【校稿】第三十篇 Statistical engineering links statistical thinking, methods, t
本帖最后由 小编D 于 2011-12-16 17:04 编辑
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本文翻译者：forwardsj
by Roger W. Hoerl and Ronald D. SneeRoger W. Hoerl及Ronald D. Snee著
The philosopher Thomas Kuhn showed that a way of thinking (a paradigm) lasts until the problems no longer adequately addressed by the paradigm are so important that a new paradigm is needed to deal with that list of unsolved problems.1哲学家哲学家托马斯。库恩展示了一种思维方式（一种模式），这种模式持续到已经不足以适当的解决问题，这时，对一种解决问题（表1）的新模式的需要变得非常的重要
Some urgent problems not being adequately addressed by existing statistically based approaches to quality improvement include:那些无法用现存的基于统计学的方法做质量改进的紧急问题包含
· Ensuring statistical projects have high impact. Too often, we experience the phenomenon that "the operation was a success, but the patient died." In other words, our application of statistics is technically impeccable, but it doesn’t actually drive tangible improvement· 保证统计项目的高影响力，我们经常会遇到“手术很成功，但是病人死了”这种现象。换句话说我们对统计学的应用在技术上面无懈可击，但无法达到实际的改进。
· Integrating concepts of statistical thinking with the application of statistical methods and tools. A gap or disconnect exists between the two, resulting in poor application of the important principles of statistical thinking and lost opportunities to apply statistical methods effectively. There’s a missing link that will drive proper application of statistical methods based on a solid understanding of statistical thinking principles.· 通过使用统计学方式以及工具对统计思维进行集成。在两者之间缺乏一种连接，导致了对重要的统计思维原理的糟糕应用。并且，无法有效的使用统计学方法。这里有一道裂痕，因此而无法基于统计思维原理的的坚实理解，来驱动统计方式的合理应用
· Providing statisticians an opportunity to become true leaders in their organizations, rather than passive consultants.· 给统计学家提供一个机会，使他们成为各自组织中的真正领导者，而不是被动的顾问。
Therefore, we propose a different paradigm for the quantitative approaches to quality improvement. We call it statistical engineering.因此，我们为质量改进的大量方法提出了一个不同的模式。叫做统计工程学
The term statistical engineering has been used before, perhaps most notably by consultant Dorian Shainin, who generally used it to indicate the application of statistical approaches that were ad hoc (but generally worked) rather than based on formal statistical theory.或许大多数人从顾问多里安。谢宁处得知统计工程学曾经用过的条款。他通常用以表明对统计方法的应用是很特别的（但通常会起作用），而不是基于正式的统计学原理。
Conversely, we use the term in its literal sense. Engineering can be defined as the study of how to best use known scientific and mathematical principles for the benefit of mankind. This contrasts with pure science, which attempts to advance our understanding of natural laws and phenomena. 相反，我们根据字面的意思来使用这些条款。工程学可以被定义为为了人类的利益而对如何最好地使用已知的科学和数学原理的研究。这种与纯科学的反差会使我们对自然规律和现象的理解更进一步。
For example, after years of research, a chemist might invent a new marketable substance in the laboratory. Chemical engineers might then determine how to scale up the process to produce this substance commercially, design the plant to manufacture it and identify how the plant should be operated to be profitable.例如，在数年的研究后，一个化学家在实验室里面可能会发明一种新的可投放市场的材料。而化学工程师们可能则会决定如何设定工艺对这种材料进行商业制造。如何设计工厂来进行生产以及如何运行这个工厂使之盈利
The statistical engineering discipline would be the study of how to use the principles and techniques of statistical science for the benefit of humankind. From an operational perspective, statistical engineering is defined as the study of how to best use statistical concepts, methods and tools, and integrate them with IT and other relevant sciences to generate improved results.统计工程学训练会是为了人类的利益而进行的关于如何使用统计科学的原理和技术。从操作的角度来看统计工程学可被定义为关于如何最好的使用统计概念，方法和工具，并集成信息技术和其他相关科学以产生改善结果的研究。
In other words, engineers—statistical or otherwise—do not focus on advancement of the fundamental laws of science, but rather how these laws might be best used for practical benefit. This is not to say engineers do not research or develop theory. Rather, it suggests engineers’ theoretical developments tend to be oriented toward the question of how to best use known science to benefit society.换句话说，统计学的或其他的工程师并非专注于基本科学原理的改进，而是专注于如何使用这些原理得到实际的效果。这并不是说工程师们并不研究发展理论。确切的说，工程师们的理论发展倾向于如何使用已知科技来造福社会 We’re not suggesting that society no longer needs research in new statistical techniques for improvement; it does. The balance needed at this time, however, is perhaps 80% for statistics as an engineering discipline and 20% for statistics as a pure science.我们并非是说社会不再需要改进的新统计技术。实际是需要的。现在需要的是，或许80%的统计工程学原理的应用和20%的纯统计科学的研究达到平衡。
For example, new strategies to better use control charts for maximum benefit in healthcare, finance and other service industries are needed even more than additional research on the mathematical properties of control charts themselves. Both are still needed, but we call for a shift in emphasis from the latter to the former.例如，为了更好的使用控制图来达成医疗，金融和其他服务业的最大效益，我们需要一种新战略而不是仅仅对控制图本身的数学特性进行复加的研究。两者都是需要的，但是我们需要从后者到前者的重心转移。
Statistical engineering example统计工程学实例
To provide a tangible example of statistical engineering, consider lean Six Sigma (LSS Critics have pointed out that LSS hasn’t actually invented any new tools, and they are right. This is one of the reasons academia have been much slower to focus attention on LSS than on improvement methods that came with new tools, such as Taguchi Methods.用精益六西格玛作为统计工程学的实例，（精益六西格玛的批评者指出精益六西格玛实际上没有发明任何新的工具，他们是正确的）这也是相对于新工具带来的改善方式（如田口方法）学术机构很迟才才关注到精益六西格玛
We believe LSS has not invented any new tools because it is not an example of statistical science, but rather statistical engineering.我们相信精益六西格玛并没有发明任何新工具，因为这是一个统计工程学的例子，而不是统计科学的例子。
For example, per critics’ claims, LSS has taken existing statistical principles and tools and integrated them with other disciplines, such as lean enterprise, quality engineering and operations research, to provide an overall method that generates more dramatic results than had been obtained previously. The novelty of LSS is not in the tools, per se. It is in the way they are integrated, deployed and supported organizationally.例如，根据批评者的主张，精益六西格玛采用现有的统计理论和工具并把他们和其他理论整合。（例如精益企业，质量工程和操作研究，）用以提供一种可以比从前的方案更有激动人心的结果的打包方案。就其本身而言，精益六西格玛的新颖之处并不在工具。而在它们有组织的整合，展开和支援的方法。
This did not occur by chance, but rather by the careful study of the limitations of previous improvement initiatives and decades of experience with Six Sigma by many organizations that eventually resulted in today’s LSS approach.今天的精益六西格玛并不是偶然发生的，而是基于对之前的改进动机的研究还有许多组织在六西格玛几十年的经验而形成的。
This is how statistical engineering works; it makes a formal discipline of how to best use the existing statistical toolkit to drive more dramatic results. In other words, statistical engineering integrates existing theory of the tools themselves with the cumulative learnings from applications in diverse settings to develop a dynamic theory of how to generate improved results.这是统计工程学如何起作用的，关于如何使用现有的统计工具来得到更加激动人心的正式原理就这样形成了。换言之，统计工程学整合了现有的工具理论何在不同设定应用的经验累积，从而发展了一个如何产生改进结果的动态理论。
This theory can then be debated by the profession, researched, tested and improved over time. This is what we have seen with Six Sigma; it has gone from Six Sigma to LSS, has incorporated additional methods (such as simulation) and has progressed from using the measure, analyze, improve, control (MAIC) framework to using the define, measure, analyze, improve and control (DMAIC) framework. Statistical engineering enables us to improve how we make improvements, just as we have seen with Six Sigma.这个理论可能会在不同行业间辩论，并时时进行研究测试和改进。这个理论是我们通过六西格玛看到的。这个理论已经从六西格玛转变为精益六西格玛，吸收了其他的方式（例如模拟），并从使用量测，分析，改进，控制（MAIC）的框架进步到使用定义，量测，分析，改进和控制（DMAIC）的框架。统计工程学使我们改进创造改进的方式，就像我们从六西格玛中看到的。
The missing link漏掉的一环
Much has been written in quality literature about statistical thinking, which includes the critical principles of a process view of work, systems thinking and the importance of understanding and reducing variation. Ideally, these principles should guide the application of statistical tools.有很多的关于统计思维的质量文字，包含了诸如工作流程，系统思考以及理解并降低变差的重要性的关键原理。从理想化的角度来看，这些原理应该指导统计工具的应用
Too often, however, we have found a disconnect between those who understand these principles and those who apply statistical tools. Some can articulate the principles and explain how they apply to specific situations, but they may struggle with applying statistical tools. This leads to lost opportunities.但是，我们常常能够看到那些理解这些原理的人和那些应用统计工具的人往往无法整合。有些人可以清晰地的阐述这些原理并能够解释这些原理是如何在不同情况下应用，但是他们可能挣扎于应用统计工具，而这会导致机会的丧失
Conversely, there are many specialists in statistical tools who don’t seem to understand the principles of statistical thinking. But they apply the tools anyway, which often results in situations where, as noted earlier, "the operation was a success, but the patient died."相反，有很多统计工具的专家却可能不太理解统计思考的原理。但是他们仍然使用这些工具，往往导致了我们之前提到过结果，“手术很成功，但是病人死了”，
Figure 1
shows how statistical engineering might be the "missing link" to help integrate statistical thinking and statistical tools. Statistical thinking is the strategic aspect of our discipline that provides conceptual understanding and the proper context. It answers the question "Why should we use statistics?" When this context is properly understood, a well-developed discipline of statistical engineering will provide statistically based improvement approaches based on theory and rigorous research.显示了统计工程学可能是这缺失的一环以帮助集成统计思维以及统计工具。统计思维在战略层面提供对原理的理解以及适合的内容。它回答了“我们为什么使用统计学”这个问题。当理解了这个问题，一个发达的统计工程学的规律会根据理论和严谨的研究提供基于统计的改善措施。
This provides the tactical aspect of statistics; it answers the question, "What overall approaches should be used?" Individual statistical methods and tools provide the operational aspect and answer the question, "How do we implement these approaches?" The strategic, tactical operational model of leadership has been around since antiquity and applied to the statistics profession at least since 1990.3这个提供了战术层面的的统计学。它回答了“应该是用什么工具”这个问题。单独的统计方法和工具提供了操作层面上的，并回答了这个问题，“我们如何实行这些方式”领导力的战略，战术和操作的模型自古就有并最少在1990年3月导入统计职业
Conversely, without statistical engineering, we have a set of proven principles and a set of tools. It is rarely obvious to practitioners how to apply the tools in a way that is consistent with the principles. Statistical engineering can do just that.相反，没有统计工程学的话，我们有一套有效的原理和工具。但对实践者来说，如何使用在和理论统一的情况下使用哲学工具却是比较困难的。。而统计工程学就是起这个作用的
For example, statistical thinking includes the key principle of reducing unwanted variation. But, much of historic statistical practice has focused tool usage primarily on improving averages.例如，统计思维包含了降低我们不希望见到的变差的关键理论。但是，过去很多统计学的应用却是主要专注于用工具来改善均值。
Through the rigor of statistical engineering, an overall improvement approach, LSS, was developed that includes the DMAIC roadmap, which shows practitioners how to apply statistical and other tools in such a way as to reduce variation. In other words, the tools are linked to the principles.通过严谨的统计工程学，人们发展了一个宏观的改善方法，精益六西格玛，。它包含了定义 量测 分析 改进 控制的路线图，这个路线图告诉实践者们如何使用统计学的或其他的工具来减少变差。换句话说，工具和原理连接起来了。
Roger W. Hoerl is manager of GE Global Research’s applied statistics lab. He has a doctorate in applied statistics from the University of Delaware in Newark. An ASQ fellow and a recipient of the ASQ Shewhart Medal and Brumbaugh Award, Hoerl is also an academician in the International Academy for Quality.
Ronald D. Snee is president of Snee Associates LLC in Newark, DE. He has a doctorate in applied and mathematical statistics from Rutgers University in New Brunswick, NJ. Snee has received the ASQ Shewhart and Grant medals, is an ASQ fellow and is an academician in the International Academy for Quality.
你好，我是小编H。请对以下文章有校稿兴趣的组员留下你的预计完成时间，并发短信息联系小编H，以便小编登记翻译者信息以及文章最终完成时的奖惩工作。
本文翻译者：forwardsj
by Roger W. Hoerl and Ronald D. SneeRoger W. Hoerl及Ronald D. Snee著
The philosopher Thomas Kuhn showed that a way of thinking (a paradigm) lasts until the problems no longer adequately addressed by the paradigm are so important that a new paradigm is needed to deal with that list of unsolved problems.1哲学家哲学家托马斯。库恩展示了一种思维方式（一种模式），这种模式持续到已经不足以适当的解决问题，这时，对一种解决问题（表1）的新模式的需要变得非常的重要
Some urgent problems not being adequately addressed by existing statistically based approaches to quality improvement include:那些无法用现存的基于统计学的方法做质量改进的紧急问题包含
· Ensuring statistical projects have high impact. Too often, we experience the phenomenon that "the operation was a success, but the patient died." In other words, our application of statistics is technically impeccable, but it doesn’t actually drive tangible improvement· 保证统计项目的高影响力，我们经常会遇到“手术很成功，但是病人死了”这种现象。换句话说我们对统计学的应用在技术上面无懈可击，但无法达到实际的改进。
· Integrating concepts of statistical thinking with the application of statistical methods and tools. A gap or disconnect exists between the two, resulting in poor application of the important principles of statistical thinking and lost opportunities to apply statistical methods effectively. There’s a missing link that will drive proper application of statistical methods based on a solid understanding of statistical thinking principles.· 通过使用统计学方式以及工具对统计思维进行集成。在两者之间缺乏一种连接，导致了对重要的统计思维原理的糟糕应用。并且，无法有效的使用统计学方法。这里有一道裂痕，因此而无法基于统计思维原理的的坚实理解，来驱动统计方式的合理应用
· Providing statisticians an opportunity to become true leaders in their organizations, rather than passive consultants.· 给统计学家提供一个机会，使他们成为各自组织中的真正领导者，而不是被动的顾问。
Therefore, we propose a different paradigm for the quantitative approaches to quality improvement. We call it statistical engineering.因此，我们为质量改进的大量方法提出了一个不同的模式。叫做统计工程学
The term statistical engineering has been used before, perhaps most notably by consultant Dorian Shainin, who generally used it to indicate the application of statistical approaches that were ad hoc (but generally worked) rather than based on formal statistical theory.或许大多数人从顾问多里安。谢宁处得知统计工程学曾经用过的条款。他通常用以表明对统计方法的应用是很特别的（但通常会起作用），而不是基于正式的统计学原理。
Conversely, we use the term in its literal sense. Engineering can be defined as the study of how to best use known scientific and mathematical principles for the benefit of mankind. This contrasts with pure science, which attempts to advance our understanding of natural laws and phenomena. 相反，我们根据字面的意思来使用这些条款。工程学可以被定义为为了人类的利益而对如何最好地使用已知的科学和数学原理的研究。这种与纯科学的反差会使我们对自然规律和现象的理解更进一步。
For example, after years of research, a chemist might invent a new marketable substance in the laboratory. Chemical engineers might then determine how to scale up the process to produce this substance commercially, design the plant to manufacture it and identify how the plant should be operated to be profitable.例如，在数年的研究后，一个化学家在实验室里面可能会发明一种新的可投放市场的材料。而化学工程师们可能则会决定如何设定工艺对这种材料进行商业制造。如何设计工厂来进行生产以及如何运行这个工厂使之盈利
The statistical engineering discipline would be the study of how to use the principles and techniques of statistical science for the benefit of humankind. From an operational perspective, statistical engineering is defined as the study of how to best use statistical concepts, methods and tools, and integrate them with IT and other relevant sciences to generate improved results.统计工程学训练会是为了人类的利益而进行的关于如何使用统计科学的原理和技术。从操作的角度来看统计工程学可被定义为关于如何最好的使用统计概念，方法和工具，并集成信息技术和其他相关科学以产生改善结果的研究。
In other words, engineers—statistical or otherwise—do not focus on advancement of the fundamental laws of science, but rather how these laws might be best used for practical benefit. This is not to say engineers do not research or develop theory. Rather, it suggests engineers’ theoretical developments tend to be oriented toward the question of how to best use known science to benefit society.换句话说，统计学的或其他的工程师并非专注于基本科学原理的改进，而是专注于如何使用这些原理得到实际的效果。这并不是说工程师们并不研究发展理论。确切的说，工程师们的理论发展倾向于如何使用已知科技来造福社会 We’re not suggesting that society no longer needs research in new statistical techniques for improvement; it does. The balance needed at this time, however, is perhaps 80% for statistics as an engineering discipline and 20% for statistics as a pure science.我们并非是说社会不再需要改进的新统计技术。实际是需要的。现在需要的是，或许80%的统计工程学原理的应用和20%的纯统计科学的研究达到平衡。
For example, new strategies to better use control charts for maximum benefit in healthcare, finance and other service industries are needed even more than additional research on the mathematical properties of control charts themselves. Both are still needed, but we call for a shift in emphasis from the latter to the former.例如，为了更好的使用控制图来达成医疗，金融和其他服务业的最大效益，我们需要一种新战略而不是仅仅对控制图本身的数学特性进行复加的研究。两者都是需要的，但是我们需要从后者到前者的重心转移。
Statistical engineering example统计工程学实例
To provide a tangible example of statistical engineering, consider lean Six Sigma (LSS Critics have pointed out that LSS hasn’t actually invented any new tools, and they are right. This is one of the reasons academia have been much slower to focus attention on LSS than on improvement methods that came with new tools, such as Taguchi Methods.用精益六西格玛作为统计工程学的实例，（精益六西格玛的批评者指出精益六西格玛实际上没有发明任何新的工具，他们是正确的）这也是相对于新工具带来的改善方式（如田口方法）学术机构很迟才才关注到精益六西格玛
We believe LSS has not invented any new tools because it is not an example of statistical science, but rather statistical engineering.我们相信精益六西格玛并没有发明任何新工具，因为这是一个统计工程学的例子，而不是统计科学的例子。
For example, per critics’ claims, LSS has taken existing statistical principles and tools and integrated them with other disciplines, such as lean enterprise, quality engineering and operations research, to provide an overall method that generates more dramatic results than had been obtained previously. The novelty of LSS is not in the tools, per se. It is in the way they are integrated, deployed and supported organizationally.例如，根据批评者的主张，精益六西格玛采用现有的统计理论和工具并把他们和其他理论整合。（例如精益企业，质量工程和操作研究，）用以提供一种可以比从前的方案更有激动人心的结果的打包方案。就其本身而言，精益六西格玛的新颖之处并不在工具。而在它们有组织的整合，展开和支援的方法。
This did not occur by chance, but rather by the careful study of the limitations of previous improvement initiatives and decades of experience with Six Sigma by many organizations that eventually resulted in today’s LSS approach.今天的精益六西格玛并不是偶然发生的，而是基于对之前的改进动机的研究还有许多组织在六西格玛几十年的经验而形成的。
This is how statistical engineering works; it makes a formal discipline of how to best use the existing statistical toolkit to drive more dramatic results. In other words, statistical engineering integrates existing theory of the tools themselves with the cumulative learnings from applications in diverse settings to develop a dynamic theory of how to generate improved results.这是统计工程学如何起作用的，关于如何使用现有的统计工具来得到更加激动人心的正式原理就这样形成了。换言之，统计工程学整合了现有的工具理论何在不同设定应用的经验累积，从而发展了一个如何产生改进结果的动态理论。
This theory can then be debated by the profession, researched, tested and improved over time. This is what we have seen with Six Sigma; it has gone from Six Sigma to LSS, has incorporated additional methods (such as simulation) and has progressed from using the measure, analyze, improve, control (MAIC) framework to using the define, measure, analyze, improve and control (DMAIC) framework. Statistical engineering enables us to improve how we make improvements, just as we have seen with Six Sigma.这个理论可能会在不同行业间辩论，并时时进行研究测试和改进。这个理论是我们通过六西格玛看到的。这个理论已经从六西格玛转变为精益六西格玛，吸收了其他的方式（例如模拟），并从使用量测，分析，改进，控制（MAIC）的框架进步到使用定义，量测，分析，改进和控制（DMAIC）的框架。统计工程学使我们改进创造改进的方式，就像我们从六西格玛中看到的。
The missing link漏掉的一环
Much has been written in quality literature about statistical thinking, which includes the critical principles of a process view of work, systems thinking and the importance of understanding and reducing variation. Ideally, these principles should guide the application of statistical tools.有很多的关于统计思维的质量文字，包含了诸如工作流程，系统思考以及理解并降低变差的重要性的关键原理。从理想化的角度来看，这些原理应该指导统计工具的应用
Too often, however, we have found a disconnect between those who understand these principles and those who apply statistical tools. Some can articulate the principles and explain how they apply to specific situations, but they may struggle with applying statistical tools. This leads to lost opportunities.但是，我们常常能够看到那些理解这些原理的人和那些应用统计工具的人往往无法整合。有些人可以清晰地的阐述这些原理并能够解释这些原理是如何在不同情况下应用，但是他们可能挣扎于应用统计工具，而这会导致机会的丧失
Conversely, there are many specialists in statistical tools who don’t seem to understand the principles of statistical thinking. But they apply the tools anyway, which often results in situations where, as noted earlier, "the operation was a success, but the patient died."相反，有很多统计工具的专家却可能不太理解统计思考的原理。但是他们仍然使用这些工具，往往导致了我们之前提到过结果，“手术很成功，但是病人死了”，
Figure 1
shows how statistical engineering might be the "missing link" to help integrate statistical thinking and statistical tools. Statistical thinking is the strategic aspect of our discipline that provides conceptual understanding and the proper context. It answers the question "Why should we use statistics?" When this context is properly understood, a well-developed discipline of statistical engineering will provide statistically based improvement approaches based on theory and rigorous research.显示了统计工程学可能是这缺失的一环以帮助集成统计思维以及统计工具。统计思维在战略层面提供对原理的理解以及适合的内容。它回答了“我们为什么使用统计学”这个问题。当理解了这个问题，一个发达的统计工程学的规律会根据理论和严谨的研究提供基于统计的改善措施。
This provides the tactical aspect of statistics; it answers the question, "What overall approaches should be used?" Individual statistical methods and tools provide the operational aspect and answer the question, "How do we implement these approaches?" The strategic, tactical operational model of leadership has been around since antiquity and applied to the statistics profession at least since 1990.3这个提供了战术层面的的统计学。它回答了“应该是用什么工具”这个问题。单独的统计方法和工具提供了操作层面上的，并回答了这个问题，“我们如何实行这些方式”领导力的战略，战术和操作的模型自古就有并最少在1990年3月导入统计职业
Conversely, without statistical engineering, we have a set of proven principles and a set of tools. It is rarely obvious to practitioners how to apply the tools in a way that is consistent with the principles. Statistical engineering can do just that.相反，没有统计工程学的话，我们有一套有效的原理和工具。但对实践者来说，如何使用在和理论统一的情况下使用哲学工具却是比较困难的。。而统计工程学就是起这个作用的
For example, statistical thinking includes the key principle of reducing unwanted variation. But, much of historic statistical practice has focused tool usage primarily on improving averages.例如，统计思维包含了降低我们不希望见到的变差的关键理论。但是，过去很多统计学的应用却是主要专注于用工具来改善均值。
Through the rigor of statistical engineering, an overall improvement approach, LSS, was developed that includes the DMAIC roadmap, which shows practitioners how to apply statistical and other tools in such a way as to reduce variation. In other words, the tools are linked to the principles.通过严谨的统计工程学，人们发展了一个宏观的改善方法，精益六西格玛，。它包含了定义 量测 分析 改进 控制的路线图，这个路线图告诉实践者们如何使用统计学的或其他的工具来减少变差。换句话说，工具和原理连接起来了。
Roger W. Hoerl is manager of GE Global Research’s applied statistics lab. He has a doctorate in applied statistics from the University of Delaware in Newark. An ASQ fellow and a recipient of the ASQ Shewhart Medal and Brumbaugh Award, Hoerl is also an academician in the International Academy for Quality.
Ronald D. Snee is president of Snee Associates LLC in Newark, DE. He has a doctorate in applied and mathematical statistics from Rutgers University in New Brunswick, NJ. Snee has received the ASQ Shewhart and Grant medals, is an ASQ fellow and is an academician in the International Academy for Quality.
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460591835 (威望:0) (江苏 南京) 电子制造 工程师
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Roger W.Hoerl&Ronald D. Snee著
The philosopher Thomas Kuhn showed that a way of thinking (a paradigm) lasts until the problems no longer adequately addressed by the paradigm are so important that a new paradigm is needed to deal with that list of unsolved problems.1
哲学家托马斯.库恩指出:当既有的方式已经不能充分解决问题时,对于未解决问题来说,新方式的出现是非常重要的.
Some urgent problems not being adequately addressed by existing statistically based approaches to quality improvement include:
无法用现有的质量改进的统计方法解决的问题有:
•Ensuring statistical projects have high impact. Too often, we experience the phenomenon that "the operation was a success, but the patient died." In other words, our application of statistics is technically impeccable, but it doesn’t actually drive tangible improvement
•保证统计项目的高影响力.我们经常会遇到这种现象:“手术很成功，但是病人死了”。换句话说我们对统计学的应用在技术上面无懈可击，但无法达到实际的改进。
•Integrating concepts of statistical thinking with the application of statistical methods and tools. A gap or disconnect exists between the two, resulting in poor application of the important principles of statistical thinking and lost opportunities to apply statistical methods effectively. There’s a missing link that will drive proper application of statistical methods based on a solid understanding of statistical thinking principles.
•统一统计理论和方法应用。由于两者相互连接的空白，导致重要的统计原理应用不足，统计方法的使用效率低下，没能实现理论指导下方法的有效应用。
•Providing statisticians an opportunity to become true leaders in their organizations, rather than passive consultants.
•给统计学家一个机会成为组织的真正领导者，而不是被动的顾问。
Therefore, we propose a different paradigm for the quantitative approaches to quality improvement. We call it statistical engineering.
因此，我们为质量改进的统计方法提出了一个新的方式，叫做统计工程学
The term statistical engineering has been used before, perhaps most notably by consultant Dorian Shainin, who generally used it to indicate the application of statistical approaches that were ad hoc (but generally worked) rather than based on formal statistical theory.
或许大多数人从多里安.谢宁顾问处得知统计工程学这个术语。他曾经常用它表达统计方法的应用，而非正式的统计理论。
Conversely, we use the term in its literal sense. Engineering can be defined as the study of how to best use known scientific and mathematical principles for the benefit of mankind. This contrasts with pure science, which attempts to advance our understanding of natural laws and phenomena.
相反，我们使用该术语的字面涵义。工程学是研究如何最好使用已知的科学和数学原理，为人类谋福利。与纯科学的这种差距使我们对自然规律和现象的理解更进一步。
For example, after years of research, a chemist might invent a new marketable substance in the laboratory. Chemical engineers might then determine how to scale up the process to produce this substance commercially, design the plant to manufacture it and identify how the plant should be operated to be profitable.
例如，经过数年的研究，一个化学家在实验室里发明一种有市场潜力的新材料。接着，化学工程师们决定如何设定工艺对这种材料进行商业制造。如何设计工厂来生产以及如何运行这个工厂使之盈利。
The statistical engineering discipline would be the study of how to use the principles and techniques of statistical science for the benefit of humankind. From an operational perspective, statistical engineering is defined as the study of how to best use statistical concepts, methods and tools, and integrate them with IT and other relevant sciences to generate improved results.
统计工程学的是一门研究如何使用统计科学的原理和技术，使人类获利的学科。从操作的角度讲，统计工程学也可以被定义研究怎样有效的利用统计概念，方法和工具，辅以信息技术以及其他科学，实现改进的学科。
In other words, engineers—statistical or otherwise—do not focus on advancement of the fundamental laws of science, but rather how these laws might be best used for practical benefit. This is not to say engineers do not research or develop theory. Rather, it suggests engineers’ theoretical developments tend to be oriented toward the question of how to best use known science to benefit society.
另一方面，统计或其他的工程师并非专注于基础科学原理的发展，而是关心这些原理的实际应用。这并不是说工程师们并不做理论研究，确切的说，工程师们的理论研究更关注如何使已知科技造福社会。
We’re not suggesting that society no longer needs research in new statistical techniques for improvement; it does. The balance needed at this time, however, is perhaps 80% for statistics as an engineering discipline and 20% for statistics as a pure science.
我们并非是说社会不需要统计学理论和技术的学术研究和进步，这是需要的。但是，现阶段更需要理论与应用的平衡发展，或许，80%的应用和20%的学术研究。
For example, new strategies to better use control charts for maximum benefit in healthcare, finance and other service industries are needed even more than additional research on the mathematical properties of control charts themselves. Both are still needed, but we call for a shift in emphasis from the latter to the former.
例如，在医疗，金融以及其它服务业的更合适的应用控制图和进一步对控制图的数学研究相比，都非常重要，是目前的重心应放在前者。
Statistical engineering example
统计工程学实例
To provide a tangible example of statistical engineering, consider lean Six Sigma (LSS Critics have pointed out that LSS hasn’t actually invented any new tools, and they are right. This is one of the reasons academia have been much slower to focus attention on LSS than on improvement methods that came with new tools, such as Taguchi Methods.
用精益六西格玛作为统计工程学的实例，精益六西格玛的批评者指出精益六西格玛实际上没有发明任何新的工具，他们是正确的，这也是学术界关注新工具带来的改善方式（如田口方法），却很迟才注意到精益六西格玛的原因。
We believe LSS has not invented any new tools because it is not an example of statistical science, but rather statistical engineering.
我们相信精益六西格玛没有发明任何新工具，因为这是统计工程学的实例，而不是统计科学的。
For example, per critics’ claims, LSS has taken existing statistical principles and tools and integrated them with other disciplines, such as lean enterprise, quality engineering and operations research, to provide an overall method that generates more dramatic results than had been obtained previously. The novelty of LSS is not in the tools, per se. It is in the way they are integrated, deployed and supported organizationally.
例如，根据批评者的主张，精益六西格玛将现有的统计理论和工具与其他理论整合，例如精益企业，质量工程和操作研究，以提供一种更有激励性的综合方法。就其本身而言，精益六西格玛的新颖不在工具，而在它在组织基础上的整合，发散和支持。
This did not occur by chance, but rather by the careful study of the limitations of previous improvement initiatives and decades of experience with Six Sigma by many organizations that eventually resulted in today’s LSS approach.
今天的精益六西格玛并非偶然，而是基于对之前改进瓶颈的不懈研究和许多组织实施六西格玛几十年的经验而形成的。
This is how statistical engineering works; it makes a formal discipline of how to best use the existing statistical toolkit to drive more dramatic results. In other words, statistical engineering integrates existing theory of the tools themselveswith the cumulative learnings from applications in diverse settings to develop a dynamic theory of how to generate improved results.
这是统计工程学的价值，它设定了一个怎样让现有的统计工具能带来更好的成果的原则。换言之，统计工程学是整合了现有的工具理论及其在不同设定下应用的经验累积，发展成为一个关于有效改进的，充满活力的理论。
This theory can then be debated by the profession, researched, tested and improved over time. This is what we have seen with Six Sigma; it has gone from Six Sigma to LSS, has incorporated additional methods (such as simulation) and has progressed from using the measure, analyze, improve, control (MAIC) framework to using the define, measure, analyze, improve and control (DMAIC) framework. Statistical engineering enables us to improve how we make improvements, just as we have seen with Six Sigma.
这个理论将不断的被业界讨论，研究，验证和改进。我们可以看到，六西格玛融合了其他的方式方法（例如模拟），从量测，分析，改进，控制（MAIC）进步到定义，量测，分析，改进和控制（DMAIC）的框架，已经转变为精益六西格玛。统计工程学教会我们改进改进的方法，就像我们从六西格玛中看到的。
The missing link
漏掉的一环
Much has been written in quality literature about statistical thinking, which includes the critical principles of a process view of work, systems thinking and the importance of understanding and reducing variation. Ideally, these principles should guide the application of statistical tools.
有很多关于统计思维的质量文字，包含了诸如工作流程化的关键思想，降低变差的重要性的系统思考和理解，这些理论原本应该指导统计工具的使用
Too often, however, we have found a disconnect between those who understand these principles and those who apply statistical tools. Some can articulate the principles and explain how they apply to specific situations, but they may struggle with applying statistical tools. This leads to lost opportunities.
但是，我们常看到的，理解原理的和应用工具的人各行其事。有些人可以清晰地的阐述这些原理并能够解释这些原理是如何在不同情况下应用，但是不擅于工具应用，这导致许多机会白白错过。
Conversely, there are many specialists in statistical tools who don’t seem to understand the principles of statistical thinking. But they apply the tools anyway, which often results in situations where, as noted earlier, "the operation was a success, but the patient died."
相反，很多应用工具的专家却不太理解统计的原理，他们在使用工具时，往往会出现前文提到的结果，“手术很成功，但是病人死了”，
Figure 1 shows how statistical engineering might be the "missing link" to help integrate statistical thinking and statistical tools. Statistical thinking is the strategic aspect of our discipline that provides conceptual understanding and the proper context. It answers the question "Why should we use statistics?" When this context is properly understood, a well-developed discipline of statistical engineering will provide statistically based improvement approaches based on theory and rigorous research.
如图1所示，统计工程学可能是“缺失的一环”，以统一统计理论和统计工具。统计理论提供战略层面的原理的理解和适合的表述，它回答了“我们为什么使用统计学”这个问题。正确理解了这个概念后，完善的统计工程会提供基于理论和严谨的研究的，统计的改善措施。
This provides the tactical aspect of statistics; it answers the question, "What overall approaches should be used?" Individual statistical methods and tools provide the operational aspect and answer the question, "How do we implement these approaches?" The strategic, tactical operational model of leadership has been around since antiquity and applied to the statistics profession at least since 1990.3
这个提供了战术层面的的统计学，它回答了“应该是用什么工具”这个问题。具体的统计方法和工具提供了操作可能，并回答“我们如何实行这些方式”。可操作的，战略战术的领导力模型由来已久，最早从1990年3月起就被应用到统计中
Conversely, without statistical engineering, we have a set of proven principles and a set of tools. It is rarely obvious to practitioners how to apply the tools in a way that is consistent with the principles. Statistical engineering can do just that.
如果没有统计工程学，我们有效的原理，也有足够的工具，但却很少用不与理论矛盾的方法应用工具。统计工程学就是做这个的。
For example, statistical thinking includes the key principle of reducing unwanted variation. But, much of historic statistical practice has focused tool usage primarily on improving averages.
例如，统计思维包含了降低变差的关键理论。但是，过去很多统计学的应用却是专注于用工具来改善均值。
Through the rigor of statistical engineering, an overall improvement approach, LSS, was developed that includes the DMAIC roadmap, which shows practitioners how to apply statistical and other tools in such a way as to reduce variation. In other words, the tools are linked to the principles.
通过严谨的统计工程学，人们发展了一个综合的改善方法，精益六西格玛，它包含了定义 量测 分析 改进 控制的路线图。这个路线图告诉我们如何使用统计学或其他工具来减少变差。换句话说，工具和原理连接起来了。
Roger W. Hoerl is manager of GE Global Research’s applied statistics lab. He has a doctorate in applied statistics from the University of Delaware in Newark. An ASQ fellow and a recipient of the ASQ Shewhart Medal and Brumbaugh Award, Hoerl is also an academician in the International Academy for Quality.
Ronald D. Snee is president of Snee Associates LLC in Newark, DE. He has a doctorate in applied and mathematical statistics from Rutgers University in New Brunswick, NJ. Snee has received the ASQ Shewhart and Grant medals, is an ASQ fellow and is an academician in the International Academy for Quality.