Data envelopment analysis for stochastic production and supply chain planning
DOI:
https://doi.org/10.35335/emod.v16i3.63Keywords:
Efficiency evaluation, Production planning, Stochastic DEA, Supply chain planning, Uncertainty managementAbstract
This research presents a stochastic Data Envelopment Analysis (DEA) model for production and supply chain planning. The objective is to evaluate the efficiency of decision-making units (DMUs) in a system considering the stochastic nature of inputs and outputs. The proposed model incorporates uncertainty by assuming normal distributions for the stochastic variables. The model formulates a linear programming problem to maximize the efficiency scores of DMUs subject to constraints that ensure the efficiency of the system. The weights assigned to DMUs and input variables provide insights into their relative importance. A numerical example is presented to demonstrate the application of the model, and the results highlight the efficiency scores and weights for the DMUs. The findings contribute to improving decision-making in production and supply chain systems under uncertain conditions. The developed model offers a practical tool for evaluating efficiency and identifying areas for improvement in real-world systems. Further research can explore extensions and variations of the model to enhance its applicability in different contexts
References
Ang, S., Zhu, Y., & Yang, F. (2021). Efficiency evaluation and ranking of supply chains based on stochastic multicriteria acceptability analysis and data envelopment analysis. International Transactions in Operational Research, 28(6), 3190–3219. https://doi.org/https://doi.org/10.1111/itor.12707
Avilés-Sacoto, S. V., Cook, W. D., Güemes-Castorena, D., & Zhu, J. (2020). Modelling efficiency in regional innovation systems: A two-stage data envelopment analysis problem with shared outputs within groups of decision-making units. European Journal of Operational Research, 287(2), 572–582.
Cikovic, K. F., Martincevic, I., & Smoljic, M. (2021). Data envelopment analysis (dea) application in supply chain management. Economic and Social Development: Book of Proceedings, 161–172.
Dotoli, M., Epicoco, N., Falagario, M., & Sciancalepore, F. (2015). A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty. Computers & Industrial Engineering, 79, 103–114.
El-Demerdash, B. E., El-Khodary, I. A., & Tharwat, A. A. (2016). A stochastic data envelopment analysis model considering variation in input and output variables. International Journal of Data Envelopment Analysis and Operations Research, 2(1), 1–6.
Ewertowska, A., Pozo, C., Gavaldá, J., Jiménez, L., & Guillén-Gosálbez, G. (2017). Combined use of life cycle assessment, data envelopment analysis and Monte Carlo simulation for quantifying environmental efficiencies under uncertainty. Journal of Cleaner Production, 166, 771–783.
Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk–Definition, measure and modeling. Omega, 52, 119–132.
Izadikhah, M., Azadi, E., Azadi, M., Farzipoor Saen, R., & Toloo, M. (2020). Developing a new chance constrained NDEA model to measure performance of sustainable supply chains. Annals of Operations Research, 1–29.
Izadikhah, M., Azadi, M., Shokri Kahi, V., & Farzipoor Saen, R. (2019). Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains. International Journal of Production Research, 57(3), 662–682.
Izadikhah, M., & Farzipoor Saen, R. (2015). A new data envelopment analysis method for ranking decision making units: an application in industrial parks. Expert Systems, 32(5), 596–608.
Jacobs, R. F., Berry, W. L., Whybark, D. C., & Vollmann, T. E. (2018). Manufacturing planning and control for supply chain management: The CPIM Reference. McGraw-Hill Education.
Jamal, M. A., El-Khodary, I. A., & Ali, D. S. (2022). A Two-Stage Rough Data Envelopment Analysis and Its Application in Three-Level Supply Chain Performance Evaluation. Ingénierie Des Systèmes d’Information, 27(2).
Jiang, B., Lio, W., & Li, X. (2018). An uncertain DEA model for scale efficiency evaluation. IEEE Transactions on Fuzzy Systems, 27(8), 1616–1624.
Ju, K.-J., Park, B., & Kim, T. (2016). Causal relationship between supply chain dynamic capabilities, technological innovation, and operational performance. Management and Production Engineering Review, 7.
Kumar, G., Subramanian, N., & Arputham, R. M. (2018). Missing link between sustainability collaborative strategy and supply chain performance: Role of dynamic capability. International Journal of Production Economics, 203, 96–109.
Liu, J., Zhu, J., & Zhang, J. (2018). A DEA-based approach for competitive environment analysis in global operations strategies. International Journal of Production Economics, 203, 110–123.
Lozić, J. (2022). Application of Data Envelopment Analysis (DEA) in information and communication technologies. Tehnički Glasnik, 16(1), 129–134.
Moheb-Alizadeh, H., & Handfield, R. (2018). An integrated chance-constrained stochastic model for efficient and sustainable supplier selection and order allocation. International Journal of Production Research, 56(21), 6890–6916.
Mönch, L., Uzsoy, R., & Fowler, J. W. (2018). A survey of semiconductor supply chain models part I: semiconductor supply chains, strategic network design, and supply chain simulation. International Journal of Production Research, 56(13), 4524–4545.
Olesen, O. B., & Petersen, N. C. (2016). Stochastic data envelopment analysis—A review. European Journal of Operational Research, 251(1), 2–21.
Panigrahi, S. S., Bahinipati, B., & Jain, V. (2018). Sustainable supply chain management: A review of literature and implications for future research. Management of Environmental Quality: An International Journal, 30(5), 1001–1049.
Ramezankhani, M. J., Torabi, S. A., & Vahidi, F. (2018). Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach. Computers & Industrial Engineering, 126, 531–548.
Ritzinger, U., Puchinger, J., & Hartl, R. F. (2016). A survey on dynamic and stochastic vehicle routing problems. International Journal of Production Research, 54(1), 215–231.
Shafiee, M, Hosseinzade Lotfi, F., & Saleh, H. (2021). Benchmark forecasting in data envelopment analysis for decision making units. International Journal of Industrial Mathematics, 13(1), 29–42.
Shafiee, Morteza. (2017). Supply chain performance evaluation with rough two-stage data envelopment analysis model: Noncooperative stackelberg game approach. Journal of Computing and Information Science in Engineering, 17(4), 41002.
Smriti, T. N., & Khan, H. R. (2018). Efficiency analysis of manufacturing firms using data envelopment analysis technique. Journal of Data Science, 16(1), 69–78.
Tayal, A., Gunasekaran, A., Singh, S. P., Dubey, R., & Papadopoulos, T. (2017). Formulating and solving sustainable stochastic dynamic facility layout problem: A key to sustainable operations. Annals of Operations Research, 253, 621–655.
Uzsoy, R., Fowler, J. W., & Mönch, L. (2018). A survey of semiconductor supply chain models Part II: demand planning, inventory management, and capacity planning. International Journal of Production Research, 56(13), 4546–4564.
Wen, M. (2015). Uncertain data envelopment analysis. Springer.
Wirtz, J. (2020). Organizational ambidexterity: cost-effective service excellence, service robots, and artificial intelligence. Organizational Dynamics, 49(3), 1–9.
Yu, J., Ryu, J.-H., & Lee, I. (2019). A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system. Applied Energy, 247, 212–220.
Zadmirzaei, M., Mohammadi Limaei, S., Amirteimoori, A., & Olsson, L. (2019). Measuring the relative performance of forest management units: a chance-constrained DEA model in the presence of the nondiscretionary factor. Canadian Journal of Forest Research, 49(7), 788–801.
Zahedi-Seresht, M., Khosravi, S., Jablonsky, J., & Zykova, P. (2021). A data envelopment analysis model for performance evaluation and ranking of DMUs with alternative scenarios. Computers & Industrial Engineering, 152, 107002.
Zhu, F., Zhong, P., Sun, Y., & Yeh, W. W. (2017). Real‐time optimal flood control decision making and risk propagation under multiple uncertainties. Water Resources Research, 53(12), 10635–10654.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Hengki Tamando Sihotang, Patrisia Teresa Marsoit, Kouvelis Geovany Ortizan
This work is licensed under a Creative Commons Attribution 4.0 International License.
