Process and Product Improvement in Manufacturing Systems with Correlated Stages
Process and Product Improvement in Manufacturing Systems with Correlated Stages
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DOI：
10.1287/mnsc.48.5.591.7804
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发表时间：
2002-05
期刊：
影响因子：
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通讯作者：
Paul F. Zantek;G. P. Wright;R. Plante
中科院分区：
文献类型：
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作者：
Paul F. Zantek;G. P. Wright;R. Plante
制造系统通常包含加工和装配阶段，其输出质量受到系统中前一阶段的输出质量的显着影响。本研究提供并实证验证了一个程序1测量每个阶段的性能对后续阶段的输出质量，包括信号产品的质量的影响，和2确定阶段的制造系统中的管理应集中投资于过程质量改进。我们提出的程序建立在系统中的阶段的优先顺序，并使用跨阶段的产品质量测量之间的相关性所提供的信息。我们的程序的出发点是一个计算机可执行的网络表示的产品质量测量之间的统计关系;执行自动转换网络到一个模糊方程模型，并估计模型参数的最小二乘法。参数估计值用于测量和排序每个阶段的性能对中间阶段和最终产品质量的可变性的影响。我们扩展我们的工作，提出了一个经济模型，它使用这些结果，以指导管理层在决定每个阶段的过程质量改进的投资额。我们报告的一些调查结果，从广泛的实证验证我们的程序使用电路板生产线的数据，从一个主要的电子制造商。这里提出的经验证据强调了会计的重要性，在确定产品质量变化的来源和B分配过程质量改进的投资的质量环节。
Manufacturing systems typically contain processing and assembly stages whose output quality is significantly affected by the output quality of preceding stages in the system. This study offers and empirically validates a procedure for 1 measuring the effect of each stage's performance on the output quality of subsequent stages including the quality of the signal product, and 2 identifying stages in a manufacturing system where management should concentrate investments in process quality improvement. Our proposed procedure builds on the precedence ordering of the stages in the system and uses the information provided by correlations between the product quality measurements across stages. The starting point of our procedure is a computer executable network representation of the statistical relationships between the product quality measurements; execution automatically converts the network to a simultaneous-equations model and estimates the model parameters by the method of least squares. The parameter estimates are used to measure and rank the impact of each stage's performance on variability in intermediate stage and final product quality. We extend our work by presenting an economic model, which uses these results, to guide management in deciding on the amount of investment in process quality improvement for each stage. We report some of the findings from an extensive empirical validation of our procedure using circuit board production line data from a major electronics manufacturer. The empirical evidence presented here highlights the importance of accounting for quality linkages across stages in a identifying the sources of variation in product quality and b allocating investments in process quality improvement.