Forecasting Sales Profiles of Products in an Exceptional Context: COVID-19 Pandemic

Sleiman, Rita; Mazyad, Ahmad; Hamad, Moez; Tran, Kim-Phuc; Thomassey, Sebastien

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.1007/s44196-022-00161-x

URL permanente :

http://hdl.handle.net/20.500.12210/84975

Titre :

Forecasting Sales Profiles of Products in an Exceptional Context: COVID-19 Pandemic

Auteur(s) :

Sleiman, Rita [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Mazyad, Ahmad [Auteur]
Hamad, Moez [Auteur]
Tran, Kim-Phuc [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Thomassey, Sebastien [Auteur]

Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]

Titre de la revue :

International Journal of Computational Intelligence Systems

Nom court de la revue :

Int. J. Comput. Intell. Syst.

Numéro :

Pagination :

Date de publication :

2022-12-02

ISSN :

1875-6891

Mot(s)-clé(s) en anglais :

Demand forecasting
Sales profiles
K-means clustering
Random forest
Sales profiles correction

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Accurate demand forecasting has always been essential for retailers in order to be able to survive in the highly competitive, volatile modern market. However, anticipating product demand is an extremely difficult task in ...
Lire la suite >Accurate demand forecasting has always been essential for retailers in order to be able to survive in the highly competitive, volatile modern market. However, anticipating product demand is an extremely difficult task in the context of short product life cycles in which consumer demand is influenced by many heterogeneous variables. During the COVID-19 pandemic in particular, with all its related new constraints, the fashion industry has seen a huge decline in sales, which makes it difficult for existing sales forecasting methods to accurately predict new product sales. This paper proposes an original sales forecasting framework capable of considering the effect of the COVID-19 related crisis on sales. The proposed framework combines clustering, classification, and regression. The main goals of this framework are (1) to predict a sales pattern for each item based on its attributes and (2) to correct it by modelling the impact of the crisis on sales. We evaluate our proposed framework using a real-world dataset of a French fashion retailer with Omnichannel sales. Despite the fact that during the lockdown period online sales were still possible, consumer purchases were significantly impacted by this crisis. Experimental analysis show that our methodology learns the impact of the crisis on consumer behavior from online sales, and then, adapts the sales forecasts already obtained.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

Génie des matériaux textiles (GEMTEX) - ULR 2641

Date de dépôt :

2023-06-20T12:09:52Z
2024-03-21T08:07:31Z

Fichiers

s44196-022-00161-x.pdf
Version éditeur
Accès libre
Accéder au document

Ce document est diffusé sous licence Attribution 3.0 United States

Numéro de version	Lien	Date de modification
2	20.500.12210/84975*	2024-03-21T08:04:47Z
1	20.500.12210/84975.1	2023-06-20T12:09:52Z

Forecasting Sales Profiles of Products in ...

BibTeX

CSV

Excel

RIS

Fichiers

Historique des modifications

Forecasting Sales Profiles of Products in ... BibTeX CSV Excel RIS

Fichiers

Historique des modifications

Forecasting Sales Profiles of Products in ...

BibTeX

CSV

Excel

RIS