Operation Research

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

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.

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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

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.

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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

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.

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New measures were published by the UK Government in November 2009 to protect communities at risk from ‘flash’ flooding when drains get overwhelmed by sudden downpours.  The Flood & Water Management Bill going before Parliament will give new powers to local authorities to manage surface water.  Environment Secretary Hilary Benn said, “We’ll see more severe weather as climate change takes hold, with heavier rainfall and potential flooding. The weather in the last couple of weeks has shown that this risk is very real.” 

Recent events in Cumbria in the UK suggest climate change really should be at the top of the agenda, and this new bill will certainly go some way to managing the risk associated with it.

Risk analysis has its place in determining the risk associated with climate change, and Palisade's risk modeling software has helped numerous organizations develop reports on likely outcomes. Cambridge University’s Judge Business School recently used @RISK to provide key input to the Stern Review on the Economics of Climate Change. Released in 2006, this report undertaken by the British government by Lord Stern discusses the effect of climate change and global warming on the world economy and is the largest and most widely referenced report of its kind.

They researched issues such as the impacts of the sea level rising and increases in temperature making land infertile or unfarmable, and balanced these against the costs of various options available to tackle global warming.  

At one end of the scale, doing nothing costs nothing, but the environmental consequences will be high. However, activity that reduces the severity of the impacts may itself be very expensive. The aim of the @RISK model was to enable people to make informed decisions on the optimum way to deal with climate change (ie how much to cut back on damaging activity and what methods to use).

@RISK risk simulation software also helped researchers tackle a key problem associated with investigating climate change, namely that the different effects of the various factors which influence it are themselves, undetermined. For example, the historical evidence does not pin down exactly how much global temperatures will increase if CO2 emissions double. @RISK enabled researchers to quantify this uncertainty in order that they have a measurement of the accuracy of their findings.

Risk analysis models should go some way to determining outcomes, and potentially help us prepare for the tragic events that have unfolded in recent days in the UK.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

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.

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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

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.    

The recent turbulence in the global economy has projected the word ‘risk’ into many everyday conversations, both commercial and personal: the unacceptable risks taken by fund managers which led to the collapse of major financial institutions; companies risking bankruptcy as a result of recession; the risk of people losing their jobs – and potentially their homes; and so on. 

As a result there is also increased talk of risk analysis, which in turn has brought disciplines known as ‘quantitative risk management’ (QRM) and ‘decision making under uncertainty’ (DMU) firmly into the business zeitgeist.  But for many small to mid-size companies, QRM and DMU are still regarded as something of a dark art and one that is not relevant to their day-to-day activities.

The truth is, that in boom or recession businesses make decisions every day – each with an associated level of risk.  Much of this decision making is undertaken by looking into the issues facing a business, putting some numbers on them to calculate their impact, and then mitigating or allowing sufficiency contingency in the event that things go wrong.

Examining business decision-making in detail shows us that most businesses could benefit from making the link to risk analysis, and from there taking a more strategic approach to the discipline. Cost estimation, budgeting, cash flow forecasting, operational risk assessments, sales forecasting – in fact any part of a business where there is uncertainty can all be made more robust and meaningful.

Recession has brought the idea of QRM to the forefront of business owners’ minds.  Essentially it is a valuable aide to making better, more informed decisions where the amount of uncertainty on which they are based is known. 

Risk analysis is no longer a dark art, but in today’s economic climate, is an essential part of the business decision-making process, no matter what size the organisation.

In my next blog we’ll look what technology is available today that will help businesses making better decisions now and in the future.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

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

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

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

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

It's a deadly game of chance, the perfect venue for Monte Carlo to meet Monte Carlo.