Monte Carlo Excel

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!

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. 

In this free webcast, Dr. William Strauss models the next 100 years, based on the last century's data. The experiment in this webcast 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? The experiment uses @RISK’s risk analysis and Monte Carlo techniques 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 Evolver (genetic algorithm optimization software) 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.

Palisade is pleased to host this presentation from Dr. William Strauss.

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). Read more of Dr. Strauss' bio here.

» Complete abstract of "Simulating the U.S. Economy: Where will we be in 100 years?" 
» Register now (FREE)  
» View archived webcasts

In Part 2 of this series I finished by asking what should be done with historical data, now that we have decided that storing it is probably a good idea. I won’t keep you waiting any longer.

Auditing and calibration of the model at both the micro and macro level. It’s as important as any other element of risk or statistical analysis, or indeed the model building itself. At the distribution level historical data helps to both parameterise the distributions and in fact select them in the first place. As a minimum a few data points will help you to understand possible central tendencies and variability for your risks, and also generate a list of feasible distributions to choose from. With a reasonable number of observations @RISK for Excel can be used to fit distributions to the data taking care of both distribution selection and parameterisation simultaneously. Only five data points are technically needed, but a reasonable fit will require either more than that or other holistic information to achieve validity.

At the macro level total project cost estimates are often ignored from the portfolio perspective. Commonly high percentiles are reported from such models to use in a ‘contingency’ calculation, such as the P90 or P95. Whilst a high percentile, the P90 (say) should still be exceeded 10% of the time! If your projects never go over this percentile then either there are some major mitigating factors not included in the model or the volatility is being consistently overstated. Likewise, the P10 for total cost (these ‘good’ percentiles are rarely if ever reported or considered in project cost estimation work) should be bettered in roughly 10% of projects. If this is not the case then the upside risk has been overstated. This may be due to misconceptions about the positive skewing present in most cost/delay risks or mistakes made in the parameterisation of the risks where the estimate (“most likely” etc.) is actually the “best case” or close to it, rather than a central tendency of the process over time. There could also be other possibilities.

No matter how you look at it, the collection and intelligent use of historical data is integral to effective and useful risk analysis and management, and critical to achieving valid Monte Carlo simulation results. If you aren’t currently recording everything you can get your hands on start right now!

 

» Part 1
» Part 2

Rishi Prabhakar
Trainer/Consultant

This Thursday, 28 January 2010 at 11am ET, Dr. William Strauss, President of FutureMetrics, will present a free live webcast entitled, "Simulating the U.S. Economy: Where will we be in 100 years?" Sign up now to attend the webcast.

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 free live webcast 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 webcast 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 (risk analysis software using Monte Carlo techniques) 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 Evolver (genetic algorithm optimization using Monte Carlo simulation) 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.

----
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).

» Register now for this FREE live webcast
» View archived webcasts

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

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

Resolved for 2010:

Having talked in previous posts as to why it’s important, and today how accessible it is for any size of organisation to adopt a healthy approach to risk, I’ll now take you through my top ten tips on how you can maximize your risk management programme:

1. Get buy-in
Risk management is not an optional extra. It is a business critical tool that is an asset and an integral part of the project. The company culture must be developed to embrace QRM (quantitative risk management) and DMU (decision making under uncertainty) in order that everyone understands their benefits and therefore accepts the need for them.

2. Get budget
Business tools cost money, but managing risk is an investment - not an overhead – and must be regarded as such. Allocating resource and making it a formal business process should be seen as an insurance policy.  Not only will it help organisations make better decisions that will save them money in the long term but, by identifying potential risks and adverse events, it can protect them against unexpected costs in the future.

3. Get words
As with any organisational change, it is essential that everyone is clear on the new processes. Therefore a common risk language – or 'glossary' – needs to be developed to avoid misunderstanding and to ensure a consistent approach to QRM and DMU.

4. Get numbers
Qualitative assessment is essential, but numbers are more powerful – for example the percentage chance of meeting a deadline or budget. Monte Carlo simulation random sampling provides the margin of error for a venture and is a good way to illustrate the consequences of different courses of action. Risk management experts must ensure everyone understands these figures, and accepts them.

5. Get structure
Managing risk in order to make better-informed decisions requires an appropriate organisational structure. Individuals and groups need clearly defined roles, and must then each take responsibility for their own area of expertise.

6. Get lateral
Every organisation has risks that it deals with on a daily basis and which must therefore be factored in to the decision-making model. However, no enterprise operates in isolation, so other external variables must be included. For example, even a small rise in fuel costs could have a major effect on revenues if raw materials need transporting long distances.

7. Get perspective
Political, cultural and social risk factors can be explored by involving all stakeholders.  Investing time and money in consultation and research ensures that businesses have a clear idea of the complete environment in which they operate, and therefore minimise the chances of products and services failing.

8. Get reporting
Risks, and the management of them, must be reviewed regularly – and the programme amended if necessary. This requires a regular reporting process, in which risks are clearly identified and prioritised.

9. Get with it
Being risk aware does not mean being risk averse. Businesses should guard against rigidly adhering to 'the way we've always done it' approach, instead keeping up-to-date, learning new tricks and not being afraid to be bold.  Although risky on the surface, these tactics prevent being left behind – much of the potentially uncertainty can also be removed with QRM and DMU.

And finally…

10. Get it documented
Back up the commitment to a thorough QRM and DMU programme with documentation. This validates the budget and buy-in requested at the start. And it’s good for business – organisations this thorough are guaranteed a competitive edge.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

It’s clear that the financial crisis has exposed a number of failings in the practice of risk management. In my last post I talked about the relevance risk analysis and the disciplines of ‘quantitative risk management’ (QRM) and ‘decision making under uncertainty’ (DMU) are to all sizes of organisations, be it large or small. 

However, how accessible are these disciplines to the average size business across the globe today?

With the need to make more informed decisions more pressing by the day, thankfully QRM and DMU and now far more accessible than ever before.  Traditionally systems tended to be expensive, enterprise-based applications targeted at large companies who were prepared to spend considerable time, money and human resources.  The result was an all-singing, all-dancing product which often ended up underused due to confusion on the part of the very employees who were supposed to make it work.

Steady increases in computer processing have given the desktops of today as much power as the high-end servers of a few years ago, meaning that risk analysis and management is now an achievable goal for businesses of all sizes.  Palisades @RISK and Decision Tools Suite software are such desktop risk and decision analysis tools – working within Microsoft Excel and therefore being accessible to a large number of users.

‘Monte Carlo Simulation’, a technique originally conceived by scientists working to develop the atomic bomb as part of the Manhattan Project, is an inherent part of @RISK, a cornerstone of the Suite.  It enables users to introduce uncertainty into their previously static spreadsheets, which lets them look at things in a probabilistic, rather than a deterministic way.  In layman’s terms, this means that rather than companies and individuals making decisions based on estimates or best guesses, they can see all the potential outcomes to a venture – and how likely these scenarios are to occur.

For many companies this significantly improves the decision-making process.  Firstly it requires a change in the methodology of employees responsible for assessing risks and opportunities and secondly for the first time employees have a tool which allows them to communicate their recommendations to management or colleagues in a transparent and standardised way.  Equally, being able to look at scheduling risk in a probabilistic and quantitative sense allows for the allocation of labour and resources in a way which minimises slack and wastage whilst maximizing ROI.

So, it would seem that the new ‘risk management’ language that is starting to develop in the workplace and being taught to a new generation of managers on MBA courses should be welcomed.  With the accessibility of the technology available to assist them, we need to make sure that organisations do more than just pay lip service to QRM and DMU if they are to reap the rewards.

In my next blog I’ll be giving you the my top ten tips to adopting a health approach to risk, that will help businesses of all sizes maximize their risk management programmes.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

Using this technique the researchers estimated that for every laboratory-confirmed case of H1N1, there were actually 79 cases.  In other words, predictions of a pandemic were not hysteria.  And while public health officials are unlikely to get hard data that will allow them to measure the actual extent and severity of H1N1 infections, no doubt without efforts to prepare for the virus, their rearview mirror methods would eventually tell a much grimmer story.    

I'll defer to Mr. Miller, whose blog is loaded with interesting details.   

As anyone who does risk analysis is aware, the flip side of opportunity is risk, or maybe opportunity is the upside of risk.  They are always there together, the two sides of chance, but it's great to occasionally see the brighter side of the coin. 

In a report issued last month, KPMG emphasizes the need for comprehensive, strategic risk management across an organization. Entitled “The Business Case for a Risk Executive: Leading Efforts to Avoid Surprises, Maneuver through Challenges, and Add Value,” the report notes that most current risk management efforts are specific to particular departments, projects, or regulations, and do not approach risk from an enterprise level. This had led to critical oversights and missed opportunities.

To address this gap, KMPG recommends the appointment of a risk executive. This person’s dedicated purpose is “to help prepare the organization to respond to change and the risks that emerge in changing times, and to turn those efforts into opportunities that benefit the organization.” More specifically, such an executive would unify risk approaches across business units and departments, standardize reporting, and establish a common risk “language.” (Note: Risk modeling software and Monte Carlo techniques play central roles in this effort.)

Expounding on the importance of risk management experts, the report concludes, "Without a risk executive, risk management efforts will likely continue to lag and hamper the organization’s effort to recover. But with a risk executive owning the process, risk management can move beyond a support role and help enable the organization to realize its strategic goals and rebuild business value."

» Read the full report (PDF)

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