在智能制造与供应链快速发展背景下，销售预测通过整合历史数据、产品特性及市场趋势，预估需求并匹配供应，是企业资源配置与战略规划的核心。目前，仓储物流机器人制造企业H公司的销售预测流程存在多重问题：预测覆盖周期短导致前瞻性不足，生产规划缺乏科学性，资源分配亦不合理。这些问题直接造成销售预测准确率仅为58%，产品交付及时率也仅达80%，严重制约了企业的运营效率。

本文运用流程改进分析（DMAIC）模型，综合采用案例分析、问卷调查、访谈及统计分析等方法，分五个环节系统优化H公司销售预测流程。定义阶段，明确企业内外部客户需求，确立提升销售预测准确率与产品交付及时率的核心目标；测量阶段，通过问卷调查锁定销售数据收集对象不明确、部门目标不一致和预测缺乏市场趋势前瞻性研究等流程问题；分析阶段，借助访谈法挖掘出数据管理体系缺陷、跨部门协同与流程设计模糊和数据质量管控体系不完善等深层原因；改进阶段，组建跨部门项目小组，实施优化组织架构、搭建“项目-区域-全球”三层滚动预测架构、建立数据评审制度等措施，并运用项目管理方法试运行，结合SPC技术动态监控调整；控制阶段，完善员工激励监督机制、开展专项能力培训、强化流程关键指标监控。

研究显示，DMAIC模型优化取得实效。组织架构上，细分销售部门并增设管理部，优化权责；流程体系中，构建三层滚动式预测架构，提升预测效率；数据管理通过评审会制度规范流程，提高数据质量；跨部门协作借助S&OP机制与计划委员会，减少协同阻碍；人员管理强化激励与培训，提升员工参与度。新流程试运行后，销售预测准确率提升至65%，产品交付及时率提高到85%，企业数据处理与跨部门协作能力得到改善，为同类型企业销售预测流程优化提供实践参考。

Against the backdrop of the rapid development of intelligent manufacturing and supply chain, sales forecasting integrates historical data, product features and market trends to estimate demand and match it with supply, serving as the core of enterprise resource allocation and strategic planning. At present, the sales forecasting process of H Company, a manufacturing enterprise of warehousing and logistics robots, has multiple problems: the short forecast coverage period leads to insufficient foresight, the production planning lacks scientificity, and the resource allocation is also unreasonable. These problems have directly caused the sales forecast accuracy rate to be only 58%, and the product delivery timeliness rate to be only 80%, which has severely restricted the operational efficiency of the enterprise.

This paper uses the process improvement analysis (DMAIC) model, and comprehensively adopts methods such as case analysis, questionnaire survey, interview and statistical analysis to systematically optimize the sales forecasting process of H Company in five links. In the definition stage, the internal and external customer needs of the enterprise are clarified, and the core objectives of improving the sales forecast accuracy rate and product delivery timeliness rate are established; in the measurement stage, the process problems such as unclear sales data collection objects, inconsistent departmental goals and lack of forward-looking research on market trends in forecasting are locked through questionnaire survey; in the analysis stage, the underlying causes such as the defects of the data management system, the ambiguity of cross-departmental collaboration and process design, and the imperfect data quality control system are excavated by means of interview; in the improvement stage, a cross-departmental project team is set up, measures such as optimizing the organizational structure, building a three-tier rolling forecast framework of "project-regional-global" and establishing a data review system are implemented, the project management method is used for trial operation, and the SPC technology is combined for dynamic monitoring and adjustment; in the control stage, the employee incentive and supervision mechanism is improved, special capacity training is carried out, and the monitoring of key process indicators is strengthened.

The research shows that the optimization of the DMAIC model has achieved practical results. In terms of organizational structure, the sales department is subdivided and a management department is added to optimize the rights and responsibilities; in the process system, a three-tier rolling forecast framework is built to improve the forecast efficiency; in data management, the process is standardized through the data review meeting system to improve the data quality; in cross-departmental collaboration, the S&OP mechanism and the planning committee are used to reduce the collaboration obstacles; in personnel management, the incentive and training are strengthened to improve the participation of employees. After the new process is put into trial operation, the sales forecast accuracy rate has increased to 65%, the product delivery timeliness rate has increased to 85%, the data processing and cross-departmental collaboration capabilities of the enterprise have been improved, which provides practical reference for the sales forecast process optimization of similar enterprises.