IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i11p2502-d1158851.html
   My bibliography  Save this article

Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect

Author

Listed:
  • Keun Hee Lee

    (School of Sciences, RMIT University, Melbourne 3000, Australia)

  • Mali Abdollahian

    (School of Sciences, RMIT University, Melbourne 3000, Australia)

  • Sergei Schreider

    (Rutgers Business School, Rutgers University, Newark, NJ 07102, USA)

  • Sona Taheri

    (School of Sciences, RMIT University, Melbourne 3000, Australia)

Abstract

Determining the optimal price of products is essential, as it plays a critical role in improving a company’s profitability and market competitiveness. This requires the ability to calculate customers’ demand in the Fast Moving Consumer Goods (FMCG) industry as various effects exist between multiple products within a product category. The substitution effect is one of the challenging effects at retail stores, as it requires investigating an exponential number of combinations of price changes and the availability of other products. This paper suggests a systematic price decision support tool for demand prediction and price optimise in online and stationary retailers considering the substitution effect. Two procedures reflecting the product price changes and the demand correlation structure are introduced for demand prediction and price optimisation models. First, the developed demand prediction procedure is carried out considering the combination of price changes of all products reflecting the effect of substitution. Time series and different well-known machine learning approaches with hyperparameter tuning and rolling forecasting methods are utilised to select each product’s best demand forecast. Demand forecast results are used as input in the price optimisation model. Second, the developed price optimisation procedure is a constraint programming problem based on a week time frame and a product category level aggregation and is capable of maximising profit out of the many price combinations. The results using real-world transaction data with 12 products and 4 discount rates demonstrate that including some business rules as constraints in the proposed price optimisation model reduces the number of price combinations from 11,274,924 to 19,440 and execution time from 129.59 to 25.831 min. The utilisation of the presented price optimisation support tool enables the supply chain managers to identify the optimal discount rate for individual products in a timely manner, resulting in a net profit increase.

Suggested Citation

  • Keun Hee Lee & Mali Abdollahian & Sergei Schreider & Sona Taheri, 2023. "Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect," Mathematics, MDPI, vol. 11(11), pages 1-28, May.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2502-:d:1158851
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/11/2502/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/11/2502/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gustavo Vulcano & Garrett van Ryzin & Richard Ratliff, 2012. "Estimating Primary Demand for Substitutable Products from Sales Transaction Data," Operations Research, INFORMS, vol. 60(2), pages 313-334, April.
    2. Evgeny A. Antipov & Elena B. Pokryshevskaya, 2020. "Interpretable machine learning for demand modeling with high-dimensional data using Gradient Boosting Machines and Shapley values," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(5), pages 355-364, October.
    3. Sushil Punia & Konstantinos Nikolopoulos & Surya Prakash Singh & Jitendra K. Madaan & Konstantia Litsiou, 2020. "Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail," International Journal of Production Research, Taylor & Francis Journals, vol. 58(16), pages 4964-4979, July.
    4. Ruth N. Bolton, 1989. "The Relationship Between Market Characteristics and Promotional Price Elasticities," Marketing Science, INFORMS, vol. 8(2), pages 153-169.
    5. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
    6. Islam, Samiul & Amin, Saman Hassanzadeh & Wardley, Leslie J., 2021. "Machine learning and optimization models for supplier selection and order allocation planning," International Journal of Production Economics, Elsevier, vol. 242(C).
    7. Maxime C. Cohen & Ngai-Hang Zachary Leung & Kiran Panchamgam & Georgia Perakis & Anthony Smith, 2017. "The Impact of Linear Optimization on Promotion Planning," Operations Research, INFORMS, vol. 65(2), pages 446-468, April.
    8. Yalç{i}n Akçay & Harihara Prasad Natarajan & Susan H. Xu, 2010. "Joint Dynamic Pricing of Multiple Perishable Products Under Consumer Choice," Management Science, INFORMS, vol. 56(8), pages 1345-1361, August.
    9. Siddharth Mahajan & Garrett van Ryzin, 2001. "Stocking Retail Assortments Under Dynamic Consumer Substitution," Operations Research, INFORMS, vol. 49(3), pages 334-351, June.
    10. Karray, Salma & Martín-Herrán, Guiomar & Zaccour, Georges, 2020. "Pricing of demand-related products: Can ignoring cross-category effect be a smart choice?," International Journal of Production Economics, Elsevier, vol. 223(C).
    11. Ma, Shaohui & Fildes, Robert, 2017. "A retail store SKU promotions optimization model for category multi-period profit maximization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 680-692.
    12. A. Gürhan Kök & Marshall L. Fisher, 2007. "Demand Estimation and Assortment Optimization Under Substitution: Methodology and Application," Operations Research, INFORMS, vol. 55(6), pages 1001-1021, December.
    13. Øyvind Thomassen & Howard Smith & Stephan Seiler & Pasquale Schiraldi, 2017. "Multi-category Competition and Market Power: A Model of Supermarket Pricing," American Economic Review, American Economic Association, vol. 107(8), pages 2308-2351, August.
    14. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.
    15. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    16. Ambrose Leung & Maureen McGregor & Justin Chesney, 2014. "Income and Substitution Effects: Graphical Analysis for Intermediate Microeconomics," Journal for Economic Educators, Middle Tennessee State University, Business and Economic Research Center, vol. 14(1), pages 97-107, Fall.
    17. Stephen A. Smith & Narendra Agrawal, 2000. "Management of Multi-Item Retail Inventory Systems with Demand Substitution," Operations Research, INFORMS, vol. 48(1), pages 50-64, February.
    18. Felipe Caro & Jérémie Gallien, 2012. "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research, INFORMS, vol. 60(6), pages 1404-1422, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Noha A. Mostafa & Abdelwahab A. Hussein & Mohab Elsheeta & Giovanni Romagnoli, 2024. "Impacts of COVID-19 and the Russian–Ukrainian Conflict on Food Supply Chain: A Case Study from Bread Supply Chain in Egypt," Sustainability, MDPI, vol. 16(3), pages 1-19, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    2. Shin, Hojung & Park, Soohoon & Lee, Euncheol & Benton, W.C., 2015. "A classification of the literature on the planning of substitutable products," European Journal of Operational Research, Elsevier, vol. 246(3), pages 686-699.
    3. Pol Boada-Collado & Victor Martínez-de-Albéniz, 2020. "Estimating and Optimizing the Impact of Inventory on Consumer Choices in a Fashion Retail Setting," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 582-597, May.
    4. Transchel, Sandra, 2017. "Inventory management under price-based and stockout-based substitution," European Journal of Operational Research, Elsevier, vol. 262(3), pages 996-1008.
    5. Fernando Bernstein & A. Gürhan Kök & Lei Xie, 2015. "Dynamic Assortment Customization with Limited Inventories," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 538-553, October.
    6. Ding, Xiaohui & Chen, Caihua & Li, Chongshou & Lim, Andrew, 2021. "Product demand estimation for vending machines using video surveillance data: A group-lasso method," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    7. Joonkyum Lee & Vishal Gaur & Suresh Muthulingam & Gary F. Swisher, 2016. "Stockout-Based Substitution and Inventory Planning in Textbook Retailing," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 104-121, February.
    8. Marshall Fisher & Ramnath Vaidyanathan, 2014. "A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations," Management Science, INFORMS, vol. 60(10), pages 2401-2415, October.
    9. Qiu, Jiaqing & Li, Xiangyong & Duan, Yongrui & Chen, Mengxi & Tian, Peng, 2020. "Dynamic assortment in the presence of brand heterogeneity," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    10. Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2023. "Machine Learning Methods for Data-Driven Demand Estimation and Assortment Planning Considering Cross-Selling and Substitutions," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 158-177, January.
    11. Transchel, Sandra & Buisman, Marjolein E. & Haijema, Rene, 2022. "Joint assortment and inventory optimization for vertically differentiated products under consumer-driven substitution," European Journal of Operational Research, Elsevier, vol. 301(1), pages 163-179.
    12. Yücel, Eda & Karaesmen, Fikri & Salman, F. Sibel & Türkay, Metin, 2009. "Optimizing product assortment under customer-driven demand substitution," European Journal of Operational Research, Elsevier, vol. 199(3), pages 759-768, December.
    13. Talebian, Masoud & Boland, Natashia & Savelsbergh, Martin, 2014. "Pricing to accelerate demand learning in dynamic assortment planning for perishable products," European Journal of Operational Research, Elsevier, vol. 237(2), pages 555-565.
    14. Yu, Yimin & Shou, Biying & Ni, Yaodong & Chen, Li, 2017. "Optimal production, pricing, and substitution policies in continuous review production-inventory systems," European Journal of Operational Research, Elsevier, vol. 260(2), pages 631-649.
    15. Lingxiu Dong & Panos Kouvelis & Zhongjun Tian, 2009. "Dynamic Pricing and Inventory Control of Substitute Products," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 317-339, December.
    16. Maria Mayorga & Hyun-Soo Ahn & Goker Aydin, 2013. "Assortment and inventory decisions with multiple quality levels," Annals of Operations Research, Springer, vol. 211(1), pages 301-331, December.
    17. Patxi J. Bernales & Yongtao Guan & Harihara Prasad Natarajan & Patricia Souza Gimenez & Mario Xavier Alvarez Tajes, 2017. "Less Is More: Harnessing Product Substitution Information to Rationalize SKUs at Intcomex," Interfaces, INFORMS, vol. 47(3), pages 230-243, June.
    18. Robert P. Rooderkerk & Harald J. van Heerde & Tammo H. A. Bijmolt, 2013. "Optimizing Retail Assortments," Marketing Science, INFORMS, vol. 32(5), pages 699-715, September.
    19. Sascha Kurz & Jörg Rambau & Jörg Schlüchtermann & Rainer Wolf, 2015. "The Top-Dog Index: a new measurement for the demand consistency of the size distribution in pre-pack orders for a fashion discounter with many small branches," Annals of Operations Research, Springer, vol. 229(1), pages 541-563, June.
    20. Yalçın Akçay & Yunke Li & Harihara Prasad Natarajan, 2020. "Category Inventory Planning With Service Level Requirements and Dynamic Substitutions," Production and Operations Management, Production and Operations Management Society, vol. 29(11), pages 2553-2578, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2502-:d:1158851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.