پیش بینی فروش در خرده فروشی مد با استفاده از شبکه های عصبی
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پیش بینی فروش در خرده فروشی مد با استفاده از شبکه های عصبی

عنوان فارسی مقاله: بررسی استفاده از شبکه های عصبی عمیق برای پیش بینی فروش در خرده فروشی مد
عنوان انگلیسی مقاله: Exploring the use of deep neural networks for sales forecasting in fashion retail
مجله/کنفرانس: سیستم های حمایتی تصمیم گیرنده - Decision Support Systems
رشته های تحصیلی مرتبط: مدیریت، مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: بازاریابی، هوش مصنوعی
کلمات کلیدی فارسی: پیش بینی فروش، خرده فروشی مد، رگرسیون بردار پشتیبانی، شبکه های عصبی مصنوعی، شبکه های عمیق عصبی
کلمات کلیدی انگلیسی: Sales forecasting، Fashion retail، Support vector regression، Artificial neural networks، Deep neural networks
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.dss.2018.08.010
دانشگاه: Faculdade de Engenharia da Universidade do Porto, INESC TEC, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 4/126 در سال 2017
شاخص H_index: 115 در سال 2019
شاخص SJR: 1/656 در سال 2017
شناسه ISSN: 0167-9236
شاخص Quartile (چارک): Q1 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 37
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E10856
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related work


3- Case study and data


4- Methodology


5- Results and discussion


6- Conclusions and future work


References

نمونه متن انگلیسی مقاله

Abstract


In the increasingly competitive fashion retail industry, companies are constantly adopting strategies focused on adjusting the products characteristics to closely satisfy customers' requirements and preferences. Although the lifecycles of fashion products are very short, the definition of inventory and purchasing strategies can be supported by the large amounts of historical data which are collected and stored in companies' databases. This study explores the use of a deep learning approach to forecast sales in fashion industry, predicting the sales of new individual products in future seasons. This study aims to support a fashion retail company in its purchasing operations and consequently the dataset under analysis is a real dataset provided by this company. The models were developed considering a wide and diverse set of variables, namely products' physical characteristics and the opinion of domain experts. Furthermore, this study compares the sales predictions obtained with the deep learning approach with those obtained with a set of shallow techniques, i.e. Decision Trees, Random Forest, Support Vector Regression, Artificial Neural Networks and Linear Regression. The model employing deep learning was found to have good performance to predict sales in fashion retail market, however for part of the evaluation metrics considered, it does not perform significantly better than some of the shallow techniques, namely Random Forest.


Introduction


Fashion retail is a highly competitive market, where inventory control plays a key role in the profitability of companies. Accurate sales forecasting is therefore fundamental to be successful in this environment. If sales forecasting is not accurate, stock-out or overstock situations might occur, which can have a direct and immediate impact on the company’s profitability (Agrawal & Schorling, 1996; Sun, Choi, Au, & Yu, 2008; Baecke, De Baets, & Vanderheyden, 2017; Sodero & Rabinovich, 2017; Xia & Wong, 2014) . The effect is not restricted to profitability performance, as quality of the customer service can also be affected by an inefficient forecasting system. For example, if a customer is faced with a stock-out situation, they might decide to shop in a different retailer (Corsten & Gruen, 2004). Additionally, it is known that the fashion industry operates with long supply chains involving a large number of actors (such as raw materials suppliers, manufacturers, distributors and retailers), which leads to orders being placed before there is an accurate understanding of the demand level for the products (H. L. Lee, Padmanabhan, & Whang, 1997; H. Huang & Liu, 2017). Despite its relevance, sales forecasting is a complex subject because the sales’ success of a product is highly dependent on the personal taste of consumers, which varies greatly (Allenby, Jen, & Leone, 1996; Choi, Hui, Liu, Ng, & Yu, 2014). In addition, the lifecycle of fashion products is typically very short, being replaced every new season by new products with no historical sales data (Choi, Hui, Ng, & Yu, 2012). Fashion collections are also composed of an extremely large number of different products in many different sizes, corresponding to many different stock keeping units (SKUs) (Liu, Ren, Choi, Hui, & Ng, 2013). Moreover, several external factors can also have a direct impact on the sales including the weather conditions, holidays, marketing actions, promotions, fashion trends and the current economic environment (Sébastien Thomassey & Fiordaliso, 2006; Sun et al., 2008; Ni & Fan, 2011).

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