Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry

Abstract

Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessitate the use of models that can contribute to the efficient forecasting of products’ sales and demand. Many researchers have tried to address this problem using conventional forecasting models that predict future demands using historical sales information. While these models predict product demand with fair to moderate accuracy based on previously sold stock, they cannot fully be used for predicting future demands due to the transient behaviour of the fashion industry. This paper proposes an intelligent forecasting system that combines image feature attributes of clothes along with its sales data to predict future demands. The data used for this empirical study is from a European fashion retailer, and it mainly contains sales information on apparel items and their images. The proposed forecast model is built using machine learning and deep learning techniques, which extract essential features of the product images. The model predicts weekly sales of new fashion apparel by finding its best match in the clusters of products that we created using machine learning clustering based on products’ sales profiles and image similarity. The results demonstrated that the performance of our proposed forecast model on the tested or test items is promising, and this model could be effectively used to solve forecasting problems.

Supply Chain Demand Forecast Based on SSA-XGBoost Model

“…in formula (5) 24. end for 25. Update the global optimal fitness value and the individual optimal position of the population 26. end for Outout: BestX: location of global optimal fitness value Convergence_Curve: global optimal fitness value for each iteration…”

Outlier-Aware Demand Prediction Using Recurrent Neural Network-Based Models and Statistical Approach

“…Clustering is an algorithm that segments datasets into clusters using similar data. This technique can help to handle the peculiarity of each product having different properties [11]- [13], [21]. In this study, we improved the prediction accuracy by clustering the model input time series among those with similar characteristics and processing the prediction results according to each cluster.…”

“…The RNN is a deep learning-based time series analysis technique, and helps to quickly analyze large amounts of data and build effective prediction models. Because of these advantages, various industries, such as fashion [10], [11], retail [12]- [21], tourism [22], electricity [23]- [26], among others [27]- [29], have attempted to improve their supply chain management by utilizing AI techniques and RNN-based demand prediction models. However, standard RNN-based demand predictions are limited by the many outliers that occur due to the characteristics of the paint industry.…”

Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models

“…Deep learning architectures that can process vast amounts of data, recognize patterns, and make accurate predictions have opened up new possibilities across various sectors, leading to increased efficiency, improved decision-making, and enhanced user experiences. It has revolutionized many industries, including manufacturing [10][11][12], finance [13,14], healthcare [15][16][17][18], environment [19], electronics [20], energy [21,22], agriculture [23,24], transportation [25,26], entertainment [27,28], retail [29,30], e-commerce [31,32], and many others, transforming the way we approach complex tasks and unlocking new possibilities. Although it is a relatively new and emerging technology, many data-driven or rule-based algorithms, from naive to complex, are already employed in various scientific fields [6,[33][34][35][36][37][38][39].…”