Intelligent demand forecasting systems for fast fashion

2016 ◽  
pp. 145-161 ◽  
Author(s):  
Brahmadeep ◽  
S. Thomassey
Keyword(s):  
Demand Forecasting ◽  
Fast Fashion

A demand forecasting model based on the improved Bass model for fast fashion clothing

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoxi Zhou ◽  
Jianfei Meng ◽  
Guosheng Wang ◽  
Qin Xiaoxuan
Keyword(s):  
Historical Data ◽  
Consumer Preferences ◽  
Demand Forecasting ◽  
Impact Factors ◽  
Bass Model ◽  
Demand Forecast ◽  
Fast Fashion ◽  
Content Type ◽  
Sales Data ◽  
Demand Forecasting Model

PurposeThis paper examines the problem of lack of historical data and inadequate consideration of factors influencing demand in the forecasting of demand for fast fashion clothing and proposes an improved Bass model for the forecasting of such a demand and the demand for new clothing products.Design/methodology/approachFrom the perspective of how to solve the lack of data and improve the precision of the clothing demand forecast, this paper studies the measurement of clothing similarity and the addition of demand impact factors. Using the fuzzy clustering–rough set method, the degree of resemblance of clothing is determined, which provides a basis for the scientific utilisation of historical data of similar clothing to forecast the demand for new clothing. Besides, combining the influence of consumer preferences and seasonality on demand forecasting, an improved Bass model for a fast fashion clothing demand forecast is proposed. Finally, with a forecasting example of demand for clothing, this study also tests the validity of the method.FindingsThe objective measurement method of clothing similarity in this paper solves the problem of the difficult forecasting of demand for fast fashion clothing due to a lack of sales data at the preliminary stage of the clothing launch. The improved Bass model combines, comprehensively, consumer preferences and seasonality and enhances the forecast precision of demand for fast fashion clothing.Originality/valueThe paper puts forward a scientific, quantitative method for the forecasting of new clothing products using historical sales data of similar clothing, thus solving the problem of lack of sales data of the fashion.

scholarly journals Intelligent Retail Forecasting System for New Clothing Products Considering Stock-out

2017 ◽  
Vol 25 (0) ◽  
pp. 10-16 ◽  
Author(s):  
He Huang ◽  
He Huang ◽  
Qiurui Liu
Keyword(s):  
Fuzzy Inference ◽  
Selection Process ◽  
New Products ◽  
Demand Forecasting ◽  
Fast Fashion ◽  
Inference System ◽  
Factors Affecting ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Forecasting System

Improving the accuracy of forecasting is crucial but complex in the clothing industry, especially for new products, with the lack of historical data and a wide range of factors affecting demand. Previous studies more concentrate on sales forecasting rather than demand forecasting, and the variables affecting demand remained to be optimized. In this study, a two-stage intelligent retail forecasting system is designed for new clothing products. In the first stage, demand is estimated with original sales data considering stock-out. The adaptive neuro fuzzy inference system (ANFIS) is introduced into the second stage to forecast demand. Meanwhile a data selection process is presented due to the limited data of new products. The empirical data are from a Canadian fast-fashion company. The results reveal the relationship between demand and sales, demonstrate the necessity of integrating the demand estimation process into a forecasting system, and show that the ANFIS-based forecasting system outperforms the traditional ANN technique.

scholarly journals Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1578
Author(s):  
I-Fei Chen ◽  
Chi-Jie Lu
Keyword(s):  
Demand Uncertainty ◽  
Prediction Models ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fashion Industry ◽  
Fast Fashion ◽  
Product Demand ◽  
Learning Machines ◽  
Forecasting Method

In today’s rapidly changing and highly competitive industrial environment, a new and emerging business model—fast fashion—has started a revolution in the apparel industry. Due to the lack of historical data, constantly changing fashion trends, and product demand uncertainty, accurate demand forecasting is an important and challenging task in the fashion industry. This study integrates k-means clustering (KM), extreme learning machines (ELMs), and support vector regression (SVR) to construct cluster-based KM-ELM and KM-SVR models for demand forecasting in the fashion industry using empirical demand data of physical and virtual channels of a case company to examine the applicability of proposed forecasting models. The research results showed that both the KM-ELM and KM-SVR models are superior to the simple ELM and SVR models. They have higher prediction accuracy, indicating that the integration of clustering analysis can help improve predictions. In addition, the KM-ELM model produces satisfactory results when performing demand forecasting on retailers both with and without physical stores. Compared with other prediction models, it can be the most suitable demand forecasting method for the fashion industry.

Ethical Issues in the Fast Fashion Industry

2018 ◽  
Vol 9 (8) ◽  
pp. 660-665
Author(s):  
Chi Sheh ◽  
◽  
Peng Chan ◽  
Wen Jun Sim ◽  
◽  
...  
Keyword(s):  
Business Ethics ◽  
Conceptual Model ◽  
Ethical Issues ◽  
Real Life ◽  
Time Frame ◽  
Fashion Industry ◽  
Fast Fashion ◽  
Life Event ◽  
Real Cost ◽  
Literature Reviews

Fast fashion is becoming more and more popular nowadays and this industry is growing rapidly. In order to supply to the big demand of fast fashion clothing, company will need to increase the production of the clothing in shorter time frame. Besides that, to out beat the competitor, company will provide more choices of clothing in cheaper price to the customers. By practicing these actions to increase the business profits, company is behaving unethical to the manufacturer of the cloth. Most consumers are not aware of these ethical issues. This paper is will used and tested the conceptual model of fast fashion business ethics based on literature reviews. The finding from this paper will manifest the “real cost” of a cheap and branded fast fashion clothing and will be supported by real life event that happened. However, after realizing the problems, some company did make some changes and the solutions are stated in the paper as well.

Net Demand Forecasting by Taguchi's T Method in Small or Middle Power Demand with PVs

2017 ◽  
Vol 137 (8) ◽  
pp. 1043-1051 ◽  
Author(s):  
Yusuke Morimoto ◽  
Shintaro Negishi ◽  
Satoshi Takayama ◽  
Atsushi Ishigame
Keyword(s):  
Demand Forecasting ◽  
Power Demand ◽  
Middle Power
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