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Manufacturing System Design meets Big Data Analytics for Continuous Improvement

机译:制造系统设计满足大数据分析的持续改进

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摘要

Desired business results are the direct result of the system design. It is also theorized that the \u27thinking\u27 within an organization creates the organization\u27s \u27structure\u27 or design, which then drives the system\u27s \u27behavior.\u27 Achievement of enduring change in a system\u27s performance must begin with a change in the thinking of all the people in the enterprise, but especially that of leadership. In the absence of such a change in the thinking, the needed structural changes within a system may result in short-lived, point solutions, resulting in localized optimization of sub-systems versus systemic improvement. Axiomatic design, applied to a manufacturing system, is a design methodology to best reflect, understand and control the inherent complexity of large-scale integrated systems. System stability, and ultimately cost and span-time reduction, are the desired objectives of system design. This paper provides an overview of the manufacturing system design decomposition, and discusses the integrated use of data analytics to identify bottlenecks for system-improvement and use of the manufacturing system design decomposition to cost-justify resource allocation decisions for improvement.
机译:期望的业务结果是系统设计的直接结果。从理论上讲,组织内部的思考会创建组织的结构或设计,然后驱动系统的行为。实现系统性能的持久变化必须从以下几点开始企业所有人的思维方式发生变化,尤其是领导者的思维方式发生变化。在思想上没有这种变化的情况下,系统内所需的结构更改可能会导致寿命短的点式解决方案,从而导致子系统的局部优化与系统性改进。公理化设计应用于制造系统,是一种最佳反映,理解和控制大规模集成系统固有复杂性的设计方法。系统设计的理想目标是系统的稳定性以及最终的成本和时间的减少。本文概述了制造系统设计分解,并讨论了数据分析的综合使用以识别系统改进的瓶颈,并讨论了制造系统设计分解的使用以成本合理化资源分配决策以进行改进。

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