Evaluating a Pricing Model in a Two-level Supply Chain by Integrating Traditional and Modern Channels with the Return Policy

Document Type : Research Paper

Authors

1 MSc., Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran.

2 Assistant Prof., Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran.

3 Ph.D., School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

Objective: This study focuses on analyzing a pricing problem within a two-echelon supply chain, comprising a manufacturer and multiple retailers. It also explores the integration of various channels, including traditional, electronic, and omni-channels such as "Buy-Online-Pickup-in-Store", "Buy-Online-Deliver-to-Home", and "Order-in-Store-Deliver-to-Home". In addition, it seeks to develop a demand function dependent on product price and return policy in electronic channels, as well as delivery times for products. Accordingly, the present study aims to investigate and evaluate a pricing model considering various distribution channels in a single-period single-product environment and to provide a solution approach with valuable performance that can be used in real problems as a decision-making tool. It also tries to investigate the impact of various factors on the number of decision variables and the profit of the entire supply chain.
Methods: The problem was formulated as a non-linear mathematical programming model and coded and implemented using GAMS software. Furthermore, several numerical examples were solved to investigate the effect of changes in some parameters on the values of decision variables, retailers' demand, and the total profit of the supply chain.
Results:The results showed that the two parameters of price sensitivity of demand and return sensitivity of demand have a critical impact on decision variables including the retailer's sales price, return price, delivery time, and profit of the entire supply chain.
 
Conclusion: This study proposed an integrated approach to evaluate the impact of pricing decisions and return policy in the supply chain. Therefore, it can serve as a valuable resource for companies in making operational decisions regarding the concurrent implementation of pricing decisions, product return policy, and delivery time. Online retailers can use the achieved results to make operational decisions regarding the application of the product return policy according to the presented analysis.

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Main Subjects

References

Didehkhani, H., Mehrani, H., Badie, F. & Yousefi Komijani, A. (2019). Evaluation of Multi-channel Marketing Strategies Based on Fuzzy ANP and TOPSIS. Iranian Journal of Trade Studies, 23(92), 55-79. (in Persian)
Farrokhi, M.A., Rasti-Barzoki, M. (2016). Pricing in a Two-Echelon Supply Chain with Manufacturers’ Competing to Seizing the Market in the Make-to-Order Environment by Using Game Theory. Journal of Industrial Engineering Research in Production Systems, 3(6), 207-219. (in Persian)
Gallino, S. & Moreno, A. (2014). Integration of online and offline channels in retail: The impact of sharing reliable inventory availability information. Management Science, 60(6), 1434-1451.
Gao, F. & Su, X. (2017). Omnichannel retail operations with buy-online-and-pick-up-in-store. Management Science, 63(8), 2478-2492.
Giri, B.C. & Sharma, S. (2014). Manufacturer's pricing strategy in a two-level supply chain with competing retailers and advertising cost dependent demand. Economic modelling, 38, 102-111.
Modak, N.M. & Kelle, P. (2019). Managing a dual-channel supply chain under price and delivery-time dependent stochastic demand. European Journal of Operational Research, 272(1), 147-161.
Mohaghar, F., Jolai, F. & Heydari, J. (2020). Modeling a Four Echelon Omni-Channel Supply Chain for Seasonal Product under Stochastic Demand. Industrial Management Journal, 12(2), 206-235. (in Persian)
Momen, S. & Torabi, S.A. (2021). Omni-channel retailing: A data-driven distributionally robust approach for integrated fulfillment services under competition with traditional and online retailers. Computers & Industrial Engineering, 157, 107353.
Momeni, M. & Zereshki, N. (2021). Modeling of Closed-Loop Supply Chain by Utilizing Scenario-Based Approaches in Facing Uncertainty in Quality and Quantity of Returns. Industrial Management Journal, 13(1), 105-130. (in Persian)
Nahofti Kohneh, J. & Teimoury, E. (2016). A model for the design of blood products supply chain at the time of the earthquake disaster considering the transfers from the other provinces (Case Study: Tehran blood transfusion network). Industrial Management Journal, 8(3), 487-513. (in Persian)
Nasiri, G.R., Deymeh, H., Karimi, B. & Miandoabchi, E. (2021). Incorporating sales and marketing considerations into a competitive multi-echelon distribution network design problem with pricing strategy in a stochastic environment. Journal of Retailing and Consumer Services, 62, 102646.
Rahnamai Rahmani, M. & Taleizadeh, A. (2019). Pricing in a two-level supply chain under carbon emission control policies. Advances in Industrial Engineering, 52(4), 585-596.
(in Persian)
Raza, S.A. (2015). An integrated approach to price differentiation and inventory decisions with demand leakage. International Journal of Production Economics, 164, 105-117.
Saboori, G., Nasiri, G.R. & Salehi, H. (2022). Integration of traditional and modern channels in a two-level supply chain with pricing decision. International Conference on Industrial and Systems Engineering, Mashhad, Iran, 38-44. (in Persian)
Sadeghi Moghadam, M.R., Momeni, M. & Nalchigar, S. (2009). Material Flow Modeling in Supply Chain Management with Genetic Algorithm Approach. Industrial Management Journal, 1(2), 71-88. (in Persian)
Salehi, H., Taleizadeh, A.A., Tavakkoli-Moghaddam, R. & Hafezalkotob, A. (2020). Pricing and market segmentation in an uncertain supply chain. Sādhanā, 45, 118.
Song, Y., Fan, T., Tang, Y. & Xu, C. (2021). Omni-channel strategies for fresh produce with extra losses in-store. Transportation Research Part E: Logistics and Transportation Review, 148, 102243.
Srivastava, P.R., Zhang, J.Z., Eachempati, P., Sharma, S.K. & Liu, Y. (2022). An Intelligent omnichannel assortment model to manage webrooming: an optimization approach. Journal of Strategic Marketing, 1-25.
Wang, F., Diabat, A., & Wu, L. (2021). Supply chain coordination with competing suppliers under price-sensitive stochastic demand. International Journal of Production Economics, 234, 108020.
Wu, X. & Chen, Z.L. (2022). Fulfillment scheduling for buy‐online‐pickup‐in‐store orders. Production and Operations Management, 31(7), 2982-3003.
Zhao, J., Wei, J., & Li, Y. (2018). Pricing decisions of complementary products in a two-level fuzzy supply chain. International Journal of Production Research, 56(5), 1882-1903.
Industrial Management Journal
Volume 15, Issue 1
2023
Pages 92-111

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