Risk Management Consulting Company

Value-Based Management Compensation

Wednesday, June 9, 2010 by Holly Bailey

Full disclosure: I am, like so many of my friends, an investor––a small-time one--and recently, I have joined in the public outrage about bankers' bonuses and executive compensation in general. Compensation is one of the hot buttons in the debate over financial reform. I keep wondering why compensation practices are what they are and how they could be adjusted to calm turmoil on Wall Street.

Enter Marwaan Karame, and his version of risk analysis.

Karame heads the New York consultancy Economic Value Advisors, which coaches major corporations on Value Based Management. Value--long-term versus right-now profit--is the foundation of the firm's philosophy. Its central principle is that any activity a business undertakes should increase the wealth of its shareholders--in the case of a privately held company, the number of shareholders may equal 1.

Karame has developed what he calls Value Based Compensation, and the goals of this are to align the self-interest of management with the self-interest of shareholders. He believes the shareholders, the company, come first. And this means a lot of decision-making under uncertainty. But Marwaan has a method for his management-shareholders balancing act, and it involves performance targets, statistical analysis, and risk assessment (in this case, managing probabilities of performance). His strategy involves maintaining a reserve of bonus funds and timing the payout of these rewards.

The point at which Monte Carlo simulation and Monte Carlo software come in is the point at which variance between performance targets and the level and timing of reward converge. He shows his his client how to click into Monte Carlo in the Excel spreadsheet and use the software to locate the tipping point between wealth for management and wealth for shareholders.

As a small--very small--shareholder, discovering that there is such a tipping point and that Karame knows how to locate it is reassuring. Makes me feel there's someone on my side.

(Data) Cleanliness Is Next To Godliness

Monday, June 7, 2010 by Steve Hunt

I’m pleased to welcome Palisade Six Sigma Partner Edward Biernat of Consulting with Impact as featured guest blogger. As well as running a successful consultancy, Ed is a noted Six Sigma educator and author.

--Steve Hunt

(Data) Cleanliness Is Next To Godliness

I recently had dinner with Eric Alden, a Master Black Belt for Xerox corporation. Eric had just gotten back from the American Society for Quality’s (ASQ) headquarters in Milwaukee where he was one of 200 Master Black Belts worldwide that generated the questions for the upcoming ASQ Master Black Belt certification examination (more on that in an upcoming post). Eric had also recently completed a mini-course for the local ASQ chapter on data integrity. We shared some war stories and came up with some common threads regarding data integrity.

1. Just because it is a number doesn’t mean it is worth anything. People get enamored with tons of data from process instrumentation, shop floor collection sources or Excel spreadsheets. There seems to be a false security with this pile of data, and managers often look to the Black Belt to ‘sort it out’, because with all that data, the answer is in there somewhere. Many a belt has crashed on the rocky reefs of bad data, often after tons of time and effort (and credibility) were wasted generating false answers.

2. GIGO. The Garbage In – Garbage Out philosophy of computing applies especially to existing corporate databases. Here a few recent examples of GIGO.

a. A belt wanted to analyze the specific timing of events in shop floor process and had tons of data from the process instrumentation that had times down to the fraction of a second. After lengthy analysis, they found a significant difference between two shifts and forced the lesser shift to adopt the sequence of the more uniform shift. After introducing costly production problems and actually hurting the overall process, the sensors were found to be faulty and the overall process subject to human manipulation to generate the ‘pretty charts’ that everyone expected.

b. Office areas are not immune. Something as simple as a checksheet to gather data to analyze when a particular computer error occurred can be in question, especially when the clerk fills in the times at the end of the shift from memory rather than logging the event as it occurs.

3. Good data in bad spreadsheets. Even if you get good data, having an inexperienced person setting up the spreadsheet can cause problems. It is analogous to a person using a word processing software and making a table using spaces and tabs. It looks like a great table until you have to manipulate it. Then it falls apart. Problems like merged cells, subtotals, random formula inserted in cells, etc. can make a Belt weep and cause significant errors in the resulting analyses.

4. Useless manipulation. Often a big issue is that management wants data sliced a certain way for no good reason. This sometimes leads to the proliferation of additional spreadsheets or databases that needlessly add to complexity. (Note: If you have an ERP system like Oracle or SAP, USE IT! They are designed to house data and protect its integrity. Plus the data entry screens typically allow for better and more accurate entry. Few things are more wasteful than entering everything in the ERP system then re-entering it into a spreadsheet to appease a manager’s inability to adapt and change.)

What are some tactics for resolving these issues?

1. On a macro level, start ensuring that the data that your company is collecting is sound data as part of the preparation for a Six Sigma launch, or a part of plain old good business. Bad data slows down or stops a Six Sigma project dead in its tracks, changing it from getting something done to fixing the data.

a. Know catalog your data databases, including the extra ones (Excel, Access) that are usually relied upon but undocumented.

b. Prioritize the data sources by synchronizing them with your Six Sigma launch sequencing.

c. Sample the data to insure its usefulness. If it is bad, fix it. This will give teams better data to start off with and will allow time for that data to accumulate for analysis.

2. For specific projects, conduct a Measurement System Analysis (MSA) on you data sources (This tool is often used in the Measure phase of the DMAIC model). We often think of MSA’s when it comes to physical measurements. It is just as critical in the ‘softer’ data.

a. Pull the correct sample size. In StatTools, under Statistical Inference there is a Sample Size Selection tool that can be used to pull the correct amount of data needed for the analysis.

b. Pull your data randomly and follow the trail to the actual entry point. That may mean watching how individuals enter data, probing for special circumstances, etc.

c. In your analysis, look for random factors such as vacation fill-ins. Both Eric and I both had several experiences where one person was filling in for someone who is out sick or on vacation and, usually do to inadequate training, varies from the expected process.

3. Pivot Tables are our friends. Start today upgrading the skill sets of the people that do the actual data entry and first level analysis. Train them in how to use tools like Picot Tables that slice the data but leave the actual spreadsheet intact. The fewer merged cells, etc. that we fight with, the better.

4. Managers – Trust your Belt. If they say the data is bad, it probably is. No matter how much you want an answer today, you may not be able to get one. The good news is that some processes can be modeled using @RISK to begin improvement that is directionally correct while waiting for the data to compile. Then the better data can be used to either update or replace the early model.

5. Go hunting. Find extraneous datasets and merge them / kill them. The fewer that are out there, the more likely you will be able to ensure the integrity of those that remain.

Remember that data analysis is a funnel. Tons of data leads to bunches of information which then can help us make some decisions. Throwing bad data into the system is similar to throwing bad tomatoes into the food distribution system. The end results can be pretty messy and difficult to clean up.

Also, don’t miss Ed Biernat’s free live webcast DMAIC and Using a Non-Intuition Approach, Thursday, 11AM Eastern Time.

https://palisade.webex.com/palisade/onstage/g.php?d=719996370&t=a

BIO:

Edward Biernat is the president of Consulting With Impact, Ltd., a training, coaching, and consultancy located in Canandaigua, NY that he founded in 1998.

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/.

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

Cost-Benefit Analysis in the Land of Buzz

Friday, April 9, 2010 by Holly Bailey

For the past couple of years, I've been following the advance of cloud computing into the marketplace. Recently, as the Cloud has begun to--I can't say materialize as that might confer some notion of definable substance, which in this line of business is to be avoided at all costs--become a presence, information officers have been increasingly interested in matters of costs and benefits. Those who are considering migrating their current computing operations to the Cloud would like to make risk assessments that weigh CAPEX--capital expense--against OPEX--operating expense--and for that they will need help calculating the TCO--the Total Cost of Ownership. To forecast the TCO, they will need to get out the Monte Carlo software to predict their potential flow of data out through the Cloud, and depending on a company's familiarity with risk analysis, this "could = hire a consultant who understands the meaning of all this."

Recently, to help clarify matters, a Computerworld blogger declared, "The fact that people are so interested in cloud TCO indicates that the general value proposition of cloud computing has been accepted and absorbed." The need for this incisive commentary he blames on the fact that "there's been an amazing amount of FUD"--Fear, Uncertainty, and Doubt--"strewn about on the topic of cloud TCO."

My problem with this discussion, as you've probably gathered, is not the efforts of smart people to grapple with the opportunities and operations management issues raised by Internet-based computing. It's the FUD that folks in computing seem to experience when it comes to clear, plain labels. They flee into the land of buzz in order to assure TO--Total Ownership--of the terms.

For starters, take the term Cloud for Internet. It all gets just a little too. . . .well, vaporous. It makes me feel like the grandmother of a man being ceremoniously installed as a dean at Cornell University a while back. Having survived into her nineties and through the morning's pomp and circumstance, she asked her grandson what exactly he would be doing in this new job, and as he started to explain, she looked as if something tasted bad. Finally she broke in. "Honey," she said, "if you can't say it in one sentence, it has got to be illegal."

Making Optimal Choices, or Just Making Choices? Part 3

Friday, March 26, 2010 by DMUU Training Team

Part 2 of this blog ended with me very quickly stating that the MotoGP tipping comp optimiser was identical in structure to a portfolio optimisation problem, where the portfolio could contain stock or other assets, or even projects. Let’s look at this in a little more detail as I’m sure you’re reading this to find how to optimise your own decisions rather than wondering how I went in the tipping competition!

In my model there was a fixed budget (though less could be spent if desired) to spend on riders, with the aim of maximising their total points haul. In the real world you may have a total budget of say $100m to invest in a range of projects perhaps many hundreds of millions of dollars in total value each of which have certain expected returns. At its simplest this decision evaluation will find the most (expected) profitable portfolio of the projects. This is an inclusion/exclusion grouping model, but it is very simple to optimise assets with a continuous level e.g. the amount of money invested in various shares etc. Another real example I have seen when working with an investment company here in Australia was a model whose goal was simply to find the portfolio mix that came closest to the total allowable spend without exceeding it.

Further realism can be included by using constraints should there be the need. A resource constraint may mean there has to be a limit to the number of projects that can be run simultaneously. There may also be a minimum number of projects determined by management as a mitigation strategy. Such constraints are very simple to employ using Evolver and add value to the decision analysis without the need to provide specific risk analysis/Monte Carlo simulation information for the model.

A slightly more sophisticated method of turning an optimisation into a useful portfolio risk management tool where uncertainty hasn’t been specifically modelled is to estimate the possible downside of each asset and include it in the calculation of the portfolio’s ‘score’. The Evolver software comes standard with over twenty example spreadsheets for your educational pleasure, of which “Portfolio Mix.xls” gives one method for doing just this.
In the next (and final) instalment of the Making Optimal Choices blog I will explore the idea that not all optimisations no matter how mathematically correct will produce the same results in good time, and that elegant modelling should always be the goal prior to firing up Evolver.

And so you know, I came second in the competition. Next year I’m hoping to go one better!

» Making Optimal Choices, Part 1
» Making Optimal Choices, Part 2

Rishi Prabhakar
Trainer/Consultant

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

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

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.

Simulating the U.S. Economy: Where will we be in 100 years?

Friday, September 4, 2009 by DMUU Training Team

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).

Dr. Strauss will present a case study at the 2009 the 2009 Palisade Conference: Risk Analysis, Applications, & Training. The conference is set to take place on 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.

Simulating the U.S. Economy:
Where will we be in 100 years?

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 work 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 work 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 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 @RISK 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.

Please join us in October in New York for software training in best practicies in quantitiative risk analysis and decision making under uncertainty, real world case studies from risk services consultants and experts, and networking with practicioners from many different fields including oil and gas, pharmaceuticals, academics, finances, Six Sigma, and more.

Think Clearly, Act Decisively, Feel Confident

Friday, August 21, 2009 by DMUU Training Team

2009 Palisade Conference in New York City

Palisade is pleased to welcome Dr. Sven Roden of Unilever's Decision Analysis Group to deliver the keynote address at the 2009 Palisade Conference: Risk Analysis, Applications, and Training in New York City, Oct 21-22, 2009. The keynote is titled, "Think Clearly, Act Decisively, Feel Confident."

In this presentation, Dr. Roden will discuss what Decision Making Under Uncertainty means to Unilever, and the Decision Analysis techniques that are at the forefront of making a cultural change to the way Unilever approaches and analyzes strategic decisions. Unilever’s relationship with Palisade has helped them in their journey; the company has trained over 400 finance managers in Decision Making Under Uncertainty using Palisade's DecisionTools Suite. Unilever's Decision Analysis Group is constantly look internally and externally to identify future trends and applications and evolve tools and models to ensure their place at the forefront of applying Decision Analysis.

Dr. Sven Roden is a senior Decision Analyst within Unilever’s Finance Academy, acting as an internal consultant leading decision analysis evaluations on problems where teams have been struggling to find a solution. He is also involved in developing new methodologies and providing expert training and coaching to Unilever's financial managers. Prior to joining Unilever, Sven worked for BNFL as a Technology Strategist and research physicist.

Is Norway’s Pension Fund Adequately Diversified?

Monday, December 1, 2008 by DMUU Training Team

As a regular visitor to Norway, it is hard not to be impressed by the wealth generation in the country. Even more impressive is the discipline of the government and population to accept that the majority of the vast oil windfall of the country should be invested for the future (in a pension fund) and not spent today (high tax rates and price levels being one testament to that).

In this blog I allow myself some raw speculation as to whether holistic risk management thinking is being adequately applied when it comes to the government’s management of the wealth generated by this windfall.

In the spring of 1997, the Ministry of Finance decided that the Government Pension Fund–Global (previously known as the Government Petroleum Fund) should invest parts of its portfolio in equities. In January 1998 the fund consisted of bond investments worth NOK 113 billion (about USD 15 billion at current exchange rates). Since then inflows of new capital into the fund (also boosted by the high oil price) have been significant; in 2007, capital inflows averaged more than USD 300 million per trading day. By January 2008 the fund was worth over NOK 2000 billion (about USD 300 billion) and it is forecast to be worth over NOK 4000 billion (about USD 600 billion) by 2015 (according to the National Budget 2009)—the ultimate in retirement planning. Over time the fund’s investment guidelines have been relaxed, with the fund currently consisting of about 50% equities and 50% bonds, including government, corporate, securitized and inflation linked bonds.

To some extent there is a natural diversification in the fund. For example, to the extent that it is believed that global equities in aggregate are negatively affected by high oil prices, then there is a natural hedge in the portfolio, as increases in the oil price will reduce the equity value but lead to increased capital inflows (although the balance of this will change as the equity portfolio becomes larger). Similarly, oil-related new inflows and the investment in inflation-linked bonds could also provide some protection against long-term inflation (arguably, equities may or may not be a good long term inflation hedge). In addition, the fund of course also uses advanced tools of portfolio management, which are surely applied with rigor. However, as we know from the credit crisis, such tools can lead one to a false sense of confidence if they miss the big picture (deckchairs on the Titanic, etc!).

In this context, I allow myself to speculate (hypothesise?!) as to whether the fund should be devoting far more significant efforts to invest in non-traditional assets. (The fund’s performance is essentially currently measured against a benchmark portfolio of bonds and equities and so such efforts or investments would be hard to justify against these objectives).

The most obvious scenario in which the fund could lose out significantly would be a shift in the world’s energy sources (over the many decades of pension obligations), which could create an environment that is simultaneously largely unfavorable for most asset classes in the fund. Conceivably the potentially massive costs associated with creating a low-carbon global economy could produce a situation that is unfavorable for most global equity investments, that could unleash inflationary forces that reduce the value of many bond investments, and potentially reduce demand for oil (and its price). Such a “nightmare” scenario for the fund does not seem beyond the realms of reality.

The most obvious strategy to mitigate the effects of this scenario would be for the fund to proactively take very large positions in alternative energy technologies. Such positions would presumably need to be very large (and possibly require the fund to itself create and support the development of new innovative companies in this area, not just to passively invest in existing ones). The costs and risks in doing so would be large (particularly as the scenario may never materialize), but it could be a prudent one, given the already very large fund that already exists for a small population base of about 5m people. Could it be so bad if 5%-10% of that fund were invested in such technologies? (Such investments would arguably support, or at least relate to, some of the fund’s other goals—such as ethical or social investments). A risk assessment would be a good idea. Now, back to the real world!

Dr. Michael Rees
Director of Training and Consulting

Palisade Conference Provokes Six Sigma Buzz

Friday, November 21, 2008 by Steve Hunt

Last week, Palisade Corporation held its North American User Conference; it was a very successful event that brought together @RISK Users from around the world. Presentations and discussions touched on topics such as the subprime mortgage crisis, financial risk management, modeling flu, project risk management and of course, the ways to Monte Carlo simulation in Six Sigma.

It was great to see such a high level of interest in the Six Sigma related presentations and buzz they created in both the social networking opportunities as well as the feedback forms that were submitted after the conference. This shows despite the economic difficulties and the natural tendency to eliminate all unessential spending, Six Sigma and Design for Six Sigma is rightfully viewed as part of the solution.

SigmaFlow’s president Jay Holstine, presented Process Mapping for Knowledge Transfer: Doing More with Less. A very pertinent topic in today’s economic times, which will be presented live as an ISSSP Focused Session on November 25 at 2pm EST. Please join us.

Ed Biernat from Consulting with Impact led a presentation on the use of Six Sigma in Process Industries. If you are interested in viewing his presentation, Lean Six Sigma Applicatin of @RISK Part I, it can be viewed online. Part II will be live on December 12, 2008 at 1pm EST where he will dive deeply into the use of @RISK in this case study. Please join us.

A recent article, Executives Switch to Survival Mode, in the Wall Street Journal indicates that two of the top issues in crisis management can be managed with a strong Lean Six Sigma program, these were:

Excellence in Execution – Whether on the shop floor or in administrative processes, there is no longer room for inaccuracies or waste.
Speed, flexibility and adaptability to change is another area where a strong Six Sigma program mitigates the effects of crisis.

The interest at our User Conference in exploring the use of @RISK to reduce project cycle times and costs indicates to me that smart business leaders are looking to reduce risks and strengthen their companies during this time of crisis.

Apples and Apples--and the RFP

Thursday, October 16, 2008 by Holly Bailey

Only slightly less risky than Russian roulette, the game called RFP can be only slightly less deadly. When a government agency puts out an RFP--request for proposal--and the bids start coming in, its decision makers are then faced with the challenge of selecting the most advantageous bid. On the face of it, this may seem like a no-brainer--look at the costs, the projected results, and pick the most convincing numbers.

But here's the catch. Each company submitting a proposal is likely to take a different approach to uncertainty, to risk assessment. When it comes to uncertainty, time and money are the two biggies, and making a seamless intersection between the two is a classic problem in operations management. The bidders use different numbers for uncertain values, and so their projections are not easily compared.

To bring competing bids into alignment, government agencies typically develop their own internal bids against which to measure. But until they can put probability into play, they are still stuck with decision making under uncertainty. The results, as the news often reminds us, can be disastrous.

Monte Carlo simulation to the rescue. At least one consulting company is now helping agencies to evaluate proposals using Monte Carlo software that specifies the range of uncertainty with probability distributions that bring time and money into the same plane and produce optimized projections. This makes the choice of proposal--and evaluation of that decision--less risky. Now an agency can compare apples and apples.