SixSig Reference Feature: Six Sigma and the PC World
Posted by: meikah | 10 May 2010 | 11:49 pm
It has been a while since I featured here the statistics component of Six Sigma. And thanks to an article on Scientific Computing for featuring such information.
Six Sigma is, at heart, a business philosophy that seeks to deliver products and services with a minimum of defects. The cost savings and increase in business efficiencies have resulted in an alignment between the business needs and the scientific community, which is bridged by the statistical world. Advances in computing power and the ready availability of computer applications such as JMP and Minitab have put the needed computing tools in the hands of the average scientist and engineer.
Filed under: Six Sigma, Statistics, Technology
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How to Handle Statistical Variation in Six Sigma
Posted by: meikah | 8 March 2010 | 10:00 pm
From Your Articles
Six Sigma metrics are more than a collection of statistics. The intent is to make targeted measurements of performance in an existing process, compare it with statistically valid ideals, and learn how to eliminate any variation. Improving and maintaining product quality requires an understanding of the relationships between critical variables. Better understanding of the underlying relationships in a process often leads to improved performance.
To achieve a consistent understanding of the process, potential key characteristics are identified; the use of control charts may be incorporated to monitor these input variables. Statistical evaluation of the data identifies key areas to focus process improvement efforts on, which can have an adverse effect on product quality if not controlled. Advanced statistical software such as Minitab or Statgraphics, are very useful if not essential for gathering, categorizing, evaluating, and analyzing the data collected throughout a Six Sigma project. Special cause variation can also be documented and analyzed. When examining quality problems, it is useful to determine which of the many types of defects occur most frequently in order to concentrate one’s efforts where potential for improvement is the greatest. A classic method for determining the “vital few†is through a Pareto chart.
Filed under: Six Sigma, Statistics
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Statistical Software for Six Sigma
Posted by: meikah | 6 October 2009 | 9:31 pm
Six Sigma and data go together. But your Six Sigma effort will only be successful if you know how to make use of your data. You may have good data, complete data, but how to make sense of it is important.
This is where statistics comes in. Peter Peterka writes about the statistical software that works well with your Six Sigma data.
Advanced statistical software such as Minitab or Statgraphics, are very useful if not essential for gathering, categorizing, evaluating, and analyzing the data collected throughout a Six Sigma project. Both Minitab and Statgraphics are powerful full standalone statistical process control software applications for performing statistical analysis. Both are highly recommended for Six Sigma use as they are tools that can help you utilize one of Six Sigma’s biggest advantages: the ability to make better decisions based upon data. They will work with the DMAIC (Define-Measure-Analyze-Improve-Control) methodology and Lean Six Sigma.
Both Minitab and Statgraphics are designed to support the Six Sigma philosophy offering a range of tools for graphical analyses, collecting powerful statistics, quality analyses with potential for a range of custom designed uses.
Filed under: Six Sigma, Software/Technology, Statistics
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What Companies Can Expect from their Lean Management Approach
Posted by: meikah | 16 February 2009 | 7:06 pm
I stumbled upon this old article on ManageSmarter, which gives out the following Lean Six Sigma stats:
- Cost to produce down 20-plus percent.
- Inventory down 50 to 80 percent.
- Lead times reduced 60 to 90-plus percent.
- Cost of quality down 50 to 70 percent.
- Throughput increased by 10 percent.
- Revenue and earnings growth at least twice those of your competition.
The lean consultants at R. Michael Donovan & Co., Inc., agree that manufacturers should expect effective Lean Sigma efforts to produce such results.
Source:
Lean Six Sigma Stats
Filed under: Lean, Lean Six Sigma, Statistics
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8 Factors in Selecting Statistical Software for Six Sigma
Posted by: meikah | 21 January 2009 | 8:39 pm
Over at Quality Magazine, Evan Miller, president and co-owner of Hertzler Systems Inc., shares eight factors to consider in selecting statistical software for Six Sigma.
According to Miller, there are two categories of statistical software: the Advanced Statistical Analysis tools and the Real-time Enterprise SPC Solutions. How to work on both or either of these categories, the following will be your guide:
- How strong a background in statistics does the typical operator have?
- What types of data are operators most likely to encounter?
- If data are mined for information, how easily can multiple approaches with multiple options be tried?
- How easy is it to create a report or presentation that can be shared with other colleagues?
- What does it take to get new data into the system?
- Does the system automatically test new data for real-time process shifts?
- Are Data Stored in a Robust Relational Database?
- How Easy is it to Slice and Dice the Data?
Filed under: Six Sigma, Software/Technology, Statistics
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Lean Six Sigma Stats
Posted by: meikah | 1 December 2008 | 8:47 pm
Over at Manage Smarter, Margery Weinstein shares some stats or results that companies may expect from an effective Lean Six Sigma initiatives.
Let me share the stats with you:
- Cost to produce down 20-plus percent.
- Inventory down 50% to 80%.
- Lead times reduced 60 to 90-plus percent.
- Cost of quality down 50% to 70%.
- Throughput increased by 10%.
- Revenue and earnings growth at least twice those of your competition.
Good numbers that should convince companies more to go into Lean Six Sigma. 
Filed under: Lean Six Sigma, Six Sigma, Statistics
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Six Sigma Means Preventing Variation
Posted by: meikah | 6 November 2008 | 11:43 pm
The statistical arm of Six Sigma uses the term Sigma, which is used to represent a measure of variation of a statistical population.
The term “six sigma process” comes from the notion that if one has six standard deviations between the mean of a process and the nearest specification limit, there will be practically no items that fail to meet the specifications.
It is thus important that your processes do not have too much variations. So, do you want to hear a variation story?
Ron Pereira of Lean Six Sigma Academy has a good one. Check out his two posts:
A Variation Story and Million Dollars Saved in 60 Minutes.
Filed under: Six Sigma, Statistics
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Desirability, an Important Skill for Six Sigma Practitioners
Posted by: meikah | 13 August 2008 | 9:13 pm
This is new to me, too. So, what do we mean by desirability here?
It is a single number that describes how well a person’s performance comes to meeting the targets for all requirements simultaneously.
An article on iSixSigma has a good discussion about it.
The math in the desirability score card requires practitioners to know what the customer wants and what they will accept, known as voice of the customer (VOC); and what can be delivered and with how much variation, known as voice of the process (VOP).
Filed under: Six Sigma, Statistics, Tools/Toolkits
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SixSig Discovers Statistical & Scientific Thinking blog
Posted by: meikah | 8 July 2008 | 8:42 pm
I went over to Curious Cat Management blog and discovered an interesting blog: Statistical & Scientific Thinking blog.
In this blog, John Dowd discusses a wide variety of topics that are useful if you’re going into Six Sigma or any other process improvement. It talks about statistics, quality, productivity, research methods and the like.
Here’s the link to his take on Six Sigma.
Check out the blog!
Filed under: Six Sigma, Statistics
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Six Sigma and Statistics
Posted by: meikah | 23 June 2008 | 9:29 pm
Deploying Six Sigma means you will be handling data, analyzing that data, and so you will surely be handling statistics.
Here are two posts that will give you good insights on statistical concepts:
- 10 Key Statistical Concepts Every Engineer Should Know from Objective DOE
- 10 Statistical Concepts Everyone Should Know from Today’s Six Sigma
These two posts show us more than just the figures; rather, these underscore the role of these data, their interpretation in a Six Sigma or quality initiative.
