关键词: 设备运行管控绩效, 模糊集定性比较分析（fsQCA）, 绩效评价分析框架, 绩效提升策略

Abstract:

In the context of industrial interconnectivity， the factors that directly or indirectly affect the operation management and control efficiency and effectiveness of production equipment are increasingly numerous， making it difficult to precisely define the key factors that decisively influence the performance outcomes of equipment operation management and control. The intrinsic mechanisms and interplay paths remain unclear， which constrains the optimization of operation management control efficiency and effectiveness. There is an urgent need to conduct in-depth research on the key influencing factors of production equipment operation management and control efficiency and effectiveness， to reveal the configurational effects and mechanisms of these factors， and to develop effective strategies for enhancing the performance of production equipment operation management and control， thereby supporting the maximization of operation management and control efficiency and effectiveness in an industrial interconnected environment. Against this backdrop， a thorough analysis of domestic and international standards， literature， and other relevant materials related to the production equipment operation management and control performance is conducted， and an analysis framework for evaluating performance is constructed based on “influencing factors-outcomes-action paths”. Using the data from 76 manufacturing enterprises， the fuzzy-set qualitative comparative analysis （fsQCA） method is employed to explore the key influencing factors and enhancement strategies for production equipment operation management and control performance from three dimensions： high-load output， lean management and control， and low-failure operation.The high-load output mode represents enterprises maintaining high， consistent performance of various types of production equipment through scientific production planning and task scheduling， supplemented by high-level equipment maintenance and daily upkeep， to effectively achieve production goals and attain high operation management and control performance. The lean management and control mode represents enterprises， on the basis of good production task scheduling， using production process routes to control process flows and parameters， optimize the supply balance of production materials and energy， and timely handle equipment anomalies and potential failures， ensuring stable equipment operation and consistent product quality performance， thereby achieving high production equipment operation management and control performance. The low-failure operation mode represents enterprises focusing on the health status of production equipment， implementing dynamic monitoring and real-time sensing of equipment operation status through cloud platforms， and conducting predictive maintenance and early warning based on data， thereby enhancing the effective uptime of production equipment and achieving high operation management and control performance. Taking a manufacturing workshop for an aviation engine transmission unit as an example， and combining its current production equipment operation management and control status， a predictive maintenance system solution based on the “low-failure operation mode” was developed. The operation management and control performance of the enterprise significantly improved， with the average process capability index of the workshop's production equipment increasing from 1.28 to approximately 1.60， the average repair time for equipment failures decreasing from 8 hours to 3 hours， and the average comprehensive efficiency of production equipment rising from 56.43% to 68.57%. The effectiveness and rationality of the proposed method for analyzing influencing factors of production equipment operation management and control performance and enhancement strategies were verified through practice.

Key words: production equipment operation management and control performance, fuzzy set qualitative comparative analysis（fsQCA）, performance analysis framework, performance improvement strategies