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

Zhou, Xin; Jian-fei, Meng; Wang, Guosheng; Qin, Xiangjing

doi:10.1108/ijcst-08-2019-0114

Cited by 13 publications

(9 citation statements)

References 33 publications

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“…It is mainly divided into two categories: traditional statistical methods that rely on the characteristics of time series [12], and artificial intelligence heuristic algorithms relying on large-scale historical data [13]. Zhou et al [14] proposed an improved Bass model for fast-fashion clothing demand forecasting based on the influence of consumer preference and seasonality on demand forecasting. The premise of using the Bass model is that the market potential and performance of the product will remain unchanged in the life cycle, and the product will not be innovated.…”

Section: Research Progress On the Sales Forecasting Modelmentioning

confidence: 99%

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Lv¹,

Han²,

Hu³

2023

JTST

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Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.

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Section: Research Progress On the Sales Forecasting Modelmentioning

confidence: 99%

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Lv¹,

Han²,

Hu³

2023

JTST

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“…In terms of predicting market trends, statistical approaches are used in experiments, including linear regression, weighted average and exponential smoothing, to discover the relationship between clothing attributes or rank them (Zhou et al ., 2021). In recent decades, machine-learning-based methods have become popular among researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

How to discover consumer attention to design topics of fast fashion: a topic modeling approach

Pan,

Li,

Luo

et al. 2023

JFMM

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PurposeIt is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side.Design/methodology/approachTo address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention.FindingsThe experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams.Practical implicationsThe issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies.Originality/valueThis study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented.

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“…The second-hand clothing market, which recycles, cleans and re-sells such clothes, can effectively extend the life of a fashion item, thus positively influencing the sustainability of the fashion and textile industry. (Zhou et al, 2020) [23] However, the fashion industry has mixed views on the sustainability of the second-hand market.…”

Section: Second-hand Fashion Market: the Reuse Of Unwanted Textilesmentioning

confidence: 99%

How Is Fashion Going: Structured Literature Review for Circular Economy in Fashion and Textile Industry

Huang¹

2022

FBEM

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In a time of global warming, sustainability claims are prevalent in the fashion and textile industry. Fashion industry is experiencing a shift from a linear economy to a circular economy. The circular economy shift in fashion textiles, however, has very many dilemmas due to the close connection with its value chain. This paper will take the form of a literature review to discuss the methods used in transforming the circular economy in fashion and textiles and the dilemmas it faces, as well as the social issues arising from the transformation, from the design to the distribution to the recycling of a fashion item.

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

Cited by 13 publications

References 33 publications

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

How to discover consumer attention to design topics of fast fashion: a topic modeling approach

How Is Fashion Going: Structured Literature Review for Circular Economy in Fashion and Textile Industry

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