Prediction of retail sales of footwear using feedforward and recurrent neural networks

Das, Prasun; Chaudhury, Subhasis

doi:10.1007/s00521-006-0077-3

Cited by 26 publications

(7 citation statements)

References 6 publications

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“…Seasonality analysis is useful when sales forecasting (Steele, 1951). A crucial aspect of demand planning, seasonality allows firm managers to generate reasonable sales projections ( Jain and Covas, 2010), and effectively manage the supply chain (Das, 2007).…”

Section: Modeling Seasonalitymentioning

confidence: 99%

Culture and gender’s role in apparel purchasing patterns

Nieves-Rodriguez

Pérez-Rivera

Longobardi

et al. 2017

JFMM

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Purpose Scholars recognize that international marketing effectiveness requires adapting to cultural values, and at the same time, paradoxically, acknowledge the possibility of cultural convergence. The purpose of this paper is to take the context of Puerto Rico as a US territory to reconcile these two propositions by analyzing culture and gender’s influence on apparel purchase. Design/methodology/approach Via multiple regression analysis, the study considers seasonality as a factor of apparel purchase patterns, developing a consumer behavior model for the apparel industry. Findings Results confirm that culture influences purchase behavior, an influence moderated by gender. Additionally, they show that seasons and special occasions are strong predictors of apparel purchase patterns. Research limitations/implications The findings assert the claims of cultural convergence, yet preserve the notion that cultural values are reflected in patterns of consumer behavior in the case of apparel. Practical implications The study develops highly explanatory models indicating that Puerto Rico expenditure reflects cultural patterns of special occasions, but overshoots expectations for its US counterpart. Originality/value The results show that Puerto Rico has appropriated several US cultural aspects (e.g. special holidays), which are expressed differently as reflected by apparel purchase behavior, supporting the notion that Puerto Rico should be treated as an international market. The study demonstrates that cross-cultural studies may be robust in absence of available Hofstede’s dimensions for a country.

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Section: Modeling Seasonalitymentioning

confidence: 99%

Culture and gender’s role in apparel purchasing patterns

Nieves-Rodriguez

Pérez-Rivera

Longobardi

et al. 2017

JFMM

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“…Punam et al [11] proposed a model based on two-level statistical analyses that help forecast store sales. In another study proposed by [12], the author uses the neural network to predict clog's weekly sales store. This forecasting helps the owner to balance the stock regularly.…”

Section: Literature Reviewmentioning

confidence: 99%

BMSP-ML: big mart sales prediction using different machine learning techniques

Ali¹,

Muneer

Almaghthawi

et al. 2023

IJ-AI

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<span lang="EN-US">Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an essential element is to clear the missing data and perform proper feature engineering to better understand them before applying them. The experimental results show that the random forest predictor has outperformed ridge regression, linear regression, and decision tree models among the four machine learning techniques implemented in this study. The performance of the proposed models has been evaluated using root mean square error (RMSE).</span>

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“…Different techniques have been applied to demand forecasting in literature for the fashion industry, including neural networks, support vector machine (SVM), fuzzy inference system, extreme learning machine, extended extreme learning machine, deep neural networks, harmony search algorithm and grey method (Thomassey et al (2005), Das andChaudhury (2007), Sun et al (2008), Au et al (2008), Carbonneau et al (2008), Thomassey (2010), Wong and Guo (2010), Choi et al (2011), Xia et al (2012, Choi et al (2014), Lu (2014), Kaya et al (2014), Brahmadeep andThomassey (2016), Pillo et al (2016), Hui and Choi (2016), Loureiro et al (2018)). In addition, a hybrid combining different techniques tend to perform better than a single method (Wong and Guo (2010), Choi et al (2014)).…”

Section: State-of-artmentioning

confidence: 99%

“…The used features were "cluster numbers", "sales units", "life cycle length", and with an addition of new feature average sales. Data normalization was performed, algorithms of regression trees, k-NN, linear regression (James et al ( 2014)), random forests (Breiman (2001)), and neural networks (Zhang ( 2003), Das and Chaudhury (2007)) were applied in prediction step. Besides, ensemble methods taking the median and average of the outputs from the five individual methods were also considered and the performance was evaluated in terms of accuracy.…”

Section: Three-step Modelmentioning

confidence: 99%

A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches

Kharfan

Chan²,

Efendigil

2020

Ann Oper Res

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Companies in the fashion industry are struggling with forecasting demand due to the short-selling season, long lead times between the operations, huge product variety and ambiguity of demand information. The forecasting process is becoming more complicated by virtue of evolving retail technology trends. Demand volatility and speed are highly affected by e-commerce strategies as well as social media usage regards to varying customer preferences, short product lifecycles, obsolescence of the retail calendar, and lack of information for newly launched seasonal items. Consumers have become more demanding and less predictable in their purchasing behavior that expects high quality, guaranteed availability and fast delivery. Meeting high expectations of customers' initiates with proper demand management.This study focuses on demand prediction with a data-driven perspective by both leveraging machine learning techniques and identifying significant predictor variables to help fashion retailers achieve better forecast accuracy. Prediction results obtained were compared to present the benefits of machine learning approaches. The proposed approach was applied by a leading fashion retail company to forecast the demand of newly launched seasonal products without historical data.

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Prediction of retail sales of footwear using feedforward and recurrent neural networks

Cited by 26 publications

References 6 publications

Culture and gender’s role in apparel purchasing patterns

Culture and gender’s role in apparel purchasing patterns

BMSP-ML: big mart sales prediction using different machine learning techniques

A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches

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