Six Sigma

Palisade is proud to announce our first Health Risk Analysis Forum in San Diego on March 31st 2010

Wednesday, March 10, 2010 by DMUU Training Team

Why attend?

This one-day forum is a great way to find out how others in the Healthcare Industry are using our software, as well as to learn new approaches to the problems Healthcare professionals face every day. We will have six software training sessions, and six real-world case studies presented by industry experts covering risk and decision analysis from all angles specific to the Healthcare sector.

You will also see how new versions of @RISK, PrecisionTree, RISKOptimizer, TopRank, NeuralTools, StatTools, and other Palisade software tools work together to give you the most complete picture possible in your situation.

Who should attend?

Professionals in risk and financial analysis in: Care Equipment & Services, Pharmaceuticals, Biotechnology & Life Sciences, Hospital Care & Management, or related services

How much?

For a limited time, the cost for attending the Health Risk Analysis Forum is has been discounted $100.

$295 covers all sessions, continental breakfast, lunch and a cocktail networking reception. Attendees will also receive a welcome package that includes a 15% discount on their next software purchase.

Please contact Jameson Romeo-Hall at jromeo-hall@palisade.com if you are interested in attending.

Location
The Westin Gaslamp Quarter
910 Broadway Circle
San Diego, CA 92101
(619) 239-2200

Book your room at a discounted rate (subject to availability.)

New business planning – measuring feasibility

Tuesday, February 23, 2010 by DMUU Training Team

The latest Business in Britain survey from Lloyds TSB Commercial shows that the UK's commercial enterprises are regaining confidence. The six monthly report charts the performance of 1,732 UK companies and their views on prospects for the coming year. Its most recent business confidence shows that expectations for both sales and orders have started to recover. The balance of firms anticipating an upturn in sales has climbed to 21% - from just 1% six months ago. And hopes for orders are also looking brighter. The balance expecting order levels to rise over the coming six months has climbed to 23%, from just 6% in the last survey.

But companies planning major new business drives for 2010 would do well to follow the example of Thales UK, which uses @RISK to enable it to assess commercial feasibility of potential new business wins. @RISK's in-depth risk analysis ensures the leading provider of mission-critical electronic information systems for aerospace, defence and security markets around the world, is fully informed when making business-critical decisions.

Thales operates in a highly competitive environment, with technologically advanced countries presenting tough opposition when it tenders for contracts. It must continually develop highly sophisticated equipment that is robust and failsafe to meet the stringent demands of its customers. Bringing products of this calibre to market is costly in terms of time and resource, so for every competitive new business opportunity, Thales must be confident that it has a reasonable chance of success.

Using Monte Carlo analysis to show all potential scenarios and the likelihood that each will occur, @RISK enables Thales to calculate the competitiveness of complex markets, measure probabilities for project costs, quantify rate of return, and even account for the effects of cumulative business, thereby providing decision-makers with the most complete picture possible. From this risk analysis, Thales can make an informed decision on the commercial viability of the potential new business offered.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

The State of Six Sigma and Process Improvement

Tuesday, February 2, 2010 by Steve Hunt

Two weeks ago, I attended IQPC’s (International Quality & Productivity Center) Lean Six Sigma and Process Improvement Summit in Orlando, Florida. During the past 4 years, I have watched the conference, the attendees, and their

projects evolve. The IQPC did an excellent job keeping the quality of the conference at an A+ level despite wrangling with the effects of a down market and near zero travel budgets for many companies. This conference has earned it place as one of the premier Six Sigma events of the year.

With attendance numbers on par with last year (which are only slightly down from a few years ago), the major difference that I noticed was the attendees' passion. As the economy has worsened and media’s perception of Six Sigma waned, practitioners and champions are more passionate and committed now than ever. Perhaps it’s because they still have jobs and their companies understand the value of cost reduction in both their processes and product/ process development programs. They - and the companies who employ them - have every right to be excited and passionate because they are making positive changes to their organizations that will hopefully lead them to recovery and stability faster than others.

Many companies, large and small, represented practically every industry. Farmers Insurance and Capital One were two representatives from the insurance and banking industries. Technology and pharmaceuticals were well represented by Seagate, Motorola, Merck and Johnson & Johnson. In addition, the energy sector was well represented, as were the military, aerospace and services sectors. (If you want a complet list of companies attending, it may be available at www.sixsigmaiq.com)

The overriding message heard over and over again, was, “We need to make your Six Sigma deployments stick.” Initially, I found this to be an interesting message since it came from a group of many highly intelligent and motivated individuals who were obviously very successful in doing just that: “Making it Stick”. This message serves as a clarion call for all of us. We need to look for new tools, philosophies and approaches to make our improvement initiative better and “stickier” so that they can pass the test of time.

The highlight of every year is the awards ceremony. There were many great projects honored this year, and congratulations to the winners and everyone who submitted their projects! At the awards ceremony I had the pleasure to meet a great group from the Bahamas Telecommunications Company. They are the pioneers for Lean Six Sigma for their company. (I tried to get them to need an onsite training session in some of the Palisade tools, but have been thus far unsuccessful!) Good luck on your Six Sigma Journey, I hope to see you accepting an award next year!

Goldilocks Had It Easy

Monday, February 1, 2010 by Holly Bailey

Ed Biernat, Consulting with Impact, has been in touch to respond to my recent question about analysis paralysis: How do you know when you've done enough decision analysis, no more, no less than will benefit you?

Here's Ed's take on the issue: "Goldilocks had it easy. She eventually got it right the third time. This issue is one that we wrestle with in Lean Six Sigma overall, because it is easy to become enamored with the analysis of data. Analysis paralysis kills the speed of an implementation and must be vanquished at all costs. Inertia is the biggest foe that we face in implementing Lean Six Sigma. It was one of the big problems with the old model with statisticians in businesses (and why it is hard to find a pure statistician around now in anything but actuarial endeavors.) What the issue really comes down to the basic question, What Problem Are You Solving?

Golf makes a quick analogy. Let’s take the greatest 7-iron player in the world. This person can play the 7-iron like nobody’s business. In fact, they use the club more than any other club in their bag, and crowd really appreciates this virtuoso of the 7-iron. But what is the purpose of the game? To use the 7-iron or to get the lowest score on the course? For risk-analysis geniuses, we can substitute the risk analysis tool for the 7-iron. It is a great tool, a powerful tool. But only if it helps us solve the problem we are facing. And that problem is probably not to build the world’s best model.

If you have addressed the question that you started with when you built the model, then you have done enough analysis. In our consultancy, our bias is to get close and move forward unless we are dealing with a mission-critical decision. We fully admit that we are not modeling experts, and we are OK with that. That is not why our clients engage our services. We solve problems and help them to change their culture. Modeling helps with that by getting the team familiar with issues and sensitivities before we do a full deployment. Once they can see the impact of this variation and their assumptions, and once they have a framework for going forward, we put the model away because it's done its job."

Thanks, Ed, for giving this some thought!

Free Webcast This Thursday: “Lean Six Sigma: History, Trends and Predictions”

Wednesday, January 6, 2010 by Steve Hunt

On Thursday, January 7, 2010, Ed Biernat will present a free live webcast entitled. "Lean Six Sigma: History, Trends, and Predictions."

Lean and Six Sigma have been buzzwords for more than a decade. Some companies have thoroughly embraced the concepts and toolsets, others have dabbled, and the rest sit on the sidelines wondering, to borrow a phrase from a television ad from the ‘80’s, “Where’s the beef?”

In this interactive webcast, we will briefly review what these strategies entail, who is using them and how, and then we’ll put on our prognosticator’s hat take a look at what the future may hold in store. We will review some of the latest “buzz” on the topic as well as recent research aimed at how well these methodologies are moving off the shop floor into wider application.

Ed welcomes any questions regarding Lean, Six Sigma, or the “new and improved” Lean Six Sigma. Although we will be focusing on the implementation side of the equation, (including the use of Palisade software), the discussion can be as free-ranging as the participants require.

Edward Biernat is the president of Consulting With Impact, Ltd., a training, coaching, and consultancy located in Canandaigua, NY that he founded in 1998. CWI’s client list includes companies ranging from the Fortune 100 to post-startups in the medical device, food and food packaging, steel, automotive, healthcare, and service sectors. He is a graduate of Clarkson University with bachelor degrees in Mechanical and Electrical Engineering, and has held positions in engineering, quality, and management at several New York companies. He is the author of numerous training programs and articles, and has presented at national and international events including the Institute of Industrial Engineers’ Annual Conference and the European Organization for Quality in Brussels, Belgium. Ed also developed part of the curriculum for and presents at the Lean Six Sigma Black Belt certification course at a local college.

» Register now (FREE)
» View archived webcasts

Free Webcast This Thursday: “Lean Six Sigma: History, Trends and Predictions”

Monday, January 4, 2010 by DMUU Training Team

Lean and Six Sigma have been buzzwords for more than a decade. Some companies have thoroughly embraced the concepts and toolsets, others have dabbled, and the rest sit on the sidelines wondering, to borrow a phrase from a television ad from the ‘80s, “Where’s the beef?”

In this interactive webcast, we will briefly review what these strategies entail, who is using them and how, and then we’ll put on our prognosticator’s hat take a look at what the future may hold in store. We will review some of the latest “buzz” on the topic as well as recent research aimed at how well these methodologies are moving off the shop floor into wider application.

In the course of the webcast, Ed would like to address participant questions regarding Lean, Six Sigma, or the “new and improved” Lean Six Sigma. After signing up for the webcast, forward any questions to: jromeo-hall@palisade.com. Questions can also be submitted real-time via the webcast chat feature.

Palisade is please to host this presentation from Edward Biernat. Ed is the president of Consulting With Impact, Ltd., a training, coaching, and consultancy located in Canandaigua, NY that he founded in 1998. CWI’s client list includes companies ranging from the Fortune 100 to post-startups in the medical device, food and food packaging, steel, automotive, healthcare, and service sectors. He is a graduate of Clarkson University with bachelor degrees in Mechanical and Electrical Engineering, and has held positions in engineering, quality, and management at several New York companies. He is the author of numerous training programs and articles, and has presented at national and international events including the Institute of Industrial Engineers’ Annual Conference and the European Organization for Quality in Brussels, Belgium. Ed also developed part of the curriculum for and presents at the Lean Six Sigma Black Belt certification course at a local college.

» Register now (FREE)
» View archived webcasts

Digital Eyes on Alien Life

Wednesday, December 9, 2009 by Holly Bailey

University of Chicago geoscientist Patrick McGuire has big plans for Mars. Previously he worked on an imager for a Mars orbiter that could identify different types of soil and rock by detecting infrared and other wavelengths, and now he is drawing on that experience to develop a space suit with digital "eyes" and a neural network that rides on the hips of the spacesuit and can sort out living biological material from other matter.

The digital eyes will detect and plot colors, and the neural net, which is known as a Hopfield neural network, will compare these color patterns to a database of information previously gathered from that area of planet in order to make an animal-vegetable-mineral determination.

This complex AI system has already been tested at the Mars Desert Research Station in Utah, and McGuire and his colleagues were satisfied that the Hopfield algorithm could learn colors from just a few images and could recognize units that had been observed earlier.

McGuire's concept is that a human wearing this neural network could simply walk around the red planet and record every nearby object, rapidly gathering information.

Obviously, such a clothing item awaits a manned Mars mission. But in the meantime, why not have the next Rover suit up?

The Cat is Out of the Bag

Thursday, December 3, 2009 by Holly Bailey

At November's supercomputing conference in Portland, Oregon, IBM announced that its researchers working with a team from Stanford University had succeeded in developing an accurate simulation of human brain function. The simulation will be capable of emulating sensation, perception, action, interaction and cognition.

This algorithm simulating a living neural network, called BlueMatter (spelled as one word like everything else in computerese these days) is an important milestone in IBM's mission to build a cognitive computing chip because it begins to advance large-scale simulation of a cortical neural network and it synthesizes neurological data. BlueMatter is built with Blue Gene (two words for this pun in the singular) architecture, which, in combination with specialized MRI images, allowed the team to create a wiring diagram of the human brain. This map of the brain is, according to IBM's press release "crucial to untangling its vast communication network and understanding how it represents and processes information."

To be more accurate, what BlueMatter has thus far demonstrated is the potential to achieve neural network technology that operates on the scale of complexity of the human brain. The algorithm's current simulation approximates the cortical system of a cat. Hence, the title of the paper announcing IBM's accomplishments: "The Cat Is Out of the Bag." Even so, this is an operations research accomplishment that dwarfs such mundane analytical tasks as option valuation, value-at-risk, or reserve estimation.

One of the goals of the company's cognitive computing program is to create a chip that operates with the energy efficiency of the human brain (20 watts). But in order to emulate the brain activity of a cat, the research team had to bring out one of the largest supercomputers in the world, the IBM Dawn Blue Gene/P--which comprises about 150 thousand processors and contains 144 terrabytes of main memory.

This cat came out of a pretty big bag.

25 Worst Tech Products

Monday, November 30, 2009 by Steve Hunt

A friend and colleague who knows I write a Six Sigma blog sent me a link to an older article on PC World, The 25 Worst Tech Products of All Time that he thought might applicable to Six Sigma.

As first blush, I thought, “What an article on PCWorld.com on the Worst Tech products would have anything to do with Six Sigma?” The answer . . . everything! Particularly after reading the piece, the number 1 or worst product of all time (in their eyes) is American Online. I agree AOL has had its difficulties, but one has to admit the service has had staying power despite this. It’s been around for 20 years, which is a lifetime in the computer world. I don’t know if they utilized Voice of the Customer (VOC) , but they did something right since they are still around.

The article mentions AOL had shown improvements over the previous years. This goes to show us, they had a good idea, but took many years to sort out the bugs and for them to position themselves correctly. At the time of initial development they probably didn’t utilize Design for Six Sigma or another Critical Parameter development methodology, but it appears they may have implemented Lean Six Sigma principles to improve their “inexcusably poor customer service,” “inaccessible dial-up numbers,” and what I’ll call “flawed billing practices.” Please know I am not necessarily agreeing with the article, or being an advocate for AOL, I’m simply pointing out how the company has appeared to have improved its product and service over time.

One can only hope and assume that companies are doing a better job up front vetting their ideas, products and designs . . . with sound initiatives such as Design for Six Sigma. If not, hopefully we won’t seem the on PC World’s next “worst of” list.

If you would like to learn more about Design for Six Sigma, May I recommend either of these two free webinars:

Six Sigma, Monte Carlo Simulation, and Kaizen for Outsourcing

Friday, November 6, 2009 by Steve Hunt

I recently tripped over a very good and interesting article written by Marcia Gulesian, titled Six Sigma, Monte Carlo Simulation and Kaizen for Outsourcing.

Despite its seemingly complex title, the article touches on the basics of Six Sigma and decision analysis where Six Sigma basic quantitative calculations are discussed - such as process capability calculations (Cp, Cpk) are used in an example for the outsourcing of a critical component. The example utilizes a Monte Carlo Simulation a model to illustrate her point.

The example model simulates the outsourcing of a critical component to 3 different vendors, and demonstrates the critical information that a Monte Carlo Simulation model can provide to make informed decisions regarding cost, volumes, supplier capabilities and internal resources. As we all know, having multiple vendors is necessary, but knowing how to distribute your demand across them and knowing the risks and costs involved is critical.

If you would like to experiment with the model, you can download it, But please know you'll need @RISK to run it, you download the @RISK free trial to run the simulation.

The over-arching topic of the article is that any process can be scrutinized for variation and cost reduction, and in my opinion should be. Companies will continue to outsource more and more so that they may focus on their core competencies. But as this happens, it becomes more imperative that a sound strategy is used to manage the potential outcomes.

I’m very happy to have found this article. Maria obviously understands the power and value of Six Sigma and Monte Carlo Simulation and look foward to future articles from her.

Risk Studies

Wednesday, October 28, 2009 by Holly Bailey

This month geographers Pierpaolo Mudu and Elise Beck put out a call for papers for the next annual meeting of the Association of American Geographers. Their session will focus on the "social geography" of risk. Social--or human--geography is devoted to identifying cultural, political, and economic patterns that play out on the physical landscape.

Needless to say, every item in the list of session topics is a virtual Pandora's box of risk. Here are a few of them: "natural" versus "non-natural" risk, perceptiion of risk, environmental risk analysis, different scales to map risks, vulnerability modeling (which I assume was comparative risk analysis). There is lots of juicy fodder for folks who enjoy taking aim at uncertainty with their computers, especially because building models that address these topics often involves integrating GIS techniques with Monte Carlo software.

While I am intrigued by the almost unfathomable risks outlined in the call for papers and the thought of all those number-crunching social scientists who have only six months to plumb the depths of these topics, I was even more intrigued by the mention of what is apparently a new emerging academic field. It's called risk studies--something like American studies, only for quant types who want to get to know the lay of the land.

Wine Aficionado? Six Sigma expert? or both?

Tuesday, October 27, 2009 by Steve Hunt

I’ve heard of Six Sigma being used in every industry from manufacturing, banking, even baking, but now . . . wine making?

Just the other night I found out a winery is using Six Sigma principles to ensure they are producing the highest quality wine available.

Yes, that’s right . . . Six Sigma Ranch and Vineyards have combined the old-world art of wine making with the science of data driven Six Sigma principles. Why not! Isn’t the origin of Design of Experiments from the agricultural world? That’s where (is that right?) RA Fisher introduced the concepts of replication, randomization, blocking and devise analysis of Variance to separate the sources of variation in the 1920s.

How many times have we read the reviews from a single winery, how some years are better than others, etc., and wondered why they can’t make the quality more consistent? Why not apply Six Sigma to wine making?

I think it makes perfect sense!

Six Sigma Ranch and Vineyards is applying Six Sigma principles in all stages of the process:

Conduct extensive analyses of soil, water and climate to find the most favorable sites for our vineyards.
Choose rootstocks that thrive best in the soil composition of a given vineyard.
Meticulously prune vines to enhance the quality of grapes and to allow consistent ripening.
Apply chemical and sensory analyses to pick the grapes at just the right time to produce optimal flavor in the wine.
Listen to the voice of the customer - whether you are a sophisticated wine drinker with well-defined preferences, a social wine drinker who knows what you like and wants the security of consistency, or you just want a good place to start

The use of Six Sigma in all business process makes good sense. There is talk that Six Sigma is dead, and that people are waiting for the next big thing. The truth of the matter is no matter how you repackage the tools, these tools will be around for decades, because good decisions are based on data analysis and that should never go away. My only hope is that they are using @RISK to analyze their data to make even better decisions.

The next time I am in California or the local wine store, I’ll have to investigate this further.

Neural Network Zeros in on Quarks

Monday, October 19, 2009 by Holly Bailey

Having successfully dodged high school physics I would not normally be sucked in by an article on quarks, but this one involved neural network computing, which I do understand pretty well.

It seems that a couple of weeks ago physicists at Fermilab, near Chicago, made the most precise measurement yet of a top quark. A quark is an elementary particle, the most fundamental building block of matter. Quarks come in six flavors (I'm not making this up!), four of which can be produced only by high-energy collisions. Think updated cyclotron. The top quark is one of these four, and first observed in 1995, it is the most recently discovered quark. The physicists--unsatisfied, of course, with having simply identified the particle--wanted to measure it.

It turns out that the way to measure a quark is to observe its decay and work backward from non-quark to quark. This involves heavy-duty statistical analysis of many, many observations. The scientists at Fermilab collected a large set of sample data on quark decay, and then in order to zero in on bona fide quarks, they trained a neural network to identify which particle events were not related to top quark decay. When the neural net had sorted out the quark imitators, the physicists could size up the real quarks more accurately.

The top quark is relatively large for an elementary particle. Until last month it was believed to be about the size of an atom of gold. What is the current estimate? Too daunting a calculation to quote. But if you go to the information the Fermilab has on display, you--or some of you, anyway--will begin to get the picture.

Introduction to DecisionTools Suite 5.5 Products: Software training at the NYC Conference

Monday, October 12, 2009 by DMUU Training Team

Palisade is gathering trainers from our New York and London offices to present software training seminars next week at the 2009 Palisade Conference: Risk Analysis, Applications, & Training. The conference is set to take place on 21 - 22 October at the Hyatt Regency in Jersey City, 10 minutes by PATH from Manhattan's Financial District.

Take this convenient and inexpensive opportunity to learn from Palisade’s trainers and software developers. Learn how to use the elements of the new DecisionTools Suite 5.5 as a comprehensive risk analysis, optimization, and statistical analysis toolkit. See how each of the products in the Suite — @RISK, RISKOptimizer, Evolver, PrecisionTree, TopRank, StatTools, and NeuralTools — can be used to solve practical problems in the real-world.

The conference also features case studies demonstrating how to use @RISK and DecisionTools Suite, from risk management experts in the fields of finance, healthcare and pharmaceuticals, energy, oil and gas, DFSS and Six Sigma, project management, operations management, manufacturing, and more.

See the full schedule for the Conference here.

Next Week: October 21-22 in NYC

Building on the success of last year’s record-breaking event, the conference will offer a wide range of software training, model building, and real-world case study sessions. Last year, the event drew over 150 practitioners and decision-makers from a broad spectrum of industries. The @RISK and DecisionTools software tracks were more popular than ever. This year, we’re expanding software training with sessions that let you walk through examples and try the tools directly. This will enable you to take some new tips back to the office. Please join us in October for a great opportunity to learn and connect with colleagues.

NOAA and the Green Blobs

Friday, October 9, 2009 by Holly Bailey

I've always thought it's fun to call up NOAA's seven-day weather forecast on the computer and watch the green blobs travel over the map and gain hotter colored splotches as the weather shown by the blobs gets worse. But I've never given much consideration to the probabilities that come with the forecasts. Today I stumbled upon the information that a prediction like "70% chance of rain" under the picture of the day's weather is the product of something called ensemble forecasting. This is a form of Monte Carlo simulation that is specially tooled to account for elements of chaos (which makes me wonder if it is used much in the financial sector).

Ensemble forecasting is the pooling of multiple--meaning many,say 50--simulation models run with Monte Carlo software, each of which starts from a slightly different set of conditions. The slightly different initial conditions are intended accommodate the element of chaos, and the pooling of probabilities from each member simulation allows for greater reliability.

Weather is a set of dynamic conditions, and forecasting a future set of these conditions goes far beyond the standard operations research puzzle. It is a legendary challenge in environmental risk analysis that only becomes more challenging the farther into the future it extends. Even with ensemble forecasting this is still true, because a small error in the initial conditions will grow with the lead time. This is the reason that, to the despair of the UK's Natural Environmental Research Council, "we can forecast major weather patterns reasonably well up to about three days ahead. Beyond that, the uncertainties in the forecasts become so large that the forecast is no longer meaningful."

For now, I'll look up the green blobs for only the next three days. After that, it all just depends on the weather.

The Evolution of Deception

Friday, October 2, 2009 by Holly Bailey

No sooner did I mention an apparent trend toward using neural networks to analyze biological processes than another fascinating study came up on my radar: in Switzerland two engineers and a biologist are using a neural network to simulate the biological evolution of social communication.

The Swiss research employs camera-equipped robots instead of animals, and it sends a group of robots out "foraging." The robots are equipped with light sensors, and the "food" at one end of the foraging area is a lighter color than the "poison" at the other end. The bots are scored for their success at locating the "food," and they "talk about" their success by flashing a blinking blue light. Which, in turn, draws robots that are computationally attracted to the blue light.

After that, the self-prepetuating algorithms of the neural network experiment begin to look a little like genetic algorithm optimization. The robots are randomly "mated," and their neural nets are mingled. In only a few generations, the robots learn to head towards the blue lights. But here there is a hitch: the "food source" is available to only a few robots at a time.

What evolves next? By the 50th generation the robots begin to do what you and I would probably do if we had the advantage of a precious resource we didn't want to share: they deceive. They stop signaling with their blue lights when they find "food" because their signals will draw a crowd of greedy robots.

Interestingly, this story of evolving robots mirrors research done on live chickens in the mid-1990s, except that there was also sex and mating motivation--the real kind--at work in those experiments. Animal behaviorists studying vocal communication observed that roosters will call to notify other chickens about the availability of food only if those other chickens are hens.

In spite of this difference, the evolutionary logic of both the chickens and the robots is the same: why share the good stuff and lose your advantage?

A Neural Network by Any Other Name

Wednesday, September 30, 2009 by Holly Bailey

You say toe-MAY-toe, and I say toe-MAH-toe. But I still know exactly what you mean: a big, round, juicy red veggie that slices up nice for the burgers. But how I know the difference between toe-MAH-toe and toe-MAY-toe is a mystery that has eluded neuroscientists. Until now.

In the many press releases I see on scientific topics, I've noticed a trend toward using neural network technology to analyze its biological namesakes, the neural networks in the human brain. One of the latest examples of this is research from a team of scientists at Hebrew University who have used computational neural networks to analyze the cellular processes by which sensory neurons adjust to differences in speech for the same word.

The differences in the way I say tomato and you say tomato are largely a matter of timing and durations, and these sounds are received by single nerve cells. The neural net algorithms devised by Dr. Robert Gutig and Dr. Haim Sompolinsky identify these differences by classifying the way the single nerve cells respond. This innovation will not only be useful in such speech decoding applications as telephone voice dialing, but they also have promise in treating auditory problems.

Toe-MAY-toe? Toe-MAH-toe? Let's call the whole thing....Naw, the two brain scientists aren't calling anything off. Their neural network is just getting started.

Have confidence in your analysis!

Monday, September 21, 2009 by Steve Hunt

Confidence intervals are the most valuable statistical tools available to decision makers, and according a recent Six Sigma IQ article written by Dr. Andrew Sleeper of Successful Statistics, they are not being used as frequently as they should. Sleeper’s article Have Confidence in Your Statistical Analysis!: Learning How to Use Confidence Intervals does an excellent job illustrating why point estimates are useless for making decisions, and how to determine what is the best confidence interval to use. Is it 90%, 95%, or some other value?

The article does not discuss how to calculate confidence intervals, since widely available software (for example, Palisade’s @RISK and StatTools) automates this task. Formulas and calculation methods are well documented in many books.

One example that Dr. Sleeper uses to illustrate his point: Suppose the CEO has decreed that we need CPK to exceed 1.50 for all critical characteristics. If I measure a sample of parts and announce “CPK is 1.63,” this sounds like good news. But then you ask a really good question: “How large is the sample size?” If you discover the sample size was only three, should you be worried? What if you discover the sample size was 300?
We have to make a decision about the capability of the population, but once again, the point estimate is not enough information by itself to make this decision. It is another useless number.

Instead, suppose I said “I am 95 percent confident that CPK is at least 1.52.” Or I could say “I am 97 percent confident that CPK is at least 1.50.” Either of these would be a true statement. And since sample size is used to make these calculations, they provide all information necessary to make the business decision.
These one-sided confidence intervals are often called lower confidence bounds, because the upper limit of each confidence interval is infinity. In the case of CPK, we usually don’t care how large it is, so a lower confidence bound is more appropriate than a two-sided confidence interval.

Because they are single numbers, point estimates are almost always above or below the parameters they are supposed to estimate. Without additional information, point estimates are useless for making decisions. But confidence interval estimates are very likely to be true, and the confidence level specifies and controls the probability that the interval estimates are true. Since properly applied confidence intervals incorporate sample size and other tested assumptions, these are reliable tools to make business decisions.

In addition to this article you can find alot of great information at Six Sigma IQ

“A point estimate by itself is just another useless number.” – Andy Sleeper, 2009

Simulating the U.S. Economy: Where will we be in 100 years?

Friday, September 4, 2009 by DMUU Training Team

William Strauss is the President and founder of FutureMetrics. He brings more than thirty years of strategic planning, project management, data analysis, and modeling experience into the company’s stock of knowledge capital. Bill’s professional history includes executive positions as director, president, and senior vice president, as well as positions as senior analyst and field coordinator. He has an MBA (specializing in Finance) and a PhD (Economics).

Dr. Strauss will present a case study at the 2009 the 2009 Palisade Conference: Risk Analysis, Applications, & Training. The conference is set to take place on 21 - 22 October at the Hyatt Regency in Jersey City, 10 minutes by PATH from Manhattan's Financial District.

See the abstract for his case study below, and see the full schedule for the Conference here.

Simulating the U.S. Economy:
Where will we be in 100 years?

There is an assumption that drives all of our expectations for how our economy will be in the future. That assumption is one of endless economic growth. Clearly endless exponential growth is impossible. Yet that is what we base all of our expectations upon. We all agree that zero or negative economic growth is bad (just look around now at the effects of the Great Recession). But we also know logically that 2% or 4% annual growth every year leads to an exponential growth outcome that is unsustainable.

To see where this growth imperative will take us we first have to see how we go to where we are today. This work first models the 20th century. The model is both complex and simple. The basic schematic of the model’s relationships is easy to understand. Furthermore, the core of the model is a simple production function that combines capital, labor, and the useful work derived from energy to generate the output of the economy. Complexity is contained in the solutions to the internal workings of the model. What is unique is that there are no exogenous economic variables. Once the equations’ parameters are calibrated, setting the key outputs to "one" in 1900 results in their time paths very closely predicting the U.S. GDP and its key components from 1900 to 2006.

The experiment in this work is about the future. If the model can very closely replicate the last 100 years, what does it have to say about the next 100 years? From 1900 to 2006 there are periods in which there was parameter switching. (The optimal parameters and the years for the switching were found using a constrained optimization technique.) That suggests that in the future there will also be changes. The experiment uses @RISK’s features to generate new combinations of parameters for each of tens of thousands of runs of the simulation. Changes in the parameters represent potential exogenous policy choices.

The "doing what you did gets you what you got" scenario leads to a surprising and unsettling outcome. The experiments using @RISK do find a path that works. Obviously if it is not "business-as-usual" that leads to a stable outcome, it is some other way. The policy choices that lead to a stable outcome suggest that the future of capitalism is not going to be what we expect it to be.

Please join us in October in New York for software training in best practicies in quantitiative risk analysis and decision making under uncertainty, real world case studies from risk services consultants and experts, and networking with practicioners from many different fields including oil and gas, pharmaceuticals, academics, finances, Six Sigma, and more.

Bausch & Lomb’s Global Director of DFSS Gets Our Focus

Wednesday, September 2, 2009 by Steve Hunt

As part of Palisade’s membership in the ISSSP, we get to participate in what are called Focused Sessions. For these webcast-like sessions, we are sponsors and exert no editorial control over their content . . . but we decide who the speaker is.

So we’ve decided to put the attendees in good hands! Jeff Slutsky, Global Director of Design for Six Sigma for Bausch & Lomb, will be giving a presentation on September 17th called Probabilistic Project Estimation Using Monte Carlo Simulation.

Registration for the event through the ISSSP is free. This presentation will feature @RISK for MS Project. If you ever wanted to find out more about @RISK for Project in Six Sigma and project estimation, this would be a good venue.

Last summer Jeff presented an excellent free live webcast: DFSS-based Design Optimization using Design of Experiments and @RISK. This is also something that can be viewed for free.

As for recommended reading in the future, Jeff is also the coauthor of Design for Six Sigma in Technology and Product Development. I'd highly recommend it, it is an excellent resource that is often used as the corner stone in many DFSS and Critical Parameter Management courses