Scenario based two stage production planning for cassava SMEs under demand uncertainty
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Abstract
Production planning in small and medium sized enterprises (SMEs) is commonly based on deterministic assumptions that do not fully reflect uncertain market demand. This study develops a scenario-based production planning approach to support feasible and cost efficient decisions under demand uncertainty. Two stage stochastic programming model with demand scenarios is applied to a real multi-product SME (small medium enterprises) case, where three demand scenarios pessimistic, most likely, and optimistic are constructed from historical data. The model incorporates production costs, raw material availability, labor capacity, and machine capacity constraints and is solved using a standard linear programming solver with actual operational data. The results indicate that optimal production quantities and total production costs vary across demand scenarios due to differences in demand limits and resource availability. While deterministic planning becomes infeasible under extreme demand conditions, the proposed Two stage stochastic programming model consistently produces feasible and cost efficient production plans, resulting in consistently feasible solutions across all demand scenarios, and highlighting its usefulness as a practical decision support tool for SMEs facing demand uncertainty.
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How to Cite
Panjaitan, D. J., Aprilia, R., & Firmansyah, F. (2026). Scenario based two stage production planning for cassava SMEs under demand uncertainty. International Journal of Basic and Applied Science, 14(4), 192–202. https://doi.org/10.35335/ijobas.v14i4.850
References
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[21] F. L. John R. Birge, Introduction to Stochastic Programming (2nd ed.), 2nd ed. Springer Heidelberg New York Dordrecht London, 2011.
[22] S. A. Seyfi, İ. Yanıkoğlu, and G. Yılmaz, “Multi-stage scenario-based stochastic programming for managing lot sizing and workforce scheduling at Vestel,” Ann. Oper. Res., vol. 344, no. 2, pp. 911–936, 2025, doi: 10.1007/s10479-023-05741-4.
[23] s. M. J. Mirzapour Al-e-hashem, A. Baboli, S. Sadjadi, and M. Aryanezhad, “A Multiobjective Stochastic Production-Distribution Planning Problem in an Uncertain Environment Considering Risk and Workers Productivity,” Math. Probl. Eng., vol. 14, Apr. 2011, doi: 10.1155/2011/406398.
[24] A. F. Rivera-Morales, N. R. Smith, A. Ruiz, and L. E. Cárdenas-Barrón, “Analysis of Optimization Models Under Different Approaches to Deal with Uncertainty Regarding Pre-Disaster Planning in Food Bank Supply Chains,” J. Ind. Eng. Manag., vol. 17, no. 3, pp. 721–752, 2024, doi: 10.3926/jiem.7771.
[25] A. Qarash, A. Ahfid, and A. Elmeshrgui, “Optimising Production Portfolios for Profitability in a Libyan Bottled-Water SME: A Linear Programming Case Study of Shimaa Food Industries Company,” J. Technol. Res., vol. 3, pp. 703–708, Dec. 2025, doi: 10.26629/jtr.2025.65.
[26] J. Englberger and F. Herrmann, “Rolling horizon planning under uncertainty,” Logist. Res., vol. 15, no. 2, pp. 1–16, 2022.
[27] S. K. Orak, N. Aydin, and E. Karatas, “Significance of stochastic programming in addressing production planning under uncertain demand in the metal industry sector,” Int. J. Optim. Control Theor. Appl., vol. 15, no. 1, pp. 14–24, 2025, doi: 10.36922/ijocta.1704.
[2] J. Wu, D. Zhang, Y. Yang, G. Wang, and L. Su, “Multi-Stage Multi-Product Production and Inventory Planning for Cold Rolling under Random Yield,” Mathematics, vol. 10, no. 4, p. 597, 2022.
[3] M. Elyasi, B. Altan, A. Ekici, O. Ö. Özener, İ. Yanıkoğlu, and A. Dolgui, “Production planning with flexible manufacturing systems under demand uncertainty,” Int. J. Prod. Res., vol. 62, no. 1–2, pp. 157–170, 2024, doi: https://doi.org/10.1080/00207543.2023.2288722.
[4] A. Shapiro, D. Dentcheva, and A. Ruszczyński, Lectures on Stochastic Programming: Modeling and Theory, Second Edition. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2014. doi: 10.1137/1.9781611973433.
[5] Y. Zhang, X. Wang, and S. Liu, “Optimization of food processing systems under demand uncertainty,” Comput. \& Ind. Eng., vol. 176, p. 108968, 2023.
[6] J. Wu, L. Shen, G. Zhang, Z. Zhou, and Q. Zhu, “Efficiency evaluation with data uncertainty,” Ann. Oper. Res., no. March, 2022, doi: 10.1007/s10479-022-04636-0.
[7] A. Shapiro, D. Dentcheva, and A. Ruszczyński, Lectures on Stochastic Programming. Philadelphia: SIAM, 2014.
[8] K. Yang, Q. Wang, and L. Zhao, “Two-stage stochastic programming for multi-objective optimization of sustainable utility systems integrating with combined heat and power units,” J. Clean. Prod., vol. 451, no. 20, p. 142143, 2024, doi: https://doi.org/10.1016/j.jclepro.2024.142143.
[9] H. T. Sihotang, D. Vinsensia, F. Riandari, and S. Chandra, “Data-driven corporate growth: A dynamic financial modelling framework for strategic agility,” Int. J. Basic Appl. Sci., vol. 13, no. 2, pp. 84–95, 2024, doi: https://doi.org/10.35335/ijobas.v13i2.485.
[10] T. Taşkıner and B. Bilgen, “Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review,” Logistics, vol. 5, no. 3, p. 52, 2021, doi: 10.3390/logistics5030052.
[11] X. Huang, L. Ji, Y. Xie, and Z. Luo, “Robust optimization of regional biomass supply chain system design and operation with data-driven uncertainties,” Food Bioprod. Process., vol. 149, no. January, pp. 176–189, 2025, doi: https://doi.org/10.1016/j.fbp.2024.11.021.
[12] W. Gu, J. Wang, and H. Dai, “Scenario-based optimization of food supply chains under uncertainty,” J. Clean. Prod., vol. 331, p. 129915, 2022.
[13] B. Ta\csk\iner and B. Bilgen, “Production planning under demand uncertainty using fuzzy approaches,” J. Intell. Manuf., vol. 32, pp. 783–799, 2021.
[14] A. M. Gobachew and H.-D. Haasis, “Scenario-based optimization of supply chain performance under demand uncertainty,” Sustainability, vol. 15, no. 13, p. 10603, 2023, doi: https://doi.org/10.3390/su151310603.
[15] X. Zhou, Y. Li, and J. Chen, “Scenario-based planning in agricultural production systems,” Agric. Syst., vol. 224, p. 103593, 2024.
[16] D. Kurniawati and R. Yuniarty, “Optimization approaches in food processing SMEs,” Int. J. Ind. Optim., vol. 3, no. 2, pp. 101–112, 2022.
[17] S. Harimurti, A. Nugroho, and E. Prasetyo, “Production planning models for Indonesian SMEs,” J. Tek. Ind., vol. 22, no. 1, pp. 45–54, 2020.
[18] Z. Yuliu, R. Shi, and M. G. Ierapetritou, “Mathematical programming approaches to supply chain optimization under uncertainty: a review,” Curr. Opin. Chem. Eng., vol. 51, p. 101207, 2026, doi: https://doi.org/10.1016/j.coche.2025.101207.
[19] M. M. Morales Chavez, W. Sarache, Y. Costa, and J. Soto, “Multiobjective stochastic scheduling of upstream operations in a sustainable sugarcane supply chain,” J. Clean. Prod., vol. 276, p. 123305, 2020, doi: https://doi.org/10.1016/j.jclepro.2020.123305.
[20] Y. Boutmir, R. Bannari, F. Fedouaki, A. Bannari, and Achraf Touil, “Integrated Production–Distribution Planning for Paper Manufacturing Under Fuzzy Uncertainty,” in Engineering proceeding, 2025, pp. 3–14. doi: https://doi.org/ 10.3390/engproc2025112066.
[21] F. L. John R. Birge, Introduction to Stochastic Programming (2nd ed.), 2nd ed. Springer Heidelberg New York Dordrecht London, 2011.
[22] S. A. Seyfi, İ. Yanıkoğlu, and G. Yılmaz, “Multi-stage scenario-based stochastic programming for managing lot sizing and workforce scheduling at Vestel,” Ann. Oper. Res., vol. 344, no. 2, pp. 911–936, 2025, doi: 10.1007/s10479-023-05741-4.
[23] s. M. J. Mirzapour Al-e-hashem, A. Baboli, S. Sadjadi, and M. Aryanezhad, “A Multiobjective Stochastic Production-Distribution Planning Problem in an Uncertain Environment Considering Risk and Workers Productivity,” Math. Probl. Eng., vol. 14, Apr. 2011, doi: 10.1155/2011/406398.
[24] A. F. Rivera-Morales, N. R. Smith, A. Ruiz, and L. E. Cárdenas-Barrón, “Analysis of Optimization Models Under Different Approaches to Deal with Uncertainty Regarding Pre-Disaster Planning in Food Bank Supply Chains,” J. Ind. Eng. Manag., vol. 17, no. 3, pp. 721–752, 2024, doi: 10.3926/jiem.7771.
[25] A. Qarash, A. Ahfid, and A. Elmeshrgui, “Optimising Production Portfolios for Profitability in a Libyan Bottled-Water SME: A Linear Programming Case Study of Shimaa Food Industries Company,” J. Technol. Res., vol. 3, pp. 703–708, Dec. 2025, doi: 10.26629/jtr.2025.65.
[26] J. Englberger and F. Herrmann, “Rolling horizon planning under uncertainty,” Logist. Res., vol. 15, no. 2, pp. 1–16, 2022.
[27] S. K. Orak, N. Aydin, and E. Karatas, “Significance of stochastic programming in addressing production planning under uncertain demand in the metal industry sector,” Int. J. Optim. Control Theor. Appl., vol. 15, no. 1, pp. 14–24, 2025, doi: 10.36922/ijocta.1704.
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