Retirement Planning

Free Webcast This Thursday: The Use of the DecisionTools Suite in Biotechnology Project and Portfolio Decision Making

Monday, August 30, 2010 by DMUU Training Team

Vertex Pharmaceuticals, Inc. is a global biotechnology company based out of Cambridge, MA. The Company's strategy is to commercialize its products both independently and in collaboration with major pharmaceutical companies. Vertex's product pipeline is focused on viral diseases, cystic fibrosis, inflammation, autoimmune diseases, cancer, and pain.

Given the uncertainty of outcomes in the biotech industry, consideration of variability is an inherent part of the decision process. Often, the mean (average) is not a relevant decision criteria. This is especially true for smaller biotech companies like Vertex – the opportunity costs are extremely high because scarce capital resources would be invested elsewhere, with a higher probability of realistic return. For example, a company may reject a project which is profitable on average (positive Net Present Value) because some of the possible outcomes are unacceptable to the decision maker. Consideration of variability allows a decision maker to bring in their own risk tolerance into the decision. A similar argument applies when estimating a safety margin above a base case (e.g. in cost budgeting).

Vertex’s strategy and analytics group within the corporate finance division seeks to provide the senior management with dynamic revenue and profit forecasting methodology that helps to identify types of drugs that should be developed given a finite amount of cash and resources. A traditional financial view allows the user to identify scenarios and potential outcomes, but lacks the ability to show the range of potential values within each and every outcome. Vertex’s team uses the DecisonTools Suite to establish the average outcome, the variability of outcomes and to pressure-test risk and uncertainty of a particular scenario throughout the decision process.

Vertex’s team built a complex financial risk analysis model using @RISK to enhance its portfolio process. Monte Carlo simulation and optimization are used to analyze and optimize project and portfolio decisions, given short and long-term corporate strategy. @RISK is also frequently used throughout the business development process: simulating across multiple sales forecasts provides BD team with a range of potential outcomes, making it easy to pinpoint a particular scenario on a curve, along with its probability and value. TopRank turns the sensitivity analysis into a quick and seamless exercise, answering multiple what-if questions within minutes. Franchise and program leaders can now see a dollar effect of their program being delayed or advanced, adding supplementary indications to the development plan and even addressing the price uncertainties all at the same time. The simple interface of PrecisionTree along with tornado chart outputs makes it easy to explain the effect and importance of a particular assumption / decision to an audience with no finance background.

As the company continues to grow, adding more drugs and collaborations to its development pipeline, we will see in this free live webcast how the DecisionsTools Suite remains one of Vertex’s analytical tools of choice to enhance and guide the decision making process.

» Register now (FREE)
» View archived webcasts

Oops! Didn’t see that coming! Part 4

Monday, August 9, 2010 by Steve Hunt

This is the conclusion of Dave Roy’s guest blog, we hope you have found them informative. Again, Dave comes to us from SSPI, Six Sigma Professionals, Inc., and taught Jack Welch and his entire staff their Six Sigma Green Belt training. Also, look for Dave’s free live webcast on August 19^th, Assessing your New Product, Process or Service Introduction Methodology: Is yours premier? Does it enable Six Sigma performance?

Oops! Didn’t see that coming! Part 4

As a continuation from the July blog, we are now concluding with the “Optimize” and “Validate” phases of the ICOV (Identify-Conceptualize-Optimize-Validate) framework of a rigorous new design process as explained in “Services Design for Six Sigma – A Roadmap for Excellence”.

These phases are important because it allows time and methodology to optimize the design, develop all of the detailed documentation, verify performance and capability under operating conditions and manage an orderly transition to the new state.

The Optimize phase consists of a single stage (Design Optimization) and associated Tollgate 5 to validate successful completion of the requirements.

The Design Optimization stage involves completing all of the detailed design documentation, building Prototypes of the design, simulating/analyzing Process Capability, preparing all Control Plans and updating the Design and Process Scorecards.

Tollgate 5 Exit Criteria:

o Agreement that functionality and performance meet the customers’ and business requirements under the intended operating conditions.

o Approval to proceed with the Validate stage.

Formal tools which can be used in this phase are Design Scorecard, Process Management, Mistake Proofing, Simulation, Change Management, Control Plans, Reliability and Robustness.

The Validate phase consists of two stages (Verification and Launch Readiness) and associated Tollgates (6 and 7) to validate successful completion of the requirements.

The Verification stage involves developing Pilot plans, Piloting the new design and process and analyzing and making adjustments to achieve the desired functionality and performance under operating conditions.

Tollgate 6 Exit Criteria:

o Agreement that functionality and performance from the pilot meet the customers’ and business requirements under the intended operating conditions.

o Approval to proceed with the Launch Readiness stage.

Formal tools which can be used in this phase are Design Scorecard, Process Management, Mistake Proofing, Change Management, Control Plans, Statistical Process Control (SPC), and Confidence Analysis.

The Launch Readiness stage involves developing Pilot plans, Piloting the new design and process and analyzing and making adjustments to achieve the desired functionality and performance under operating conditions.

Tollgate 7 Exit Criteria:

o Agreement that transition plans and training plans have been developed and are executable.

o Approval to proceed with the Production stage.

Formal tools which can be used in this phase are Transition Plans, Training Plans, Process management, Change Management and Control Plans.

Following the ICOV model we have now used a formal methodology that allows us to validate performance at progressive economical stages and have improved the ability to detect unknown risks thus avoiding the Oops! Didn’t see that coming!. It should be mentioned that the methodology should be flexible and scalable to adjust for level of invention and risk. A brand new invention (Research & Development) that has never been deployed in similar conditions is much different than implementing a known solution (Application Engineering) under new conditions.

» Part 1
» Part 2
» Part 3

BIO:

David Roy is an integral part of the Six Sigma community. He taught GE’s Jack Welch and entire staff Six Sigma, as well as served as Senior Vice President of Textron Six Sigma. He is a Certified GE Master Black Belt, was instrumental in developing GE’s DMADV (DFSS) methodology, and has taught 3 waves of DFSS Black Belts. David holds an BS in Mechanical Engineering from The University of New Hampshire. He is also the co-author “Services Design for Six Sigma – A Roadmap for Excellence”

Tackling the energy crisis by managing demand, using risk modeling software

Thursday, July 29, 2010 by DMUU Training Team

One of the side-effects of the recession appeared to be a reduction in the demand for electricity as businesses and consumers alike looked to make savings on their outgoings. However, economic recovery seems to render this trend as temporary, meaning that the global need to tackle energy-consumption is as pressing as ever.

BC Hydro, Canada's third largest electrical utility provides an interesting case study in how to ascertain the most effective ways to tackle the gap between supply and demand of electricity in British Columbia. Trends such as an expanding population growth, and the increase in energy usage per customer, have led to a rise in the demand for electricity across the region. By legislation, BC Hydro must aim to meet these energy needs through implementing cost-effective energy conservation approaches before it can turn to increasing the supply.

The company has set itself one of the most aggressive targets in North America, with a plan to meet almost 75 percent of its incremental load through Demand Side Management (DSM) over the next 20 years. DSM projects include compact fluorescent light promotions; subsidies for energy efficient appliances; variable speed motor promotions (for home furnaces); and promotional activity aimed at motivating customers to use less energy.

BC Hydro uses @RISK risk analysis software to measure the uncertainty around its energy conservation efforts, both at the project stage and at a higher portfolio level. Around 60 projects were analysed on a case-by-case basis, and a probability distribution around the forecast outcome was developed. @RISK helps BC Hydro to capture the level of uncertainty of the estimated savings for each individual DSM venture.

In recognition that projects do not operate in isolation, BC Hydro also uses @RISK to explore the interrelationships between key uncertainties: the participation and savings per participant, and the participation across projects. The analysis showed that if a 'conservation culture' was developed in the province, it would result in an increase in energy savings across all programmes. However, it also illustrated that, if this culture failed to materialise, the performance of all programmes will be dragged down.

Exploring uncertainty using @RISK allowed BC Hydro to find the best balance between the uncertainties of supply side resources and those of relying heavily on energy conservation. Employing Decision Making Under Uncertainty helps BC Hydro to meet both financial risk analysis and environmental risk analysis goals. As a result, it expects to meet the majority of its incremental load growth through conservation measures.

» Read the BC Hydro case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

Oops! Didn’t see that coming! Part 3

Monday, July 19, 2010 by Steve Hunt

We are pleased to welcome back to my blog consultant and trainer David Roy from Six Sigma Professionals, Inc.

Oops! Didn’t see that coming! Part 3

As a continuation from the June blog, we are now covering the “Conceptualize” phase of the ICOV framework of a rigorous new design process as explained in “Services Design for Six Sigma – A Roadmap for Excellence”.

This phase is important because it conceives, evaluates and selects good design solutions through robust process methodology which ensures alignment to the customer and the business needs.

Many design solutions skip this phase and become typically named as “Launch and Learn”.

The Conceptualize phase consists of two stages and associated Tollgates to validate successful completion of the requirements.

The Concept Development stage involves translating Customer requirements into solution free Functional requirements, developing the System Level Conceptual Design, generating Concepts for required functions, Concept selection and translation of the Functional Requirements to Design Parameters.

An example of a Functional Requirement for a Customer Want of “Speedy Service” could be “Speed of Service” and a Design Parameter could be “Waiting Time”

Tollgate 3 Exit Criteria:

Assessment that the Conceptual Development Plan & Cost will satisfy the customer base
A Decision the design represents an economic opportunity (if appropriate)
Verification adequate funding will be available to perform Preliminary Design
Identification of the Tollgate Keeper & the appropriate staff
An action plan to continue flow-down of the design Functional Requirements

The Preliminary Design stage involves creating the design documentation and configuration management, performing design analysis and testing, translating the Design Parameters into Process Variables and formulating the Production strategy.

An example of further mapping the Design Parameter of “Waiting Time” to a Process Variable could be “Number of Phone Lines”

Tollgate 4 Exit Criteria:

Acceptance of the selected Solution/Design
Agreement the Design is likely to satisfy all Design Requirements
Agreement to proceed with the next stage of the selected Solution/Design
An action plan to finish the flow-down of the design Functional Requirements to design parameters and process variables

Formal tools which can be used in this phase are QFD, TRIZ/Axiomatic design, Measurement System Analysis (MSA), Failure Mode effect Analysis (FMEA), Design scorecard, Process mapping, Process management, Pugh Concept Selection, Robust Design, Design Scorecards, Design for X and Design reviews.

The next and final blog will cover the Optimize and Validate phases.

BIO:

David Roy is an integral part of the Six Sigma community. He taught GE’s Jack Welch and entire staff Six Sigma, as well as served as Senior Vice President of Textron Six Sigma. He is a Certified GE Master Black Belt, was instrumental in developing GE’s DMADV (DFSS) methodology, and has taught 3 waves of DFSS Black Belts. David holds a BS in Mechanical Engineering from The University of New Hampshire. He is also the co-author “Services Design for Six Sigma – A Roadmap for Excellence”

» Part 1
» Part 2

Ensuring water supply when the heavens rarely open, using risk simulation software

Wednesday, July 7, 2010 by DMUU Training Team

The UK finally seems to be heading into summer after an unusually long and cold winter. However, despite the amount of rain that falls on our 'green and pleasant land' (to the extent of major flooding on occasions), one of the anomalies of the UK weather system is that any prolonged warm period seems to be accompanied by the underlying threat of a hosepipe ban.

This is in stark contrast to many regions around the world that, despite seeing far less precipitation, manage a robust water supply. Abu Dhabi for example has no rain for several months of the year, and relies almost completely on desalinated seawater for its potable water requirements. The desalination process is challenging in terms of operation, costs, and environmental impact. Whilst over-production capacity is expensive, at the other end of the scale it is essential that Abu Dhabi has sufficient water production capacity to support the Abu Dhabi government development plan (Abu Dhabi Plan 2030).

This plan means that the Abu Dhabi Water & Electricity Company (ADWEC) is required by the Regulation and Supervision Bureau (RSB), the regulatory body of Abu Dhabi, to use a risk-based methodology to assess the water demand and required capacity. As a result ADWEC uses @RISK risk analysis software to help it to forecast as accurately as possible the demand for water and electricity across the Emirate in order to plan for the optimum expansion as well as the efficient and effective use of water production plants.

@RISK enables ADWEC to model all feasible uncertainties in the variables that determine the quantity of water required over specific timescales, such as per capita water consumption rates and the rate of population growth. The variables input into the @RISK risk simulation software model are based on the water demand categories such as domestic, agricultural and industrial. Factors with inherent uncertainties that affect the demand forecast outcome and must be modelled include: seasonal variation, distillers' unplanned outages, water losses, population growth rates and demand for housing.

By undertaking risk analysis of the variables involved in assessing demand and supply, ADWEC minimises the potential for water production capacity to be over or under deployed. As a result of using @RISK to assist with its forecasting, planning and management strategies, ADWEC has been able to consistently meet with almost complete accuracy the Abu Dhabi Emirate water demand forecasts.

A useful lesson...

» Read the full ADWEC case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

Oops! Didn’t see that coming! Part 2

Tuesday, June 15, 2010 by Steve Hunt

Guest blogger David Roy Six Sigma Professionals, Inc., and taught Jack Welch and his entire staff their Six Sigma Green Belt training. Dave also has a quick survey for your input on structuring DFSS training. brings us the second installment of his four-part blog. Dave comes to us from SSPI,

--Steve Hunt

Oops! Didn’t see that coming! Part 2

We’d like to ask for your guidance by completing a short marketing survey to help SSPI structure our training in a way that is most useful to our community. This 8 question survey should take less than 5 minutes, and is anonymous. Your opinions are greatly appreciated.

As a continuation from the May blog, we are now covering the “Identify” phase of the ICOV framework of a rigorous new design process.

This phase is important because it establishes the framework for the concept, establishes the level of rigor required for the project management process, estimates the development cost, collects the Customer and Business requirements and the criteria for success.

The level of project management needs to be flexible and scalable depending on the Level of Effort (cost) and the Level of Innovation (risk) of the new concept.

Surely a project that will take a month to develop and has been done elsewhere requires less rigor that a concept that will take 3 years to develop and represents a brand new invention which has never been done before.

The I phase consists of two Tollgates during which an objective steering committee will decide whether to refine the work in the current phase, proceed or cancel the project.

Tollgate 1 Exit Criteria are:

o Decision To Collect The Voice Of The Customer To Define Customer Needs, Wants And Delights

o Verification adequate funding is available to define Customer Needs

o Identification of the Tollgate Keepers1 leader & the appropriate staff

Tollgate 2 Exit Criteria is successful demonstration of:

o Assessment of market opportunity

o Command a reasonable price or be affordable

o Commitment to development of the Conceptual Designs

o Verification adequate funding is available to develop the Conceptual Design

o Identification of the Gate Keepers leader (gate approver) & the appropriate staff

o Continue flow down of CTSs to Functional Requirements

Formal tools which can be used in this phase are Market/Customer research tools, Product Roadmaps, Process Roadmaps, Technology Roadmaps, Multigenerational plans, Quality Functional Deployment (House of Quality).

Market/Customer research tools may include Customer Relationship Management (CRM) Data, Surveys, Focus Groups, Conjoint Analysis and Kano Model Analysis.

The next blog will cover the Conceptualize phase

BIO:

David Roy is an integral part of the Six Sigma community. He taught GE’s Jack Welch and entire staff Six Sigma, as well as served as Senior Vice President of Textron Six Sigma. He is a Certified GE Master Black Belt, was instrumental in developing GE’s DMADV (DFSS) methodology, and has taught 3 waves of DFSS Black Belts. Dave’s experience includes Product and Transactional so his examples are of interest to all. David holds an BS in Mechanical Engineering from The University of New Hampshire. He is also the co-author “Services Design for Six Sigma – A Roadmap for Excellence”

» Part 1

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.

Another take on the BP Oil Spill

Friday, May 28, 2010 by Steve Hunt

We are pleased to introduce you to consultant and trainer Sandi Claudell, today’s featured guest blogger. Sandi is CEO of MindSpring Coaching, and has been a valued Palisade Six Sigma Partner for quite some time. She is a Six Sigma Master Black Belt (Motorola), and is a Lean Master (Toyota Motors - Japan) among other notable achievements.

--Steve Hunt

Part 1: The Platform Disaster

Much has been said about the disastrous BP oil spill in New Orleans. If we use the theory of probability and reliability then have too many different companies responsible for a very complex construction and operation added to the chance of failure.

There is probably a cultural issue at work where each entity wanted to give the other what they wanted to hear rather than the truth. (For historic and recent examples: NASA Challenger and recent Toyota Prius problems). When we lose sight of quality and reliability of parts, construction, maintenance, testing under ALL conditions rather than the obvious few, etc. then we run high risks of failure. When you build 100+ wells and avoided disasters . . . perhaps people fool themselves into thinking there never WILL be a disaster. They don’t look at a model that demonstrates the longer you go without such an event (given the input factors of how each element can and will fail) the closer you come to the event we all want to avoid.

They may or may not have used an integrated Systems Design . . . not simply an engineering system but the system on how individuals work together, communicate with each other, act as a conforming unit or a more self-directed autonomous unit looking for and generating solutions outside the box. A team that is innovative and willing to look at all the possibilities and create a breakthrough design that was / is more mistake proof.

If they had used DFSS (Design for Six Sigma) then their designs would be more robust taking into consideration all the necessary safety precautions for human life as well as immediate response to a potential failure. As part of DFSS we use a statistical tool call Design of Experiments (Strategy of Formulations, Central Composites, etc.) where we can try very complex interactions (factors) with minimal effort / cost and maximum statistical accuracy. DoE creates prediction equations that allow us to model and ask questions of what would happen under different conditions. More importantly we can look at many different quality metrics (responses, outcomes, etc.) with the same experimental trial. If we replicate the test then we can even forecast what elements cause variation (very hard to detect in highly complex systems without the use of statistics).

If they had used an FMEA (Failure Mode Effect Analysis . . . a tool used in Six Sigma) then they could have anticipated failures and put error proofing devices in place to detect and/or respond to potential faults BEFORE it is irreversible. If we add a Monte Carlo simulation to potential working conditions then the model forecasts probability plots and identifies key factors that will be critical to success or failure.

Perhaps they did indeed use a Monte Carlo using Crystal Ball. It is a good product but if they used Palisade’s @RISK and added some of the other tools provided by Palisade such as RISK Optimizer, Neural Tools, etc. then they could have analyzed the system in other dimensions besides a simple Monte Carlo, thus uncovering weaknesses BEFORE designing and/or building the platform and well.

Part 2: Capping the well head

In Lean there is a whole discipline called “Error Proofing Devices”. As part of the design effort we need to create first and foremost safety and other devices that prevent the error from occurring in the first place. If that line of defense fails then there should be devices built into the process designed to cap the well if your error proofing fails. If that line of defense fails then there should be a disaster response plan created and practiced and tested to ensure that the spill is repaired immediately.

Part 3: Treating the resulting spill

Again, Design of Experiments could test different materials, chemicals and methods to find the right combination to contain or otherwise manage the resulting oil spill. Trying one chemical only may be the age old definition of madness . . . trying the same thing over and over again expecting different results. Again, a robust design of experiments could aid in the process of finding a solution that is most effective and with multiple tests on the same samples ensure that is it the most safe for the environment and the population most directly in the path of the oil spill. These tests are ideally run years before such a spill however, doing something now is better than simply standing by and watching it happen.

Last but not least:

Management (Executives down to line managers) should have coaches. Coaches who can speak to the culture, the systems design, the tools and methods used in Lean Six Sigma and who can verify data analysis and help with the accurate interpretation of the data. These coaches should be independent . . . not a full time employee of the corporation as they are more likely to speak the truth and highlight risks as well as opportunities.

Now BP and all the other entities may have done some of what I mentioned above. But I would assume they must have left out one or more of the listed items or we wouldn’t be looking at the oil traveling into the wetlands around New Orleans right now. Hindsight is always brilliant but we can learn from our mistakes. We can create better cultures, systems, error proofing devices, Experimental Designs etc.

BIO:

Sandi Claudell is CEO of MindSpring Coaching. She is a Master Black Belt in Six Sigma, a Lean Master and has worked as a consultant for many companies to initiate worldwide improvements. For more information or to contact Sandi please visit http://www.mindspringcoaching.com/.

Neural Nets vs. the Ripple Effect

Thursday, April 1, 2010 by Holly Bailey

About a week ago the Financial Times ran an article about a "new" investment analysis technique that could cut through turbulence in the financial markets: neural network analysis. I thought okay, this isn't new but maybe the application is innovative. Besides, I liked the metaphor the reporter used, a metal ball dropped in a vat of oil and the ensuing ripples that disturb the oil.

The article is about software developed by a Danish investment firm that turned its back on "linear" models to adopt a neural network approach that continually reclassifies investments in a portfolio and then makes suggestions about which equities to buy and which to sell. The proprietary software chews through a heap of data--prices, price-earnings ratio, and interest rates, for starters, and its performance bench mark is the Russell 1000 index.

The test portfolio used to proof the method was acquired in 2007, just before the ball dropped into the oil. For a time it seemed to hold up but then got caught in the turbulence and its undertow. It has now recovered nicely, ahead of the Russell 1000 in fact, and the asset managers are looking for more investors. This is a sweet success story, especially given the demon turbulence looming over the project and the fact that the assets are apparently owned by the Danish state pension plan.

I understood the use of neural network software to counter nonlinear events like market turbulence, and I understood the continual classification and reclassification. But I was intrigued that nowhere in the article was there a mention of risk, risk analysis, or even risk assessment. Maybe it was there all the time, incorporated in the proprietary software, and maybe it just wasn't mentioned. Certainly the asset managers who developed the program were aware they were at risk--they were chewing their nails as their fund slid down right beside all the other funds that were dropping in value. But assessing risk doesn't seem to have been a factor in the firm's new defense against mayhem in the markets.

So. Is it time to shut down your Monte Carlo software? I don't think so. . . .

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

Rumors of Death

Monday, March 15, 2010 by Holly Bailey

Allan Roth, who writes a blog for CBS Money Watch called "The Irrational Investor," recently asked his readers a rhetorical question: Is Financial Monte Carlo Simulation Dead? Since rhetorical questions demand an answer in less time than it takes the questioner to draw breath, Roth obliged.

While expressing sympathy for the investors who were victims of poor risk assessment and forecasting when the financial markets shook themselves down to rubble in 2008, Roth is taking a very politely defensive swing at one of the many critics of risk analysis who have turned up the volume since then--one Jim Otar of Otar Retirement Solutions and the author of Unveiling the Retirement Myth.

Roth is an experienced user of Monte Carlo software who knows the pitfalls of overoptimistic assumptions. He says he finds 99 percent of the Monte Carlo models he's see over the years to be inadequate because of this flaw. Jim Otar, for his part, finds other flaws as well: in the generation of randomness and trends and in the sequence of returns. Otar's modeling method does not rely on randomness but on a century's worth of historical data.

Our two worthy opponents put their models up against one another in a match that crunched identical inputs. Their models produced very, very similar results, apparently satisfying each analyst as to the superiority of his method. But while Roth said nice things about Otar and his model, he pointed out the limitations of relying on historical information alone. In other words, he doesn't concede.

For any kind of retirement planning models, he says, the cure to flaws is conservative input. Then he giddily sends his readers to one of those rudimentary online Monte Carlo calculators that investment firms love to offer their clients.

Rumors of this death are greatly exaggerated.

How can the UK public services prepare for unpredictable, extreme weather?

Friday, March 5, 2010 by DMUU Training Team

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

New business planning – measuring feasibility

Tuesday, February 23, 2010 by DMUU Training Team

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

Free Webcast this Thursday: "Simulating the U.S. Economy: Where will we be in 100 years?"

Tuesday, January 26, 2010 by DMUU Training Team

There is an assumption that drives all of our expectations for how our economy will be in the future. That assumption is one of endless economic growth. Clearly endless exponential growth is impossible. Yet that is what we base all of our expectations upon. We all agree that zero or negative economic growth is bad (just look around now at the effects of the Great Recession). But we also know logically that 2% or 4% annual growth every year leads to an exponential growth outcome that is unsustainable.

In this free webcast, Dr. William Strauss models the next 100 years, based on the last century's data. The experiment in this webcast is about the future. If the model can very closely replicate the last 100 years, what does it have to say about the next 100 years? The experiment uses @RISK’s risk analysis and Monte Carlo techniques to generate new combinations of parameters for each of tens of thousands of runs of the simulation. Changes in the parameters represent potential exogenous policy choices.

The “doing what you did gets you what you got” scenario leads to a surprising and unsettling outcome. The experiments using Evolver (genetic algorithm optimization software) do find a path that works. Obviously if it is not “business-as-usual” that leads to a stable outcome, it is some other way. The policy choices that lead to a stable outcome suggest that the future of capitalism is not going to be what we expect it to be.

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

William Strauss is the President and founder of FutureMetrics. He brings more than thirty years of strategic planning, project management, data analysis, and modeling experience into the company’s stock of knowledge capital. Bill’s professional history includes executive positions as director, president, and senior vice president, as well as positions as senior analyst and field coordinator. He has an MBA (specializing in Finance) and a PhD (Economics). Read more of Dr. Strauss' bio here.

» Complete abstract of "Simulating the U.S. Economy: Where will we be in 100 years?"
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Free Live Webcast this Thursday: Simulating the U.S. Economy: Where will we be in 100 years?

Monday, January 25, 2010 by DMUU Training Team

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

There is an assumption that drives all of our expectations for how our economy will be in the future. That assumption is one of endless economic growth. Clearly endless exponential growth is impossible. Yet that is what we base all of our expectations upon. We all agree that zero or negative economic growth is bad (just look around now at the effects of the Great Recession). But we also know logically that 2% or 4% annual growth every year leads to an exponential growth outcome that is unsustainable.

To see where this growth imperative will take us we first have to see how we go to where we are today. This free live webcast first models the 20th century. The model is both complex and simple. The basic schematic of the model’s relationships is easy to understand. Furthermore, the core of the model is a simple production function that combines capital, labor, and the useful work derived from energy to generate the output of the economy. Complexity is contained in the solutions to the internal workings of the model. What is unique is that there are no exogenous economic variables. Once the equations’ parameters are calibrated, setting the key outputs to “one” in 1900 results in their time paths very closely predicting the U.S. GDP and its key components from 1900 to 2006.

The experiment in this webcast is about the future. If the model can very closely replicate the last 100 years, what does it have to say about the next 100 years? From 1900 to 2006 there are periods in which there was parameter switching. (The optimal parameters and the years for the switching were found using a constrained optimization technique.) That suggests that in the future there will also be changes. The experiment uses @RISK’s features (risk analysis software using Monte Carlo techniques) to generate new combinations of parameters for each of tens of thousands of runs of the simulation. Changes in the parameters represent potential exogenous policy choices.

The “doing what you did gets you what you got” scenario leads to a surprising and unsettling outcome. The experiments using Evolver (genetic algorithm optimization using Monte Carlo simulation) do find a path that works. Obviously if it is not “business-as-usual” that leads to a stable outcome, it is some other way. The policy choices that lead to a stable outcome suggest that the future of capitalism is not going to be what we expect it to be.

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William Strauss is the President and founder of FutureMetrics. He brings more than thirty years of strategic planning, project management, data analysis, and modeling experience into the company’s stock of knowledge capital. Bill’s professional history includes executive positions as director, president, and senior vice president, as well as positions as senior analyst and field coordinator. He has an MBA (specializing in Finance) and a PhD (Economics).

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Data Issues Part 1

Tuesday, January 12, 2010 by DMUU Training Team

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

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

Digital Eyes on Alien Life

Wednesday, December 9, 2009 by Holly Bailey

University of Chicago geoscientist Patrick McGuire has big plans for Mars. Previously he worked on an imager for a Mars orbiter that could identify different types of soil and rock by detecting infrared and other wavelengths, and now he is drawing on that experience to develop a space suit with digital "eyes" and a neural network that rides on the hips of the spacesuit and can sort out living biological material from other matter.

The digital eyes will detect and plot colors, and the neural net, which is known as a Hopfield neural network, will compare these color patterns to a database of information previously gathered from that area of planet in order to make an animal-vegetable-mineral determination.

This complex AI system has already been tested at the Mars Desert Research Station in Utah, and McGuire and his colleagues were satisfied that the Hopfield algorithm could learn colors from just a few images and could recognize units that had been observed earlier.

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?

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.

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Two Sides of the Coin

Wednesday, November 18, 2009 by Holly Bailey

Maybe it's because of fallout from the past year's financial crisis, but I have been noticing that almost all the press mention for risk analysis or Monte Carlo simulation is in connection with fending off the bad stuff--loss, adversity, or failure of various kinds. So it was refreshing to come across a story of decision evaluation being used to analyze the good stuff, that is, innovation and opportunity.

In 2008, Dell sponsored a student team from the Tauber Institute at the University of Michigan to compare the opportunity scenarios for designing new laptops that would use emerging wireless technology. Dell's challenge to the engineering and business students was to determine the most profitable way to approach new laptops for new markets.

Out came the laptops, out came the Monte Carlo software. In went the inputs--the possible cards, the cost of components, retail discounts vs. direct sales, necessary changes in internal organization. What was the value-at-risk? An already pretty profit picture from the laptop sales of the previous year.

It was the most positive kind of problem to solve. And what was the outcome of the team's efforts? "A Profit-Based Simulation Model for Laptop Planning"-- an optimistic title if there ever was one. But I suppose the title could have been "Modeling Potential Loss from New Laptop Design." There were quite a number of good-news scenarios at the institute that year. I mention the Dell team because of the intensive decision analysis element.

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