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    • 1. 发明授权
    • Method and system for determining manufacturing throughput target
    • 确定制造吞吐量目标的方法和系统
    • US08185227B2
    • 2012-05-22
    • US12685393
    • 2010-01-11
    • Sugato BagchiLindsay E. BurnsSteven C. CatlettChing-Hua Chen-Ritzo
    • Sugato BagchiLindsay E. BurnsSteven C. CatlettChing-Hua Chen-Ritzo
    • G06F19/00
    • G06Q10/06
    • A first linear combination of a local moving aggregated quantity derived from a WIP distribution and a global aggregated quantity derived from the WIP distribution is calculated for each range for a given product type in a manufacturing line. The first linear combination serves as a first throughput target for the range and product. A second linear combination of a standard deviation of the non-zero portion of the WIP distribution and the global aggregated quantity is calculated for the product type in the manufacturing line. The coefficients of this second linear combination are predetermined. This second linear combination serves as a second throughput target. A throughput target for each range is determined by determining the minimum of the first throughput target, which can be different for each range, and the second throughput target, which is common across all ranges.
    • 对于生产线中给定产品类型的每个范围,计算从WIP分布导出的局部移动聚合量和从WIP分布导出的全局聚合量的第一线性组合。 第一个线性组合作为范围和产品的第一个吞吐量目标。 对于生产线中的产品类型计算WIP分布的非零部分的标准偏差与全局总计量的第二线性组合。 该第二线性组合的系数是预定的。 该第二线性组合用作第二吞吐量目标。 通过确定对于每个范围可以是不同的第一吞吐量目标的最小值和在所有范围内共同的第二吞吐量目标来确定每个范围的吞吐量目标。
    • 2. 发明申请
    • METHOD AND SYSTEM FOR DETERMINING MANUFACTURING THROUGHPUT TARGET
    • 用于确定制造目标的方法和系统
    • US20110172801A1
    • 2011-07-14
    • US12685393
    • 2010-01-11
    • Sugato BagchiLindsay E. BurnsSteven C. CatlettChing-Hua Chen-Ritzo
    • Sugato BagchiLindsay E. BurnsSteven C. CatlettChing-Hua Chen-Ritzo
    • G06F17/50
    • G06Q10/06
    • A first linear combination of a local moving aggregated quantity derived from a WIP distribution and a global aggregated quantity derived from the WIP distribution is calculated for each range for a given product type in a manufacturing line. The first linear combination serves as a first throughput target for the range and product. A second linear combination of a standard deviation of the non-zero portion of the WIP distribution and the global aggregated quantity is calculated for the product type in the manufacturing line. The coefficients of this second linear combination are predetermined. This second linear combination serves as a second throughput target. A throughput target for each range is determined by determining the minimum of the first throughput target, which can be different for each range, and the second throughput target, which is common across all ranges.
    • 对于生产线中给定产品类型的每个范围,计算从WIP分布导出的局部移动聚合量和从WIP分布导出的全局聚合量的第一线性组合。 第一个线性组合作为范围和产品的第一个吞吐量目标。 对于生产线中的产品类型计算WIP分布的非零部分的标准偏差与全局总计量的第二线性组合。 该第二线性组合的系数是预定的。 该第二线性组合用作第二吞吐量目标。 通过确定对于每个范围可以是不同的第一吞吐量目标的最小值和在所有范围内共同的第二吞吐量目标来确定每个范围的吞吐量目标。
    • 5. 发明申请
    • Data quality management using business process modeling
    • 数据质量管理采用业务流程建模
    • US20070198312A1
    • 2007-08-23
    • US11357134
    • 2006-02-21
    • Sugato BagchiXue BaiJayant Kalagnanam
    • Sugato BagchiXue BaiJayant Kalagnanam
    • G06F17/50
    • G06Q10/067G06F16/215G06F17/18G06Q10/06G06Q10/06311G06Q10/06375G06Q10/06395
    • A business process modeling framework is used for data quality analysis. The modeling framework represents the sources of transactions entering the information processing system, the various tasks within the process that manipulate or transform these transactions, and the data repositories in which the transactions are stored or aggregated. A subset of these tasks is associated as the potential error introduction sources, and the rate and magnitude of various error classes at each such task are probabilistically modeled. This model can be used to predict how changes in transactions volumes and business processes impact data quality at the aggregate level in the data repositories. The model can also account for the presence of error correcting controls and assess how the placement and effectiveness of these controls alter the propagation and aggregation of errors. Optimization techniques are used for the placement of error correcting controls that meet target quality requirements while minimizing the cost of operating these controls. This analysis also contributes to the development of business “dashboards” that allow decision-makers to monitor and react to key performance indicators (KPIs) based on aggregation of the transactions being processed. Data quality estimation in real time provides the accuracy of these KPIs (in terms of the probability that a KPI is above or below a given value), which may condition the action undertaken by the decision-maker.
    • 业务流程建模框架用于数据质量分析。 建模框架表示进入信息处理系统的事务的来源,处理或转换这些事务的过程中的各种任务,以及存储或聚合事务的数据存储库。 这些任务的子集与潜在的错误引入源相关联,并且对每个这样的任务的各种错误类别的速率和幅度进行概率建模。 该模型可用于预测事务卷和业务流程中的变更如何影响数据存储库中的聚合级别的数据质量。 该模型还可以解释错误纠正控件的存在,并评估这些控件的布局和有效性如何改变错误的传播和聚合。 优化技术用于放置符合目标质量要求的误差校正控制,同时最小化操作这些控制的成本。 这种分析还有助于开发业务“仪表板”,使决策者能够根据正在处理的交易的汇总来监测和反应关键绩效指标(KPI)。 实时数据质量评估提供了这些KPI的准确性(根据KPI高于或低于给定值的概率),这可能影响决策者采取的行动。