IDEAS home Printed from https://ideas.repec.org/a/ers/journl/vxxivy2021i3p316-334.html
   My bibliography  Save this article

Shelf Space Allocation for Specific Products on Shelves Selected in Advance

Author

Listed:
  • Kateryna Czerniachowska
  • Marcin Hernes

Abstract

Purpose: The aim of the research is to develop the shelf space allocation model for specific products on shelves selected in advance, based on the practical retail requirements. Retailers and manufacturers may impose particular conditions for the appearance of products on the shelf based on its package type, brand, price, form and size. Shelf space allocation must be in line with the store positioning strategy of the retailer. Design/Methodology/Approach: This paper proposed dynamic programming to solve the profit maximization problem on small problem sizes considering extra allocation parameters such as capping and nesting. The distribution of shelf space to products has a direct effect on the competitiveness of the retail store. Findings: The paper presented major profit differences between shelf space allocation without capping and nesting parameters and including them. The computational experiments were performed to test the additional gains received with the usage of capping and nesting parameters. Practical Implications: This research provided qualitative insights for the retailers by comparing the profit gained with and without the capping and nesting allocation possibilities proposed in the model. Originality/Value: In this paper the basic shelf space allocation problem was simplified with selection of the shelf to place the products in advance, next the basic shelf space allocation model was extended with capping and nesting on-shelf allocation methods.

Suggested Citation

  • Kateryna Czerniachowska & Marcin Hernes, 2021. "Shelf Space Allocation for Specific Products on Shelves Selected in Advance," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 316-334.
    • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:3:p:316-334
    as

    Download full text from publisher

    File URL: https://www.ersj.eu/journal/2356/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hansen, Jared M. & Raut, Sumit & Swami, Sanjeev, 2010. "Retail Shelf Allocation: A Comparative Analysis of Heuristic and Meta-Heuristic Approaches," Journal of Retailing, Elsevier, vol. 86(1), pages 94-105.
    2. Yang, Ming-Hsien, 2001. "An efficient algorithm to allocate shelf space," European Journal of Operational Research, Elsevier, vol. 131(1), pages 107-118, May.
    3. Pisinger, David, 1995. "An expanding-core algorithm for the exact 0-1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 87(1), pages 175-187, November.
    4. Wongkitrungrueng, Apiradee & Valenzuela, Ana & Sen, Sankar, 2018. "The Cake Looks Yummy on the Shelf up There: The Interactive Effect of Retail Shelf Position and Consumers’ Personal Sense of Power on Indulgent Choice," Journal of Retailing, Elsevier, vol. 94(3), pages 280-295.
    5. Pisinger, David, 1995. "A minimal algorithm for the multiple-choice knapsack problem," European Journal of Operational Research, Elsevier, vol. 83(2), pages 394-410, June.
    6. Raymond R. Burke, 2006. "The Third Wave of Marketing Intelligence," Springer Books, in: Manfred Krafft & Murali K. Mantrala (ed.), Retailing in the 21st Century, pages 113-125, Springer.
    Full references (including those not matched with items on IDEAS)

    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. Kateryna Czerniachowska, 2022. "A genetic algorithm for the retail shelf space allocation problem with virtual segments," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 364-412, March.
    2. Bianchi-Aguiar, Teresa & Hübner, Alexander & Carravilla, Maria Antónia & Oliveira, José Fernando, 2021. "Retail shelf space planning problems: A comprehensive review and classification framework," European Journal of Operational Research, Elsevier, vol. 289(1), pages 1-16.
    3. Ostermeier, Manuel & Düsterhöft, Tobias & Hübner, Alexander, 2021. "A model and solution approach for store-wide shelf space allocation," Omega, Elsevier, vol. 102(C).
    4. Masoud Rabbani & Navid Salmanzadeh-Meydani & Amir Farshbaf-Geranmayeh & Vahed Fadakar-Gabalou, 2018. "Profit maximizing through 3D shelf space allocation of 2D display orientation items with variable heights of the shelves," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 337-360, June.
    5. Bianchi-Aguiar, Teresa & Silva, Elsa & Guimarães, Luis & Carravilla, Maria Antónia & Oliveira, José F., 2018. "Allocating products on shelves under merchandising rules: Multi-level product families with display directions," Omega, Elsevier, vol. 76(C), pages 47-62.
    6. Wishon, Christopher & Villalobos, J. Rene, 2016. "Robust efficiency measures for linear knapsack problem variants," European Journal of Operational Research, Elsevier, vol. 254(2), pages 398-409.
    7. Higgins Michael J. & Rivest Ronald L. & Stark Philip B., 2011. "Sharper p-Values for Stratified Election Audits," Statistics, Politics and Policy, De Gruyter, vol. 2(1), pages 1-37, October.
    8. Kim, Gwang & Moon, Ilkyeong, 2021. "Integrated planning for product selection, shelf-space allocation, and replenishment decision with elasticity and positioning effects," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    9. Kyungmin Kim & Minseok Song, 2022. "Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs," Energies, MDPI, vol. 15(10), pages 1-16, May.
    10. Hoto, Robinson & Arenales, Marcos & Maculan, Nelson, 2007. "The one dimensional Compartmentalised Knapsack Problem: A case study," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1183-1195, December.
    11. Flamand, Tulay & Ghoniem, Ahmed & Haouari, Mohamed & Maddah, Bacel, 2018. "Integrated assortment planning and store-wide shelf space allocation: An optimization-based approach," Omega, Elsevier, vol. 81(C), pages 134-149.
    12. Mavrotas, George & Figueira, José Rui & Florios, Kostas, 2009. "Solving the bi-objective multidimensional knapsack problem exploiting the concept of core," MPRA Paper 105087, University Library of Munich, Germany.
    13. Haahr, J.T. & Lusby, R.M. & Wagenaar, J.C., 2015. "A Comparison of Optimization Methods for Solving the Depot Matching and Parking Problem," ERIM Report Series Research in Management ERS-2015-013-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. Christian Tipantuña & Xavier Hesselbach, 2020. "NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms," Future Internet, MDPI, vol. 12(10), pages 1-31, October.
    15. Yan-Kwang Chen & Shi-Xin Weng & Tsai-Pei Liu, 2020. "Teaching–Learning Based Optimization (TLBO) with Variable Neighborhood Search to Retail Shelf-Space Allocation," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
    16. Düsterhöft, Tobias & Hübner, Alexander & Schaal, Kai, 2020. "A practical approach to the shelf-space allocation and replenishment problem with heterogeneously sized shelves," European Journal of Operational Research, Elsevier, vol. 282(1), pages 252-266.
    17. Subhash C. Sarin & Hanif D. Sherali & Seon Ki Kim, 2014. "A branch‐and‐price approach for the stochastic generalized assignment problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(2), pages 131-143, March.
    18. Li, Xin & Qian, Zhuzhong & You, Ilsun & Lu, Sanglu, 2014. "Towards cost efficient mobile service and information management in ubiquitous environment with cloud resource scheduling," International Journal of Information Management, Elsevier, vol. 34(3), pages 319-328.
    19. David Pisinger, 2000. "A Minimal Algorithm for the Bounded Knapsack Problem," INFORMS Journal on Computing, INFORMS, vol. 12(1), pages 75-82, February.
    20. Lotfi, M.M. & Torabi, S.A., 2011. "A fuzzy goal programming approach for mid-term assortment planning in supermarkets," European Journal of Operational Research, Elsevier, vol. 213(2), pages 430-441, September.

    More about this item

    Keywords

    Retailing; decision making/process; merchandising; shelf levels; product package;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

    Statistics

    Access and download statistics

    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:ers:journl:v:xxiv:y:2021:i:3:p:316-334. 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: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .

    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.