Are solar panels a sound investment? A risk analysis case study

Friday, August 27, 2010 by DMUU Training Team
The UK's new coalition government has said that, as part of its 'Green Deal', it will encourage home energy efficiency improvements paid for by savings from energy bills. It seems likely that, in the year that energy regulator Ofgem warned of 20 percent electricity price hikes by 2020, this initiative will include solar panel technology

Currently the UK still lags behind many other countries in Europe and the rest of the world when it comes to harnessing solar power. Not only do we have less hours of sunshine than many regions, but there is a lack of clarity as to the 'payback' time when it comes to users seeing a return on investment.

This is where Palisade customer, the California-based Tioga Energy, makes an interesting case study. Whilst it may seem unfair to compare the UK with the west coast of America when talking about solar-related matters, the sunnier climate does not reduce the need to prove ROI for customers with solar energy agreements.

Tioga Energy provides project financing through its solar Power Purchase Agreements (PPAs), and maintains and operates solar systems on behalf of its customers. Tioga’s offering delivers predictably priced power and enables organisations to to both 'green' their operations and reduce energy costs. To illustrate the benefits of solar, estimating future electricity prices and making comparisons by showing the savings from a new solar system, Tioga enlisted the help of @RISK for risk analysis solutions.

To forecast possible price increases, Tioga Energy inputs California's historical electricity rate data into a quantitative risk analysis model developed using @RISK. This generates a probability distribution for electricity rate rises over the 20-year PPA period, which shows that there is a 25 percent likelihood that price increases will be less than 4.8 percent, and a 25 percent chance that rate rises would be more than 8.7 percent.

The @RISK risk analysis model therefore helps Tioga Energy evaluate the likelihood that a customer will save money for a variety of PPA scenarios (i.e. the rate at which electricity would initially be charged and the amount by which it would then increase each year). It also calculates the magnitude of savings for the different combinations of first year costs and subsequent rises. Consumers are therefore able to better understand the pricing and make an informed decision about whether to sign up for a PPA.

Using historical data and @RISK's risk modelling software capacity, Tioga offers consumers a robust view of the potential benefits of a solar PPA. This enables them to hedge against rising electricity rates, as well as feel confident that they are playing a part in tackling global warming.

» Read the Tioga Energy case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis
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Introduction, by Way of Retraction

Friday, July 9, 2010 by Holly Bailey
Just after I posted my last blog questioning a recent Investopedia column in the San Francisco Chronicle, I had a congenial note from the author of that column, David Harper.  His column compared Monte Carlo Simulation with two other methods of calculating Value-at-Risk, and I was concerned that its view of risk and risk analysis techniques was overly simplified. David   was surprised to discover that column had just appeared because he wrote it five years ago!

The five-year lag explains a lot--Monte Carlo simulation was not nearly so widely adopted or carried about by so many software tools as it is today--and I should have suspected the article was a vintage piece before I started carping.

So I happily retract my concerns to introduce to you David Harper, CPA and certified Financial Risk Manager.  In response to my comment about the attitudes and techniques that led to last year's collapse of the financial markets, David says that, now that the black swan has flown, "the crisis should implicate both HistoricalSim VaR and parametric VaR (at least multivariate normal!) and point toward Monte Carlo Sim. I've been thinking for a while that all of this [I think he means lack of accuracy in specifying risk] should really boost Monte Carlo."

Investment commentary is only one of David's activities.  He is the founder of Bionic Turtle, a business devoted to e-learning about financial risk and preparation for the certification exam for financial risk managers. This is a worthy enterprise--I was relieved to discover that there are hoops financial risk managers have to got through to be called that--and for anyone who would like to know more about quantitative techniques for risk analysis, its website is worth prowling. 

Thank you, David, for setting me straight.  
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Health Care Management: Decision Making at Two Levels

Tuesday, June 1, 2010 by Holly Bailey
Reading recent reviews of two books on healthcare caused me to realize that in spite of the rapidly increasing number of clinical studies that use risk analysis and neural networks to sort out the best treatment choices, there has been very little published on how to use quantitative tools like decision trees and Monte Carlo software to manage health care better. Given the recent national debates on health care reform, this is actually quite surprising. 
 
There's health care management, and then there's health care management.  On the macro level, decision evaluation focuses on the organization. Marian C. Jennings's Health Care Strategy for Uncertain Times (2000) prescribes ways for corporate health care managers to reshape the ways their organizations deal with uncertainty by adopting the same quantitative techniques used in the commercial realm by enterprises like investment firms and utility companies.  On the micro level, health care management focuses on you, your body. Thomas Goetz's The Decision Tree (2010) prescribes how to apply a number of these same decision analysis techniques to your own health. 
 
Essentially, what both books are saying is, "Look, the only certainty is uncertainty.  But you have some numbers.  Here are the tools to turn those numbers into plans you can reasonably rely on." These tools shouldn't be news to you as a reader of this blog, but apparently, if the popularity of Goetz's book and renewed attention to Jennings's are any indication at all, the health care management arena is plenty ripe for quantitative decision support tools.
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June 2010 - Worldwide Training Schedule

Tuesday, May 11, 2010 by 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
London
Brasil
Latin-America
Asia-Pacific
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Put More Science into Cost Risk Analysis

Tuesday, May 4, 2010 by DMUU Training Team
At the 2010 Palisade Risk Conference in London, John Zhao of Statoil used a mock cost estimate contingency model to demonstrate how @RISK simulation functions can yield a more realistic project contingency through integrated qualitative risk assessment and quantitative risk analysis.

While future oil prices may be hard to predict due to low manageability, it is absolutely possible to scientifically forecast the sizes of risks that companies are willing to take, and such risks may include the probabilistic volumes of newly discovered reserves, the probability of meeting a project development schedule, chances of project cost overruns, and the likelihood of eroding entire project profitability. To achieve these goals, @RISK has lent a helping hand to business analysts for easier operation of complicated mathematical modelling.

Statoil, an international oil company, takes risk management seriously and has applied Monte Carlo simulation techniques in core and support businesses using @RISK. Such applications not only include the solo use of individual applications, but integrated combinations from drilling, reserve estimation, and well completion to cost and schedule controls at project execution. Besides the widespread uses of the software, Zhao discussed a specific application of @RISK to convincingly simulate required capital project contingency  in detail.

A simplistic line-item ranging exercise using @RISK Monte Carlo simulation is no longer adequate to derive large capital project contingency, as empirical data confirmed that many disastrous cost overrunning projects were lack of contingency to cover the covert risks. In order to show management a complete risk picture on a project, both systemic risks (which empirical history has indicated a likelihood of occurring), and specific risks (which have discrete probabilistic characteristics), should be included in the overall project risk analysis. Therefore the combination of continuous PDF for project cost estimates, and discrete PDF for project risk registers, may prevail and provide management with a more convincing project cost contingency.

John Zhao is Quality and Risk Manager at StatoilHydro Canada Limited. He has 22 years project management experience in the petrochemical industry. He has authored many papers and made numerous presentations worldwide on the subject of risk and contingency management. In the past 10 years, John has developed his expertise in cost engineering and risk analysis for large downstream and oilsands upstream projects across Canada. His extensive knowledge in construction project qualitative risk assessment process has made him an expert on the subject in North America; his proprietary Monte Carlo model using @RISK is a popular tool for project contingency and escalation simulation. The quantitative model that John has built has integrated @RISK with PrecisionTree to help corporations conduct risk-based strategic decision-making.

» View the complete abstract and PDF presentation of "Put More Science into Cost Risk Analysis"
» Read Zhao's whitepaper, "Put More Science into Quantitative Risk Analysis"

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Free Webcast this Thursday: Targeted Analyses and Compelling Communication: A Formula for Successful Value Creation in Management Science

Monday, May 3, 2010 by DMUU Training Team
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 free live webcast describes steps we as quantitative scientists can take to foster understanding, generate novel insights and stimulate actionable results with our clients, as well as demonstrates some of the tools we use - including @RISK.

» Register now (FREE) 
» View archived webcasts
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Robust Risk Analysis for the Time/Expertise Poor – Part 1

Tuesday, April 13, 2010 by DMUU Training Team
I have recently spoken to several clients whom have all came to the same conclusion about the risk analysis solution they think is most appropriate. They don’t want to do it, and I have no problem with that!

Of course that’s not precisely true. The benefits of Monte Carlo techniques in risk analysis are quite well understood and there is plenty of buy-in from businesses in the Australasian region. The trouble these businesses face (particularly in the realm of project cost estimation) is that the specific process of quantifying their risks for stochastic analysis and the ensuing simulation is not well understood and the means to ameliorate this appears to be beyond their reach. The modelling and simulation components of the project risk management process are not given adequate resources to be performed well, and certainly not to the extent that they provide the most useful information.

It is the case that many companies do not employ dedicated quantitative analysts. This means they have to rely upon some (maybe one) person in the team who has a non-zero quantity of experience and possibly training with risk simulation software to create a valid and credible stochastic model. This person is also not likely to be given enough time to do said task, thus the model inevitably suffers. It is my experience that most models – and all project cost estimation models – can be improved or actually need to be fixed.

So the corporate mind is willing, but the flesh is weak. How can this be addressed? No amount of additional training will suddenly allow you to overcome your time and resource constraints. Perhaps you can’t get the budget for training anyway or don’t want to master risk analysis software when it’s not really core to your role? The solution is one that I personally endorse (and provide!) as a risk analysis consultant – custom Excel programming.

VBA for Excel is a fairly simple language to learn, yet very powerful tool for automating repetitive or sometimes complex spreadsheet tasks. A customised solution involves writing VBA code to perform the tasks we’d rather not do ourselves in the risk analysis model. The “we” here refers to companies that find themselves in the situations previously described whereby they are incapable of creating and operating these models, not necessarily though any fault of their own. In my next blog I’ll examine some modelling problems/requirements and how they might be dealt with effectively using customisation.

Rishi Prabhakar
Trainer/Consultant
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New Approaches to Risk and Decision Analysis

Wednesday, March 17, 2010 by DMUU Training Team


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


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Quantitative risk assessment under utilised for infrastructure projects

Friday, March 12, 2010 by DMUU Training Team
Why is it that most of the high profile projects managed by the government in the UK all ultimately become beset by problems? A number of projects jump to mind – the Millennium Dome, Wembley Stadium and currently the NHS IT. All three have been plagued by developmental delays and financial mismanagement.

Recently, yet another worthy, but ambitious project has been announced – the North-South high speed rail line to connect London to Scotland. One wonders if the government undertakes detailed quantitative project risk analysis for its infrastructure initiatives?

A good example to highlight in this context is ENGCOMP, a Saskatchewan-based engineering consulting firm that has worked with the Canadian Department of National Defence (DND) to help define budgets for the fourth phase of construction of its Fleet Maintenance Facility at Canadian Forces Base Esquimalt in Victoria, British Columbia. Using @RISK, a Monte Carlo simulation tool, ENGCOMP helped the DND define and secure budget approval from the Federal Government’s Treasury Board. The consultancy firm was able to estimate the impact of the variability and uncertainties pertaining to risks, costs and scheduling. This assessment enabled it to estimate the project risk budget or the risk reserve and schedule contingency, which were both factored in when defining the total project cost of the infrastructure project.

The fact is, in the world of business, risk is inherent and unavoidable. Whilst one cannot completely control risk, one can certainly help reduce uncertainty, greatly increasing the chances of project success. For instance, a key finding of the project risk analysis conducted by ENGCOMP was that, taking into account all the risk and uncertainties on the project, there is an 85 per cent chance that the Fleet Maintenance Facility project will be completed in January 2014. A fairly positive result for the DND, given the scale and complexity of this project in question.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis
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What Should You Get From a Simulation? Part 3

Tuesday, March 9, 2010 by DMUU Training Team
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
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The role of software in risk management

Thursday, January 7, 2010 by DMUU Training Team
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
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Adopting a healthy approach to risk

Tuesday, December 29, 2009 by DMUU Training Team
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
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Making Risk & Decision analysis accessible to all

Friday, December 18, 2009 by DMUU Training Team
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
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Risk & Decision analysis – it’s not a dark art

Wednesday, December 16, 2009 by DMUU Training Team
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
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Free Webcast This Thursday: “Integrated Project Risk Analysis - Structuring the Model Effectively”

Monday, November 30, 2009 by DMUU Training Team
On Thursday, December 3, 11am-Noon ET, Jay O’Connor will present a free Live Webcast about project risk management.

A project risk analysis is only as good as the model that was used to prepare it. It is critical that the model be constructed to reflect the risks specifically associated with the project. The model must be able to accurately reflect the risks associated with schedule, quantities, cost and the residual unmitigated risk items from the qualitative risk analysis. The model should also take into account the interrelationships and dependencies of these items.
This webcast will address these issues and present examples of how results can vary based on the level of detail used in preparing the risk analysis, and will include the use of @RISK, and @RISK for Project.

Palisade is pleased to host Jay O’Connor’s presentation. With over 25 years of experience in the areas of estimating, planning and quantitative risk analysis for international projects, Jay understands the complexities that are associated with identifying and assessing project risks. His experience includes both the owner’s and contractor’s side of engineering and construction projects. He has worked in the upstream and downstream oil and gas industry sectors and the pulp and paper sector. His career has taken him to the United Kingdom, Japan, Indonesia, Malaysia, Singapore and Australia.

» Register now (FREE)  
» View archived webcasts

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Six Sigma, Monte Carlo Simulation, and Kaizen for Outsourcing

Friday, November 6, 2009 by Steve Hunt

I recently tripped over a very good and interesting article written by Marcia Gulesian, titled Six Sigma, Monte Carlo Simulation and Kaizen for Outsourcing.

Despite its seemingly complex title, the article touches on the basics of Six Sigma and decision analysis where Six Sigma basic quantitative calculations are discussed - such as process capability calculations (Cp, Cpk) are used in an example for the outsourcing of a critical component. The example utilizes a Monte Carlo Simulation a model to illustrate her point.

The example model simulates the outsourcing of a critical component to 3 different vendors, and demonstrates the critical information that a Monte Carlo Simulation model can provide to make informed decisions regarding cost, volumes, supplier capabilities and internal resources. As we all know, having multiple vendors is necessary, but knowing how to distribute your demand across them and knowing the risks and costs involved is critical.

If you would like to experiment with the model, you can download it,  But please know you'll need @RISK to run it, you download the @RISK free trial to run the simulation.

The over-arching topic of the article is that any process can be scrutinized for variation and cost reduction, and in my opinion should be. Companies will continue to outsource more and more so that they may focus on their core competencies. But as this happens, it becomes more imperative that a sound strategy is used to manage the potential outcomes.

I’m very happy to have found this article. Maria obviously understands the power and value of Six Sigma and Monte Carlo Simulation and look foward to future articles from her. 
 

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

Monday, October 19, 2009 by DMUU Training Team
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.
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The Scientific Method for Management

Wednesday, January 21, 2009 by DMUU Training Team
The scientific method for management is growing in popularity because it allows for organizational decisions—whether by business or government—to be formulated under more rigorous considerations. The quantitative approach to risk and decision making, with tools such as Palisade’s DecisionTools Suite, is one method for making management decisions with the aid of data and science.

In contrast, traditional ad hoc methods, whether for day-to-day operations management or  monumental management decisions, are being exposed as amateur approaches. We’ve seen so many companies, governments, and economies in trouble as artificial pillars of value have crumbled. Risk assessment was devalued, ignored, or applied haphazardly. 

The challenge for accurate decision making remains. In the presence of uncertainty and unknowns (lack of information), the scientific method for management decision-making allows for more defendable decisions. Sensitivity and scenario analyses in the context of probabilistically-defined Monte Carlo models can be applied to paint a picture of where an organization sits in an apparent asteroid field of risk.

The practice of quantitative risk management and decision making has been widely adopted in the U.S., the U.K. and many parts of the western world, and now the Chinese government has planted the seed. We’re seeing examples of the “Scientific Method for Management” in leading companies such as CNPC and Sinopec as a new wave of protocol in China. Statistical software that is able to help with decision making under uncertainty will play a large part in helping to implement better decision making in the future.

Mark Meurisse
Managing Director, Palisade Asia-Pacific Pty Ltd.
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Lack of Risk Management Cited as Key Cause of Credit Crunch

Monday, January 12, 2009 by DMUU Training Team

In a recent policy paper, the Association of Chartered Certified Accountants (ACCA) named the “failure of institutions to appreciate and manage the inter-connection between the risks inherent in their business activities” as a key factor that led to the credit crisis. The paper goes on to list the lack of influence of risk management departments and weakness in risk reporting as additional primary factors. According to the ACCA, “[Senior managers] did not understand the risks and were using risk assessment with tools which were inappropriate.”

As the global recession deepens, the ACCA paper underscores the growing emphasis on risk analysis in financial institutions and all businesses. @RISK and DecisionTools Suite software is specifically designed for risk analysis using Monte Carlo simulation and other techniques which can show virtually all possible outcomes, however unlikely. In today’s current economic climate, objective quantitative analysis of all risks cannot be overlooked.

» Read the policy paper "Climbing out of the Credit Crunch"
» Learn more about the DecisionTools Suite
» Learn more about risk analysis

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Read This While You Can Still Access It

Thursday, January 8, 2009 by Holly Bailey

The best-laid plans are. . . .subject to change.  An article by Joe Nocera in this week's New York Times Magazine causes me to put on the back burner my plans to blog on the concept of  probability and its various expressions.  I can do that later, but right now I want to persuade you to read Nocera.

Offering a really good capsule history of Value-at-Risk modeling for the uninitiated, Nocera delves into a theme that has pervaded my recent blogs for Palisade Corporation: it may not be the model but more likely the person managing the modeling who introduces slop into risk analysis.  He has talked to a good many risk management experts, and is able to present a balanced view of both the limitations of VaR techniques and the shortcomings of the people who relied so heavily on their risk assessment techniques as to bring about the collapse of those sectors of the financial markets that depend on hedging and mortgages.

One thing that will be a relief to any of you who are doing quantitative risk assessment, Nocera never points a finger at Monte Carlo software or any other category of quantitative analysis software.  So, the problem isn't the tools.  It may be the---

I don't want to spoil this excellent article for you.

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