利用机器学习解码客户对时尚新品偏好的需求预测模型

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Complexity Pub Date : 2024-07-11 DOI:10.1155/2024/8425058
S. Anitha, R. Neelakandan
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引用次数: 0

摘要

由于流行趋势不断变化、交货期较长、季节性变化和产品激增,时尚业新产品的需求预测一直面临挑战。要进行准确的需求预测,就必须全面了解消费者的偏好。本研究提出了一种基于机器学习的模型,用于分析顾客偏好和预测新产品需求。为了解顾客偏好,我们对试衣间数据进行了分析,并创建了顾客档案。应用无监督机器学习算法 K-means 聚类,通过将相似的资料分组来形成聚类。聚类的权重与每个聚类中产品的百分比有关。在聚类过程之后,使用决策树分类模型将新产品归入预定义的聚类之一,以预测新产品的需求。这种需求预测方法将使零售商能够库存符合客户偏好的产品,从而最大限度地减少过剩库存。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A Demand Forecasting Model Leveraging Machine Learning to Decode Customer Preferences for New Fashion Products

Demand forecasting for new products in the fashion industry has always been challenging due to changing trends, longer lead times, seasonal shifts, and the proliferation of products. Accurate demand forecasting requires a thorough understanding of consumer preferences. This research suggests a model based on machine learning to analyse customer preferences and forecast the demand for new products. To understand customer preferences, the fitting room data are analysed, and customer profiles are created. K-means clustering, an unsupervised machine learning algorithm, is applied to form clusters by grouping similar profiles. The clusters were assigned weights related to the percentage of product in each cluster. Following the clustering process, a decision tree classification model is used to classify the new product into one of the predefined clusters to predict demand for the new product. This demand forecasting approach will enable retailers to stock products that align with customer preferences, thereby minimising excess inventory.

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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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