大多数中小规模企业对需求预测管理重要性认识不足，更是疏于对需求预测管理，少数中小企业进行需求预测管理，也大多停留在简单易操作的定性预测层面，造成企业计划工作难度大，库存管理成本高，流动资金压力大，客户满意度低。随着业务规模发展、市场发展需要，需求预测管理对提升企业运营效率和核心竞争力的重要性受到更多企业重视。

本文案例企业V公司是一家收单设备企业，集研发、生产、销售、售后为一体的中小型企业，受管理成本、信息系统建设、人员能力等因素限制，公司需求预测管理采用粗放型管理模式：没有制定需求预测管理规范；采用操作简单，对预测人员能力要求相对较低的客户经理判定法。随着产品生命周期进入成熟期后期，越来越多的因为需求预测管理不足造成的公司管理和运营问题逐渐呈现。

作者通过梳理国内外需求预测相关文献，对需求预测管理概念、需求预测分类、需求预测重要性、需求预测方法、需求预测管理等方面进行研究及总结归纳。以案例企业V公司主营产品X9产品作为研究对象，介绍X9产品主要客户的集中招标采购模式；统计分析2017年X9产品年上市以来五年历史出货数据；对公司内部需求预测管理相关人员进行访谈，了解公司现行需求预测管理现状；运用需求预测管理理论分析V公司需求预测管理现状运营问题。在对V公司需求预测管理现状、管理模式进行分析后，作者选用ARIMA时间序列预测模型理论，基于2017-2021年出货数据，利用时间序列预测方法对V公司X9产品需求预测进行ARIMA（p，d，q）预测模型构建。模型构建过程中，使用SPSS软件对X9产品进行发货数据相关性分析，并确定模型参数完成模型构建。利用所构建的ARIMA（p，d，q）模型对V公司X9产品在不同客户进行需求预测结果有效验证。在优化需求预测方法的同时，对需求预测管理提出优化建议和方案，为V公司提供需求预测管理便捷高效的优化方案。

Most small and medium-sized enterprises always ignore the importance of demand forecasting management, neglect the demand forecasting management. A few small enterprises have demand forecasting management, most of them stay in the simple and easy qualitative forecasting level, it’s resulting in difficulty of supply planning, inventory management costs, working capital pressure, low customer satisfaction. With the development of business scale and market, more and more enterprises attach the importance of demand forecasting management in improving the operation efficiency and core competitiveness of enterprises.

Company V is a small and medium-sized enterprise integrating POS terminal R&D, production, sales and service business. Limited by management costs and personnel capacity, the company's demand forecasting management adopts extensive management mode. No demand forecast management standards. Adopts the customer manager judgment method which is simple in operation and low ability requires of forecasting personnel for management. In the product lifecycle later stage of maturity, more and more management and operational problems caused by demand forecasting management emerge.

By combing domestic and foreign literature on demand forecasting, the author studies and summarizes the concept of demand forecasting management, classification of demand forecasting, importance of demand forecasting, methods of demand forecasting, and demand forecasting management. In terms of case study, the author takes X9, the main product of company V, as the research object, introduces the centralized bidding and procurement mode of major customers of X9 product, calculates the shipping data of X9 product for five years since 2017, and uses the interview method to review the current operation status of the company's current demand forecasting. Use the theory of demand forecasting to analyze the current operation problems of company V. After analyzing the demand forecasting management mode and current situation of company V, the author selects the ARIMA time series forecasting model theory, and constructs the ARIMA (p, d, q) forecasting model for X9 product demand forecasting based on the shipment data from 2017 to 2021 by using the time series forecasting method. The author used SPSS software to analyze the relevance of X9 product shipment data, determined model parameters. The constructed ARIMA (p, d, q) model is used to effectively verify the demand prediction of X9 products in different customers. At the same time of optimizing demand forecasting method, the author also put forward optimization suggestions and schemes for demand forecasting management process, so as to provide more convenient and efficient optimization schemes for company V demand forecasting management.