The Calculation of Inventory Turnover in Retail Services -- Based on Improved Neural Network
Inventory turnover is defined as the ratio of cost of goods sold to average inventory, and it is commonly used to measure performance of inventory managers, the speed of cash flow and level of competitiveness of the retail business. Therefore, it is an important index for investors and managers. Not every company publishes it in practice. In the previous studies, we usually use the multi-linear regression method to calculate inventory turnover. In fact, inventory turnover is often not necessarily a linear relationship with other variables but is non-linear relationship. In this paper, we uses an improved neural network, and makes book-to-value, gross margin, income of persons, and retail price index as the inputs of model to calculate inventory turnover. It can overcome the shortcomings of the traditional neural network that is lack of training samples to reduce its accuracy of calculation. Improved neural network firstly uses panel data model to extract the variables playing significant roles in the inventory turns and put them as inputs of improved neural network. The regression coefficients of significant variables of entity fixed-effect model are initial weights of improved neural network. The simulation results show the effectiveness of the improved neural network.
inventory turnover improved neural networks panel data analysis fixed effects retail industry
Hong Nie Shugang Li
School of Mechanical Engineering, Shanghai Jiao Tong University, China
国际会议
The Institute Industrial Engineera Asian Conference 2011(2011年国际工业工程师协会亚洲会议)
上海
英文
589-596
2011-06-10（万方平台首次上网日期，不代表论文的发表时间）