Risk Analysis Solutions

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

Friday, August 27, 2010 by DMUU Training Team

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

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

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

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

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

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

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

» Read the Tioga Energy case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

Market decline versus speed to market – ‘A bird in the hand...’

Wednesday, August 25, 2010 by DMUU Training Team

I recently saw an interesting @RISK cashflow model from the portable phone industry. It modeled the uncertainty in the length and decline of overall market demand for a particular technology against five strategies for getting various application products to market as soon as possible.

Using @RISK’s Simtable function, combined with Excel’s Index function, it was possible to run multiple simulations and see which strategy could take best advantage of the potential market, given the uncertainties in the development process, the possibility of competitors, the market take-up and the margins that might be achieved.

As is often the case in all aspects of life, the simulation revealed that ‘a bird in the hand is better than two in the bush’; it’s very comforting to know that @RISK risk analysis solutions can cut through loads of detail and come back with an answer that echoes received wisdom!

Ian Wallace, ACMA
Palisade Training Team

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”

Prediction Markets

Tuesday, August 3, 2010 by Holly Bailey

Although they've been around for the last 20 years or so, prediction markets have begun to make news for their application in business operations. Heralded early on in books like James Surowiecki's The Wisdom of Crowds, prediction markets are a fascinating alternative to traditional forecasting methods, such Monte Carlo simulation, which extrapolate future events from past patterns. Essentially a betting exchange where participants stake something on the accuracy of the information they offer up, a prediction market is a way of capturing emerging patterns.

Prediction markets can be public or closed private exchanges, as in most business applications. Here's how it might work: a business sets up an online portal to gather intelligence from its employees on such issues as scheduling or production costs. Each employee has a limited number of points to wager with the information he or she offers, and these points are value-at-risk, which means that an employee is likely to offer only information that is accurate enough to be worth the points.

Why bother to play at all? Darwinian competition. With each winning piece of information, the participant gains collective respect. Maybe he or she advances in rank on a leader board or maybe the company honors its top participants in a ceremony.

While the accuracy of prediction markets is still a topic of some fairly warm debate in applied mathematics, a number of risk analysis services are concentrating their solution portfolios on predictive markets.

Customised Solutions Using @RISK and VBA for Excel

Thursday, July 8, 2010 by DMUU Training Team

If you missed Palisade trainer Rishi Prabhakar's webcast "Customised Solutions Using @RISK and VBA for Excel," you can still view it in our archive.

The hour-long presentation explores the use of VBA for Microsoft Excel to control @RISK functionality, to simplify the process of risk analysis for resource-strapped businesses. Rishi explains the advantages (and limitations) of macro control for modelling and running simulations.

Simple examples are worked through to show the XDK (@RISK’s automation library) in action, from generic examples to a cost estimation model. This addresses elements of model construction, various simulation settings and finally reporting. The emphasis is on exposing the viewer to the various possibilities the XDK lends to the user rather than an in-depth VBA for Excel coding session.

Rishi Prabhakar holds a BSc in Mathematics from the University of Technology, Sydney Australia. Rishi has experience in the resources, infrastructure and primary industries, telecommunications, scientific research, banking and finance with an emphasis on operational risk.

With technical skills in the areas of modelling, simulation, statistical analysis, cost estimation, time series forecasting, customised solutions utilising VBA for Excel, and extreme value theory, Rishi has provided training and consulting services in risk and decision analysis for Palisade’s Asia Pacific office since 2005.

» Customised Solutions Using @RISK and VBA for Excel
» Webcast archive

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/.

Oops! Didn’t see that coming!

Wednesday, May 12, 2010 by Steve Hunt

We are pleased to introduce you to consultant and trainer David Roy, our first guest blogger in my blog. Dave comes to us from SSPI, Six Sigma Professionals, Inc., and taught Jack Welch and his entire staff their Six Sigma Green Belt training. David’s blog will be the first in a series, and this initial entry also has a quick survey at the end for your input on structuring DFSS training.

--Steve Hunt

Oops! Didn’t see that coming!

How often do we hear these words after we have made a change to product, service or process?

We frequently solve one problem only to discover a new problem; or the solution we selected didn’t really resolve the problem.

There are many reasons for these surprises. Problem Solving sometimes addresses the symptoms and not the root cause. Useful solutions often have compromising harmful effects that we did not consider.

You may now be thinking; “Wow, if everything we do is going to turn out bad let’s not change anything.” The reality is that change is inevitable. Whether driven by rising customer expectations, innovative new technologies or even variation in inputs over time; change will occur.

Managing the design and implementation of these changes requires a more formal methodology than the prominent “Launch and Learn” method.

The sophistication of the methodology will vary depending on the magnitude of the risks associated with the change. If we are problem solving for variation in a standard process and trying to regain control simple tools such as Cause and Effect diagram and Failure Mode Effects Analysis and Standard Work may be all that is required.

When we start to explore reducing variation or introducing new technologies or process then we need to bring on a Design For Six Sigma (DFSS) methodology which incorporates elements such as Change Management, Robust Design, Reliability, Modeling & Simulation and Piloting & Prototyping.

Over the next 4 blogs we will cover the four phases of a DFSS project under the framework of I-dentify, C-onceptualize, O-ptimize, and V-erify or ICOV for short.

We will give a high level look at the steps within these phases and the tools used to reduce the risk of the change and un-intended consequences.

On another note, if you are able, 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.

http://www.surveymonkey.com/s.aspx?sm=2aQk8QF1eLB5MFQJC1pUXA_3d_3d.

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”

The Economics of Supply Chain Risk Management using @RISK

Monday, May 3, 2010 by DMUU Training Team

At the 2010 Palisade Risk Conference in London, David Inbar of Minet Technologies presented a talk on supply chain risk management.

Supply chain risk management is an emerging field which has been growing significantly in importance because of modern management concepts such as lean, globalization and outsourcing. The mutual dependencies and close collaboration in modern supply chains create unique risks and challenges. Supply chain risk management is an economic process and choosing the elements and amount of risk mitigations should be based on economic measures.

Inbar's talk gave an overview of the concepts and process of supply chain risk management, and demonstrated how using Monte Carlo simulation techniques with @RISK risk analysis software adds value to the decision making under uncertainty processes and enables managers to purchase the most cost effective mitigations. Says Inbar, "An organization with the right risk management process can assure peace of mind to customers and supply chain partners."

David Inbar is the founder and managing director of Minet Technologies, a provider of professional services and technologies in supply chain and purchasing. Minet is active in the interfaces between business, processes and technologies in the world of supply chain and purchasing, creating methodologies and delivering projects and solutions.

» View a PDF of the presentation here
» Abstracts and presentations from the 2010 Palisade Risk Conference in London

Robust Risk Analysis for the Time/Expertise Poor – Part 2

Thursday, April 15, 2010 by DMUU Training Team

In my last blog I introduced the idea of a customised risk analysis solution to problems commonly faced in project risk management, especially cost estimation. Of course this idea is not uniquely applicable to project costs, but this paradigm is the simplest to explore, and that’s what I’m about to do.

Picture a risk register in a worksheet that has been created at a macro level to encapsulate most (all?) of the risks your projects may face. For any given project only a subset of these will be relevant – what is the best way to get these risks into a risk model on the next worksheet? By pressing a button of course! It is almost trivial to write code that picks up all selected risks and places them and the relevant data fields in the model worksheet. Sure beats manually copying and pasting individual line items and the transcribing errors that follow.

The next problem is utilising the workshopped parameters (likelihood of event, three-point estimates for severity etc.) in a logical way to be referenced by appropriate @RISK functions. Once a model structure has been agreed upon a macro button can place @RISK distributions where they ought to go, either logically due to the paradigm (using RiskBinomial, for example) or via a drop-down selection for dollar impact (RiskPert or RiskGamma, say). My clients have been especially thankful when I limit their choice of distribution and provide a simple flow-chart to follow to make this very decision. Reducing the propensity for arguments in risk workshops is worth its weight in gold; if we can assume that reducing this risk ‘weighs’ plenty!

Similarly one or two instances of the simulation settings are likely to satisfy all requirements, so these too can be activated by macro buttons. In this way a user can’t run a ‘poor’ simulation thus creating spurious results. The simulation output that is required can be placed into a report template attached to the model template and generated using yet another simply-labelled macro button. In this way there will be consistent reporting across the organisation allowing decision makers to become familiar and comfortable with simulation results they might otherwise ignore or be unaware of.

A risk model created by this process may not be the theoretically optimal one, but it will be valid and in context with its intended use. It will certainly be easy to use! The results will be consistent and should satisfy management’s desires as well as regulatory requirements.
The project cost estimation is but one example, and the above possibilities are far from the only ones imaginable. Additional complexity or alternate needs would be just as easily met simply with different code essentially without any practical limits. You don’t need to be an expert in Monte Carlo techniques and software to run robust, credible risk analyses. All you need is a risk analysis consultant who macro-controls the cumbersome and probabilistic elements, some appropriate simulation options and reporting procedures. Ask for me by name!

» Robust Risk Analysis for the Time/Expertise Poor – Part 1

Rishi Prabhakar
Trainer/Consultant

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

Profitability Projections in a Manufacturing Environment of High Uncertainty

Monday, April 12, 2010 by Steve Hunt

The other night, I had the opportunity to watch a free webcast titled “Use of @RISK for Probabilistic Decision Analysis of a Manufacturing Forecast in an Environment of High Uncertainty”. This presentation was extremely timely, since many companies are struggling to survive in these challenging economic times. Dr. Jose Briones did an excellent job discussing and illustrating how profitability projections in a manufacturing environment are directly tied to how the sales forecast fits with the capability of the operation, and how different manufacturing capacities and productions rates impact the output of the plant and the allocation of the fixed cost of production.

In the example he presents, a company is trying to decide how best to balance the sales of certain families of products to maximize revenue, maintain a diverse product line, and properly price each individual product based on the impact to the manufacturing schedule and fixed cost allocation.

He spends an appropriate amount of time discussing different input distributions such as the Triangular, Normal, Pert and Gamma distributions as well as sharing his recommendations on when to use them. He also shares his expertise on fixed cost allocation by product and the dangers in using the common method of dividing the fixed cost by the total production, and recommends doing so by allocating the fixed costs based on the projected run time of each product family. Lastly, he spends some time discussing the interpretation of the results, which I feel does a great job wrapping up the information presented in the webcast.

Dr. Jose A. Briones is currently the Director of Operations for SpyroTek Performance Solutions, a diversified supplier of specialty materials, BPM software and innovation consulting services. Dr. Briones has a PhD in Chemical Engineering from Clemson University and is a graduate of the Business Administration Program of Wharton Business School. If you have any questions about the webcast, you can contact Jose at Brioneja@SpyroTek.com or through Jameson Romeo-Hall at Palisade Corporation.

Making Optimal Choices, or Just Making Choices? Part 4

Tuesday, March 30, 2010 by DMUU Training Team

It has taken four entries but I’ll finish this blog stream now with a discussion on optimisation optimisation. That’s not a typo. It’s an art form that is analogous to elegant modelling, as opposed to ‘just’ modelling. The tipping competition model not only opened my eyes to the world of optimisation but also that not all models are created equally, even if they are numerically equivalent.

A few rounds into the season we were allowed to buy and sell riders, which was great if you’d bought some duds at the start! But from a modelling point of view the complexity had increased exponentially. There was now a time component to the model structure as well as different prices for the riders based on their performance to date. My first attempt to model this was quite cumbersome with dozens of 0/1 decision cells to indicate buying riders at the start of the season and then the buying and selling of riders after four rounds. While mathematically correct I wasn’t doing Evolver any favours by having such complex dependencies between so many decision cells. The optimisation was taking far too long to converge, so much so that the final solution when the optimisation was stopped after what I considered to be a reasonable length of time was greatly impacted by the initial solution.

Now this of course isn’t usually the way things work with Evolver; as a sophisticated genetic algorithm optimiser the global optimal solution should be found regardless of the initial conditions. However the time taken to get to such a solution can be extended greatly if optimising an unnecessarily convoluted model. After initially blaming the software I realised I could simplify the model by turning two decisions (“buy” and “sell”) into one (“change status”). This immediately removed one third of the decision variables and straight away the optimisation converged quickly to a global optimal solution regardless of the initial feasible solution. Evolver can only work with the model you give it!

The act of optimising the optimisation can be a subtly tricky one, but is necessary if you are to have confidence in your optimised solutions and thus the decisions made. Building a model that ‘works’ is only the first piece to the puzzle. If the solutions aren’t stable then can you really be sure you’re producing the best answer? No. And if the model is being used to decide which of the multi-million dollar projects you will proceed with (or some other equally critical decision) I’m sure you’d want to have some certainty around the answer provided by Evolver. Of course if you’d like some help with an optimisation model from experienced risk analysis consultants feel free to contact the consulting team at Palisade!

» Making Optimal Choices, Part 1
» Making Optimal Choices, Part 2
» Making Optimal Choices, Part 3

Rishi Prabhakar
Trainer/Consultant

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.

Use of @RISK for Probabilistic Decision Analysis of a Manufacturing Forecast in an Environment of High Uncertainty

Monday, March 8, 2010 by DMUU Training Team

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

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

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

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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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

New Approaches to Risk & Decision Analysis at the 2010 Conference in London

Friday, November 13, 2009 by DMUU Training Team

Following on from the resounding success of the last Palisade Risk Conference in London, which attracted over 110 attendees from industry and academia, the 2010 Palisade Risk Conference will be taking place on April 14th-15th. The location for this event will again be the Institute of Directors on Pall Mall, London, and already there are a number of exciting presentations confirmed from the likes of Unilever, Pricewaterhouse Coopers and Halcrow.

The 2010 Palisade Risk Conference will be a two-day forum which will cover a wide variety of innovative approaches to risk and decision analysis. Featuring real-world case studies from industry experts, best practices in risk and decision analysis, risk analysis software training, and sneak previews of new software in the pipeline, the event is also an excellent opportunity to network with other professionals and find out how they’re using Palisade risk analysis solutions to make better decisions.

Call for Papers

If you have an unusual or interesting application of Palisade software which you would like to present, please send a short abstract to cferri@palisade.com. The closing date for abstracts to be submitted is Friday, 11th December, 2009.

Batch Fitting in @RISK Risk Analysis Software

Wednesday, November 11, 2009 by DMUU Training Team

@RISK allows you to use historical data to fit data to a probability distribution. The process is very simple: first select the range where the data is located, and then select the Distribution Fitting button. @RISK will guide through the fitting process where you can select a variety of statistical tests such as Chi-Square, Anderson-Darling, Kolmogorov Smirnov, and the Root-Mean Squared Error. View a short tutorial about Distribution Fitting in risk analysis models below.

While the Distribution Fitting functionally is very useful, in some real life cases we need to fit hundreds of distributions, or create filters for certain date ranges or conditions. If we are to do this manually, the fitting process can be overwhelming. A batch fitting function streamlines the process in your risk modeling software.

Palisade’s Custom Development Team has been helping many of our customers automate this process using the @RISK Developers Kit. With a custom batch fitting add-in, we are able to extract information from external databases and organize data so that the fitting process can be done automatically. The resulting distributions can be dropped with ease into risk analysis models.

If you are interested in this type of consulting support for risk analysis models, please let us know. Feel free to contact your Palisade sales representative.

>> View @RISK tutorials

Javier Ordóñez, Ph.D
Director of Custom Solutions

Allocating Contingencies to Risk Events that were identified in a Risk Register

Friday, October 30, 2009 by DMUU Training Team

In a previous blog, I presented a very simple way to allocate contingencies to uncertain cost elements in the project risk management process. However, that methodology works well when there are not risk events that affect a cost element or a group of cost elements.
A risk event is described by two elements: the probability of occurrence and the conditional impact to the project given its occurrence. For example, we have a risk that describes the possibility of a new regulation. If it occurs, it will increment the cost of group of cost elements by a minimum of 10%, most likely 15%, and a maximum 20%. If the risk does not occur, no impact will be observed. Using a Discrete and a PERT distribution, we can model such risk such as:

When sampling from this distribution approximately only 20% of the time will generate a multiplier with a minimum of 1.1, most likely 1.5 and a maximum of 1.2; in 80% of instances the multiplier will be 1. That means that only 20% of the time the risk will increment the cost of selected cost elements by the multiplier previously described as show in the figure below:

In addition to risk events in our cost risk analysis models, we often use distributions that describe cost uncertainties. These distributions model ranges are mostly in a different order of magnitude. Therefore, the variance will also be in a even greater order of magnitude. For example, the cost of Item 3 modeled using a 3-point estimate (i.e., min 100,000, ML 120,000, and max 150,000) has a variance of 87,698,412.70), while the variance of the risk event is 0.0036.

If we are to distribute the contingency using the % of contribution of the variance method, the risk event that we just modeled will be ignored even though we know that such risk event has an impact that we cannot dismiss. Given this practical scenario, the method of variance contribution will not work appropriately.

As an alternative, we can use a tornado diagram that results from @RISK’s sensitivity analysis. Here we can use the regression coefficients to understand what risk events or uncertainties are affecting the total cost in a more drastic way. In the case that you also incorporated events that represent an opportunity to reduce cost, you will observe that the coefficient is negative; in your allocation calculations you should not consider negative coefficients.

In the figure below you can observe the Regression Tornado. Here risk events and uncertainties are represented in a scale that goes from 0 to +/-1:

Knowing the regression coefficient of each input that affects the total cost in a negative way, we can construct a table and obtain a normalized percent that can be used to distribute contingency. If for example, we have a contingency of $100,000, it can be distributed to each input proportionally to the regression coefficient as shown below.

Some risk management experts do not distribute the entire amount of the calculated contingency. It is common practice to distribute only a percentage of it (i.e., 70%). The remaining amount will be used as a reserve that will handle unidentified risks.

Javier Ordóñez, Ph.D
Director of Custom Solutions

Allocating Contingencies to Uncertain Cost Elements in a Cost Risk Analysis Model

Tuesday, October 20, 2009 by DMUU Training Team

In a previous entry to this blog I discussed how to assess the contingency required in a cost risk analysis study. The next step is to allocate the calculated contingency to uncertain cost elements that drive the variation in the total cost of the project. In this way, the contingency can be better managed and controlled throughout the life of a project.

While reviewing literature on this topic, I found a practical way to do this. This methodology uses the percentage contribution of each uncertain variable (usually 3 point estimate distributions) to the variance of the resulting distribution of the total cost.

To apply this method, we need to report the variance of each input distribution and the variance of the end result. In case that input distributions are independent from each other, we can just add up individual variances to estimate the variance of the total. However, this is hardly the case since correlation between input variables is expected in cost models.

@RISK allows reporting statistics from an input distribution without running a simulation as well as statistics that describe an output. These functions are from part of the @RISK functions library: Statistic Functions> Theoretical and Statistic Functions>Simulation Results, respectively. These functions can be accessed using the fx icon from the @RISK toolbar.

To report the variance of input distributions we can use the RiskTheoVariance and for the output RiskVariance. The construction of the allocation model is shown below.

In the project risk management model above, it can be observed that the % Contribution to the Variance of the Total Cost is calculated as a proportion of the input variance to the total variance. Once these percentages are determined we can use them to allocate the management contingency to each cost element. It can be also observed that the engineering allowance is also calculated, and the decision maker now has criteria to manage and control contingencies.

Javier Ordóñez, Ph.D
Director of Custom Solutions