The Douglass Index, Keeping Good Data from Going to Waste
Posted by: meikah | 21 July 2010 | 3:15 am
Every day, we are faced with data. The greater challenge is how to evaluate the data and make them work for us.
According to iSixSigma article,
The Douglass Index allows practitioners to convert data with poor-to-marginal repeatability and reproducibility to a marginal process capability (Cp) level.
Filed under: Data, Data Analysis, Data Quality, Six Sigma
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Quality Quiz from PQ Systems e-Line
Posted by: meikah | 14 April 2010 | 7:52 pm
PQ Systems through it’s Quality eline newsletter brings us another quality quiz by Professor Leary.
For this month’s quiz, and a chance to win a copy of the newly-released collection of Quality Quiz Classics, do the Quality Quiz. Submit your response by April 30 to be entered in the drawing.
Here’s the quiz:
Last month we looked again at the ongoing struggles of Quinn Quip, who has been trying to understand and implement the binomial central limit theorem. He has learned several lessons related to taking many sample proportions of the size 100 from a population with a mean of .50 and then forming a distribution with these sample proportions. Among the lessons:
- The mean of this distribution of sample proportions will be close to .50;
- The shape would follow a normal distribution;
- The variability of these sample proportions about the population mean of .50 would be
Winners of last month’s quiz and a copy of the Quality Quiz Classics DVD:
Alice Akelman (S.L. Alekman Associates, Inc.)
Michael Borton (Xerox Corporation)
Owen Gutteridge (MSP Group)
Bruce Jefferson (Bruns Machine)
Patty Walton (Cleveland Clinic)
Congratulations!
Filed under: Data Analysis, PQ Systems eLine, Quality Quiz, Six Sigma
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On Measurement
Posted by: meikah | 24 February 2010 | 3:41 am
A large part of Six Sigma is dealing with data and making sense of them. That is why statistics plays a major role in any Six Sigma implementation.
As they say, you cannot manage what you cannot measure. But an article on BusinessWeek warns everyone that measuring data should be treated with utmost care. It cites Einstein saying, “Not everything that can be counted counts, and not everything that counts can be counted.” Thus, measurement has its right time and place.
Companies should guard against overmeasuring not only data but using metrics in processes. The article cites four problems with overemphasizing measurement’s role in management.
- Degradation by over-measurement
- Dehumanization
- A false sense of trust
- Increased cost and risk
Filed under: Data, Data Analysis, Data Quality, Metrics, Six Sigma
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The SixSig Trivia
Posted by: meikah | 9 December 2009 | 9:00 pm
It’s 16 days before Christmas, and I’m sure by now, many of you have already Christmas trees in your homes. Do you get a real tree or those artificial ones?
When my mom was much younger, she would always make her own Christmas trees. Sometimes she would get a real one, other times, she would create a replica of a tree full of snow (soap suds, really). About 10 years ago, she resigned to putting up the same green plastic Christmas tree year after year, and adorn it with different balls or colored-paraphernalia each year.
However, according to the American National Association of Christmas Tree Growers, although artificial Christmas trees are more convenient, “real†trees are better for the environment.
They came up with this chart that shows Christmas tree preferences since 2002.
Source: http://www.christmastree.org/statistics_consumer.cfm#type
Via: PQ Systems eLine, Data in everyday life
Filed under: Data, Data Analysis, Environment, Six Sigma, SixSig Trivia, Sustainable Business
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The SixSig Trivia
Posted by: meikah | 10 November 2009 | 7:48 pm
This year, Americans will celebrate Thanksgiving Day on the 26th of November.
In the United States, Thanksgiving Day is celebrated as a way of giving thanks to food collected from a good harvest in 1621. It started somehow as a religious festival, but has evolved into a secular one. American families gather together on this day and prepare a feast. The symbol of Thanksgiving Day is a stuffed turkey.
Now you can just imagine how many turkeys will be served on the tables during that day. Here’s an interesting data about that.
Quality eLine’s Data in Everyday Life presents data on turkey production, and which states produce the most number of turkeys.
Data Source: U.S. Census Bureau
If production of turkey fails, whatever will happen to Thanksgiving Day celebration?
Filed under: Data, Data Analysis, SixSig, Thanksgiving Day
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Are You Data-Driven?
Posted by: meikah | 16 July 2009 | 8:02 pm
As Six Sigma practitioners, data is your best friend. So, how data-driven are you?
Over at Quality Mag, an article analyzes the concept of data-driven and which kind Six Sigma practitioners fall.
The Data Cost / Data Value Matrix is a simple two-by-two matrix that helps us talk about what it means to be “data driven.” On the horizontal axis we have the cost of data, going from high to low. On the vertical axis we have the value of data, going from low to high. The matrix gives us four quadrants. The “data driven” organization lives in Quadrant A, in the upper-right corner. Data are inexpensive and of high value. In my experience very few businesses meet this definition. Some have pockets of excellence, but most fall short of realizing their full potential.
While there may be an infinite number of ways companies fall short of being “data driven,” my experience is they fall into three broad categories, and they are represented by the other three quadrants on the matrix. I’ve seen all of these companies, and maybe you have too.
Companies in Quadrant B gain high value from their data, but pay way too much for the knowledge. From what I’ve seen, many Six Sigma companies fall into this bucket. For the most part these people understand how to maximize the value of the data. They know how to use the full array of statistical tools. They apply proven, disciplined techniques of project management and statistical problem solving to get to the bottom of chronic, entrenched problems. Payback from these programs can be huge.
Filed under: Data, Data Analysis, Quality, Six Sigma
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The Data Aspect in Six Sigma
Posted by: meikah | 25 June 2009 | 6:55 pm
Data is important to your Six Sigma initiative. Thus, if you are thinking of going into Six Sigma, you better learn how to gather and interpret data at the very least.
Oracle put out a white paper titled, Increasing Return on Investment with Data Services.
As the number of applications and platforms generating data increases, so does an organization’s need to give users access to that data through a unified, standard system compatible with multiple interfaces. Oracle Data Service Integrator is a powerful solution that allows organizations to build data services that unify business processes and data for easier use and improved customer service.
Filed under: Data, Data Analysis, Data Quality, Six Sigma, Six Sigma References
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Innovation of the Week: Stream Computing from IBM
Posted by: meikah | 22 May 2009 | 3:46 am
The software enables massive amounts of data to be analyzed in real time. The new software is called IBM System S.Big Blue is making System S trial code available at no cost to help clients better understand the software’s capabilities and how they can take advantage of it for their business. This trial code includes developer tools, adapters and software to test applications.
Filed under: Data, Data Analysis, Data Quality, IBM, Innovation, Innovation Update
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SixSig Looks at Some Important Data on Thanksgiving Day
Posted by: meikah | 27 November 2008 | 12:25 am
Six Sigma is a data-driven initiative, and so if you are to apply it to any process, you must start with some data.
Over at ThomasNet, they shared Thanksgiving by the Numbers. Figures that will matter to mostly service industries, e.g. hospitality, transportation, retail, among others.
EATING
- 271 Million – The preliminary estimate of turkeys raised in the U.S. in 2008, with 49 million expected to come from Minnesota [Source: USDA National Agricultural Statistics Service, U.S. Census Bureau]
- 46 Million – The number of turkeys predicted to be eaten at Thanksgiving, with the average turkey weighing 15 lbs [Source: National Turkey Federation]
TRAVELING
- 41 Million – The number of Americans anticipated to travel more than 50 miles from home this Thanksgiving weekend [Source: AAA]
- 33.2 Million – Holiday travelers expected to go via car, a 1.2 percent decrease from 2007 [Source: AAA]
- $1.89 – National average price of gasoline per gallon as of Nov. 24 [Source: Energy Information Administration]
SHOPPING
- 34% – The percentage of shoppers hitting the stores on “Black Friday” — the day after Thanksgiving [Source: Maritz, Inc.]
- $875 – Amount to be spent on gifts by 33 percent of Black Friday shoppers; 17 percent plan to spend over $1,000 [Source: Maritz, Inc.]
- 1.2% – Expected sales gain on Black Friday, down 7.1 percentage points from last year’s 8.3 percent sales increase [Source: BDO Seidman, LLP]
To my friends in the U.S., Happy Thanksgiving!
Filed under: Data, Data Analysis, Data Quality, Events/Announcements, SixSig, Thanksgiving Day
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The Six SIgma Breakthrough Formula
Posted by: meikah | 27 August 2008 | 9:06 pm
You cannot improve what you can’t measure, we often hear this. But, it’s true! Because in the first place you wouldn’t know if your processes are still in synch or are already deviating from the norm.
Thus, data is valuable but can only be useful when you have the right metrics for it. Dorothy Miller shares her insights on improving business intelligence, the Six Sigma way. In that article on DM Review, Ms. Miller cites the Six Sigma breakthrough formula.
The breakthrough formula, that is often used in general business Six Sigma programs, is a foundation for my proposed Six Sigma BI Continuing Improvement Model (BI-CIM). The formula is: Y = f(X) + E. It defines the relationships between BI quality and the factors that cause or impact quality. The formula may be called breakthrough because the improvements are often dramatic. The formula, as I apply it to BI, means that quality (Y) is a result of, and dependent on, all the impacting factors (X). Y is the quality of the BI product. X includes all those components that cause or impact product quality. E is the uncertainty factor. Because the Six Sigma BI continuing improvement program discussed in this column is based on the breakthrough formula, it is important to define Y and X and the metrics for X for Y.
What is your breakthrough formula?
