Fashion retailing

Intelligent sales forecasting for fashion retailing using harmony search algorithms and extreme learning machines... 

To provide a flexible, robust and effective methodology for fashion sales forecasting, this chapter examines the sales forecasting problem based on the forecasting process in real-world fashion retailing, which forecasts the total sales amount of each fashion item category or each city (all categories) on a medium-term basis (annually, quarterly and monthly). 

In the fashion retail market, time series of sales of most item categories are strongly seasonal, such as knitted short-sleeved dresses (spring/summer) and coats (fall/winter). In this study, same-period time series, comprising sales data only from the same period of past years, are also used to observe the change trend... 

According to the forecasting results based on real fashion retail data and benchmark data from M3 competition, it can be concluded that the proposed model is widely applicable, since it is capable of generating accurate forecasts for a variety of time series with irregular patterns as well as strong seasonal patterns. 

Exterrsive experiments were condircted to validate the proposed HI model in terms of real fashion retail data The experimental resirlts have shown that the HI model can tackle the medium-term sales forecasting problem effectively, which also demorrstrates that the proposed model can provide much superior performance over traditional ARIM A models and two recently developed sales forecasting NN models. A further experiment was presented based on seven irregular annual data sets from M3 competitiort, which further validates the effectiveness of the proposed HI model and shows that the HI model is more powerful to tackle the time series with sufficient sample data. Firrthermore, since the time series tackled in this chapter involve various patterns such as irregirlarity and seasonality, the proposed model is widely applicable and can be easily extended to solve other forecasting problems with similar time-series patterns. 

Au, K., Choi, T. and Yu, Y, 2008. Fashion retail forecasting by evolutionary neural networks. IntemationalJoumal of Production Economics, 114(2), 615-630. 

Intelligent product cross-selling system in fashion retailing using radio frequency identification (RFID) technology, fuzzy logic and rule-based expert system... 

Intelligent product cross-selling system in fashion retailing 197... 

System architecture of RFID-enabled SFS for cross-selling in the fashion retail industry. 

To validate the performance of the proposed IPCS system on matching recommendations for cross-selling in a real-life environment, 48 fashion items of a fashion retailer, including a total of 538 expert rules, were used for experimental testing. These samples of fashion items belong to five product types, including dress, jacket, skirt, top and trousers. The distribution of apparel items in each product type and the potential 829 matching pairs are listed in Table 10.3. 

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