Sales Forecasting for Fashion Retailing Service Industry: A Review

Abstract

Sales forecasting is crucial for many retail operations. It is especially critical for the fashion retailing service industry in which product demand is very volatile and product's life cycle is short. This paper conducts a comprehensive literature review and selects a set of papers in the literature on fashion retail sales forecasting. The advantages and the drawbacks of different kinds of analytical methods for fashion retail sales forecasting are examined. The evolution of the respective forecasting methods over the past 15 years is revealed. Issues related to real-world applications of the fashion retail sales forecasting models and important future research directions are discussed.

Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model

“…This paper contributes in three ways. 1) First, to the best of our knowledge, this paper is the first one which examines the panel data-based method for conducting fashion sales forecasting [42], [49]. 2) Second, our experimental results indicate that the proposed panel data models outperform both the traditional statistical and intelligent methods, which provide a strong evidence on the importance of employing the panel-data approach.…”

“…These two choices are not taken arbitrarily. In fact, ARIMA is a simple yet commonly adopted method for conducting sales forecasting [42]. It is based on the integration of auto-regression and moving average [41].…”

A survey on retail sales forecasting and prediction in fashion markets

“…In addition, for an overview on older research on fashion sales forecasting, the work of Liu et al (2013) and that of are recommended. Nenni, Giustiniano, and Pirolo (2013) give also a review of the actual sate of fashion sales forecasting, however, with the focus on the different supply chain strategies.…”

“…Email: bek@biba.uni-bremen.de mining models. In order to increase the forecasting performances, more often hybrid models are developed to use the advantages of different models for a new combined approach (Liu, Ren, Choi, Hui, & Ng, 2013). Especially, hybrid models seem to be accurate in sales forecasting (Aburto & Weber, 2007;Khashei & Bijari, 2011;Lee, Shih, & Chen, 2012).…”

Predicting the Final result of Student National Test with Extreme Learning Machine

“…Many applications are used to support in modeling forecasts such as stock forecasting, weather and so on [2]. Artificial Neural network (ANN) is able to recognize an event based on past data.…”