| dc.description.abstract | Under the trend of fast fashion in the apparel industry, the needs of end customers are often quite uncertain. The business model of the garment manufacturing industry in the middle and lower reaches of the textile industry chain is mostly B2B. The customers are usually international fashion brand retailers such as GAP, H&M,ZARA, etc. In order to reduce costs and risks, retailers mostly use a small number of different orders with short lead times. The garment manufacturing industry needs to cooperate with customers to respond quickly and collaboratively. However, under the bullwhip effect of the supply chain, inventory Cost is one of key factors that affect profit directly. Therefore, it is necessary to be more sensitive to market demand. The accuracy of sales forecasts will be the key to improving profitability and rapid response. Production and demand planning can be optimized to make the operation of the supply chain more efficient. It is efficient and fast, and reduces waste and saves costs.
This study takes a domestically listed garment manufacturing supplier as an example. Its sales forecast relies on the integration of manual sales forecasts by business personnel based on subjective experience, past customer sales-related
data and order transparency and other information. However, the sales history is big data. There are no good big data analysis tools to assist in forecasting. This study conducted a preliminary comparison of machine model prediction performance. The experimental results show that the prediction performance of each data set in this case study,ridge regression model is better than others. Whether it is in the default parameters or the relatively best combination of parameters. Linear regression and SVR have poor predictive performance. In the performance leading group models with default parameters,that further execute prediction after using Grid search to find the relative best hyper parameters , due to the best parameter combination uses MAE as the evaluation index, the MAE value of each model is better than expected.Many models’ RMSE value has also improved. However, the prediction performance of a few models has not improved or the degree of improvement is limited or not much different, such as the GBR and DTR models. Obviously, the stability of these models is not suitable for the data set of this research case. And searching for the best hyper parameters of the model is quite time-consuming in this case study. In addition, when the model training data is increased into two years to predict the next year, its prediction error rate will increase. The exception is that only ANN′s prediction performance is better. | en_US |