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论文编号：	14860
作者编号：	2320223890
上传时间：	2024/12/4 20:24:41
中文题目：	A公司数字孪生生产计划管理系统设计研究
英文题目：	Research on the Design of A Company''''''''s Digital Twin Production Planning Management System
指导老师：	焦媛媛
中文关键字：	数字孪生技术;生产计划管理;智能制造;数字化转型
英文关键字：	Digital twin technology;Production planning management;Intelligent manufacturing;Digital transformation
中文摘要：	随着工业4.0和智能制造的兴起，数字孪生技术逐渐成为企业数字化转型的关键技术之一。在这一背景下，A公司作为航空领域的短舱零部件生产商，面临着多品种小批量的生产挑战，其生产计划管理的现状存在诸多问题，如生产流程监控不足、资源配置效率低、质量控制困难等。为了提升生产效率和产品质量，A公司迫切需要一种创新的方法来优化其生产计划管理。本研究以A公司的生产计划管理为研究对象，探索了数字孪生技术在优化生产计划管理中的应用和影响，旨在通过构建数字孪生模型，实现对生产流程的实时监控、模拟和优化，有效提升生产效率和产品质量。 本研究采用了文献研究法、案例分析法和模拟试验法，对A公司的生产流程进行了深入分析，并基于数字孪生技术构建了一套计划管理优化方案。该方案通过集成OA系统、AMS考勤系统、SAP供应链管理系统、Andon生产现场支持系统和MLTS物料生命周期控制系统，实现了数据的实时采集、处理和分析，并应用此系统进行生产计划模拟，变推动式生产为拉动式生产，根据模拟数据制定资源投入策略，提高了决策的效率和质量。研究过程中，通过模拟不同的生产场景，包括需求波动、资源变化和潜在的生产中断，测试了计划管理流程的响应能力和适应性，评估了数字孪生技术在计划管理中的具体效果，为优化计划管理流程提供了定量的依据。 研究结果表明，数字孪生生产计划管理系统的实施，使得A公司在生产流程优化、风险管理、质量管理等方面取得了显著成效。具体表现在产品制造周期缩短，库存降低，产能增加。此外，该系统还增强了生产过程的透明度，推动了管理模式的创新，从经验管理向数据驱动的精准管理转变。尽管研究取得了一定成果，但也存在一些不足，如数据集成的复杂性、模型精确度的提升、实时反馈机制的优化等问题。未来研究将关注技术深化与创新、跟踪技术发展、扩大适用范围等方面，以实现更高效、智能的生产运营管理。通过持续的技术创新和实践探索，数字孪生技术将助力企业在数字化转型的道路上行稳致远。
英文摘要：	With the rise of Industry 4.0 and intelligent manufacturing, digital twin technology has gradually become one of the key technologies for enterprise digital transformation. In this context, Company A, as a short cabin component manufacturer in the aviation industry, faces the challenge of producing multiple varieties in small batches. The current situation of its production plan management has many problems, such as insufficient monitoring of the production process, low resource allocation efficiency, and difficulty in quality control. In order to improve production efficiency and product quality, Company A urgently needs an innovative method to optimize its production plan management. This study focuses on the production planning management of Company A, exploring the application and impact of digital twin technology in optimizing production planning management. The aim is to construct a digital twin model to achieve real-time monitoring, simulation, and optimization of production processes, effectively improving production efficiency and product quality. This study used literature research, case analysis, and simulation experiments to conduct an in-depth analysis of the production process of Company A, and constructed a plan management optimization scheme based on digital twin technology. This solution integrates OA system, AMS attendance system, SAP supply chain management system, Andon production site support system, and MLTS material lifecycle control system to achieve real-time data collection, processing, and analysis. The system is applied to simulate production plans, transforming push based production into pull based production. Based on simulated data, resource investment strategies are formulated to improve decision-making efficiency and quality. During the research process, the responsiveness and adaptability of the planning management process were tested by simulating different production scenarios, including demand fluctuations, resource changes, and potential production interruptions. The specific effects of digital twin technology in planning management were evaluated, providing a quantitative basis for optimizing the planning management process. The research results indicate that the implementation of the digital twin production planning management system has achieved significant results in optimizing production processes, risk management, quality management, and other aspects for Company A. Specifically manifested in shortened product manufacturing cycles, reduced inventory, and increased production capacity. In addition, the system enhances the transparency of the production process and promotes innovation in management models, shifting from experience management to data-driven precision management. Although research has achieved certain results, there are still some shortcomings, such as the complexity of data integration, the improvement of model accuracy, and the optimization of real-time feedback mechanisms. Future research will focus on technological deepening and innovation, tracking technological development, expanding applicability, and other aspects to achieve more efficient and intelligent production and operation management. Through continuous technological innovation and practical exploration, digital twin technology will assist enterprises in steadily advancing on the path of digital transformation.
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