新疆棉花种植面积占全国85%，产量占全国91%。2023/24年度新疆棉花发运量约为334万吨，同比减少37%。国内棉花市场需求疲软，外部制裁进一步抑制了棉花加工机械行业的投资热情。面对行业低迷，各企业纷纷转向提升客户满意度和优化技术服务。Z公司棉包刷唛机作为关键生产设备，其技术服务流程在安装调试、技术培训和售后服务方面存在问题，直接影响客户满意度和服务效率，亟需优化以增强竞争力。

本文基于客户满意度、质量功能展开（QFD）和流程优化理论，结合定性与定量分析，从Z公司内部人员和客户的双重视角出发，系统探讨棉包刷唛机技术服务流程中的问题根源，为服务流程优化提供科学依据。首先，本文通过对Z公司内部人员的访谈，发现安装调试流程存在上门响应不及时等问题，技术培训流程存在培训标准不统一等问题，售后服务流程存在售后服务响应时间长等问题。其次，本文基于ACSI模型构建了Z公司棉包刷唛机客户满意度模型，通过线性回归分析识别出影响客户满意度的关键因素，发现感知服务质量对客户满意度具有显著影响，而感知产品质量的影响相对较小，提升客户感知价值、减少客户抱怨、增强品牌形象可提高客户满意度。再次，本文基于QFD方法构建了关键客户需求-质量特性矩阵，将客户的核心需求与技术质量特性关联，通过专家打分评估需求与质量特性间的关联强度，明确各质量特性的重要度，为流程优化提供量化依据。最后，本文在优化路径方面提出了安装调试、技术培训和售后服务流程的优化措施。进一步设计了保障措施，以确保制定的优化措施能够有效实施。流程优化效果和评价的结果表明优化措施显著提升了客户满意度和服务效率，有效实现了成本控制，增强了Z公司的市场竞争力。

本文为Z公司棉包刷唛机技术服务流程的优化提供了实践参考，尤其在行业向智能化和数字化转型的背景下，研究成果有助于推动企业服务流程的标准化与现代化，为提升棉花加工机械行业的竞争力提供了重要的实证依据。

Cotton farming in Xinjiang accounts for 85% of the national planting area and 91% of total output. In the 2023/24 season, Xinjiang’s cotton shipments were approximately 3.34 million tons, marking a 37% year-on-year decrease. Domestic demand for cotton remains weak, and external sanctions have further dampened investment enthusiasm within the cotton processing machinery industry. In response to this downturn, companies are focusing on improving customer satisfaction and optimizing technical services. As a key piece of production equipment, Z Company's Marking Printing of Bales machine has encountered challenges in its technical service process—specifically in installation, technical training, and after-sales service—issues that directly impact customer satisfaction and service efficiency. These processes urgently need improvement to strengthen competitiveness.

This thesis applies customer satisfaction, Quality Function Deployment (QFD), and process optimization theories, combining both qualitative and quantitative analysis. It examines the root causes of issues in the technical service process of Z Company’s cotton packaging brush marking machines from both internal personnel and customer perspectives, providing a scientific basis for service process optimization.Firstly, interviews with Z Company’s internal staff reveal issues such as delayed on-site response in the installation and commissioning process, inconsistent training standards in the technical training process, and long response times in the after-sales service process.Secondly, based on the ACSI model, this thesis constructs a customer satisfaction model for Z Company’s cotton packaging brush marking machines. Through linear regression analysis, key factors affecting customer satisfaction are identified. The study finds that perceived service quality has a significant impact on customer satisfaction, while perceived product quality has a relatively smaller effect. Enhancing perceived customer value, reducing complaints, and strengthening brand image can increase customer satisfaction.Furthermore, the thesis constructs a key customer requirement-quality characteristic matrix using the QFD method, linking core customer needs with technical quality features. Expert scoring is used to assess the strength of the relationship between customer needs and quality characteristics, identifying the importance of each quality characteristic and providing a quantitative basis for process optimization.Finally, the thesis proposes optimization measures for the installation and commissioning, technical training, and after-sales service processes. Further, it designs safeguard measures to ensure the effective implementation of these optimizations.The results of the process optimization and evaluation show that the implemented measures significantly improved customer satisfaction and service efficiency, effectively controlled costs, and enhanced Z Company's market competitiveness.

This thesis provides theoretical support and practical insights for optimizing Z Company’s technical service processes for its Marking Printing of Bales machines. Amid the industry’s shift toward smart and digital transformation, the findings contribute to standardizing and modernizing service processes, offering valuable empirical evidence to enhance the competitiveness of the cotton processing machinery sector.

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