第二十八篇 Sometimes finding a solution just requires hard work and fo
本帖最后由 小编D 于 2012-3-7 13:54 编辑
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本文翻译者：jin_cai08 校稿：14baby
**
by Lynne B. HareSeveral years ago, during a difficult Bach passage, our conductor stopped the chorus and addressed the bass section: "Gentlemen, I ask for champagne, and you give me beer."几年前，在演唱巴赫一段难度较高的音乐篇章时，我们的指挥停了下来，他对低音部分的演唱者们说：“先生们，我需要的是香槟，而你们给我的却是啤酒。”
"What’s wrong with beer?" I remember thinking. You probably drink beer. I used to, but not anymore (Not after last night! Just kidding But I do remember the conductor’s remark helped shape us up. He knew we could do better, and so did we. We just needed to focus and to work harder.“啤酒有什么问题吗？”我记得是这样想得。你可能也喜欢喝啤酒。我就是这样，但现在已经不喝了（我是说昨晚到现在我还没喝，开个玩笑！）。但是我印象深刻，那个指挥的批评使我们的乐队更好的成长了。他知道我们可以做得更好，事实也的确如此。我们当时需要的就是集中精力，更加努力的练习。
The episode came to mind recently when I was asked for my views on a report intercepted on its way to a member of the top brass. It contained tables of Cpk indexes by plant, month, product and response. 最近，当有人询问我对一份要呈交给最高管理层的报告的意见时，我又想起了当年的那个插曲。那份报告包含了按车间、月份、产品以及响应量索引的Cpk图表。
Recall that Cpk is the distance from the mean to the nearer specification limit divided by three times the capability, or within-group, standard deviation. To aid interpretation, the Cpk values greater than one appeared in green in the report, while those less than one were in red. The implication was that attention should be focused on areas with red Cpk values.回想一下，Cpk是均值与较近的规格限之间的距离，而规格限是根据3倍组内标准差划分的。为了更好的进行解释，那份报告中大于1的Cpk数值是绿色的，而那些不到1的则是红色的。言下之意是，我们应该更加关注红色Cpk值的区域。
It’s difficult to argue with that. Attention probably should be focused there. But, while the report called attention to areas in need of improvement, however, it also took the heat off some other areas, assuming we are content with Cpk greater than one. Some say it should be greater than 1.3. 这样做是无可争议的。我们是因该关注那些红色的区域。但是，尽管该报告呼吁关注的是需要改进的领域，但假设我们满足于Cpk大于1，它也减轻了其他区域的压力。有些人认为Cpk应大于1.3。
I said a lot more in an earlier column, ranting about having realistic specifications—having a process that is in control generates normally distributed data and has at least 100 observations.1 These are all prerequisites to using Cpk, and they are resoundingly ignored in practice.在之前的专栏里，我对获得实际的控制限进行了许多的阐述。受控过程数据的正态分布以及至少100个观测值，这些都是使用Cpk的前提条件，这些也都是他们在实践中所忽略的。
Always plot the data经常绘制数据
It’s true that nobody put me in charge of quality indexes, but maybe I can illustrate the point. From the intercepted report, I noticed for one plant, one product, one month and one response, the Cpk was 1.09. It was in the clear—no problem. Look elsewhere. But wait a minute. I recalled someone saying, "Always, always, always—without exception—plot the data, and look at the plot." 说实在的，尽管没有人让我负责质量指标，但也许我可以说明这一点。从那份报告中，我注意到的有一个工厂的一个产品的一个月的一个响应量的Cpk值是1.09。很明显，其不存在什么问题。再去看看其他的区域。但是，再等一下！我记得有人说过“总是，总是，总是——毫无例外地——将你的数据绘制到图上，然后看看你的图。”
Well, the computer program that generated the Cpk also drew a histogram. That’s a plot, isn’t it? Besides, the data look roughly normal, and there are 187 points. True, the specifications came off a ceiling tile, but that’s the best we can do. We’re sticking to it.那么，计算机程序在产生的Cpk的同时，也绘制了直方图。这就是上面所说的图，不是么？此外，数据看起来大致正态，并且包含了187个点。诚然，规格限达到了天花板，但这是我们所能做的最好的，我们是一直这样坚持的。
Maybe I should have said, "Always, always, always—without exception—plot the data more than one way, and look at the plots (plural" No rule works for everything. But one plot that’s always worth a look is a time plot which shows all the raw data in sequential order. Figure 1 shows the time plot with the mean and upper and lower specification limits. As Yogi Berra said, "You can observe a lot just by watching." So watch Figure 1 carefully.也许我应该这样说，“总是，总是，总是——毫无例外地——以多种方法将你的数据绘制到图上，然后看看你的图（此处的图是复数）。没有什么规则是适用于一切的。但是有一种总是值得一看的图便是时序图。时序图按照相继的顺序，展示了原始数据。图1就是一个时序图，其上包含了均值线、规格上限及规格下限。正如尤吉贝拉所说，仅仅通过看，你就可以观察到许多。所以，请仔细的看看图1.
http://www.asq.org/img/qp/AR_0 ... 1.gif
At (circled) point one, there is an extreme low value. Maybe someone made an adjustment after seeing it because the next value is very close to the upper specification limit. 在点1处，其数值特别低。也许有人看到后会进行调整，因为其下一个值是非常接近于上规格限的数值。
It is possible this new high value was countered with another adjustment because two observations afterward, the observed value is below the mean. Then, starting at point two, there is a steady run upward. Did someone make another adjustment two steps before point three to bring the process downward? 有可能我们也会对这个新的高点进行调整，因为随后的两个点低于了平均值。然后，从点2开始，出现了一个向上运行趋势。在点3前的两个点，有人采取措施将过程向下调整了么？
Whatever happened at point three, the process started to creep up and was brought downward again. But after point four, there is another steady run upward.无论在点3处发生了什么，这个过程又开始攀升，但是被再次向下带回来。但是在点4之后，再次出现了一个稳定向上运行趋势
It is fun to imagine what might have happened. Do you suppose the operator had a fight with his wife the night before? His thoughts drift from the process to the argument until suddenly he sees a value in excess of 900. "Holy Toledo, I have to act quickly! I’ll show her," he thinks as he grabs the wheel and adjusts down hard.来想象这期间究竟发生了什么是一件有趣的事情。难道是前一天晚上，操作者与他的妻子吵架了吗？直到他发现有一个数值超出了900，他的思绪才从吵架的事情上转移到了他所操作的过程。 “我的天啊，托莱多，我必须迅速采取行动！我会告诉她！”他一边想，一边抓住旋轮，猛烈的向下调整。
Then you come to points five, six and seven. Do you see something unusual? The values repeat. Was there a data transcription error? Did some well-meaning soul copy values to build up the data set? Repeats like that don’t happen by chance alone. We’ll probably never know the true cause of the repeated observations, but it is clear they don’t represent the real process behavior.然后，我们来看点5，点6和点7。你看到一些不寻常的了吗？数值在重复，难道是数据转录的时候出现了错误？是一些好心人（讽刺语—译者按）为了建立数据组而把这些数据复制了？像这样的重复不会仅仅是，但是很明显，他们并不能代表实际的过程运行情况。
Forget Cpk for now暂时忘记Cpk
Oops—wait a minute. We forgot about Cpk. Instead, we got caught up in detective work. And while we did that, we didn’t worry about specifications, the process being in or out of control, the normality of the data, or the number of observations. Instead, we saw an opportunity to mend an undisciplined process, and we have something with a little more value for quality and productivity improvement.哎呀，等等。我们陷入了侦查工作，却把Cpk给忘了。当我们这样做时，我们不担心规格限、过程是否处在受控状态、数据的正态性、或者是观察值的数量。相反，我们看到了一个修正过程的机会，其对质量改进或是生产能力提高是有一些价值的。
We did this by forsaking the routine, by focusing and by working a little harder. Our working harmony produced champagne, not beer.通过放弃日常的工作，通过专注与更加的努力，我们才能做到这一点。工作的协调带来将是香槟，而不是啤酒。
Lynne B. Hare is a statistical consultant. He holds a doctorate in statistics from Rutgers University in New Brunswick, NJ. He is a past chairman of the ASQ Statistics Division and a Fellow of ASQ and the American Statistical Association. He sings in the bass section of the Pro Arte Chorale in Ridgewood, NJ.Lynne B. Hare，统计顾问。罗格斯大学统计学博士学位，其位于新泽西州新不伦瑞克。他是过去的ASQ统计学部门的主席，同时也是ASQ和美国统计协会的研究员。他在新泽西州箴当代艺术合唱团低音区演唱。
**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：jin_cai08 校稿：14baby
**
by Lynne B. HareSeveral years ago, during a difficult Bach passage, our conductor stopped the chorus and addressed the bass section: "Gentlemen, I ask for champagne, and you give me beer."几年前，在演唱巴赫一段难度较高的音乐篇章时，我们的指挥停了下来，他对低音部分的演唱者们说：“先生们，我需要的是香槟，而你们给我的却是啤酒。”
"What’s wrong with beer?" I remember thinking. You probably drink beer. I used to, but not anymore (Not after last night! Just kidding But I do remember the conductor’s remark helped shape us up. He knew we could do better, and so did we. We just needed to focus and to work harder.“啤酒有什么问题吗？”我记得是这样想得。你可能也喜欢喝啤酒。我就是这样，但现在已经不喝了（我是说昨晚到现在我还没喝，开个玩笑！）。但是我印象深刻，那个指挥的批评使我们的乐队更好的成长了。他知道我们可以做得更好，事实也的确如此。我们当时需要的就是集中精力，更加努力的练习。
The episode came to mind recently when I was asked for my views on a report intercepted on its way to a member of the top brass. It contained tables of Cpk indexes by plant, month, product and response. 最近，当有人询问我对一份要呈交给最高管理层的报告的意见时，我又想起了当年的那个插曲。那份报告包含了按车间、月份、产品以及响应量索引的Cpk图表。
Recall that Cpk is the distance from the mean to the nearer specification limit divided by three times the capability, or within-group, standard deviation. To aid interpretation, the Cpk values greater than one appeared in green in the report, while those less than one were in red. The implication was that attention should be focused on areas with red Cpk values.回想一下，Cpk是均值与较近的规格限之间的距离，而规格限是根据3倍组内标准差划分的。为了更好的进行解释，那份报告中大于1的Cpk数值是绿色的，而那些不到1的则是红色的。言下之意是，我们应该更加关注红色Cpk值的区域。
It’s difficult to argue with that. Attention probably should be focused there. But, while the report called attention to areas in need of improvement, however, it also took the heat off some other areas, assuming we are content with Cpk greater than one. Some say it should be greater than 1.3. 这样做是无可争议的。我们是因该关注那些红色的区域。但是，尽管该报告呼吁关注的是需要改进的领域，但假设我们满足于Cpk大于1，它也减轻了其他区域的压力。有些人认为Cpk应大于1.3。
I said a lot more in an earlier column, ranting about having realistic specifications—having a process that is in control generates normally distributed data and has at least 100 observations.1 These are all prerequisites to using Cpk, and they are resoundingly ignored in practice.在之前的专栏里，我对获得实际的控制限进行了许多的阐述。受控过程数据的正态分布以及至少100个观测值，这些都是使用Cpk的前提条件，这些也都是他们在实践中所忽略的。
Always plot the data经常绘制数据
It’s true that nobody put me in charge of quality indexes, but maybe I can illustrate the point. From the intercepted report, I noticed for one plant, one product, one month and one response, the Cpk was 1.09. It was in the clear—no problem. Look elsewhere. But wait a minute. I recalled someone saying, "Always, always, always—without exception—plot the data, and look at the plot." 说实在的，尽管没有人让我负责质量指标，但也许我可以说明这一点。从那份报告中，我注意到的有一个工厂的一个产品的一个月的一个响应量的Cpk值是1.09。很明显，其不存在什么问题。再去看看其他的区域。但是，再等一下！我记得有人说过“总是，总是，总是——毫无例外地——将你的数据绘制到图上，然后看看你的图。”
Well, the computer program that generated the Cpk also drew a histogram. That’s a plot, isn’t it? Besides, the data look roughly normal, and there are 187 points. True, the specifications came off a ceiling tile, but that’s the best we can do. We’re sticking to it.那么，计算机程序在产生的Cpk的同时，也绘制了直方图。这就是上面所说的图，不是么？此外，数据看起来大致正态，并且包含了187个点。诚然，规格限达到了天花板，但这是我们所能做的最好的，我们是一直这样坚持的。
Maybe I should have said, "Always, always, always—without exception—plot the data more than one way, and look at the plots (plural" No rule works for everything. But one plot that’s always worth a look is a time plot which shows all the raw data in sequential order. Figure 1 shows the time plot with the mean and upper and lower specification limits. As Yogi Berra said, "You can observe a lot just by watching." So watch Figure 1 carefully.也许我应该这样说，“总是，总是，总是——毫无例外地——以多种方法将你的数据绘制到图上，然后看看你的图（此处的图是复数）。没有什么规则是适用于一切的。但是有一种总是值得一看的图便是时序图。时序图按照相继的顺序，展示了原始数据。图1就是一个时序图，其上包含了均值线、规格上限及规格下限。正如尤吉贝拉所说，仅仅通过看，你就可以观察到许多。所以，请仔细的看看图1.
http://www.asq.org/img/qp/AR_0 ... 1.gif
At (circled) point one, there is an extreme low value. Maybe someone made an adjustment after seeing it because the next value is very close to the upper specification limit. 在点1处，其数值特别低。也许有人看到后会进行调整，因为其下一个值是非常接近于上规格限的数值。
It is possible this new high value was countered with another adjustment because two observations afterward, the observed value is below the mean. Then, starting at point two, there is a steady run upward. Did someone make another adjustment two steps before point three to bring the process downward? 有可能我们也会对这个新的高点进行调整，因为随后的两个点低于了平均值。然后，从点2开始，出现了一个向上运行趋势。在点3前的两个点，有人采取措施将过程向下调整了么？
Whatever happened at point three, the process started to creep up and was brought downward again. But after point four, there is another steady run upward.无论在点3处发生了什么，这个过程又开始攀升，但是被再次向下带回来。但是在点4之后，再次出现了一个稳定向上运行趋势
It is fun to imagine what might have happened. Do you suppose the operator had a fight with his wife the night before? His thoughts drift from the process to the argument until suddenly he sees a value in excess of 900. "Holy Toledo, I have to act quickly! I’ll show her," he thinks as he grabs the wheel and adjusts down hard.来想象这期间究竟发生了什么是一件有趣的事情。难道是前一天晚上，操作者与他的妻子吵架了吗？直到他发现有一个数值超出了900，他的思绪才从吵架的事情上转移到了他所操作的过程。 “我的天啊，托莱多，我必须迅速采取行动！我会告诉她！”他一边想，一边抓住旋轮，猛烈的向下调整。
Then you come to points five, six and seven. Do you see something unusual? The values repeat. Was there a data transcription error? Did some well-meaning soul copy values to build up the data set? Repeats like that don’t happen by chance alone. We’ll probably never know the true cause of the repeated observations, but it is clear they don’t represent the real process behavior.然后，我们来看点5，点6和点7。你看到一些不寻常的了吗？数值在重复，难道是数据转录的时候出现了错误？是一些好心人（讽刺语—译者按）为了建立数据组而把这些数据复制了？像这样的重复不会仅仅是，但是很明显，他们并不能代表实际的过程运行情况。
Forget Cpk for now暂时忘记Cpk
Oops—wait a minute. We forgot about Cpk. Instead, we got caught up in detective work. And while we did that, we didn’t worry about specifications, the process being in or out of control, the normality of the data, or the number of observations. Instead, we saw an opportunity to mend an undisciplined process, and we have something with a little more value for quality and productivity improvement.哎呀，等等。我们陷入了侦查工作，却把Cpk给忘了。当我们这样做时，我们不担心规格限、过程是否处在受控状态、数据的正态性、或者是观察值的数量。相反，我们看到了一个修正过程的机会，其对质量改进或是生产能力提高是有一些价值的。
We did this by forsaking the routine, by focusing and by working a little harder. Our working harmony produced champagne, not beer.通过放弃日常的工作，通过专注与更加的努力，我们才能做到这一点。工作的协调带来将是香槟，而不是啤酒。
Lynne B. Hare is a statistical consultant. He holds a doctorate in statistics from Rutgers University in New Brunswick, NJ. He is a past chairman of the ASQ Statistics Division and a Fellow of ASQ and the American Statistical Association. He sings in the bass section of the Pro Arte Chorale in Ridgewood, NJ.Lynne B. Hare，统计顾问。罗格斯大学统计学博士学位，其位于新泽西州新不伦瑞克。他是过去的ASQ统计学部门的主席，同时也是ASQ和美国统计协会的研究员。他在新泽西州箴当代艺术合唱团低音区演唱。
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