【校稿】第十篇——Adding Screening Tools Can Speed Up Six Sigma Projects
本帖最后由 小编H 于 2011-6-21 14:34 编辑
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Adding Screening Tools Can Speed Up Six Sigma Projects
添加筛选工具，可以加快六西格项目
Although they are not part of the typical DMAIC approach, organizations that use these tools can decrease the time needed to find solutions.
By Gregg Young
尽管它们（筛选工具）不属于典型的DMAIC方法，但是利用这些工具可以减少找到解决方法的时间。
作者：Gregg Young
Lean Six Sigma has led to improvements for many of the companies that use it; a few companies have even achieved spectacular results. However, not every company is pleased with its progress with Six Sigma. One area that may lead to this disparity of results is the use of screening tools developed by Dorian Shainin. Although they are not part of the typical DMAIC approach, organizations that use these tools can decrease the time needed to find solutions. Their effect can be seen by examining problem-solving cycle time, comparing a typical approach with one using the screening tools.
在精益六西格玛的帮助下，很多公司取得了很大的改善，有些公司甚至取得了瞩目的成绩。但是，并不是每家公司在实施六西格的过程中都是愉快的。使用Dorian Shainin发明的筛选工具可以有效地减少这些公司的不愉快性。尽管它们（筛选工具）不属于典型的DMAIC方法，但是利用这些工具可以减少找到解决方法的时间。使用筛选工具后，可以明显看到整个问题解决周期缩短了。
Traditional Approach
In a typical Six Sigma deployment, the speed of problem solving is determined by constraints in the system. Sometimes the core problem is an inability to decide where to start when approaching a new problem. Because of Pareto’s Law (80 percent of effects come from 20 percent of the causes), project teams are usually aware that only a handful of factors are critical out of dozens to hundreds of possibilities, however they do not know which factors are critical. The team also may have difficulties quickly identifying the critical factors without wasting time or effort pursuing non-critical factors.
传统方法
在一个典型的六西格玛项目里面，解决问题的时间受制于项目中的制约因素。有时关键的问题是无法决定从哪里开始。根据柏拉图定律（80%的结果来自于20%的原因），项目人员普遍认为在很多的因子里面只有极少数是关键因子，但他们不知道这些关键因子是哪些。因为他们没有快速识别关键因子的能力，所以不得不花大量时间在非关键因子上面。
Often, the team begins problem solving by brainstorming an exhaustive list of possible factors to consider. Then, the team must decide which ones to pursue first. The probability of the team selecting even one factor correctly the first time, let alone all of them, is quite low. Typically, they pursue a first set of factors, discover they are non-critical, and try again.
通常，项目团队通过头脑风暴，列出一长串可能的原因（因子）。然后，他们必须决定哪些因子作为第一批进行验证。可是第一次选择因子的正确性是很低的。通常情况下，他们验证完第一批因子后发现不是关键因子，又得从下面的因子中选择一批进行验证。如果再不行，再继续。
Eventually, the team discovers a critical factor. Then, they must decide whether to keep searching for other root causes, or to stop work on this problem, declare victory and go on to another problem. The irony in this approach is that while the foundation of Six Sigma is data-based decision making, project teams may make their initial decision – which factors to pursue first – without the benefit of data.
最后，他们找到了一个关键因子。这时他们需要做一个决定，是继续寻找其他的关键因子，还是结束这个问题，宣布胜利。具有讽刺意义的是，六西格玛的决策是基于数据基础的，他们的这个决定并没有有效的数据支持。
An analogy using plain and peanut M&M’s might be helpful. Peanut M&M’s represent the critical factors, while plain M&M’s are the non-critical factors. When a team approaches a new problem, it is like having just one, two or three peanut M&M’s hiding in a candy dish full of plain M&M’s. Brainstorming possible root causes and choosing from the list is like blindly sampling the candies with a spoon. It usually takes several spoonfuls to find one peanut candy, and many more spoonfuls to find them all.
为了更好的说明，我们使用M&M 的花生巧克力举个例子。花生巧克力代表关键因子，普通巧克力代表非关键因子。项目成员需要解决这样一个问题，从装满了普通巧克力的盘子里找出一个，两个或三个花生巧克力。而头脑风暴的结果是这样子的，他们使用一个勺子盲目的从盘子里勺出一些巧克力来寻找花生口味的。通常情况下需要勺几勺才能找到一个，剩下的需要更多勺来寻找。
Introducing the Screening Tools
In the ideal situation, the team would immediately know which factors are critical and which are not, so they could ignore all the non-critical factors and simply pursue the critical ones.
引入筛选工具
理想情况下，项目成员能立即知道哪些是关键因子，哪些不是。所以他们能忽略掉所有其他的非关键因子，只是简单地追踪关键因子。
In the M&M’s analogy, practitioners can do this with a candy dish that has a slotted opening in one side. The opening is taller than the plain M&M’s, but thinner than the peanut candies. By tipping the dish, all the plain candies slide out through the slot, leaving only the few peanut M&Ms behind. This example is analogous to adding Shainin’s screening tools to Six Sigma.
在M&M的巧克力例子里，项目成员可以做一个盘子，一个开口的盘子。这个开口比普通巧克力大，但比花生巧克力小。通过倾斜盘子，普通巧克力会滑出开口，只留下花生巧克力。六西格玛里加入的筛选工具就类似于这个盘子的开口。
The screening tools eliminate brainstorming and hypothesizing at the beginning of a project. The project team first makes non-invasive observations of the operation. They ignore everything in the middle, and only compare examples of the very best and very worst outputs, searching for consistent differences. The guidelines are simple:
 Any factor that is consistently different when the best and worst outputs occur is deemed critical, and the team pursues it.
 Any factor that is not consistently different is deemed non-critical, and the team ignores it.
筛选工具一开始就消除头脑风暴和假设。医学工作者首先对操作进行非创观察。他们忽略掉中间过程，只关注在输出表现中一直表现最好或一直表现最差的因子。准则非常简单：
 输出表现一直最好或最差的为关键因子，追踪它；
 输出表现没有一致性，即忽好忽坏的，为非关键因子，忽略它。
With this screening process, practitioners non-invasively observe and review data from the existing operation, and are usually able to separate the critical and non-critical factors more quickly than with the traditional trial-and-error approach.
有了这个筛选过程，医学工作者通过非创观察和审核数据，就能够区分出关键因子和非关键因子，这比传统的试错方法快的多。
How They Work
The screening tools follow four fundamental principles:
它们如何工作
筛选工具遵循四个基本原则：
Case Study Examples
The following case studies help illustrate the benefits of using the screening tools.
典型案例
下面的案例研究能帮助解释使用筛选工具带来的好处。
Case #1: Out-of-Square Grills
An appliance manufacturer makes grills for barbeques out of heavy wire that comes on large spools. The plant workers straighten the wire, cut it to a specified length and send it to the assembly line. The line workers then put pieces of wire in fixtures and weld the grills together. Usually they make flat, square grills, but sometimes they make warped, out-of-square grills. Company engineers had studied the problem repeatedly, but they could not identify the cause of the problem.
案例1：不规则的烤架
一家制作治具的公司，需要从一大卷很粗的金属条里制作烤架。工人们将金属条拉直，切成特定的长度，然后送到装配线。装配线的工人们将金属条放置在特定的治具里进行焊接，从而制成烤架。通常情况下烤架是平整且是方形的，但有时却是弯的，不规则的。工程人员已经研究了很久，仍没有找出导致烤架不规则的根本原因。
The company introduced screening tools to the direct line workers in a one-day workshop. These workers used rulers and protractors to compare six flat, square grills to six warped, out-of-square grills, and they solved the problem in just one week.
公司引进筛选工具来解决问题。项目成员要求同一天，同一个工作间的一线工人做以下工作：使用尺子和量角器测量六个平的，规则的烤架，六个弯的，不规则的烤架。如此这般，他们在仅有的一个星期内就将问题解决了。
The workers made 17 measurements on each grill, looking for consistent differences between the good and bad grills. Measurements included lengths, angles and the straightness of various pieces. They discovered that straightness was the only critical factor, which guided them to the root cause: the curvature of the wire on the spool.
工人们对每个烤架分别进行17次的测量，并比较好的和差的烤架之间的区别。这些测量包括组成烤架的每条金属条的长度，角度和直线度。他们发现直线度是唯一的关键因子，而导致直线度的直接原因是：缠绕在卷轴上的金属条的曲率。
At the beginning of the spool, the arc was mild, and the straightener made the wire straight in just one pass. At the tail end of the spool, the arc was much tighter and the wire retained a small amount of residual curvature after only one pass through the straightener. The workers had been putting all the same-length pieces – both straight and slightly curved – into a bundle that went to the welding line. The line workers randomly selected the pieces from the bundles, which is why the engineers were not able to discover any patterns. Everyone assumed the slight curvature was negligible, and that the fixtures provided the final straightening as they held the wire in position, but the opposite was happening; the residual curvature was bending the grills.
开始从卷轴上抽出的金属条弧度较小，只需通过矫直器一次就可以拉直。可是到卷轴的末端时，弧度变得很大，这时的金属条在通过矫直器一次后仍会存在一定的残余曲率。工人们将这些相同长度的金属条（直的和轻微弯曲的）扎成一捆送到焊接工序。焊接工人从中随机抽取进行焊接，而工程人员却没有发现任何不妥的地方。每个人都认为轻微的弯曲是可以忽略的，因为最终的定位治具可以提供最终的矫正。但实际情况却是相反的：残留曲率使得最终焊接出来的烤架出现了弯曲。
The solution was to check the straightness of the pieces coming out of the straightener and run them through again if there was any residual curve.
解决方法变得很简单，检查每条金属条的直线度，如果存在残留曲率，则继续通过矫直器进行再次矫直。
Case #2: Pigment Dispersion
A chemical manufacturer makes a pigment material that is a dispersion of solid particles. Usually, the manufacturer makes an excellent dispersion of fine particles, but occasionally a second material precipitates out with the pigment and the batch must be reworked.
案例2：颜料分散剂
一家化学公司，生产一种用于分散固体颗粒的颜料分散剂。通常，这些分散剂能很好地分散固体颗粒。但偶尔会出现颜料沉淀的情况，这样他们不得不将这批分散剂重新返工制作。
In many situations, including the previous example, little or no data on the operation exists, so workers have to make non-invasive measurements to look for the critical factors. Today, most chemical companies have the opposite problem: data overload. Automated reactor controllers record large amounts of data on every batch, and problem solvers may not know which data is critical. In this case, the automated controller was tracking 105 factors on every batch.
在包括上一个案例的例子中，很少甚至没有数据存在，这使得项目成员们不得不先进行无创测量。可是现今的化学公司存在相反的问题：数据过量。自动反应装置控制器记录每批次的数据，问题解决人员很难知道哪些数据是关键的。在这个案例中，自动控制器在每批次中记录105个因子。
To sort through this data, the team focused on eight good batches and eight bad batches. They assembled a spreadsheet of all 105 factors for these 16 batches and looked for consistent differences. In just one day, they determined that 104 of the factors were non-critical, and just one, percent non-volatiles of the finished dispersion, was consistently different. Low solids dispersions were always the worst of the worst outputs, and high solids dispersions were always best of the best outputs.
为了分析这些数据，项目成员们只关注8个好的批次和8个坏的批次。他们建立了一个拥有16批次，105个因子的工作表，试图从中找出差异性。仅仅一天，他们就剔除了其他的104个因子，找出了唯一的关键的因子：不易挥发百分比。低的固体分散剂表现糟糕，高的固体分散剂表现优异。
This guided the team to focus on how batches ended up at different solids levels, given that the controller feeds all the reactants into the reactor automatically. It turned out that the reaction must be run at lower temperatures, colder than the reactor’s cooling jacket can achieve. The solution was for the operators to add ice to the reactor manually. The amount of ice varies, and this determines the percentage of solids of the product. The team learned that they had to limit the amount of ice addition in order to achieve good dispersions every time. They could even estimate the maximum allowable amount of ice, based solely on these initial observations. As long as the operator kept the non-volatiles level above 7.5 percent, the product would be good every time.
这给项目成员指明了一个方向，他们控制所有的反映物，观察输出的不同的固体水平。事实表明，反应必须在低温的环境下进行，给反应器的降温头降温能达到这个目的。解决方法是通过工人们手动添加冰块。冰块数量变化量决定产品的固体含量。项目成员发现他们不得不限制冰块数量从而保证每一次生产出好的产品。他们甚至能根据之前的观察数据评估出冰块的最大使用量。只要工人们能控制非挥发性的百分比在7.5%以上，生产出来的就是好产品。
About the Author: Gregg Young is president of Young Associates Inc. He has spent more than 20 years developing, teaching and implementing quality improvement systems in both large corporations and small businesses. Young is author of “Seventh Sigma Tools: Best Practices in Six Sigma” and two e-book sequels, “Productivity Tools for Decision Makers: Go from Good to Great,” for manufacturing companies, and “Best Practice Problem Solving: The Six Universal Tools,” designed for any area of human endeavor. He can be reached at mailto:gregg@youngassocinc.com .
关于作者：Gregg Young，Young Associate公司总裁。他花了超过20年的时间在各类企业（大的和小的）发展，教学和实施质量改善系统。《第七个六西格玛工具：六西格玛最佳实践》的作者。两本电子书续集的作者，一本主要应用于制造型公司：《决策者的生产力工具：从优秀在卓越》；另一本用于人类的各个方面：《最佳问题解决方案：六个通用工具》。
你可以通过以下邮箱联系到他：mailto:gregg@youngassocinc.com
大家好，我是小编H。组员们对第十篇文章翻译有什么意见或者建议，请跟帖回复讨论，以便对文章翻译进行最后的确认
Adding Screening Tools Can Speed Up Six Sigma Projects
添加筛选工具，可以加快六西格项目
Although they are not part of the typical DMAIC approach, organizations that use these tools can decrease the time needed to find solutions.
By Gregg Young
尽管它们（筛选工具）不属于典型的DMAIC方法，但是利用这些工具可以减少找到解决方法的时间。
作者：Gregg Young
Lean Six Sigma has led to improvements for many of the companies that use it; a few companies have even achieved spectacular results. However, not every company is pleased with its progress with Six Sigma. One area that may lead to this disparity of results is the use of screening tools developed by Dorian Shainin. Although they are not part of the typical DMAIC approach, organizations that use these tools can decrease the time needed to find solutions. Their effect can be seen by examining problem-solving cycle time, comparing a typical approach with one using the screening tools.
在精益六西格玛的帮助下，很多公司取得了很大的改善，有些公司甚至取得了瞩目的成绩。但是，并不是每家公司在实施六西格的过程中都是愉快的。使用Dorian Shainin发明的筛选工具可以有效地减少这些公司的不愉快性。尽管它们（筛选工具）不属于典型的DMAIC方法，但是利用这些工具可以减少找到解决方法的时间。使用筛选工具后，可以明显看到整个问题解决周期缩短了。
Traditional Approach
In a typical Six Sigma deployment, the speed of problem solving is determined by constraints in the system. Sometimes the core problem is an inability to decide where to start when approaching a new problem. Because of Pareto’s Law (80 percent of effects come from 20 percent of the causes), project teams are usually aware that only a handful of factors are critical out of dozens to hundreds of possibilities, however they do not know which factors are critical. The team also may have difficulties quickly identifying the critical factors without wasting time or effort pursuing non-critical factors.
传统方法
在一个典型的六西格玛项目里面，解决问题的时间受制于项目中的制约因素。有时关键的问题是无法决定从哪里开始。根据柏拉图定律（80%的结果来自于20%的原因），项目人员普遍认为在很多的因子里面只有极少数是关键因子，但他们不知道这些关键因子是哪些。因为他们没有快速识别关键因子的能力，所以不得不花大量时间在非关键因子上面。
Often, the team begins problem solving by brainstorming an exhaustive list of possible factors to consider. Then, the team must decide which ones to pursue first. The probability of the team selecting even one factor correctly the first time, let alone all of them, is quite low. Typically, they pursue a first set of factors, discover they are non-critical, and try again.
通常，项目团队通过头脑风暴，列出一长串可能的原因（因子）。然后，他们必须决定哪些因子作为第一批进行验证。可是第一次选择因子的正确性是很低的。通常情况下，他们验证完第一批因子后发现不是关键因子，又得从下面的因子中选择一批进行验证。如果再不行，再继续。
Eventually, the team discovers a critical factor. Then, they must decide whether to keep searching for other root causes, or to stop work on this problem, declare victory and go on to another problem. The irony in this approach is that while the foundation of Six Sigma is data-based decision making, project teams may make their initial decision – which factors to pursue first – without the benefit of data.
最后，他们找到了一个关键因子。这时他们需要做一个决定，是继续寻找其他的关键因子，还是结束这个问题，宣布胜利。具有讽刺意义的是，六西格玛的决策是基于数据基础的，他们的这个决定并没有有效的数据支持。
An analogy using plain and peanut M&M’s might be helpful. Peanut M&M’s represent the critical factors, while plain M&M’s are the non-critical factors. When a team approaches a new problem, it is like having just one, two or three peanut M&M’s hiding in a candy dish full of plain M&M’s. Brainstorming possible root causes and choosing from the list is like blindly sampling the candies with a spoon. It usually takes several spoonfuls to find one peanut candy, and many more spoonfuls to find them all.
为了更好的说明，我们使用M&M 的花生巧克力举个例子。花生巧克力代表关键因子，普通巧克力代表非关键因子。项目成员需要解决这样一个问题，从装满了普通巧克力的盘子里找出一个，两个或三个花生巧克力。而头脑风暴的结果是这样子的，他们使用一个勺子盲目的从盘子里勺出一些巧克力来寻找花生口味的。通常情况下需要勺几勺才能找到一个，剩下的需要更多勺来寻找。
Introducing the Screening Tools
In the ideal situation, the team would immediately know which factors are critical and which are not, so they could ignore all the non-critical factors and simply pursue the critical ones.
引入筛选工具
理想情况下，项目成员能立即知道哪些是关键因子，哪些不是。所以他们能忽略掉所有其他的非关键因子，只是简单地追踪关键因子。
In the M&M’s analogy, practitioners can do this with a candy dish that has a slotted opening in one side. The opening is taller than the plain M&M’s, but thinner than the peanut candies. By tipping the dish, all the plain candies slide out through the slot, leaving only the few peanut M&Ms behind. This example is analogous to adding Shainin’s screening tools to Six Sigma.
在M&M的巧克力例子里，项目成员可以做一个盘子，一个开口的盘子。这个开口比普通巧克力大，但比花生巧克力小。通过倾斜盘子，普通巧克力会滑出开口，只留下花生巧克力。六西格玛里加入的筛选工具就类似于这个盘子的开口。
The screening tools eliminate brainstorming and hypothesizing at the beginning of a project. The project team first makes non-invasive observations of the operation. They ignore everything in the middle, and only compare examples of the very best and very worst outputs, searching for consistent differences. The guidelines are simple:
 Any factor that is consistently different when the best and worst outputs occur is deemed critical, and the team pursues it.
 Any factor that is not consistently different is deemed non-critical, and the team ignores it.
筛选工具一开始就消除头脑风暴和假设。医学工作者首先对操作进行非创观察。他们忽略掉中间过程，只关注在输出表现中一直表现最好或一直表现最差的因子。准则非常简单：
 输出表现一直最好或最差的为关键因子，追踪它；
 输出表现没有一致性，即忽好忽坏的，为非关键因子，忽略它。
With this screening process, practitioners non-invasively observe and review data from the existing operation, and are usually able to separate the critical and non-critical factors more quickly than with the traditional trial-and-error approach.
有了这个筛选过程，医学工作者通过非创观察和审核数据，就能够区分出关键因子和非关键因子，这比传统的试错方法快的多。
How They Work
The screening tools follow four fundamental principles:
它们如何工作
筛选工具遵循四个基本原则：
- Pareto’s Law is universal. When teams start with a one-in-dozens or one-in-hundreds chance of choosing correctly from a brainstormed list of possible factors, they sometimes must go through several iterations before they find a root cause. Once they have found one, they may stop searching. If there are actually three critical factors at work and they fix one of them, the other two factors remain active and continue generating defects. When teams must go through many non-critical factors exhaustively to find one to three critical factors, Pareto’s Law is working against them. When teams start by using screening tools to eliminate the non-critical factors, they can discover the critical factors quickly.
- If an operation usually works well, but occasionally generates defects, then the operation is fundamentally sound. When Six Sigma teams use the screening tools to approach a problem, they start with these correct conditions, and then discover which factors are critical and vary excessively whenever defects occur. The solution they develop is simply tighter control of these critical factors.
- Effects have causes. Whenever an operation that usually works well generates a defect, something about the operation has changed. The optimum solution is to identify what has changed and then tighten control. When a team using the screening tools encounters additional defects, they assume there are other root causes that have yet to be identified, like the peanut M&M’s still lurking in the bowl of plain candies.
- Comparing performance extremes can reveal consistent differences. The screening tools focus only on performance extremes – the very best and very worst outputs. Whichever critical factors are causing the defects to occur, the values of these critical factors will show the widest difference whenever extreme performance occurs.
Case Study Examples
The following case studies help illustrate the benefits of using the screening tools.
典型案例
下面的案例研究能帮助解释使用筛选工具带来的好处。
Case #1: Out-of-Square Grills
An appliance manufacturer makes grills for barbeques out of heavy wire that comes on large spools. The plant workers straighten the wire, cut it to a specified length and send it to the assembly line. The line workers then put pieces of wire in fixtures and weld the grills together. Usually they make flat, square grills, but sometimes they make warped, out-of-square grills. Company engineers had studied the problem repeatedly, but they could not identify the cause of the problem.
案例1：不规则的烤架
一家制作治具的公司，需要从一大卷很粗的金属条里制作烤架。工人们将金属条拉直，切成特定的长度，然后送到装配线。装配线的工人们将金属条放置在特定的治具里进行焊接，从而制成烤架。通常情况下烤架是平整且是方形的，但有时却是弯的，不规则的。工程人员已经研究了很久，仍没有找出导致烤架不规则的根本原因。
The company introduced screening tools to the direct line workers in a one-day workshop. These workers used rulers and protractors to compare six flat, square grills to six warped, out-of-square grills, and they solved the problem in just one week.
公司引进筛选工具来解决问题。项目成员要求同一天，同一个工作间的一线工人做以下工作：使用尺子和量角器测量六个平的，规则的烤架，六个弯的，不规则的烤架。如此这般，他们在仅有的一个星期内就将问题解决了。
The workers made 17 measurements on each grill, looking for consistent differences between the good and bad grills. Measurements included lengths, angles and the straightness of various pieces. They discovered that straightness was the only critical factor, which guided them to the root cause: the curvature of the wire on the spool.
工人们对每个烤架分别进行17次的测量，并比较好的和差的烤架之间的区别。这些测量包括组成烤架的每条金属条的长度，角度和直线度。他们发现直线度是唯一的关键因子，而导致直线度的直接原因是：缠绕在卷轴上的金属条的曲率。
At the beginning of the spool, the arc was mild, and the straightener made the wire straight in just one pass. At the tail end of the spool, the arc was much tighter and the wire retained a small amount of residual curvature after only one pass through the straightener. The workers had been putting all the same-length pieces – both straight and slightly curved – into a bundle that went to the welding line. The line workers randomly selected the pieces from the bundles, which is why the engineers were not able to discover any patterns. Everyone assumed the slight curvature was negligible, and that the fixtures provided the final straightening as they held the wire in position, but the opposite was happening; the residual curvature was bending the grills.
开始从卷轴上抽出的金属条弧度较小，只需通过矫直器一次就可以拉直。可是到卷轴的末端时，弧度变得很大，这时的金属条在通过矫直器一次后仍会存在一定的残余曲率。工人们将这些相同长度的金属条（直的和轻微弯曲的）扎成一捆送到焊接工序。焊接工人从中随机抽取进行焊接，而工程人员却没有发现任何不妥的地方。每个人都认为轻微的弯曲是可以忽略的，因为最终的定位治具可以提供最终的矫正。但实际情况却是相反的：残留曲率使得最终焊接出来的烤架出现了弯曲。
The solution was to check the straightness of the pieces coming out of the straightener and run them through again if there was any residual curve.
解决方法变得很简单，检查每条金属条的直线度，如果存在残留曲率，则继续通过矫直器进行再次矫直。
Case #2: Pigment Dispersion
A chemical manufacturer makes a pigment material that is a dispersion of solid particles. Usually, the manufacturer makes an excellent dispersion of fine particles, but occasionally a second material precipitates out with the pigment and the batch must be reworked.
案例2：颜料分散剂
一家化学公司，生产一种用于分散固体颗粒的颜料分散剂。通常，这些分散剂能很好地分散固体颗粒。但偶尔会出现颜料沉淀的情况，这样他们不得不将这批分散剂重新返工制作。
In many situations, including the previous example, little or no data on the operation exists, so workers have to make non-invasive measurements to look for the critical factors. Today, most chemical companies have the opposite problem: data overload. Automated reactor controllers record large amounts of data on every batch, and problem solvers may not know which data is critical. In this case, the automated controller was tracking 105 factors on every batch.
在包括上一个案例的例子中，很少甚至没有数据存在，这使得项目成员们不得不先进行无创测量。可是现今的化学公司存在相反的问题：数据过量。自动反应装置控制器记录每批次的数据，问题解决人员很难知道哪些数据是关键的。在这个案例中，自动控制器在每批次中记录105个因子。
To sort through this data, the team focused on eight good batches and eight bad batches. They assembled a spreadsheet of all 105 factors for these 16 batches and looked for consistent differences. In just one day, they determined that 104 of the factors were non-critical, and just one, percent non-volatiles of the finished dispersion, was consistently different. Low solids dispersions were always the worst of the worst outputs, and high solids dispersions were always best of the best outputs.
为了分析这些数据，项目成员们只关注8个好的批次和8个坏的批次。他们建立了一个拥有16批次，105个因子的工作表，试图从中找出差异性。仅仅一天，他们就剔除了其他的104个因子，找出了唯一的关键的因子：不易挥发百分比。低的固体分散剂表现糟糕，高的固体分散剂表现优异。
This guided the team to focus on how batches ended up at different solids levels, given that the controller feeds all the reactants into the reactor automatically. It turned out that the reaction must be run at lower temperatures, colder than the reactor’s cooling jacket can achieve. The solution was for the operators to add ice to the reactor manually. The amount of ice varies, and this determines the percentage of solids of the product. The team learned that they had to limit the amount of ice addition in order to achieve good dispersions every time. They could even estimate the maximum allowable amount of ice, based solely on these initial observations. As long as the operator kept the non-volatiles level above 7.5 percent, the product would be good every time.
这给项目成员指明了一个方向，他们控制所有的反映物，观察输出的不同的固体水平。事实表明，反应必须在低温的环境下进行，给反应器的降温头降温能达到这个目的。解决方法是通过工人们手动添加冰块。冰块数量变化量决定产品的固体含量。项目成员发现他们不得不限制冰块数量从而保证每一次生产出好的产品。他们甚至能根据之前的观察数据评估出冰块的最大使用量。只要工人们能控制非挥发性的百分比在7.5%以上，生产出来的就是好产品。
About the Author: Gregg Young is president of Young Associates Inc. He has spent more than 20 years developing, teaching and implementing quality improvement systems in both large corporations and small businesses. Young is author of “Seventh Sigma Tools: Best Practices in Six Sigma” and two e-book sequels, “Productivity Tools for Decision Makers: Go from Good to Great,” for manufacturing companies, and “Best Practice Problem Solving: The Six Universal Tools,” designed for any area of human endeavor. He can be reached at mailto:gregg@youngassocinc.com .
关于作者：Gregg Young，Young Associate公司总裁。他花了超过20年的时间在各类企业（大的和小的）发展，教学和实施质量改善系统。《第七个六西格玛工具：六西格玛最佳实践》的作者。两本电子书续集的作者，一本主要应用于制造型公司：《决策者的生产力工具：从优秀在卓越》；另一本用于人类的各个方面：《最佳问题解决方案：六个通用工具》。
你可以通过以下邮箱联系到他：mailto:gregg@youngassocinc.com
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小编H (威望:4) (广东 广州) 互联网 员工
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不知不觉已经第十篇了哦！