DFSS Training

Risk analysis and decision-making tools are relevant to most organisations, in most industries around the world.  This is demonstrated by the speaker line-up at this year's European User Conference, an event at which we believe it is important to bring together customers from a wide range of market sectors.

We are holding 'New Approaches to Risk and Decision Analysis' at the Institute of Directors in central London on 14th and 15th April 2010.  As with previous years, the programme aims to provide everyone attending with practical advice to enhance the decision-making capabilities of their organisation.  Customer presentations, which offer insight into a wide variety of  business applications of risk and decision analysis, include:

• CapGemini: Faldo's folly or Monty's Carlo – The Ryder Cup and Monte Carlo simulation
• DTU Transport: New approaches to transport project assessment; reference scenario forecasting and quantitative risk analysis
• Georg-August University Research: Benefits from weather derivatives in agriculture: a portfolio optimisation using RISKOptimizer
• Graz University of Technology: Calculation of construction costs for building projects – application of the Monte Carlo method
• Halcrow: Risk-based water distribution rehabilitation planning – impact modelling and estimation
• Pricewaterhouse Coopers: PricewaterhouseCoopers and Palisade: an overview
• Noven: Use of Monte Carlo simulations for risk management in pharmaceuticals
• SLR Consulting: Risk sharing in waste management projects - @RISK and sensitivity analysis
• Statoil: Put more science into cost risk analysis
• Unilever: Succeeding in DecisionTools Suite 5 rollout – Unilever's story

We will also look at the recently-launched language versions of @RISK and DecisionTools Suite, which are now available in French, German, Spanish, Portuguese and Japanese.  Software training sessions will provide delegates with practical knowledge to ensure they can optimise their use of the tools and implement business best practise and methodologies.

With over 100 delegates from around the world attending, the event is also a good opportunity to network and knowledge-share with risk professionals from around the world.

» Complete programme schedule, more information on each presentation,
   and registration details

Something has troubled me for some time regarding the choices being made in risk land. I train and work with many clients whom have adopted Monte Carlo simulation techniques (via @RISK for Excel) into the day-to-day running of their businesses. By doing so they (hopefully) now have a good understanding of the exposure they are facing be it in project cost estimation, discounted cash flow analysis or, well, anything really. But this is only one facet of risk and decision assessment, specifically dealing with the descriptive statistical output from a simulation. What of the decision evaluation component? Why aren’t more of my customers analysing the decisions they make, or better yet actually optimising them? I have a few ideas why.

If you’re in business you have to make decisions. Big ones, little ones, yes/no, multiple state and continuous value decisions. Decisions that impact other decisions in simple or complex dependency structures. But are you making the best decisions possible? I’m sure important decisions aren’t being made completely randomly (I hope!) but I see many companies who rely completely upon qualitative techniques for their decision making (experience, gut feel, etc.) which of course means optimality is no more than a hoped for outcome rather than something that is actually being worked towards.
Firstly the decision model must be identified and then quantified, and this can be a difficult task. There is a level of modelling aptitude necessary for effective modelling that goes beyond merely knowing Excel and its functions, and into the construction of logical mathematical descriptions of possibly complicated processes. Relevant decisions need to be identified and the impact of those decisions combined into a formula that can be mathematically optimised. A critical component to all this is the knowledge that spreadsheet models can actually be optimised, and that in cases where Excel’s Solver fails there are Palisade products (Evolver and RISKOptimzer) that can perform optimisations under virtually any circumstance.

I too used to focus on Monte Carlo simulation rather than decision evaluation, and this was mainly a product of the clients I was dealing with almost exclusively when I first worked for Palisade. In my next blog I’ll tell you why that changed and also get a little more into the nuts and bolts of optimisation.

Rishi Prabhakar
Trainer/Consultant

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.)

I read an interesting article on the causes of the Global Financial Crisis by John B. Taylor. Although the topic is interesting enough already, especially for a member of a risk analysis-specialising company, something else caught my eye. I have observed in training workshops, onsite consulting and now academic papers a phenomenon regarding probabilistic modelling. Many of those using the methods don’t understand what they should actually be getting from the methodology. There is an intellectual leap from the deterministic to the probabilistic that sometimes does not get made. This limits the usefulness of Monte Carlo simulation, and the value of performing such statistical analyses.

Back to the article which spurred me to write this blog in the first place. Or rather, the graph. Yes a single graph of housing starts vs. time (and its brief description) leapt out at me. One of the lines on the graph was claimed to show model simulations of housing starts using the actual interest rate, compared to the interest rate ‘predicted’ by the Taylor Rule and a third line showing actual data.

So what’s the problem?

The problem is that simulation techniques should not be used to create a single value. The single ‘simulation’ line implies a single modelled/returned value for each time period. This is deterministic modelling. There may be a particular scenario that has been modelled, but it certainly isn’t a simulation that is being represented by that single line. Simulations produce thousands of data, observed values and their associated percentiles as well central moments (mean, variance etc.). Not just one value (sorry Value at Risk – that includes you too) that can be plotted as a single line. I would guess that if a simulation were run as I understand the term then the line in the chart was probably constructed using the simulated means. But I shouldn’t be guessing.

This is far from the only time I’ve seen simulation results reduced to a single entity. I have heard from clients in the past “the simulation gave $X” with little to no context around it, and this is supposed to both mean something to me and to their customers and help to make better decisions under uncertainty…

In the next blog I will explore this idea further and discuss the sorts of results that should be gleaned from a simulation. In particular, why narrowing simulation results down to a single number is counterproductive to healthy business practices.


Rishi Prabhakar
Trainer/Consultant

Palisade Training services show you how to apply @RISK and the DecisionTools Suite to real-life problems, maximizing your software investment. All seminars include free step-by-step books and multimedia training CDs that include dozens of example models.

North America

• March 1-2, 2010, Houston, TX
  Monte Carlo Simulation for the Oil and Gas Industry using @RISK
• 3-4 March 2010, Dayton/Versailles, OH
  Decision-Making and Quantitative Risk Analysis using @RISK
• 10-11 March 2010, Jersey City, NJ
  Decision-Making and Quantitative Risk Analysis using @RISK
• 23-24 March 2010, Denver, CO
  Decision-Making and Quantitative Risk Analysis using @RISK
• 23-25 March 2010, Denver, CO
  Decision-Making & Quantitative Risk Analysis using the DecisionTools Suite

Brazil

• 3-5 de março 2010,Rio de Janeiro
  Seminário de Avaliação de Riscos na Indústria Petrolífera e de Gás (3 Días)
• 17-19 de março 2010, São Paulo, Brasil
  Avaliação do Risco para Usuários Iniciantes/Intermediários (em português)

Latin-America

• 8 al 10 de marzo del 2010,Medellín, Colombia
  Risk in Supply Chain (en español)
• 15 al 17 de marzo del 2010,Lima, Peru
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)
• 18 al 19 de marzo del 2010,Lima, Peru
  Administración de Riesgos en Proyectos con @RISK para Project (en español)

Asia-Pacific

• 10-11 March 2010, Melbourne
  Decision-Making and Quantitative Risk Analysis using @RISK

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!

In my last blog I mentioned a ‘fact’ about data that came up during a recent public training course (Decision-Making and Quantitative Risk Analysis). This fact stuns me every time I think about it, and certainly floored me the first time I encountered it. So many companies just don’t have it.

Data, that is. Historical data from completed projects, sometimes billion-dollar projects, is simply not collected especially in resources and infrastructure cost estimation. Instead every risk is re-estimated from scratch in every new project based entirely upon an estimator’s recollections or guesses. This is not a suggestion that estimators don’t know what they’re talking about, rather that the benefits of adding historical data to the analysis far outweigh the cost of gathering the information in the first place.

I first worked in the banking sector, hence my surprise to learn of this lack of data storage in certain areas of risk analysis. Project cost estimation, especially in resources and infrastructure – I’m talking to you. In financial circles there are literally millions of data points collected daily across the entire organisation. Gathering data (and then analysing it for some benefit) is simply ‘what we do’, and this process isn’t challenged. Some of the data is quite ‘small’, such as the number of seconds a particular caller was kept on hold before being answered, and others are quite ‘big’, such as multi-million dollar losses due to fraudulent activities. Regardless, it’s all kept in the knowledge that information is power – in this case the power to make intelligent decisions in the future.

How can you judge the efficacy of an estimation process (workshops etc.) if you don’t track the final observed outcomes specifically to make such a judgment? Well, you can’t. And that leaves your company’s risk and decision assessment process in limbo. Without measurement there can be no process improvement or corporate learning. Are you ‘passing’ or ‘failing’ with your use of Monte Carlo simulation via risk analysis software?

Generally the observed outcomes for risks in models will be near the estimated value, and this is to be expected. However the main role of risk analysis is to adjudge exposure to the unexpected. Far too many cost estimation models have very little volatility in their line items. I am very curious to know just how often the realised value of a given line item is outside the range of “possible values” as defined in the model. And what about the total project costs overall? This hints at and leads to the big question which is what could/should be done with such data if it were to be recorded?

I shall address these questions in the next blog. I know you’re excited to find out!

Rishi Prabhakar
Trainer/Consultant

An anonymous comment in the Vail (Colorado) Daily News about the dangers of overanalyzing a decision reminded me that, while the benefits of risk analysis have been much vaunted, the costs of decision evaluation have not been clearly defined.  Sure, it's pretty easy to come up with a figure for a DFSS training effort or a budget for an entire risk management department. But what about the statistical analysis process itself?  

Well, there's staff time or your own time (which is worth something), Monte Carlo software, some portion of your computing costs,data acquisition, and on and on. Many variables. But the kind of costs I'm thinking of are the kind you rack up while you're analyzing, say, option valuation, and not doing something else.  These are opportunity costs.  They are what really limit how thoroughgoing your risk analysis becomes, which layer you drill down to--and they are very difficult to quantify.

How do you calculate whether the time you're spending in risk assessment is cost-effective? It's a problem of operations risk.  So I suppose you could enumerate all the other activities that would consume the same amount of time and model their paybacks.  But that would cost you more time in statistical analysis. . . . and you would be left in a positive feedback loop.

In the days ahead I'll be talking to risk management and operations research folks to find out how they decide how much analysis is just the right amount--not too much, and not too little.   I'll be surprised if I turn up any computational approaches--but who knows?  

In a recent public training workshop (for @RISK for Excel) I was reminded of an unusual fact regarding data.

Commonly @RISK for Excel is used to fit distributions to historical data for use in risk modelling, and it sure beats wildly guessing obscure parameters. However there are (naturally) a litany of woe-inducing problems with all historical data sets: non-stationary data series, extreme values/outliers, data recording errors, seasonality and heteroskedasticity to name a few. Excessive ‘cleansing’ of the data set is commonly prescribed, but the statistician in me cringes to even type those words! Quality control and transforming the data will help to eliminate most of those problems, but what about outliers?

In the early Naughties I was working for a large Australian bank, forecasting their daily call centre volumes for the purpose of planning staff levels and predicting service levels. A particular call centre averaged 30,000 calls per weekday. Yet on September 12th, 2001, calls dropped to less than 10,000. Along with the rest of the world, Australians were watching the terrorist attacks on television and the internet rather than calling to fix spelling mistakes in their contact details or transfer small sums of money between accounts. But what to do with that data point? Presuming the forecasting model is not intended to include such extreme events as terrorist attacks then the point could simply be filtered out of the data set and not thought of again.

But now consider a process that should include rarer events, such as flood damage or operational risk, as one of the risks in a model. If you have 10 years of good data (say), but the set includes an event that should only occur every 100 years. This level of impact is thus drastically overrepresented in the data and any fitted distribution will be biased toward such extremes. Yet the data point can not be completely ignored as such values can occur and the simulation models must have the capacity to sample such values (though with a reasonable likelihood). In this case the artistry that is fitting distributions to data comes to the fore. The data point could be removed from the set but not from our decision making process.

From the range of distributions that can be selected, the optimal choice should not only represent the remaining data well but also have a tail that samples events in the vicinity of those that have been excluded from the analysis with reasonable probability. No, that’s not always easy to do. But as with many elements of probabilistic modelling it simply must be done in order to provide useful information to decision makers.

Thus the context of the modelling can go a long way to determine the most appropriate steps to take with your data set. If that sounds like a subjective guideline then you read it correctly. Not enough people realise just how important experience and intuition can be in the seemingly prescriptive fields of mathematics and statistics. Fitting distributions to data is no different.

And yet that isn’t the unusual fact I was reminded of in the workshop! But I’ll leave that for Part 2 of my Data Issues blog.

Rishi Prabhakar
Trainer/Consultant

Palisade Training services show you how to apply @RISK and the DecisionTools Suite to real-life problems, maximizing your software investment. All seminars include free step-by-step books and multimedia training CDs that include dozens of example models.

North America

• 9-11 February 2010, Ottawa, ON
  Decision-Making & Quantitative Risk Analysis using the DecisionTools Suite
• 16-17 February 2010, Cleveland, OH
  Decision-Making and Quantitative Risk Analysis using @RISK
• 23-24 February 2010, Orlando, FL
  Decision-Making and Quantitative Risk Analysis using @RISK

Europe

• 8-10 Feb 2010, London (Central)
  @RISK and the DecisionTools Suite: An Intensive 2.5 Day Course

Latin-America

• 10 al 12 de febrero del 2010,Santa Cruz, Bolivia
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)
• 24 al 26 de febrero del 2010,Distrito Federal, México
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)

Today there is a heightened appetite for risk management due to global economic circumstances. But risk management has always been an intrinsic aspect of business to a higher or lesser degree. However, in the current technology-led business environment, the use of software to effectively manage risk makes logical sense. It provides a level of sophistication that the traditional processes simply cannot offer. Let me explain why.

Risk management essentially involves three stages – identification, quantification, and the on-going management of risks. In reality, these stages are not completely distinct from each other, with each stage influencing and informing the others. For example, an initial quantification of risks may lead to the conclusion that some of the identified risks are in fact not serious enough to warrant further consideration, or that the original description of the risk was not sufficiently precise for meaningful risk management measures to be put in place.

Each of these stages can benefit from the use of supporting risk modeling software. For instance, Microsoft Excel can be used to create a risk register, i.e. a database that records the risks identified, the assessment of the likelihood and impact of each of these risks, the mitigating actions that have been planned, and the assignment of responsibilities for these actions. However, there are many other software tools available, each designed for a specific purpose and focus. To illustrate, enterprise-wide risk management software focuses on the creation of integrated and holistic risk management systems, whereas Monte Carlo simulation and decision tree software place their emphasis on enhancing the quantitative analysis of risks.

The selection of the appropriate risk analysis software should involve very careful thought. The right decision can lead to a very effective implementation, whereas the wrong decision may result in a large amount of wasted investment.

There are some key considerations to bear in mind when selecting the risk modeling software. Choosing software based on how many staff will genuinely be required for the day-to-day risk management process is crucial. It is easy to select software based on the ideal situation that there will be a wide staff involvement in the risk management process. In reality, this may not be possible, potentially resulting in a cumbersome and inflexible solution being chosen over a more stand-alone and flexible application.

Similarly, knowing the level of risk quantification required is important. In fact, best practice risk management now involves the use of quantitative techniques, often using Monte Carlo simulation. When correctly conducted, the process of quantifying risks is rigorous and structured, can expose hidden or biased assumptions, as well as provide a more solid rationale upon which to base the major decisions.

Finally, determining the extent of on-going risk management needed for your business can assist with software selection. 

Needless to say, any software application will be most successful when used by appropriately trained and motivated staff, and when used as a supporting tool within an overall risk management process. Software is not a replacement for process.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

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

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

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:

1. Accelerating Product Design with Simulation and Stochastic Optimization by Andy Sleeper of Successful Statistics
2. DFSS-based Design Optimization using Design of Experiments and @RISK by Jeff Slutsky Global Director of DFSS for Bausch & Lomb.
    

Following on from the resounding success of the last Palisade Risk Conference in London, which attracted over 110 attendees from industry and academia, the 2010 Palisade Risk Conference will be taking place on April 14th-15th. The location for this event will again be the Institute of Directors on Pall Mall, London, and already there are a number of exciting presentations confirmed from the likes of Unilever, Pricewaterhouse Coopers and Halcrow.

The 2010 Palisade Risk Conference will be a two-day forum which will cover a wide variety of innovative approaches to risk and decision analysis. Featuring real-world case studies from industry experts, best practices in risk and decision analysis, risk analysis software training, and sneak previews of new software in the pipeline, the event is also an excellent opportunity to network with other professionals and find out how they’re using Palisade risk analysis solutions to make better decisions.

Call for Papers

If you have an unusual or interesting application of Palisade software which you would like to present, please send a short abstract to cferri@palisade.com. The closing date for abstracts to be submitted is Friday, 11th December, 2009.

Wayne Winston is the newest blogging personality at the Huffington Post! His first post, “The Importance of Schedule Strength in Sports,” appeared yesterday. Wayne will focus on the interface between math and sports, with detailed explanations of statistical analysis and spreadsheet modeling, including @RISK risk analysis models. You can find a link to the Wayne Winston blog from the newly-launched HuffPost Sports.

Wayne is the John and Esther Reese Professor of Decision Sciences at Indiana University’s nationally ranked Kelly School of Business. He has won over 30 teaching awards, and written over 20 journal articles and 15 books.  Wayne has consulted for many organizations including the Dallas Mavericks, USA Diving, Cisco, Microsoft, US Army, Eli Lilly, Diamond Consulting, Tellabs and Medtronics. He has also developed online spreadsheet modeling and mathematics courses for Harvard Business School Publishing. And, Wayne is a two time Jeopardy! champion!

Wayne’s latest book, Mathletics, provides an introduction to the use of math by baseball, football, and basketball teams. He has also authored several books published by Palisade, including Financial Models Using Simulation and Optimization I, Financial Models Using Simulation and Optimization II: Investment Valuation, Options Pricing, Real Options & Product Pricing Models, and Decision Making Under Uncertainty with RISKOptimizer.


DMUU Training Team

Palisade Training services show you how to apply @RISK and the DecisionTools Suite to real-life problems, maximizing your software investment. All seminars include free step-by-step books and multimedia training CDs that include dozens of example models.


North America

• 2-4 December 2009, Portland, OR
  Decision-Making & Quantitative Risk Analysis using the DecisionTools Suite
• 17-18 December 2009, Atlanta, GA
  Decision-Making and Quantitative Risk Analysis using @RISK

Asia-Pacific

• 2-3 Dec 2009, Perth
  Decision-Making and Quantitative Risk Analysis using @RISK
• 16-17 Dec 2009, Sydney
  Decision-Making and Quantitative Risk Analysis using @RISK

Latin-America

• 9-11 de diciembre del 2009, Miami, FL
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)

Brazil

• 2-4 dezembro 2009, Recife, Brasil
  Avaliação do Risco para Usuários Iniciantes/Intermediários

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.

Michael A. Kubica is Founder and President of Applied Quantitative Sciences, Inc. He has over 18 years' experience within the healthcare industry, and has been providing quantitative sciences consultancy since 1999. Michael has extensive experience in providing quantitative decision support solutions for leading pharmaceutical, medical device/diagnostics, and biotechnology companies, addressing a wide range of business issues. Prior to establishing AQS, Michael held the position of Vice President, Operations for Magellan Health Services. During his career Michael has also held positions of Director of Quality Management, Regional Director of Business Operations & Finance, and Hospital Administrator. Throughout his career, Michael employed sophisticated quantitative methods to forecast performance, streamline operations, and improve quality. Michael has an MBA and Master’s of Science in psychology. He serves as Adjunct Professor of Research Design and Statistical Analysis at St. Thomas University in Miami, FL. Applied Quantitative Sciences, Inc. (AQS) is a consultancy specializing in assisting medical device, pharmaceutical and biotechnology companies make decisions under conditions of complexity and uncertainty. They are a market leader in providing simulation and optimization models which are used by industry leaders for the purposes of forecasting, new technology valuation, business and strategic planning, supply chain management, and resource planning.

Mr. Kubica will present a case study later this week at the 2009 Palisade Conference: Risk Analysis, Applications, & Training, 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.

Targeted Analyses and Compelling Communication: A Formula for Successful Value Creation in Management Science

The value of quantitative science projects too often remains unrealized for would-be consumers. Despite flawless analyses, sophisticated reports and dazzling presentations, the message goes unheeded by those who could most benefit: If only they understood how to operationalize the results. The clarity with which quantitative scientists view the practical application of results is often paralleled only by their inability to generate that same clarity in their customers. The result is that good management science is at best ignored and worst, misunderstood (and misapplied). This workshop describes steps we as quantitative scientists can take to foster understanding, generate novel insights and stimulate actionable results with our clients. 

This 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.

Andrea Dickens is a Decision Analysis Group Leader at Unilever’s Finance Academy. Andrea joined Unilever in 1988 as a statistician. Since then she has had a number of roles in Unilever, but all with one thing in common: managing and analysing uncertainty.

Andrea now leads the Decision Analysis Group, which has been developing and applying a wide range of decision analysis techniques. These techniques have been deployed on probabilistic business cases and complex decision problems throughout Unilever. The group also leads the development and rollout of training courses for Unilever Managers in Decision Making techniques, and provides coaching to course participants. In addition, the Decision Analysis Group has an internal consultancy role where they provide facilitative leadership to analyse complex business decisions. These tend to be the large, difficult and sensitive problems, high stake one-off decisions, or problems that cross organisational boundaries.

Ms. Dickens will present a case study next week at the 2009 Palisade Conference: Risk Analysis, Applications, & Training, 21 - 22 October at the Hyatt Regency in Jersey City (10 minutes by PATH from Manhattan's Financial District).

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

So You Think You Have the Right Data?

Have you often wondered how good your data is? Collecting data about future uncertainties from experts has a number of hidden traps. In this interactive session we will make you aware of some of the most common sources of bias, and suggest ways to overcome them.

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.