Stability in production manufacture

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Stability in Production Manufacture

Introduction to Production Stability

Stability in production manufacturing is a critical factor for ensuring efficiency, quality, and competitiveness in various industries. Stability refers to the ability of a production system to maintain consistent performance and output over time, despite internal and external disturbances. This article synthesizes research findings on different aspects of production stability, including sequence stability, autonomous control, and process stability evaluation.

Sequence Stability in Automotive Production

Indicators and Performance Measures

In the automotive industry, sequence stability is essential for maintaining efficiency and competitiveness. Original equipment manufacturers (OEMs) need to implement indicators to quantify and visualize stability in their production systems. These indicators help in understanding the overall performance and managing stabilized production facilities effectively. Different performance measures are applied for long-term, mid-term, and short-term planning processes, each with specific applications and specifications.

Simulation Models for Stability Estimation

To address the complexity of modern automotive production, discrete event-based simulation models are used to estimate possible stability levels. These models provide crucial information for transforming high-volume production sites into stabilized production systems. Empirical studies have shown that such models can identify various influence factors and offer recommendations for improving stability.

Autonomous Control in Production Networks

Mathematical and Engineering Approaches

Stability analysis of autonomously controlled production networks involves both mathematical and engineering perspectives. The dynamics of these networks are modeled using differential equations and discrete event simulations. Stability conditions are calculated using mathematical systems theory, ensuring that the defined state of the system remains bounded over time. Autonomous control methods, such as the queue length estimator (QLE) and pheromone-based (PHE) methods, enhance decision-making on the shop floor level.

Distributed Control Methods

Distributed production control methods, such as the nonidling-nonexceeding (NINE) policies, are investigated for their stability and performance. These methods track the solution of continuous-flow models and ensure stability by meeting specific conditions, such as the contraction of tracking delays in feedback loops. Simulation experiments have demonstrated that near-zero work-in-process and finished-parts inventory can be achieved even under high demand conditions.

Process Stability Evaluation

Sensitivity Analysis for One-of-a-Kind Production

Evaluating process stability in one-of-a-kind production is challenging due to the lack of sufficient samples. A novel sensitivity analysis-based approach has been proposed to address this issue. This method involves establishing a variation evaluation model, analyzing performance distribution, and calculating the process stability index. The approach has been validated through case studies, contributing to innovative quality control methods and process stability evaluation.

Hazard Function and Event History Analysis

Modern trends in reliability analysis emphasize the importance of identifying and compensating for factors that destabilize production processes. Stability analysis using hazard functions and event history analysis methods has proven effective in understanding and mitigating machine failures and other disruptive factors.

Conclusion

Stability in production manufacturing is a multifaceted concept that requires a combination of quantitative indicators, simulation models, autonomous control methods, and sensitivity analysis. By implementing these approaches, manufacturers can enhance the efficiency, quality, and competitiveness of their production systems. The research findings discussed in this article provide valuable insights and practical recommendations for achieving and maintaining stability in various manufacturing contexts.