Salo, A and Wu, HQ (2016). Frontier projection and efficiency decomposition in two-stage processes with slacks-based measures. European Journal of Operational Research, 250(2), 543-554. · Zbl 1346.90418 |
| [8] | Chen, L, Wang, YM and Huang, Y (2019). Cross-efficiency aggregation method based on prospect consensus process. Annals of Operations Research, 288(1), 115-135. · Zbl 1437.90085 |
| [9] | Chu, JF, Wu, J, Chu, CB and Zhang, TL (2020). DEA-based fixed cost allocation in two-stage systems: Leader-follower and satisfaction degree bargaining game approaches. Omega, https://doi.org/10.1016/j.omega.2019.03.012. |
| [10] | Cook, WD, Liang, L and Zhu, J (2010). Measuring performance of two-stage network structures by DEA: A review and future perspective. Omega, 38(6), 423-430. |
| [11] | Despotis, DK, Sotiros, D and Koronakos, G (2016). A network DEA approach for series multi-stage processes. Omega, 61, 35-48. · Zbl 1481.90222 |
| [12] | Du, J, Liang, L, Chen, Y, Cook, WD and Zhu, J (2011). A bargaining game model for measuring performance of two-stage network structures. European Journal of Operational Research, 210(2), 390-397. · Zbl 1210.90037 |
| [13] | Färe, R and Grosskopf, S (1996). Productivity and intermediate products: A frontier approach. Economic Letters, 50(1), 65-70. · Zbl 0900.90164 |
| [14] | Färe, R and Grosskopf, S (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35-49. |
| [15] | Fang, L (2020). Stage efficiency evaluation in a two-stage network data envelopment analysis model with weight priority. Omega, https://doi.org/10.1016/j.omega.2019.06.007. |
| [16] | Hassanzadeh, A and Mostafaee, A (2019). Measuring the efficiency of network structures: Link control approach. Computers & Industrial Engineering, 128, 437-446. |
| [17] | Izadikhah, M, Azadi, E, Azadi, M, Saen, RF and Toloo, M (2020). Developing a new chance constrained NDEA model to measure performance of sustainable supply chains. Annals of Operations Research, https://doi.org/10.1007/s10479-020-03765-8. · Zbl 1502.90024 |
| [18] | Kahneman, D and Tversky, A (1979). Prospect theory: An analysis of decisions under risk. Econometrica, 47(2), 263-291. · Zbl 0411.90012 |
| [19] | Kalantary, M and Saen, RF (2019). Assessing sustainability of supply chains: An inverse network dynamic DEA model. Computers & Industrial Engineering, 135, 1224-1238. |
| [20] | Khodakarami, M, Shabani, A, Saen, RF and Azadi, M (2015). Developing distinctive two-stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management. Measurement, 70, 62-74. |
| [21] | Li, HT, Chen, CL, Cook, WD, Zhang, JL and Zhu, J (2018). Two-stage network DEA: Who is the leader?Omega, 74, 15-19. |
| [22] | Liang, L, Cook, WD and Zhu, J (2008), DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55(7), 643-653. · Zbl 1160.90528 |
| [23] | Lozano, S (2016). Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector. Omega, 60, 73-84. |
| [24] | Meng, JJ and Weng, X (2018). Can prospect theory explain the disposition effect? A new perspective on reference points. Management Science, 64(7), 3331-3351. |
| [25] | Tone, K (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509. · Zbl 0990.90523 |
| [26] | Tone, K and Tsutsui, M (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243-252. · Zbl 1157.90465 |
| [27] | Tversky, A and Kahneman, D (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323. · Zbl 0775.90106 |
| [28] | Vipin, B and Amit, RK (2019). Describing decision bias in the newsvendor problem: A prospect theory model. Omega, 82, 132-141. |
| [29] | Wang, K, Huang, W, Wu, J and Liu, YN (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5-20. |
| [30] | Wang, CX and Webster, S (2009). The loss-averse newsvendor problem. Omega, 37(1), 93-105. |
| [31] | Wu, J, Jiang, HH, Chu, JF, Wang, YH and Liu, XH (2019). Coordinated production target setting for production-pollutant control systems: A DEA two-stage bargaining game approach. Journal of the Operational Research Society, 71(8), 1216-1232. |
| [32] | Yin, PZ, Chu, JF, Wu, J, Ding, JJ, Yang, M and Wang, YH (2020). A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective. Omega, https://doi.org/10.1016/j.omega.2019.02.004. |
| [33] | Zhou, XY, Luo, R, Tu, Y, Lev, B and Pedrycz, W (2018). Data envelopment analysis for bi-level systems with multiple followers. Omega, 77, 180-188. |
| [34] | Zhou, ZB, Sun, L, Yang, WY, Liu, WB and Ma, CQ (2013). A bargaining game model for efficiency decomposition in the centralized model of two-stage systems. Computers & Industrial Engineering, 64(1), 103-108. |
| [35] | Zhu, YC, Li, YJ and Liang, L (2018). A variation of two-stage SBM with leader-follower structure: An application to Chinese commercial banks. Journal of the Operational Research Society, 69(6), 840-848. |
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