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Shelf space allocation in retailing: a literature review.

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Title: Shelf space allocation in retailing: a literature review.
Authors: Ziari, Matineh1 (AUTHOR), Sajadieh, Mohsen Sheikh2 (AUTHOR) sajadieh@aut.ac.ir
Source: RAIRO: Operations Research (2804-7303). 2025, Vol. 59 Issue 5, p2721-2748. 28p.
Subjects: Retail space allocation, Retail industry, Data mining, Mathematical optimization, Systems theory, Empirical research, Consumer preferences
Abstract: Efficient allocation of shelf space is vital for attracting customers and maximizing profits in the retail industry, particularly given limited display areas. This paper offers a comprehensive review of shelf-space allocation (SSA) modeling, optimization techniques, and relevant case studies from 1969 to 2023. We categorize the literature into five key areas: first, mathematical optimization, which includes deterministic, uncertain, dynamic, and joint optimization models that form the foundation of SSA literature and enhance decision-making in complex retail environments. Second, we explore data mining techniques, demonstrating how retailers can implement consumer preference insights and purchasing patterns to transition from intuition-based decisions to data-driven strategies. Third, the case studies section illustrates real-world applications of SSA, highlighting the challenges and successes faced by retailers. Fourth, we synthesize methodologies, evaluating various SSA approaches through empirical studies that identify best practices and guide efficient resource utilization. Finally, we outline significant gaps in current research and suggest directions for future inquiry, encouraging ongoing exploration of innovative methods to improve shelf-space strategies. By systematically addressing these categories, this paper aims to provide a clearer understanding of the complexities of SSA, ultimately contributing to both academic discourse and practical applications within the retail sector. [ABSTRACT FROM AUTHOR]
Copyright of RAIRO: Operations Research (2804-7303) is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Shelf space allocation in retailing: a literature review.
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  Data: <searchLink fieldCode="AR" term="%22Ziari%2C+Matineh%22">Ziari, Matineh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sajadieh%2C+Mohsen+Sheikh%22">Sajadieh, Mohsen Sheikh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sajadieh@aut.ac.ir</i>
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  Data: <searchLink fieldCode="JN" term="%22RAIRO%3A+Operations+Research+%282804-7303%29%22">RAIRO: Operations Research (2804-7303)</searchLink>. 2025, Vol. 59 Issue 5, p2721-2748. 28p.
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  Data: <searchLink fieldCode="DE" term="%22Retail+space+allocation%22">Retail space allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Retail+industry%22">Retail industry</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+theory%22">Systems theory</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+preferences%22">Consumer preferences</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Efficient allocation of shelf space is vital for attracting customers and maximizing profits in the retail industry, particularly given limited display areas. This paper offers a comprehensive review of shelf-space allocation (SSA) modeling, optimization techniques, and relevant case studies from 1969 to 2023. We categorize the literature into five key areas: first, mathematical optimization, which includes deterministic, uncertain, dynamic, and joint optimization models that form the foundation of SSA literature and enhance decision-making in complex retail environments. Second, we explore data mining techniques, demonstrating how retailers can implement consumer preference insights and purchasing patterns to transition from intuition-based decisions to data-driven strategies. Third, the case studies section illustrates real-world applications of SSA, highlighting the challenges and successes faced by retailers. Fourth, we synthesize methodologies, evaluating various SSA approaches through empirical studies that identify best practices and guide efficient resource utilization. Finally, we outline significant gaps in current research and suggest directions for future inquiry, encouraging ongoing exploration of innovative methods to improve shelf-space strategies. By systematically addressing these categories, this paper aims to provide a clearer understanding of the complexities of SSA, ultimately contributing to both academic discourse and practical applications within the retail sector. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of RAIRO: Operations Research (2804-7303) is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189386729
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        Value: 10.1051/ro/2025037
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      – Code: eng
        Text: English
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        PageCount: 28
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      – SubjectFull: Retail space allocation
        Type: general
      – SubjectFull: Retail industry
        Type: general
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Mathematical optimization
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      – SubjectFull: Systems theory
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      – SubjectFull: Consumer preferences
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            NameFull: Sajadieh, Mohsen Sheikh
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            – D: 01
              M: 09
              Text: 2025
              Type: published
              Y: 2025
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