Calculating the Encouragement and Hesitation Coefficients of Customers When Increasing Discounts for Perishable Products

Document Type : Original Article

Authors

1 M.Sc. Student in Industrial Engineering, Tarbiat Modares University

2 Professor of Industrial Engineering, Tarbiat Modares University

Abstract

Human beings have various needs, including the essential, daily need for food. Edible goods, such as many other products, reach customers through supply chains. However, due to the nature of food items, quality deterioration is unavoidable for this group of products throughout the supply chain. This decline in quality can result from factors such as time and environmental conditions. In any case, this quality deterioration affects customers' willingness to purchase the product. The resulting decrease in demand increases product accumulation, leading to increased product spoilage. Some sellers use increasing discounts to stimulate demand and prevent product spoilage. The extent of encouragement and hesitation created can aid the organization in making better decisions. The present study aims to examine the impact of discounts on changing the level of customer encouragement and hesitation. An integrated decision-making method for calculating the changes in these coefficients is proposed and solved using Microsoft Excel 2016 software. The research results indicate that increasing discounts simultaneously increases customers' encouragement and hesitation toward purchasing the product. However, it sustains customers' willingness to purchase the goods at approximately 48%, in stark contrast to the nearly negligible willingness observed in the no-discount scenario.

Keywords

Main Subjects

References

Amiri, M., Darastani Farahani, A., & Mohajeb Qudsi, M. (2016). Multi-criteria decision-making. Kian Academic Publishing.
Cooper, M. C., Lambert, D. M., & Pagh, J. D. (1997). Supply chain management: More than a new name for logistics. The International Journal of Logistics Management, 8(1), 1-14. https://doi.org/10.3390/encyclopedia3010006
Dagne, T. B., Jayaprkash, J., & Gebeyehu, S. G. (2020). Design of supply chain network model for perishable products with stochastic demand: An optimized model. Journal of Optimization in Industrial Engineering, 13(1), 29-37. https://doi.org/10.22094/JOIE.2019.563130.1551
Dehghan-Nayeri, M., Azar, A., & Hadad, R. (2020). A model for optimal pricing of perishable products: A case study of the Ofoq Kourosh chain store. Journal of Organizational Resource Management, 10(4), 81-102.
Elionand, S., & Mallaya, R. (1996). Issuing and pricing policy of semi-perishables. In Proceedings of the 4th International Conference on Operational Research.
Esteso, A., Alemany, M., & Ortiz, Á. (2021). Impact of product perishability on agri-food supply chains design. Applied Mathematical Modelling, 96, 20-38. https://doi.org/10.1016/j.apm.2021.02.027
Faraji, M., & Maralani, M. (2009). Identifying suitable promotional and advertising tools for Persian hand-woven carpets in the domestic market. Golja Scientific-Research Journal, 5(13), 87-106.
Grillo, H., Alemany, M., Ortiz, A., & Fuertes-Miquel, V. S. (2017). Mathematical modeling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Applied Mathematical Modelling, 49, 255-278. https://doi.org/10.1016/j.apm.2017.04.037
Hassoun, A., Alhaj, M. A., & Khamis, M. (2023). Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0. Food Chemistry, 409, 135303. https://doi.org/10.1016/j.foodchem.2022.135303
He, Y., Huang, H., & Li, D. (2020). Inventory and pricing decisions for a dual-channel supply chain with deteriorating products. Operational Research, 20(3), 1461-1503. https://doi.org/10.1007/s12351-018-0393-2
Huang, H., He, Y., & Li, D. (2018). Coordination of pricing, inventory, and production reliability decisions in deteriorating product supply chains. International Journal of Production Research, 56(18), 6201-6224. https://doi.org/10.1080/00207543.2018.1480070
Ince, M., Yigit, T., & Isik, A. (2017). AHP-TOPSIS method for learning object metadata evaluation. International Journal of Information and Education Technology, 7(12), 884-887. https://doi.org/10.18178/ijiet.2017.7.12.989
Khadkhodaei, M., Jafarzadeh, M. R., & Abbasi, R. (2021). Ranking methods for promoting optimal household water consumption in metropolitan areas using a combined Grey-AHP and Grey-TOPSIS model. Journal of Water and Wastewater, 32(1), 27-40.
Manouchehri, F., Nookabadi, A. S., & Kadivar, M. (2020). Production routing in perishable and quality degradable supply chains. Heliyon, 6(2), e03336. https://doi.org/10.1016/j.heliyon.2020.e03376
Sadeghi, M., Razavi, S. H., & Saberi, N. (2013). Application of grey TOPSIS in preference ordering of action plans in balanced scorecard and strategy map. Informatica, 24(4), 619-635.
Shrivastava, H., Dutta, P., Krishnamoorthy, M., & Suryawanshi, P. (2017). Designing a resilient supply chain network for perishable products with random disruptions. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 2, pp. 870-875).
Swami, S., & Shah, J. (2013). Channel coordination in green supply chain management. Journal of the Operational Research Society, 64, 336-351. https://doi.org/10.1057/jors.2012.44
Tsao, Y.-C., & Sheen, G.-J. (2008). Dynamic pricing, promotion and replenishment policies for a deteriorating item under permissible delay in payments. Computers & Operations Research, 35(11), 3562-3580. https://doi.org/10.1016/j.cor.2007.01.024
Wang, W., & Liu, Z. Z. (2007). Contractors selection based on the grey decision model. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing (pp. 5301-5304). IEEE. https://doi.org/10.3846/jbem. 2010.03
Wee, H.-M. (1997). A replenishment policy for items with a price-dependent demand and a varying rate of deterioration. Production Planning & Control, 8(5), 494-499. https://doi.org/10.1080/095372897235073
Wu, X., Nie, L., Xu, M., & Yan, F. (2018). A perishable food supply chain problem considering demand uncertainty and time deadline constraints: Modeling and application to a high-speed railway catering service. Transportation Research Part E: Logistics and Transportation Review, 111, 186-209. https://doi.org/10.1016/j.tre.2018.01.002
Yang, S., Xiao, Y., & Kuo, Y.-H. (2017). The supply chain design for perishable food with stochastic demand. Sustainability, 9(7), 1195. https://doi.org/10.3390/su9071195
Zebardast, E. (2001). The application of the analytic hierarchy process in urban and regional planning. Fine Arts, 10, 13-21.
Zhang, Y., Zhao, L., & Qian, C. (2017). Modeling of an IoT-enabled supply chain for perishable food with two-echelon supply hubs. Industrial Management & Data Systems, 117(9), 1890-1905. https://doi.org/10.1108/IMDS-10-2016-0456
Zhao, W., & Zheng, Y.-S. (2000). Optimal dynamic pricing for perishable assets with nonhomogeneous demand. Management Science, 46(3), 375-388. https://doi.org/10.1287/mnsc.46.3.375.12063
Zhu, G.-N., Hu, J., Qi, J., Gu, C.-C., & Peng, Y.-H. (2015). An integrated AHP and VIKOR for design concept evaluation based on rough number. Advanced Engineering Informatics, 29(3), 408-418. https://doi.org/10.1016/j.aei.2015.01.010.
 
Journal of Data Analytics and Intelligent Decision-making
Volume 1, Issue 2 - Serial Number 1
September 2025
Pages 13-22

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