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论文编号：	15578
作者编号：	2320233814
上传时间：	2025/12/6 13:56:02
中文题目：	S公司生产计划研究
英文题目：	Research on Production Plan Optimization for S Company
指导老师：	石鉴
中文关键字：	工程机械；需求预测；价值流程图；智能排产；体系协同
英文关键字：	Construction Machinery；Demand Forecasting；Value Stream Mapping (VSM)；Intelligent Scheduling；System Collaboration
中文摘要：	当前，中国工程机械行业正加速全球化布局，多品种生产、跨区域运营成 为主流发展态势，国内行业普遍面临需求预测难以覆盖国际市场差异、生产周 期不能满足海外客户需求、库存多为非客户所需机型等问题，这些问题直接导 致企业库存积压、订单交付延误，制约国际竞争力提升。本文以中国工程机械S 公司挖机品类为研究核心，跳出传统生产计划“单点优化”局限，聚焦其“销 售预测适配性不足、生产周期偏长、计划排产协同断层”三大问题，结合需求 预测理论、精益生产、运筹优化及智能制造技术，构建“预测精准化－周期精 益化－排产智能化”三维协同优化体系，开展系统性研究。 研究背景显示，随着S公司国际化业务占比超60%，原国内导向的生产计划 体系短板凸显：国际市场差异化需求难以被预测覆盖，生产周期较行业头部企 业差距显著，多层级计划协同不足，导致库存积压与订单交付延误频发。研究 采用案例研究、数据建模与实证分析结合的方法，以“需求牵引－效率支撑 执行反馈”为闭环逻辑设计方案：销售预测层面，构建分区域动态模型，纳入 雨季、关税等国际特色外生变量并动态校准参数，提升预测精度；生产周期层 面，以核心机型为样本绘制价值流程图现状图，通过消除非增值浪费、工序整 合压缩周期；计划排产层面，重构“中长期－月度－周度－排产定序”四级体 系，引入智能算法，实现计划高效调整，且三者形成协同——预测为周期优化 指明靶向方向，周期缩短为排产释放动态空间，排产通过实时数据反向校准前 两者，形成“1+1+1>3”的系统效应。 研究成果有效破解S公司生产计划困境，推动预测偏差从19.2%降至7%以 内、生产周期缩短45%、交期兑现率从68%升至90%，同时形成“分区域预测+ 价值流程图+智能排产”的协同方法论，为工程机械行业多品种、国际化企业提 供“精益+数字化”的生产计划优化框架，呼应智能制造与全球化趋势。研究过 程涉及图14幅，表15个，参考文献43篇。
英文摘要：	Currently, China's construction machinery industry is accelerating its global layout, with multi-variety production and cross-regional operation having become mainstream development trends. The traditional "single-point optimization" model for production planning can no longer adapt to industry needs—it generally faces common problems such as demand forecasting failing to cover differences in the international market, production cycles lagging behind those of leading enterprises, and insufficient collaboration among multi-level plans. These issues directly lead to inventory backlogs and order delivery delays for enterprises, restricting the improvement of their international competitiveness. This study takes the excavator category of China's construction machinery company S as the research focus, breaks away from the limitation of traditional "single-point optimization" in production planning, and addresses three core issues of the company: inadequate adaptability of sales forecasting, prolonged production cycles, and poor collaboration in planning and scheduling. By integrating demand forecasting theory, lean production (Value Stream Mapping, VSM), operational research optimization, and intelligent manufacturing technologies, the study establishes a three-dimensional collaborative optimization system of "predictive precision-cycle leanization-scheduling intelligence" and conducts systematic research. The research background shows that as the proportion of S Company’s international business exceeds 60%, the shortcomings of its original domestic oriented production planning system have become increasingly prominent: the differentiated demands of the international market cannot be fully covered by the existing forecasting system, the production cycle lags significantly behind that of leading enterprises in the industry, and the poor collaboration among multi-level plans leads to frequent inventory backlogs and order delivery delays. Adopting a combination of case study, data modeling, and empirical analysis, the study designs optimization solutions based on the closed-loop logic of "demand-driven-efficiency supported-execution-feedback"At the sales forecasting level: A regional dynamic forecasting model is built, which incorporates international-specific exogenous variables (such as rainy seasons and tariffs) and dynamically calibrates parameters to improve forecasting accuracy. At the production cycle optimization level: Taking core excavator models as samples, a VSM current-state map is drawn to identify and eliminate non-value-added waste; production cycles are shortened through process integration and pull production. At the planning and scheduling level: A four-level system of "mid-to-long-term planning- monthly planning- weekly planning- scheduling sequencing" is reconstructed; intelligent algorithms are introduced to integrate over 35 constraints, enabling efficient formulation and adjustment of plans. Notably, the three dimensions form close collaboration: Forecasting points out the targeted direction for cycle optimization; shortened cycles release dynamic space for scheduling; scheduling reversely calibrates the previous two dimensions through real-time data, ultimately achieving a "1+1+1>3" systematic effect. The research results effectively solve the production planning dilemmas of S Company: the prediction error is reduced from 19.2% to less than 7%, the production cycle is shortened by 45%, and the on-time delivery rate is increased from 68% to 90%. Meanwhile, a collaborative methodology of "regional forecasting + VSM + intelligent scheduling" is formed, providing a "lean + digital" production planning optimization framework for multi-variety and internationalized enterprises in the construction machinery industry, which aligns with the trends of intelligent manufacturing and globalization. The study involves 14 figures, 15 tables, and 43 references. I
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