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

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.

Pricing Game Model of Wasted Clothing Recycling and Remanufacturing Based on Government Subsidy

In the global environment of pursuing resource regeneration and green environmental protection, more and more wasted clothing need to be solved. In order to make full use of the wasted clothing and save land and soil resources, an idea of wasted clothing's recycling and remanufacturing is put forward. In the new idea a pricing game model is established basing on Stacklberg differential game theory between traditional and remanufactured clothing. In this model, the differences in consumers' willingness to pay and the government's subsidies are considered. Government's optimal subsidy are obtained which ensure not only the interests of manufacturers but also environmental reputation and maximum social benefits. The study is helpful to push the wasted clothing's recycling and remanufacturing plan. It makes some index more precise quantification as government's subsidy, manufacturers and the social benefits. Government and manufactures can make the detailed cooperation plan reference to it.

Dyeing of S/J Cotton Knit Fabric with Natural Dye Extracts from Green Walnut Shells: Assessment of Mordanting Effect on Fastness Properties

In this study, aqueous extraction method is used because of its high extraction ratio, light fastness and also functional properties. In 1st phase, for dyeing S/J cotton knit fabric with green walnut power ferrous sulfate is considered as a mordant. In this study, three different mordanting methods such as pre-, meta-, and post-mordanting are conveyed the dyeing process with the state of metallic mordant and without metallic salt mordants. In 2nd phase, in dyeing for fixation ferrous sulfate was considered as mordants. Furthermore, the analysis and evaluation of each colour dyed material was done through following two terms for instance CIELAB (L*, a*, and b*) and K/S values. According to AATCC test methods, colour fastness to washing, crocking, perspiration of the dyed samples is determined whereas according to the ISO standard, the colour fastness to light was estimated and tested. When dyeing was carried out on S/J cotton knit fabric through considering optimum parameter like at 80&degC for 60 min and at pH 4 which showed optimum results. From the results we can see, very good wash fastness was obtained while there is no fading of the colour, whereas the outstanding and moderate level of colour fastness to light and crocking is achieved.

Global Fabric Defect Detection Based on Unsupervised Characterization

Fabric texture intelligent analysis comprises the following characteristics:objective detection results,high detection efficiency,and accuracy.It is significantly vital to replace manual inspection for smart green manufacturing in the textile industry,such as quality control and rating,and online testing.For detecting the global image,an unsupervised method is proposed to characterize the woven fabric texture image,which is the combination of principal component analysis(PCA)and dictionary learning.First of all,the PCA approach is used to reduce the dimension of fabric samples,the obtained eigenvector is used as the initial dictionary,and then the dictionary learning method is operated on the defect-free region to get the standard templates.Secondly,the standard templates are optimized by choosing the appropriate dictionary size to construct a fabric texture representat ion model that can effectively characterize the defec-free texture region,while ineffectively representing the defective sector.That is to say,through the mechanism of identifying normal texture from imperfect texture,a learned dictionary with robustness and discrimination is obtained to adapt the fabric texture.Thirdly,after matching the detected image with the standard templates,the average filter is used to remove the noise and suppress the background texture,while retaining and enhancing the defect region.In the final part,the image segmentation is operated to identify the defect.The experimental results show that the proposed algorithm can adequately inspect fabrics with defects such as holes,oil stains,skipping,other defective types,and non-defective materials,while the detection results are good and the algorithrm can be operated flexibly.