Does demand forecasting matter to retailing?

Wesley Marcos Almeida () and Claudimar Pereira Veiga ()
Additional contact information
Wesley Marcos Almeida: Pontifical Catholic University of Parana (PUCPR)
Claudimar Pereira Veiga: Federal University of Paraná

Journal of Marketing Analytics, 2023, vol. 11, issue 2, No 8, 219-232

Abstract: Abstract Time series forecasting in retail plays an important role for retailers due to its capacity to aid managers in strategic planning to meet demand. In this article, a comparative experiment between two demand forecasting models is conducted to forecast an aggregate time series in Brazilian food retail on a twelve-year horizon. Performance was analyzed by comparing the accuracy of a linear model, Seasonal Autoregressive Integrated Moving Average (SARIMA), and a non-linear model, and Wavelets Neural Network (WNN). Performance was validated by the Mean Absolute Percentage Error (MAPE) and U-Theil, with a view to achieving the most accurate measurement of demand responsiveness, using the Fill Rate (FR). WNN had the best performance, with a FR of 98.43%, whereas SARIMA achieved a FR of 102.23%. Demand forecasting using WNN can be considered a resource for differentiation between retail organizations when it is incorporated into their operations. This article makes three contributions to the field with an application to retail and strengthening the argument for choosing a promising technique that can be used by managers to aid their decision making.

Keywords: Forecasting; SARIMA; Wavelets neural networks; Retail; Decision making (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41270-022-00162-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jmarka:v:11:y:2023:i:2:d:10.1057_s41270-022-00162-x

Ordering information: This journal article can be ordered from
http://www.springer. ... gement/journal/41270

DOI: 10.1057/s41270-022-00162-x

Access Statistics for this article

Journal of Marketing Analytics is currently edited by Maria Petrescu and Anjala Krishnen

More articles in Journal of Marketing Analytics from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().