Decision Trees

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.)

In the last two blogs I have challenged the idea that simulation results can be boiled down to a single statistic with any positive benefit. The context of a statistic is incredibly important, which is another reason why many statistics and charts/tables should be reported on, not simply one figure. And here’s a compelling reason why.

Consider two competing, similarly-sized projects, of which a company can only pursue one. Now let’s say this company would like to take on the project that has the “least risk”. If they are only familiar with generating the P90 for the total project cost they will be forced to select the project with the lowest P90. But what if the key drivers for exceeding the P90 are easier to mitigate in one project compared to the other? Perhaps the project with the lower P90 also has a higher P95 or P99 – this means the catastrophic failure is actually greater despite a lower P90 and is the mathematical equivalent of “when things go bad, they go really bad”. Not all P90s are created equally! Such an adverse outcome might sink a smaller company where a larger one could wear the loss. The context of the company running the analysis also impacts the context of the analysis itself.

So you can see not only do simulations generate results with which informed decisions can only be made if approached holistically, but if the language used is restrictive this outcome will never be achieved. Risk analyses are a necessary part of business because most of us wish to minimise the chance that something bad will happen, quite simply. Even if a manager tells you they “want the P90” what they are really asking is “tell me about the risk we’re facing”. The answer to this fundamental question is not found in a single figure taken from a simulation, but in a range of charts and tables which require correct interpretation.

More so, Monte Carlo simulation itself is only one piece of the risk and decision assessment pie. Decision modelling and optimisation, predictive modelling and statistical analyses should also form part of the quantitative approach to uncertainty. There is life beyond just risk simulation software, and I intend on exploring that in future blogs.

» Part 1
» Part 2

Rishi Prabhakar
Trainer/Consultant

This Thursday, 11 March 2010 at 11am ET, Dr. Jose A. Briones, SpyroTek Performance Solutions, will present a free live webcast entitled, Use of @RISK for Probabilistic Decision Analysis of a Manufacturing Forecast in an Environment of High Uncertainty

Profitability projections in a manufacturing environment are directly tied to how the sales forecast fits with the capability of the operation. When a company has a large portfolio of products with very different operational production rates, the manufacturing capacity of the plant will be significantly impacted by the product mix to be produced. This in turn will have a radical effect on the output of the plant and the allocation of the fixed cost of production. In this case we present an example where a company is trying to decide how best to balance the sales of certain families of products to maximize revenue, maintain a diverse product line, and properly price each individual product based on the impact to the manufacturing schedule and fixed cost allocation.

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

The UK Met Office is not going to ‘live down’ its weather forecast of a ‘barbeque Summer and a mild Winter’ for 2009, anytime soon. There was ample rain through the Summer, the Cumbrian region saw severe flooding in November and now the nation is gripped by sub-zero temperatures not experienced for more than 30 years.

The inaccurate weather forecast is not a criticism of the Met Office. Forces of nature cannot be controlled, but these severe weather conditions do highlight the need for a more risk-led approach to public service planning. As we are seeing, the lack of planning to combat the current Arctic conditions engulfing the nation has thrown the country in turmoil, not to mention the substantial losses incurred by businesses. 

Global Warming is now often touted as the reason for such vagaries in weather, which according to environmentalists is set to intensify in the coming years. There is a very strong case for the government to undertake a scientific, risk-led approach to assess the potential effects of extreme weather, so that the required planning and realistic fund allocation can be made to deal with unforeseen weather situations. 

For instance, Halcrow Group Ltd, specialising in providing planning, design and management services for infrastructure development, works very closely with the UK Environment Agency on its Flood Defense programme. It conducts risk analysis on several of the Agency’s projects, using Palisade’s @RISK. Through flood risk management, the UK’s Environment Agency can reduce the probability of flooding from rivers and the sea through the management of land, river systems, and flood and coastal defenses. This also works to helps to reduce the damage floods can do through effective land use planning, flood warning and emergency responses.

There is now a dire need to extend this risk analysis-based approach beyond just flood defense, so that pre-emptive actions can be taken to reduce the adverse impact of extreme weather on the nation.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

Where I left off last time was lamenting the use of Monte Carlo simulation to create a single value (statistic etc.) from a model. It might still not be clear why this is anathema to me, so here goes.

A simulation is not a number. It’s not one possible (future) outcome – that’s a scenario. Monte Carlo simulation is a methodology for understanding one’s exposure to outcomes not situated close to the central tendency of the process/project in question. Note the plural “outcomes”. Risk analysis, when done properly, should let you know essentially all possible outcomes and how likely they are for your model. Output from a simulation can include a plot of means (over time), or P5s, or P95s, or the mean ± one standard deviation or any number of statistics. But that’s not plotting a simulation! Let’s not give a minimalist graph too much credit.

Such statements also perpetuate the idea that simulation is only used for creating means (or other centrally tending statistics) and ignores the wealth of information available. Risk simulation software exists to help you do risk analysis which must include not only several statistics but also sensitivity information. It is all too easy to turn a risk assessment into a hunt for a regularly asked for percentile (such as the P90) and there ends the task. I see this a lot, especially in project cost estimation where the pressure both from management and regulatory bodies is to accurately estimate some large percentile. Once found there is usually scant further risk analysis.

Nothing good ensues. When risk analyses are run “to get ‘the’ number” they become simply another box to tick in a process and ultimately any benefits (perceived or actual) will be forgotten and lost to the ages. The notion of context is also lost. No single number by itself really means anything, or at least shouldn’t mean anything to a decision maker. I have often heard phrases like “the model returned/gave $1.2m” followed by an audience nodding in agreement. Huh? Which statistic are you talking about there, and how about reporting a few other numbers around it to place that $1.2m somewhere meaningful?

In the next installment I will look further into this issue of context and hopefully prove the necessity of an holistic approach to understanding and reporting simulation results.

» Part 1

Rishi Prabhakar
Trainer/Consultant

Both the Conservative Party and the Labour Government have indicated that they will raise the state pensions age of men and women to help reduce the UK’s national debt.  In addition, more and more employers in the private sector are closing good pension schemes. The Association of Consulting Actuaries’ (ACA) recent survey on pension trends has revealed that 59% of employers are set to review pensions ahead of 2012 and 24% of employers will consider pension benefit reductions when they have to auto-enroll all employees into a scheme.

With taxes on business and individuals likely to rise over the next few years, it is difficult to see anything other than a deteriorating climate for pension savings unless there is a radical change of approach, says the ACA. It has proposed a standing Pension Commission that will challenge the legal and regulatory hurdles standing in the way of sensible long-term pension designs.

Perhaps, a more in-depth risk analysis may help the ACA make a stronger case to the government. As a related example, in the US, the Society of Actuaries and the Casualty Actuary Society, sponsored a research project with the Illinois State University to develop a model for projecting economic indices such as interest rates, equity price levels, inflation rates, unemployment rates, and real estate price levels. The model was created using Palisade’s @RISK and Microsoft Excel. In fact, @RISK’s built-in probability distribution functions, correlation matrices, and simulation results were essential to the study.

The UK ‘pensions’ landscape is set to undergo tremendous change, which will impact each and every one of us. Using scientific, risk analysis techniques, actuarial industry bodies can develop a strong argument and lobby the government so that informed policy decisions are made that are right for both the financial health of the nation and its citizens.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

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

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

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

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

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

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

On Thursday, February 18, 2010, Prakash Shrivastava will present a free live webcast entitled. "Cost-Effectiveness Analysis of Patient Care using The DecisionTools Suite"

Cost-effectiveness analysis is often used to evaluate effectiveness of medical interventions and is one of the main topics in healthcare research. This analysis requires data evaluation, including building and testing decision analysis models. This free live webcast will illustrate how such models are easily built using the DecisionTools Suite. The first part of the webcast will introduce process, cost drivers and measures in healthcare. In the second part, cost-analysis examples will be presented. The webcast will conclude with a sensitivity analysis example.

Prakash Shrivastava is a Principal at Strategic Management International, Inc. He specializes in Research, Analysis and Simulation of Control Systems, Business Models and Processes. He worked in Automotive Industry for over 22 years and Aerospace Industry for 7 years. He holds a Masters Degree from Indian Institute of Technology, Bombay, a Doctoral degree from Princeton University, NJ and a Masters degree in Management of Technology from RPI.

» Register now (FREE)
» View archived webcasts

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

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)