@RISK Quick Tips: Automating @RISK risk analysis software with VBA

Tuesday, August 31, 2010 by DMUU Training Team
Presented at the Palisade Risk & Decision Analysis Conference New York City
Chris Albright, author of the book VBA for Modelers, presented a number of examples of how to automate @RISK, RISKOptimizer, and StatTools in Excel using Excel's VBA and Palisade's built-in object-oriented Excel Developer Kit. These examples include production applications, scheduling applications, World Series simulation, and more. All examples include macros written by Dr. Albright, so you'll need to enable macros when you open them.

» Download the examples
» Order Dr. Albright's book "VBA for Modelers"

Free Webcast This Thursday: The Use of the DecisionTools Suite in Biotechnology Project and Portfolio Decision Making

Monday, August 30, 2010 by DMUU Training Team
Vertex Pharmaceuticals, Inc. is a global biotechnology company based out of Cambridge, MA. The Company's strategy is to commercialize its products both independently and in collaboration with major pharmaceutical companies. Vertex's product pipeline is focused on viral diseases, cystic fibrosis, inflammation, autoimmune diseases, cancer, and pain.

Given the uncertainty of outcomes in the biotech industry, consideration of variability is an inherent part of the decision process. Often, the mean (average) is not a relevant decision criteria. This is especially true for smaller biotech companies like Vertex – the opportunity costs are extremely high because scarce capital resources would be invested elsewhere, with a higher probability of realistic return. For example, a company may reject a project which is profitable on average (positive Net Present Value) because some of the possible outcomes are unacceptable to the decision maker. Consideration of variability allows a decision maker to bring in their own risk tolerance into the decision. A similar argument applies when estimating a safety margin above a base case (e.g. in cost budgeting).

Vertex’s strategy and analytics group within the corporate finance division seeks to provide the senior management with dynamic revenue and profit forecasting methodology that helps to identify types of drugs that should be developed given a finite amount of cash and resources. A traditional financial view allows the user to identify scenarios and potential outcomes, but lacks the ability to show the range of potential values within each and every outcome. Vertex’s team uses the DecisonTools Suite to establish the average outcome, the variability of outcomes and to pressure-test risk and uncertainty of a particular scenario throughout the decision process.

Vertex’s team built a complex financial risk analysis model using @RISK to enhance its portfolio process. Monte Carlo simulation and optimization are used to analyze and optimize project and portfolio decisions, given short and long-term corporate strategy. @RISK is also frequently used throughout the business development process: simulating across multiple sales forecasts provides BD team with a range of potential outcomes, making it easy to pinpoint a particular scenario on a curve, along with its probability and value. TopRank turns the sensitivity analysis into a quick and seamless exercise, answering multiple what-if questions within minutes. Franchise and program leaders can now see a dollar effect of their program being delayed or advanced, adding supplementary indications to the development plan and even addressing the price uncertainties all at the same time. The simple interface of PrecisionTree along with tornado chart outputs makes it easy to explain the effect and importance of a particular assumption / decision to an audience with no finance background.

As the company continues to grow, adding more drugs and collaborations to its development pipeline, we will see in this free live webcast how the DecisionsTools Suite remains one of Vertex’s analytical tools of choice to enhance and guide the decision making process.

» Register now (FREE)
» View archived webcasts

The Better to Be Believed

Friday, August 27, 2010 by Holly Bailey
In his blog yesterday for Smart Data Collective, Dean Abbott, makes a worthy, commonsense observation: no matter how accurate a predictive model is, it is of no use to the enterprise unless it is presented in such a way that all the decision makers understand what factors and techniques went into the analysis and why.
 
The reason that the 'best understood' model is more effective than the 'best' model is that when the people with authority over a particular decision are presented with a statistical analysis that is beyond their ken, they may or may not pretend to understand it.  But in any event, they are not likely to buy into the results if they can't retell the story the model describes.  
 
Take for instance, a Monte Carlo simulation that focuses on credit risk analysis for a particular loan.   Everyone in the line of authority will be held responsible for real world outcome of what the Monte Carlo software describes in the Excel spreadsheet.   And if you are one of these decision makers, how can you take responsibility for something you may not quite understand?
 
The problem of acceptance of a predictive model presents the analyst with a tough question: Do I present the model that I know is true and statistically accurate?  Or do I present a ruder, cruder analysis that presents a story that can be immediately understood?
 
Abbott suggests a compromise: streamline your plot by masking (Abbott says "removing") fields that contribute to the robustness of the analysis but involve statistical twists and turns that are distracting to decision makers who may not be fascinated with technique and just want to see how the story turns out. This, he explains, allows you to work from a model both you and the decision makers can believe in.

Your thoughts? 

Market decline versus speed to market – ‘A bird in the hand...’

Wednesday, August 25, 2010 by DMUU Training Team
I recently saw an interesting @RISK cashflow model from the portable phone industry. It modeled the uncertainty in the length and decline of overall market demand for a particular technology against five strategies for getting various application products to market as soon as possible. 

Using @RISK’s Simtable function, combined with Excel’s Index function, it was possible to run multiple simulations and see which strategy could take best advantage of the potential market, given the uncertainties in the development process, the possibility of competitors, the market take-up and the margins that might be achieved.

As is often the case in all aspects of life, the simulation revealed that ‘a bird in the hand is better than two in the bush’; it’s very comforting to know that @RISK risk analysis solutions can cut through loads of detail and come back with an answer that echoes received wisdom!

Ian Wallace, ACMA
Palisade Training Team

@RISK Quick Tips: Event and Operational Risk Analysis

Tuesday, August 24, 2010 by DMUU Training Team
@RISK risk analysis software using Monte Carlo simulation is used for a wide variety of applications. In this model, we have an example of a general usage to address Operational Risk.

In many circumstances one wishes to calculate the aggregate impact of many possible yes/no type events. For example, it is often important to answer questions such as "What is the loss amount that will not be exceeded in 95% of cases?" @RISK simulation can be used to answer such questions.

» Download the example: EventandOperationalRisks.xls

Rating the Polls

Monday, August 23, 2010 by Holly Bailey
With the New York State primaries coming up September 14 and the general election on November 2, I predict that as soon as summer turns the corner into September, we'll start hearing lots and lots about polls that predict election outcomes.  To find out if there was any early discussion of polls, polling, and outcomes, I returned to my favorite election forecast site from the 2008 presidential elections, FiveThirtyEight: Politics Done Right.
 
Sure enough, there it was, a comparative rating of pollsters. This will give people like me, who tend to believe any poll just because it's covered in the news, a way to assess the poll reliability. FiveThirtyEight is the brainchild of Nate Silver, and 538 is the number of members of the Electoral College.  Silver's primary business is Baseball Prospectus, which is also fueled by Monte Carlo simulation and other risk analysis techniques, but FiveThirtyEight has done well enough for the New York Times to want incorporate it in its online coverage during the coming elections.
 
Silver's grasp of statistical analysis becomes immediately evident when you go to his page on the pollsters, and he's more than happy to discuss the statistical methods he uses to rate the pollsters--regression analysis of raw data, Monte Carlo software in an Excel spreadsheet, weighting of poll performance data, and so forth. His take on these matters may be of practical interest to any of you who use these techniques in financial risk analysis.

Elections are all about decision making under uncertainty, especially voter decisions under uncertainty, and according to Nate Silver, only polls taken within 21 days of an election are reasonably reliable.  So when the national campaigns are ramping up in October, keep one eye on the polls and one on FiveThirtyEight.  



Is @RISK a forecasting tool or a decision-making tool?

Thursday, August 19, 2010 by DMUU Training Team
Most people understand that @RISK and Monte Carlo simulation are designed to be an improvement on single-point estimates.  In practice, however, I often see people using @RISK as a forecasting tool to get yet another single-point estimate, such as the 90th percentile, without putting it into the context of the potential range of outcomes.

This is probably the difference between a forecasting and a decision-making.  The former tends to focus on historical or observed trends and developing specific scenarios (e.g. best, most likely, worse) based on expert opinion, while the latter is concerned with confidence ranges and likelihood.

Indeed, it’s not until you add probability, as with @RISK, that you start to measure the quality of your forecasts (i.e. your confidence level) and calculate the margin of error – something that’s crucial in all walks of life!

In my opinion, therefore, @RISK is much more of a decision-making tool than a forecasting tool.  Both involve trying to predict the future but the addition of probability gives decision-makers vital insight to a problem. 

Don’t you just love semantics!

Ian Wallace, ACMA
Palisade Training Team

Free Webcast This Thursday: “Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance?”

Monday, August 16, 2010 by Steve Hunt
On Thursday, August 19, 2010, David Roy will present a free live webcast entitled. "Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance? "

As companies make changes to or introduce new Products, Processes or Services, we observe a wide spectrum of methodology; from well defined process with trained resources, effective tools and excellent results - to no process, ad hoc application of tools, and frequent cycles of “Launch and Learn.”

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.


» Register now (FREE)
» View archived webcasts

Free Webcast This Thursday: “Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance?”

Monday, August 16, 2010 by DMUU Training Team
On Thursday, August 19, 2010, David Roy will present a free live webcast entitled. "Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance? "

As companies make changes to or introduce new Products, Processes or Services, we observe a wide spectrum of methodology; from well defined process with trained resources, effective tools and excellent results - to no process, ad hoc application of tools, and frequent cycles of “Launch and Learn.”

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.


» Register now (FREE)
» View archived webcasts

Taking the Price

Friday, August 13, 2010 by Holly Bailey
Everyone should be allowed at least one vice, and mine is horses.  I love them, spend as much time around them as feasible, and find that after years of this I'm still learning. Recently I've met a couple of people know a whole lot about horse racing.  They don't know a thing about the horse itself, but they have a very sophisticated understanding of the mathematics of predicting performance.
 
So that I could keep up my end of our conversations, I looked further into handicapping and discovered that horse races themselves are only a kind of graphical display to show the results of some massive efforts at statistical analysis, including some of the quantitative forecasting techniques used by financial analysts and whole lot of custom Excel programming.  This should surprise no one--after all, what is betting on a horse if not decision making under uncertainty?--but what did surprise me is level of technical discussion about the math and how to work it through in Microsoft Excel statistics.
 
Take a look, for instance at a recent blog on "taking the price" from the U.K.'s Simon "The God of Odds" Rowland.  Taking the price is locking in the odds when you bet.  He discusses how to correlate a horse's rating--the amount of weight the horse has been assigned to carry--with the actual odds on this competitor.  He then gives the mathematical recipe for his custom Excel spreadsheet, which combines Monte Carlo simulation and the related Markov Chains technique. He wraps up his demonstration with a standard disclaimer: "It must be immediately apparent that this process is very susceptible to the GIGO (garbage in, garbage out) principle. No manner of mathematical manipulation will make up for essential shortcomings in the ratings and in the confidence attributed to those ratings."
 
No matter how good your model, it's still You Play, You Pay.  And Rowland's disclaimer echoed a comment an influential racing veterinarian made to me: "Never invest in something that eats while you sleep."     

Graphing with your Mouse – Part II: Overlay Graphs

Wednesday, August 11, 2010 by DMUU Training Team
In @RISK (risk analysis software using Monte Carlo simulation), you can easily overlay two graphs directly from any graph window.  Just click on a button to identify the variables to overlay, and you’re done. Alternatively, you can drag thumbnails of different simulated results onto the same graph to create overlays. These overlays in the risk analysis models are useful for comparing different strategies, showing trends over time, and more.

A quick video showing how to do this:



» View short videos on recently added @RISK features

@RISK Quick Tips: RiskSimtable to Perform Multiple Simulations

Tuesday, August 10, 2010 by DMUU Training Team
@RISK's use of Monte Carlo simulation allows for powerful features, like RiskSimtable.

The RiskSimtable feature can be used to run multiple simulations to test the sensitivity of the risk analysis model, for example to changes in the parameters of a distribution. This model is of a business with a base case expected revenue of 100 and cost of 80, giving a profit of 20.

The risk model assumes that the revenue and cost distributions are determined from a mean and standard deviation. The RiskSimtable feature is used to test the sensitivity of the distribution of profit to changes in the standard deviation of the revenues. Three values are tested of which the first is our original @RISK model. The number of simulations is therefore set at 3. A RiskSimtable can be set up either by directly typing in the required format, or by inserting it as for other Excel functions via the Insert Function menu option. The model also uses some @RISK statistical analysis functions to report the probability for each simulation that the profit exceeds 50.

» Download the example: BasicBusiness.Simtable.xls

Oops! Didn’t see that coming! Part 4

Monday, August 9, 2010 by Steve Hunt

This is the conclusion of Dave Roy’s guest blog, we hope you have found them informative. Again, Dave comes to us from SSPI, Six Sigma Professionals, Inc., and taught Jack Welch and his entire staff their Six Sigma Green Belt training. Also, look for Dave’s free live webcast on August 19th, Assessing your New Product, Process or Service Introduction Methodology: Is yours premier? Does it enable Six Sigma performance?



Oops! Didn’t see that coming! Part 4
 

 

As a continuation from the July blog, we are now concluding with the “Optimize” and “Validate” phases of the ICOV (Identify-Conceptualize-Optimize-Validate) framework of a rigorous new design process as explained in “Services Design for Six Sigma – A Roadmap for Excellence”.

 

These phases are important because it allows time and methodology to optimize the design, develop all of the detailed documentation, verify performance and capability under operating conditions and manage an orderly transition to the new state.

 

The Optimize phase consists of a single stage (Design Optimization) and associated Tollgate 5 to validate successful completion of the requirements. 

 

The Design Optimization stage involves completing all of the detailed design documentation, building Prototypes of the design, simulating/analyzing Process Capability, preparing all Control Plans and updating the Design and Process Scorecards.

 

Tollgate 5 Exit Criteria:

o    Agreement that functionality and performance meet the customers’ and business requirements under the intended operating conditions.

o    Approval to proceed with the Validate stage.

 

Formal tools which can be used in this phase are Design Scorecard, Process Management, Mistake Proofing, Simulation, Change Management, Control Plans, Reliability and Robustness.

 

The Validate phase consists of two stages (Verification and Launch Readiness) and associated Tollgates (6 and 7) to validate successful completion of the requirements. 

 

The Verification stage involves developing Pilot plans, Piloting the new design and process and analyzing and making adjustments to achieve the desired functionality and performance under operating conditions.

 

Tollgate 6 Exit Criteria:

o    Agreement that functionality and performance from the pilot meet the customers’ and business requirements under the intended operating conditions.

o    Approval to proceed with the Launch Readiness stage.

 

Formal tools which can be used in this phase are Design Scorecard, Process Management, Mistake Proofing, Change Management, Control Plans, Statistical Process Control (SPC), and Confidence Analysis.

 

The Launch Readiness stage involves developing Pilot plans, Piloting the new design and process and analyzing and making adjustments to achieve the desired functionality and performance under operating conditions.

 

Tollgate 7 Exit Criteria:

o    Agreement that transition plans and training plans have been developed and are executable.

o    Approval to proceed with the Production stage.

 

Formal tools which can be used in this phase are Transition Plans, Training Plans, Process management, Change Management and Control Plans.

 

Following the ICOV model we have now used a formal methodology that allows us to validate performance at progressive economical stages and have improved the ability to detect unknown risks thus avoiding the Oops! Didn’t see that coming!. It should be mentioned that the methodology should be flexible and scalable to adjust for level of invention and risk. A brand new invention (Research & Development) that has never been deployed in similar conditions is much different than implementing a known solution (Application Engineering) under new conditions.

 » Part 1
 » Part 2
 » Part 3
 

 

 

BIO:

 

David Roy is an integral part of the Six Sigma community. He taught GE’s Jack Welch and entire staff Six Sigma, as well as served as Senior Vice President of Textron Six Sigma. He is a Certified GE Master Black Belt, was instrumental in developing GE’s DMADV (DFSS) methodology, and has taught 3 waves of DFSS Black Belts. David holds an BS in Mechanical Engineering from The University of New Hampshire. He is also the co-author “Services Design for Six Sigma – A Roadmap for Excellence”

 

Graphing with your Mouse – Part I: Drag and Drop

Thursday, August 5, 2010 by DMUU Training Team
Monte Carlo simulation is a very powerful tool for modeling uncertainty. But perhaps the most critical step in any simulation analysis is the meaningful presentation of results to others. Decision makers won’t act on the results of a simulation if they don’t understand what they are seeing. Graphs are the most powerful way to communicate these important insights.

There are lots of ways to make graphs from the data generated by Monte Carlo simulations. But what is the easiest? Microsoft Excel statistics offers its own graphing engine, but you have to tell it which data to use for what. 

@RISK comes with a powerful graphing engine built-in, and you can create meaningful graphs just by dragging and clicking things with your mouse. In this three-part series, we’ll cover the most common ways to get valuable graphical results in @RISK without ever touching your keyboard.

First off, @RISK automatically generates thumbnail graphs of input variables and output results during a simulation. These are accessible in the @RISK Model window (for inputs) and @RISK Results window (for outputs). You can expand any small thumbnail graph from these windows to full size just by dragging it off the window and onto the spreadsheet. 

Here is a quick video showing how easy it is to do this:



» View short videos on recently added @RISK features

Prediction Markets

Tuesday, August 3, 2010 by Holly Bailey
Although they've been around for the last 20 years or so, prediction markets have begun to make news for their application in business operations. Heralded early on in books like James Surowiecki's The Wisdom of Crowds, prediction markets are a fascinating alternative to traditional forecasting methods, such Monte Carlo simulation, which extrapolate future events from past patterns.  Essentially a betting exchange where participants stake something on the accuracy of the information they offer up, a prediction market is a way of capturing emerging patterns. 
 
Prediction markets can be public or closed private exchanges, as in most business applications. Here's how it might work: a business sets up an online portal to gather intelligence from its employees on such issues as scheduling or production costs.  Each employee has a limited number of points to wager with the information he or she offers, and these points are value-at-risk, which means that an employee is likely to offer only information that is accurate enough to be worth the points. 
 
Why bother to play at all?  Darwinian competition.  With each winning piece of information, the participant gains collective respect.  Maybe he or she advances in rank on a leader board or maybe the company honors its top participants in a ceremony. 
 
While the accuracy of prediction markets is still a topic of some fairly warm debate in applied mathematics, a number of risk analysis services are concentrating their solution portfolios on predictive markets.  

@RISK Quick Tips: Using Percentile Distribution Parameters

Tuesday, August 3, 2010 by DMUU Training Team
@RISK, Palisade's risk analysis software using Monte Carlo simulation, has many features that help you create powerful models for decision making under uncertainty.

This model demonstrates the use of the alternate, percentile parameter formulation. In this case we assume that we have decided to use a Normal distribution to represent the arrival time of someone at work. The use of traditional parameters would require knowledge of the standard deviation of the arrival time, which may be hard to estimate. The use of the alternative parameter formulation allows data to be estimated by others in a more natural way. In the first case, the traditional parameters are used (mean and standard deviation). In the second case, the mean is still used, and the P90 is used in place of the standard deviation, i.e. the time before which the person arrives in 90% of cases. In the second case, the P10 and the P90 is used in place of the standard deviation i.e. the time before which the person arrives in 10% of cases, and in 90% of cases respectively.

» Download the example: AltPars.ArrivalTime.xls

A Little Limelight

Tuesday, July 27, 2010 by Holly Bailey
Limelight--and by this I mean positively glowing publicity-- shines only occasionally on quantitative analysis, and rarely on Monte Carlo simulation.  But there was, 6 years ago, Michael Lewis's Moneyball, which established a place for statistical analysis in major league baseball.  Now there is Relativity Media, LLC, currently one of the heaviest hitting movie production companies in the business, and, more specifically, there is Ryan Kavanaugh, its CEO, and Ramon Wilson, its executive vice-president of business development.
 
Two things about Kavanaugh and Wilson make them unusual: they are leading a movie production firm that is not only alive but growing, and they use quantitative analysis for lots of decision making under uncertainty.  What can be more uncertain than investing in a movie? Only somewhat unusual for the movie business is the fact that these two decision makers are under thirty-five--it's a youth oriented business--and maybe this is correlated with their emphasis on making decisions by the numbers.  
 
"You can't think of it as money," Kavanaugh has been quoted as saying.  "You have to think of it as math."  Given the multimillion-dollar budgets Relativity underwrites--the raw size of the risks involved--it's probably more comfortable for everybody at Relativity to think math.  The kind of math Kavanaugh is particularly devoted to is Monte Carlo simulation, and he talks quite openly about his company's use of it.  When it comes to variables, he names names: principal actor,  genre, director, release date, PG  or R, although in all probability (sorry), each of these variables is probably a set of variables.  
 
"Everything has to run on the principle of profit.  We'll never let creative decisions rule our business decisions.  If it doesn't fit the model, it doesn't get done."  That doesn't mean, he has explained, that if he really likes a project, he and Wilson can't juggle the variables to make the film project fit the model.  They change the parameters to reveal the path to profit.  And profit he has--the estimated assets of Relativity are about $2 billion.  

So Kavanaugh qualifies as a mogul, a math-for-movies mogul.  When the spotlight falls on him, Monte Carlo simulation isn't far out of it.

  

@RISK Quick Tips: Use of RiskTheo to Represent Distributions as Discrete Ones

Tuesday, July 20, 2010 by DMUU Training Team
@RISK, risk analysis software using Monte Carlo simulation, has many powerful features that help you create powerful models for decision making under uncertainty.

For example, you can use the RiskTheo function in @RISK to determine the parameters of a discrete distribution based on a continuous one. In this example, the RiskTheo functions of @RISK work out the P10, P50, and P90 percentiles of a continuous distribution (in this case the LogNormal), and the Mean and Standard Deviation of a RiskDiscrete distribution which has these X-values and some assumed probabilities (or weights). It then uses Excel's Solver to work out the probabilities required so that the discrete distribution based on these percentiles and probabilities would have the same mean and standard deviation as the continuous distribution.

» Download the example: CtsToDiscrete.xls
» See "Uses of the RiskTheo functions in
   @RISK to match distributions
"
 » See "DMAIC Failure Rate using RISKTheo" for a
    Six Sigma application of the RISKTheo function

Oops! Didn’t see that coming! Part 3

Monday, July 19, 2010 by Steve Hunt

We are pleased to welcome back to my blog consultant and trainer David Roy from Six Sigma Professionals, Inc.

 

 

Oops! Didn’t see that coming! Part 3
 

 

As a continuation from the June blog, we are now covering the “Conceptualize” phase of the ICOV framework of a rigorous new design process as explained in “Services Design for Six Sigma – A Roadmap for Excellence”.

 

This phase is important because it conceives, evaluates and selects good design solutions through robust process methodology which ensures alignment to the customer and the business needs.

 

Many design solutions skip this phase and become typically named as “Launch and Learn”.

 

The Conceptualize phase consists of two stages and associated Tollgates to validate successful completion of the requirements. 

 

The Concept Development stage involves translating Customer requirements into solution free Functional requirements, developing the System Level Conceptual Design, generating Concepts for required functions, Concept selection and translation of the Functional Requirements to Design Parameters.Click to Enlarge

An example of a Functional Requirement for a Customer Want of “Speedy Service” could be “Speed of Service” and a Design Parameter could be “Waiting Time

 

Tollgate 3 Exit Criteria:

  • Assessment that the Conceptual Development Plan & Cost will satisfy the customer base
  • A Decision the design represents an economic opportunity (if appropriate)
  • Verification adequate funding will be available to perform Preliminary Design
  • Identification of the Tollgate Keeper & the appropriate staff
  • An action plan to continue flow-down of the design Functional Requirements

 

The Preliminary Design stage involves creating the design documentation and configuration management, performing design analysis and testing, translating the Design Parameters into Process Variables and formulating the Production strategy.

An example of further mapping the Design Parameter of “Waiting Time” to a Process Variable could be “Number of Phone Lines

 

Tollgate 4 Exit Criteria:

  • Acceptance of the selected Solution/Design
  • Agreement the Design is likely to satisfy all Design Requirements
  • Agreement to proceed with the next stage of the selected Solution/Design
  • An action plan to finish the flow-down of the design Functional Requirements to design parameters and process variables

 

Formal tools which can be used in this phase are QFD, TRIZ/Axiomatic design, Measurement System Analysis (MSA), Failure Mode effect Analysis (FMEA), Design scorecard, Process mapping, Process management, Pugh Concept Selection, Robust Design, Design Scorecards, Design for X and Design reviews.

 

The next and final blog will cover the Optimize and Validate phases.

 

BIO:

 

David Roy is an integral part of the Six Sigma community. He taught GE’s Jack Welch and entire staff Six Sigma, as well as served as Senior Vice President of Textron Six Sigma. He is a Certified GE Master Black Belt, was instrumental in developing GE’s DMADV (DFSS) methodology, and has taught 3 waves of DFSS Black Belts. David holds a BS in Mechanical Engineering from The University of New Hampshire. He is also the co-author “Services Design for Six Sigma – A Roadmap for Excellence”

 


 » Part 1
 » Part 2