Production Forecasting

@RISK Quick Tips: Automating @RISK risk analysis software with VBA

Tuesday, August 31, 2010 by DMUU Training Team

Presented at the Palisade Risk & Decision Analysis Conference New York City

Chris Albright, author of the book VBA for Modelers, presented a number of examples of how to automate @RISK, RISKOptimizer, and StatTools in Excel using Excel's VBA and Palisade's built-in object-oriented Excel Developer Kit. These examples include production applications, scheduling applications, World Series simulation, and more. All examples include macros written by Dr. Albright, so you'll need to enable macros when you open them.

» Download the examples
» Order Dr. Albright's book "VBA for Modelers"

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

Monday, August 30, 2010 by DMUU Training Team

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

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

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

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

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

» Register now (FREE)
» View archived webcasts

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

Rating the Polls

Monday, August 23, 2010 by Holly Bailey

With the New York State primaries coming up September 14 and the general election on November 2, I predict that as soon as summer turns the corner into September, we'll start hearing lots and lots about polls that predict election outcomes. To find out if there was any early discussion of polls, polling, and outcomes, I returned to my favorite election forecast site from the 2008 presidential elections, FiveThirtyEight: Politics Done Right.

Sure enough, there it was, a comparative rating of pollsters. This will give people like me, who tend to believe any poll just because it's covered in the news, a way to assess the poll reliability. FiveThirtyEight is the brainchild of Nate Silver, and 538 is the number of members of the Electoral College. Silver's primary business is Baseball Prospectus, which is also fueled by Monte Carlo simulation and other risk analysis techniques, but FiveThirtyEight has done well enough for the New York Times to want incorporate it in its online coverage during the coming elections.

Silver's grasp of statistical analysis becomes immediately evident when you go to his page on the pollsters, and he's more than happy to discuss the statistical methods he uses to rate the pollsters--regression analysis of raw data, Monte Carlo software in an Excel spreadsheet, weighting of poll performance data, and so forth. His take on these matters may be of practical interest to any of you who use these techniques in financial risk analysis.

Elections are all about decision making under uncertainty, especially voter decisions under uncertainty, and according to Nate Silver, only polls taken within 21 days of an election are reasonably reliable. So when the national campaigns are ramping up in October, keep one eye on the polls and one on FiveThirtyEight.

Is @RISK a forecasting tool or a decision-making tool?

Thursday, August 19, 2010 by DMUU Training Team

Most people understand that @RISK and Monte Carlo simulation are designed to be an improvement on single-point estimates. In practice, however, I often see people using @RISK as a forecasting tool to get yet another single-point estimate, such as the 90th percentile, without putting it into the context of the potential range of outcomes.

This is probably the difference between a forecasting and a decision-making. The former tends to focus on historical or observed trends and developing specific scenarios (e.g. best, most likely, worse) based on expert opinion, while the latter is concerned with confidence ranges and likelihood.

Indeed, it’s not until you add probability, as with @RISK, that you start to measure the quality of your forecasts (i.e. your confidence level) and calculate the margin of error – something that’s crucial in all walks of life!

In my opinion, therefore, @RISK is much more of a decision-making tool than a forecasting tool. Both involve trying to predict the future but the addition of probability gives decision-makers vital insight to a problem.

Don’t you just love semantics!

Ian Wallace, ACMA
Palisade Training Team

@RISK Quick Tips: Six Sigma Design of Experiments: Welding

Tuesday, August 17, 2010 by DMUU Training Team

A key application of @RISK is Six Sigma and quality analysis. This model demonstrates how @RISK can be used for a DOE analysis of a welding project.

Suppose you are analyzing a metallic burst cup manufactured by welding a disk onto a ring. You need to make sure weld strength is within safety limits. The model relates the weld strength to process and design factors, models the variation for each factor, and forecasts the product performance in relation to the engineering specifications. @RISK is used to model the variation in each factor, and simulate different outcomes for weld strength. Output includes After Six Sigma statistics for Cpk-Upper, Cpk-Lower, Cpk, and PPM Defects (or DPM). Standard @RISK statistical analysis functions (like RiskMean) are also used.

» Download the example: SixSigmaDOE.xls

Free Webcast This Thursday: “Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance?”

Monday, August 16, 2010 by Steve Hunt

On Thursday, August 19, 2010, David Roy will present a free live webcast entitled. "Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance? "

As companies make changes to or introduce new Products, Processes or Services, we observe a wide spectrum of methodology; from well defined process with trained resources, effective tools and excellent results - to no process, ad hoc application of tools, and frequent cycles of “Launch and Learn.”

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.

» Register now (FREE)
» View archived webcasts

Free Webcast This Thursday: “Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance?”

Monday, August 16, 2010 by DMUU Training Team

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.

» Register now (FREE)
» View archived webcasts

Taking the Price

Friday, August 13, 2010 by Holly Bailey

Everyone should be allowed at least one vice, and mine is horses. I love them, spend as much time around them as feasible, and find that after years of this I'm still learning. Recently I've met a couple of people know a whole lot about horse racing. They don't know a thing about the horse itself, but they have a very sophisticated understanding of the mathematics of predicting performance.

So that I could keep up my end of our conversations, I looked further into handicapping and discovered that horse races themselves are only a kind of graphical display to show the results of some massive efforts at statistical analysis, including some of the quantitative forecasting techniques used by financial analysts and whole lot of custom Excel programming. This should surprise no one--after all, what is betting on a horse if not decision making under uncertainty?--but what did surprise me is level of technical discussion about the math and how to work it through in Microsoft Excel statistics.

Take a look, for instance at a recent blog on "taking the price" from the U.K.'s Simon "The God of Odds" Rowland. Taking the price is locking in the odds when you bet. He discusses how to correlate a horse's rating--the amount of weight the horse has been assigned to carry--with the actual odds on this competitor. He then gives the mathematical recipe for his custom Excel spreadsheet, which combines Monte Carlo simulation and the related Markov Chains technique. He wraps up his demonstration with a standard disclaimer: "It must be immediately apparent that this process is very susceptible to the GIGO (garbage in, garbage out) principle. No manner of mathematical manipulation will make up for essential shortcomings in the ratings and in the confidence attributed to those ratings."

No matter how good your model, it's still You Play, You Pay. And Rowland's disclaimer echoed a comment an influential racing veterinarian made to me: "Never invest in something that eats while you sleep."

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.

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

Thursday, July 29, 2010 by DMUU Training Team

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

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

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

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

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

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

» Read the BC Hydro case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

A Little Limelight

Tuesday, July 27, 2010 by Holly Bailey

Limelight--and by this I mean positively glowing publicity-- shines only occasionally on quantitative analysis, and rarely on Monte Carlo simulation. But there was, 6 years ago, Michael Lewis's Moneyball, which established a place for statistical analysis in major league baseball. Now there is Relativity Media, LLC, currently one of the heaviest hitting movie production companies in the business, and, more specifically, there is Ryan Kavanaugh, its CEO, and Ramon Wilson, its executive vice-president of business development.

Two things about Kavanaugh and Wilson make them unusual: they are leading a movie production firm that is not only alive but growing, and they use quantitative analysis for lots of decision making under uncertainty. What can be more uncertain than investing in a movie? Only somewhat unusual for the movie business is the fact that these two decision makers are under thirty-five--it's a youth oriented business--and maybe this is correlated with their emphasis on making decisions by the numbers.

"You can't think of it as money," Kavanaugh has been quoted as saying. "You have to think of it as math." Given the multimillion-dollar budgets Relativity underwrites--the raw size of the risks involved--it's probably more comfortable for everybody at Relativity to think math. The kind of math Kavanaugh is particularly devoted to is Monte Carlo simulation, and he talks quite openly about his company's use of it. When it comes to variables, he names names: principal actor, genre, director, release date, PG or R, although in all probability (sorry), each of these variables is probably a set of variables.

"Everything has to run on the principle of profit. We'll never let creative decisions rule our business decisions. If it doesn't fit the model, it doesn't get done." That doesn't mean, he has explained, that if he really likes a project, he and Wilson can't juggle the variables to make the film project fit the model. They change the parameters to reveal the path to profit. And profit he has--the estimated assets of Relativity are about $2 billion.

So Kavanaugh qualifies as a mogul, a math-for-movies mogul. When the spotlight falls on him, Monte Carlo simulation isn't far out of it.

Oops! Didn’t see that coming! Part 3

Monday, July 19, 2010 by Steve Hunt

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

Oops! Didn’t see that coming! Part 3

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

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

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

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

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

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

Tollgate 3 Exit Criteria:

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

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

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

Tollgate 4 Exit Criteria:

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

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

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

BIO:

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

» Part 1
» Part 2

@RISK Quick Tips: Oil & Gas: Production and Economic Forecast using Exponential Decline.

Tuesday, July 13, 2010 by DMUU Training Team

@RISK has many applications for oil and gas exploration and production. This quantitative risk analysis model forecasts production, revenues, and present value based on exponential decline. Uncertain input factors include yearly production, decline rate, GOR, price of gas, price of oil, and rate of increase in oil and gas prices.

A SimTable function is also used in the Discount Rate input that is used to calculate Total NPV. This contains two possible values for Discount Rate – 12% and 14% - enabling you to run two back-to-back simulations to compare the effect of different discount rates on your Total NPV.

» Download the example: Declin.xls

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

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

Wednesday, July 7, 2010 by DMUU Training Team

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

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

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

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

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

A useful lesson...

» Read the full ADWEC case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

@RISK Quick Tips: Asset Price Random Walks and Options Valuation.

Tuesday, June 29, 2010 by DMUU Training Team

@RISK risk modeling software is used for a wide variety of applications in financial risk analysis forecasting, investments, and banking. This model is one example of how @RISK can help in risk analysis decision making.

Models of the prices of assets (stocks, property, commodities) very often assume a random walk over time, in which the periodic price changes are random, and in the simplest models are independent of each other. The future price level of the asset may result in some contract or payoff becoming valuable, such as in the case of financial market options. In these cases, the value of the contract (contingent payment or option) is calculated as the average discounted value of the future payoff. In the special case of European options on a traded underlying asset, the value calculated from the simulation may be compared with mathematical formulas that analytically provide the valuation, such as the Black-Scholes equation. In many more complex cases, the pertinent analytic formulas may be unknown or very complex to derive, and one may wish to rely on simulation techniques. This particular model compares the average simulated payoff for European Call and Put options with the Black-Scholes valuation.

» Download the example: AssetPrices.Options.BS.Multi.xls