企业基于组合预测模型的采购需求计划研究

Research on Procurement Demand Planning for Enterprises Based on Combination Forecasting Models

摘要: 针对空调压缩机部件采购计划的精准预测是空调企业推进精益管理目标实现过程中所不可或缺的一个环节，这对于企业降本增效等方面均具有重要的现实意义。然而，现实中仍有很多空调企业在规划采购计划时，过度依赖历史数据和过往经验，这种预测方式往往带有较强的主观色彩，导致预测结果与实际情况存在较大偏差。为了解决其问题，本研究选取了空调行业内颇具代表性的X公司作为研究对象，并结合其实际运营情况，提出了一种融合灰度GM预测模型与ARIMA时间序列预测模型的组合预测方法。这一方法的提出，旨在为企业提供一个更为客观、精准的采购需求预测工具，进而助力企业实现更为高效的运营管理和成本控制。

Abstract: Accurate prediction of the procurement plan for air conditioning compressor components is an indispensable link in the process of promoting lean management goals in air conditioning enterprises, which has important practical significance for reducing costs and increasing efficiency. However, in reality, there are still many air conditioning companies that overly rely on historical data and past experience when planning procurement plans. This prediction method often has a strong subjective color, leading to significant deviations between the predicted results and the actual situation. In order to solve its problem, this study selected X Company, which is a representative company in the air conditioning industry, as the research object, and combined with its actual operation situation, proposed a combined prediction method that integrates grayscale GM prediction model and ARIMA time series prediction model. The proposal of this method aims to provide enterprises with a more objective and accurate procurement demand forecasting tool, thereby helping them achieve more efficient operational management and cost control.