Statistical Engineering: Statistical Methods for Process Improvement and Decision Making

统计工程：过程改进和决策的统计方法

• 批准号: RGPIN-2017-04062
• 负责人: Steiner, Stefan
• 金额: $ 2.48万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=685373
• 关键词: Statistical; Engineering; Methods; Process; Improvement

The general goals of this research include improving efficiency, reducing costs and better decision making and allocation of resources in business, industrial and medical applications. More specifically, I propose to pursue the following three related research themes: estimating current process performance from streaming data, augmenting the design and analysis of experiments using baseline data and improving the assessment of measurement systems.*******There are many situations where we obtain data at regular time intervals and wish to estimate the current process performance. For example, we may run weekly customer satisfaction surveys. Here we expect satisfaction to change slowly over time as we address customer concerns, develop new products, react to actions by our competitors, etc. This research aims to develop robust methods for assessing the current process condition that takes into account sampling variation, possible changes over time and the effect of covariates. New methods that combine data in a sensible way can greatly improve decision making. There are many related application areas, such as monitoring hospital or medical lab performance.*******Experiments, where we deliberately change a process to assess the effect of the change, are widely used in virtually all scientific inquiry. In this research I focus specifically on the use of experiments in improvement projects for existing processes. In this context, we usually have a lot of available observational “baseline” data. The goals of this project are to show how to best use this available process data to improve the planning of experiments and also to quantify the gains in efficiency resulting from incorporating the baseline data in the analysis. Since experiments are typically expensive and disruptive to run, such improvements would save time and money.*******Measurement systems are essential in virtually all areas of scientific inquiry. Without precise and well calibrated measurement systems process understanding and/or improvement are difficult to achieve. This research focuses on improving the assessment of binary measurement systems that are essential as inspection systems in industry and as diagnostic tests in medicine. Efficient and effective measurement system assessments and comparisons would enhance our ability to make informed decisions about which measurement systems to use and how to improve existing measurement systems. **

这项研究的总体目标包括提高效率，降低成本，更好地决策和资源分配在商业，工业和医疗应用。更具体地说，我建议追求以下三个相关的研究主题：从流数据中估计当前的过程性能，使用基线数据增强实验的设计和分析，以及改进测量系统的评估。在许多情况下，我们以固定的时间间隔获得数据，并希望估计当前的过程性能。例如，我们可能每周进行一次客户满意度调查。在这里，我们希望满意度随着时间的推移，我们解决客户的关注，开发新产品，我们的竞争对手的行动作出反应，等这项研究的目的是开发强大的方法来评估当前的过程条件，考虑到抽样变化，可能的变化随着时间的推移和协变量的影响。以合理的方式将联合收割机数据结合起来的新方法可以大大改善决策。有许多相关的应用领域，如监控医院或医疗实验室的性能。实验，我们故意改变一个过程来评估改变的效果，被广泛用于几乎所有的科学研究。在这项研究中，我特别关注在现有流程的改进项目中使用实验。在这种情况下，我们通常有很多可用的观测“基线”数据。该项目的目标是展示如何最好地利用这些可用的过程数据来改进实验规划，并量化将基线数据纳入分析所带来的效率增益。由于实验通常是昂贵的和破坏性的运行，这样的改进将节省时间和金钱。测量系统在几乎所有的科学研究领域都是必不可少的。如果没有精确和良好校准的测量系统，就难以实现过程理解和/或改进。本研究的重点是改善二进制测量系统的评估，这是必不可少的工业检测系统和医学诊断测试。高效率和有效的衡量系统评估和比较将提高我们就使用哪些衡量系统和如何改进现有衡量系统作出知情决定的能力。**