【作者】 张婧；

【导师】 刘芳；

【作者基本信息】 四川师范大学 ， 计算机应用技术， 2008， 硕士

【摘要】 随着经济全球化的扩展及WTO对我国保护期的结束,外资零售企业将无限制地进入我国,面对巨大的竞争和挑战,如何生存已是我国零售企业面临的重要问题。而作好销售预测及在此基础上作出正确的销售决策是致胜的关键。在商品的销售预测中,一般针对商品在将来某个时段的销售状态和销售量进行预测,预测的变量只涉及到商品的销售数量,有极少的一部分考虑到了销售金额,但都忽略了一个最重要的因素,那就是利润,利润是决定零售企业赢利及发展的关键因素。因此,本文在销售预测的输入变量的选取上,除了用到销售数量、销售金额、季节指数等商品属性之外,还选取了利率这一重要变量,增加了预测的准确性。本文利用数据挖掘技术,提出了一个新的商品销售预测模型——SPI-M模型,该模型用于零售业商品销售预测,为企业良好经营和决策部门作出重要决策提供帮助。该模型的构建过程是:首先用基于统计学的季节分析模型(S模型)进行销售市场的季节规律分析,计算出商品的季度、月份的季节指数。其次利用数据挖掘技术中的K-均值聚类算法,建立了利率等级模型(P模型),由P模型的输出结果得到销售的利率等级分类。然后将季节指数、利率等级、销售数量、销售金额等作为ID3算法的输入变量来构建决策树模型(I模型),得出历史数据的年、季、月的销售状态,最后利用统计学中的马尔科夫预测模型(M模型)预测商品将来某时段内销售状态的转移情况,而在将来某时段内商品的需求量是以前相同时段相同状态该商品的销售量的同期平均值与平均增长量之和。最后本文实现了一个基于SPI-M模型的商品销售预测系统,通过实验对SPI-M模型及单独使用季节分析模型、马尔科夫模型的预测结果进行对比分析,得出了在商品销售预测中,使用SPI-M模型预测准确性更高的结论。更多还原

【Abstract】 With the expansion of economic globalization and the deadline of protection for our country from the WTO, the foreign-invested retail enterprises will unrestrictedly pour into China, facing those enormous competitions and challenges, how to survive is the most important issue for Chinese retail enterprises have to face with. It is the key to success that making sales forecasts and making the correct marketing decisions on the basis of this.In the commodities sales forecasts, the general commodities in the future for a certain period of the sales state and sales volume, the variables involved of forecast only the sale volume of commodities, a small part of taking into account the amount of sales, but has not been the most important factor, that is, profit, profit is the key factor of retail enterprises winning profit and development. Therefore, in this thesis, the sales forecasts for the selection of input variables, in addition to the commodities attribute used in sales volume of commodities and the amount of sales, seasonal index, etc, it also has been selected the important variable profit rates, for an increase of the accuracy of the forecasts.Using data mining technology in the thesis, I put forward a new commodities sales forecasting model——SPI-M model, which used in retail commodities sales forecasts help the enterprise operations and decisions-maker make important decisions. The procedure of the SPI-M model construction is: first , we analyse the marketing seasonal laws with the season analysis model (S model) based on statistics, calculate the seasonal index of commodities quarter and month. Second, we establish the profit rate grade model (P model) with the K-means algorithm on data mining technology, which Classifies the grade of profit rate .Third, we build decision trees model (I model) within seasonal index, profit rate grade, sales volume, the amount of sales as input variables of ID3 algorithm, obtain the sales state of yearly, quarterly and monthly historical data. Finally, we forecasted the transfer of sale state of commodity within a certain period in future with Markov model(M model) on statistics, its demand of market in the same period of time in future is the sum of the corresponding period average sales involve and average increasing sales involve in same time before.At last, the thesis established a commodity sale forecasting system based on SPI-M model. Meanwhile, we compare and analyse the results of SPI-M model, Season model and Markov model through experiments, and get the conclusion that we can reach higher correctness if we use SPI-M model than rest of forecast model.更多还原