外部經濟條件對女性服飾公司之營運影響研究–以台灣某知名女性服飾品牌業者為例

Abstract

因女性服飾零售市場具有特殊產業特性，極需發展一套有效的銷售預測工具，而縮減式向量自我迴歸模型(Vector Autoregressive，簡稱VAR)是應用最廣的時間序列模型，可以同時加入多個內生變數，討論這些變數的交互影響關係，以做出適當的預測。本研究探討台灣外部經濟條件對女性服飾公司之營運影響，並特別應用VAR，選擇棉花價格、匯率、衣著類CPI指數及台灣各行業受雇員工每人月平均薪資四個變數分析國內某女性服飾公司之實際銷售數據。在衝擊反應分析方面，本研究發現薪資與銷售量最具連動關係，若薪資提高，則下一期銷售量就會增加，反之亦然。此外，誤差變異數分解發現，除銷售量自身變動因素外，薪資具有較大影響力，長期時可解釋21.5%的變異；其次為匯率，長期時可解釋10%的變異。以上經驗顯示，本研究所建模型可提供服裝銷量預測的一個新方向。

The women’s apparel industry has specific characteristics and requires suitable tools for sales forecast. The proposed model, VAR, widely used in various fields, can incorporate multiple endogenous variables and the interactions of these variables would lead to appropriate forecast. This study discusses how the external economic conditions in Taiwan affect women’s apparel sales performance. It applies the reduced autoregression model to analyze data collected from a certain fashion retailer. Four variables, cotton price, exchange rate, CPI index of apparel, and the average monthly salary per job in Taiwan, are chosen as the external economics environmental parameters in this article. The result from impulse response function shows the sales revenue will grow following the month of salary increase, and vice versa. It indicates a corresponding relationship between sales and salary. After analyzing the variance decomposition, it is found that excluding sales’ fluctuation, salary has the most impact on sales, accounting for 21.5% of the variation in the long term. In addition, exchange rate explains 10% as well. In conclusion, the proposed model has introduced a new direction in sales forecast for women’s apparel industry.