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Number Cruncher or Decision Professional?

Andrea Dickens and Dr Sven Roden

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Are quantitative analysts appropriately called upon by decision-makers? Certainly decision-makers like our ability to quantify complex situations that include huge amounts of uncertainty. But do they really view us as trusted advisors that they should automatically consult when they face difficult decisions? Or do they prefer to simply have us as back-office number crunchers? This presentation will discuss some of the ways that we could get ourselves more integrated into the decision-making process. We will also explore how we could act as a community to organise ourselves so we are seen as Decision Professionals and not just number crunchers.



Palisade Overview, and Avoiding "The Number"

Sam McLafferty and Randy Heffernan

Having just celebrated its 25th anniversary, Palisade stands at the forefront of risk and decision software analytics. Sam will provide a bit of background on Palisade’s history and will describe what sets Palisade apart in the market. He will give an overview of Palisade’s best-selling @RISK and DecisionTools Suite of analytical tools for Excel, with a special emphasis on the just-released new language versions in French, German, Spanish, Portuguese, and Japanese. Sam will describe the latest enhancements and additions to the product line before providing a glimpse into what’s coming next from the company.

There’s been a lot of talk about the need for “proper risk analysis” in the last couple of years. However, risk analysis can be both qualitative and quantitative. Any meaningful risk analysis must be done probabilistically, but what does that mean? Palisade Vice President Randy Heffernan will talk about probabilistic risk analysis: what it is, why it’s important, and how you can benefit. And why there’s no such thing as “the” number.


Action from Insight

Jan Paul Van Driel

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Based on sophisticated analysis enabled through use of decision analysis and science application and supporting tools (@RISK) leadership is endowed with an increasing amount of insight that supports decision making. During the conference we have seen multiple examples and case studies that illustrate what is possible. This presentation explores how these insights can directly address the concerns from decision makers to help them make trade-offs that require thought, discussion and the reality of implementation. We see decision making not as an event – it is a process and ultimately a capability.


Custom Software using
@RISK and the DecisionTools Suite

Dr Javier Ordóñez
Palisade Corporation

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@RISK and DecisionTools Suite software ship with full-featured development environments that allow you to create custom applications using Palisade technology directly in Excel. Palisade offers custom software development services to take full advantage of these Excel Developer Kits (XDKs), creating applications tailored to your needs right in your spreadsheet. We can also create custom applications using @RISK and other technology for any Windows-based application outside of Excel.

Using a custom interface, we will show how to define uncertain elements in each model and how to interpret the simulation results. We will present several examples to reveal how @RISK can be used to plan investment strategies for retirement, manage a portfolio of assets, perform a cost risk analysis and assess the risks in prospecting for oil. These examples demonstrate how users can run a model tailored to their needs without learning how to use @RISK.



Benefits from weather derivatives in agriculture: a portfolio optimisation using RISKOptimizer

Ulla Kellner

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Crop farmers in Germany‘s federal state of Brandenburg have one of the highest volatilities of family income in Europe. The main reason is that levels of rainfall in Brandenburg are low, and the predominant sandy soils there retain water poorly. Although climatic circumstances create high production risks in Brandenburg, crop farming is the main line of agricultural production. It is expected that fluctuations in temperature and rainfall will be much higher in the future and this would again raise the fluctuation in yields, caused mainly by absence of water in the main growth period in the region of North-East Germany.

Just like a portfolio manager, a farmer has different possible elements, with different amounts of expected values and different amounts of risk, to till his fields. Usually all crops have a high positive correlation with each other. So risk hedging needs to be done. Many crop farmers in Germany employ price hedging in the form of forward contracts. But there are very few instruments available to help them handle their yield risk. So the question arises: Why do crop farmers not use different kinds of weather risk management instruments? In many countries such as the United States and Spain, crop farmers take out multi-peril crop or farm income insurance. Index-based weather hedging instruments are used especially in developing countries to stabilise family farm income. In Germany crop farmers do not demand insurance contracts besides insurances against catastrophic risks like for example hail.

In this paper we evaluate which kind of management strategy would be optimal for crop farmers in a specific county of Brandenburg and provide hints to insurers on how to design an effective instrument for this specific region. In a first step we create a put option for weather derivative speculation.

To quantify the benefits of specific risk management instruments we built a whole farm risk program planning approach, using empirical data from the German Ministry for Agriculture. To determine the level of risk aversion, we derive a limit for the allowed standard derivation of the total gross margin from the production program of the last years. We then look for an additional benefit gained from crop rotation and weather derivatives as an on-farm and a market-based instrument. Weather derivatives have asymmetric pay-off-structure and that is why an algebraic optimization cannot be used for optimization. This is accomplished using a program which combines a stochastic simulation with genetic algorithm. Palisade’s RISKOptimizer gives us the possibility to work with a normal MS-Excel LP-tableau and lets us use a simulation based optimisation tool.


Bushing blocks optimization for an external gear pump

Maria Pia D’Ambrosio
Marco Manara

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This project aims at the optimization of the design of the bushing blocks assembled into a specific type of external gear pump produced by Casappa, one of the world leader manufacturer of this kind of machinery. The bushing block is one of the key parts of a gear pump. Its optimization depends on a large amount of parameters and has to fulfil multiple economical, functional and mechanical requirements. The proper optimization of the blocks is a crucial element of the pump design.
In the specific case here analyzed, due to important economical needs and to constraints coming from the production process, a re-design of many key geometrical characteristics of the blocks is required. The purpose of this study is to optimize several responses simultaneously. These responses come from different mathematical models (up to four) and are related to performance, noise level and cost of the pump. The optimization is carried out using statistical definition of the process tolerances and DFSS (Design For Six Sigma) tools, such as Monte Carlo simulation, which is performed through @RISK.

The presentation discusses how automation can be applied to the process measurements (using CMM - Coordinate Measuring Machine - data acquisition) of the block dimensions, in order to define the process natural variation and the type of distribution better approximating the real data. These evaluations are then used as input of a Monte Carlo simulation with multiple models and responses. The above mentioned techniques will lead to a combined optimization of the design parameters. This optimization will enable to achieve all the targets and to satisfy both economical requirements and process constraints.


Calculation of Construction Costs for Building Projects – Application of the Monte Carlo Method

Dr Christian Hofstadler

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Construction time is of crucial importance when it comes to utilizing the production factors in an effective and efficient way. Construction periods that are too short usually result in higher cost, poorer quality and a larger number of disputes. This presentation sets out to demonstrate the calculation of construction time and cost whilst considering key construction management parameters. Beyond a simple, deterministic method, other options for calculation are shown that rely on probability calculus. The approaches described to determine construction time and cost are illustrated by a building project example.

The deterministic method results in one value per each calculation process (calculation mode 1). In calculation mode 2, probability calculus is applied in a simple fashion. Both range and probability of occurrence can be considered for the relevant input variables. For the third calculation mode, the Monte Carlo method is applied using Palisade’s @RISK software. For each of the parameters to be determined, this method shows a probability distribution.

Using a high-rise building project, the application of the Monte Carlo method (calculation mode 3) to determine construction time and costs is demonstrated. Weighted triangles are used as distribution functions, which makes it possible to consider minimum and maximum values, as well as expected values. The correlation between probability of occurrence and construction times is reflected by a probability distribution.


Cellulosic Bioethanol Plant Project Risk Management via Simulation

Scott Mongeau

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Cellulosic bioethanol has garnered much interest as a promising low-carbon-footprint, alternative fuel which does not consume edible foodstuffs (as traditional corn and sugar-based biofuels controversially do). However, the science and systems behind producing cellulosic bioethanol profitably on a large scale have yet to be definitively and consistently implemented. As such, entrepreneurs interested in this compelling new approach to energy are eager to better map risks and unknowns before committing investment capital to such ventures. Many use the traditional, static Net Present Value (NPV) model to chart project risks in spreadsheets. However, given the multiple, varied, overlapping, and dynamically fluctuating unknowns (commodity prices, exchange rates, energy costs, manufacturing costs, productivity rates, market dynamics, variable tax benefits, etc.), static NPV models are limited if not dangerously misleading to prospective investors. Using Palisade @RISK, a robust, dynamic cellulosic ethanol plant is simulated to give broad insight into risks and unknowns, the better to chart and control these factors. The simulated model includes dynamic economic variables, expense, market, revenue, and productivity elements. The model offers flexibility to tailor the virtual plant to specific configurations, and thus has promise as a due-diligence tool for prospective bioethanol entrepreneurs, investors, project managers, and corporations seeking to become involved in this compelling new field. At a more basic level, the model is an interesting example of using @RISK to model NPV for a dynamic manufacturing business, taking into account economic factors, sales projections, revenue predictions, varying expenses, etc., and thus is of potential interest to finance generalists.


Decisions, Decisions
How cognitive bias affects decision outcomes

Dr Michael Brand

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At the most basic level, making a decision involves choosing between two alternatives, where the outcomes and probabilities of each choice are known. Making a decision becomes complicated when the outcomes are uncertain and/or the probabilities are unknown. Risk analysis in its broad­est sense finds solutions to these difficult problems.

It is not sufficiently well recognised that decision outcomes can be influenced by cognitive bias. Although it has been well established that these cognitive biases do exist, it is not straightforward to either demonstrate them, or convince decision makers of their existence.
Examples of cognitive bias include:

  • Wishful thinking - the decision maker desires an outcome
  • Counter-intuition - the correct decision is opposite to the intuitive guess
  • Sample representation - belief that small and large samples are similarly representative
  • Risk aversion - the tendency to select outcomes with lowest variation
  • Loss aversion - the tendency to avoid negative outcomes

This presentation will use @RISK models to demonstrate these cognitive biases among the audi­ence present.

The objective of this presentation is to allow decision makers to become more aware of cognitive biases in reaching correct decisions in risk analysis. It also demonstrates the versatility of @RISK as an educational tool in teaching decision theory.


The economics of Supply Chain Risk Management using @RISK

David Inbar

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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.

The lecture will give an overview of the concepts and process of supply chain risk management and will demonstrate how using Monte Carlo simulation techniques with @RISK adds value to the decision making processes and enable us to purchase the most cost effective mitigations.


Faldo's folly or Monty's Carlo - The Ryder Cup & Monte Carlo Simulation

Stefan Sadnicki

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The Ryder Cup is arguably the most prestigious and most exciting golf tournament in the World. It is a team event contested once every 2 years between 12 golfers from Europe and 12 golfers from the United States of America. For the 12 singles matches on the final Sunday, each captain selects the order in which his players tee off. In 2008, after an eventual US victory, the sporting press was hugely critical of Nick Faldo’s (the European captain) slate selection. This article looks to explore the justification of such criticism. First, existing academic results are reviewed and, where necessary, updated for 2008. Second, using Monte Carlo simulation, we consider the scheduling of players who react differently under pressure. This simple sporting example illustrates how Monte Carlo simulation can be used to analyse a range of potential scenarios enabling better, more informed decisions. Within a business context, where a winning outcome is essential, non-operational research practitioners must understand how operational research techniques can be used to make better, more informed decisions. This presentation concludes by discussing how the Ryder Cup model, together with a related example analysing interdependent project risks, was successfully used within a consultancy environment to introduce non-OR practitioners to the theory behind and the potential of Monte Carlo simulation.


How can we help decision makers feel more comfortable with Monte Carlo?

Dr Pierre Delfiner

A black box is any device whose workings are not understood by or accessible to its user. This definition fits exactly what some decision makers in the Oil & Gas industry think about Monte Carlo simulation, especially if they come from an engineering background. It is not only a question of training, there is a genuine need for those who bear the responsibility of important decisions to fully grasp the information that is presented to them, and exercise their judgment on concrete cases. However, without a proper model of risks and uncertainties, the selection of a few deterministic scenarios remains subjective and there is no way to know how representative they are. For complex projects, such as a large offshore development or an LNG plant, whose resource base includes many fields and prospects, Monte Carlo simulation is the only way to correctly explore the space of possible outcomes and optimize decisions.

The challenge is to be both correct and understood. A number of principles and techniques can be proposed to achieve this goal and will be discussed. An important rule is to distinguish the analysis itself, which should have the level of complexity required to get correct results, from the presentation of the results which must be kept as simple as possible. All too often technical people want to share the beauties and intricacies of their models… and only manage to puzzle their audience. The “Collapse and Expand Child Branches” of PrecisionTree is a good model for introducing complexity only as needed. A technique that proved useful to dispel the black-box image is to collect all simulated values in @RISK and store them in an Excel™ worksheet, which is presented as a database of scenarios. These are traceable, they can be inspected individually, and impossible ones can be deleted. Any decision or strategy may be tested against the database by using appropriate filters.

Acceptability is greatly improved if the analysis provides determi­nistic validation points that decision makers can relate to and helps them gain confidence in the results. Application specific examples related to the optimization of an appraisal strategy will be presented.


Internal Modelling under Solvency II

Hans Waszink

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Under upcoming regulations for the Insurance Industry in Europe, insurers will be given the opportunity to submit results from their own internal models to the regulatory authorities for the assessment of Solvency Requirements. In this presentation we will explore how Monte Carlo simulation techniques can be used to comply with the new regulations, and how insurers can use these techniques to their own advantage.
A case study will be developed of an insurance company with multiple lines of business, a reinsurance program, and a varied asset portfolio. We will show how @RISK models can be used to:
  • Demonstrate Capital Adequacy at a desired confidence level over a specified time horizon;
  • Optimise reinsurance and other risk mitigation programs given constraints on capital availability, and risk appetite.
  • Evaluate different asset allocation strategies.
  • Evaluate other business decisions on a purely economic basis 


New Approaches to Transport Project Assessment: Reference Scenario Forecasting and Quantitative Risk Analysis

Dr Kim Salling

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This presentation sets out a new methodology for examining the uncertainties relating to transport decision making based on infrastructure appraisals. Traditional transport infrastructure projects are based upon cost-benefit analyses in order to appraise the projects feasibility. Recent research however has proved that the point estimates derived from such analyses are embedded with a large degree of uncertainty. Thus, a new scheme was proposed in terms of applying quantitative risk analysis (QRA) and Monte Carlo simulation in order to represent the uncertainties within the cost-benefit analysis. Additionally, the handling of uncertainties is supplemented by making use of the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits (user demands i.e. travel time savings) and underestimating investment costs.


Pricewaterhouse Coopers and Palisade:
An Overview

Frank Lyhne Hansen

An insight into how Pricewaterhouse Coopers have used Palisade solutions over the years in their consulting projects. As a provider of advisory services to companies in a wide selection of industries, all of whom are faced with different issues and challenges, this presentation provides an overview of how one of the world's leading consultancies has utilised the flexibility and versatility of @RISK.


Put More Science into Cost Risk Analyses

John Zhao

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Economists have nicknamed next “te(e)n” years as ”debtcade” due to the unexpected but already anticipated naughty financial behaviour. The crisis had been forecasted by those quant specialists but was ignored by those management generals. Quantitative analyses have the merits to quantify measurable risk probability and to forecast foreseeable risk consequences. The application of risk analyses using @RISK is pervasive in many industries, and its use has already been proliferated. In energy industry, oil price drives profitability in many ways; higher-than-budget costs of capital projects and operating facilities however effectively erode such profit margins. Whilst the futuristic oil price may be hard to predict due to its low manageability, it is absolutely possible to scientifically forecast the sizes of risks that companies are willing to take, and such risks may include the probabilistic volumes of newly discovered reserves, probability of meeting a project development schedule, chances of project cost overruns, and the likelihood of eroding entire project profitability. To achieve these goals, @RISk has lent a helping hand to business analysts for easier operation of complicated mathematical modelling. Statoil, an international oil company, takes risk management seriously and has applied Monte Carlo simulation techniques in their core and support businesses using @RISK package. Such applications not only include the solo use of individual application but integrated combinations from drilling and well completion to cost and schedule controls at project execution. Besides the widespread uses of the package, a specific application of @RISK to convincingly simulate required capital project contingency is worth discussing in details. A simplistic line-item ranging exercise using @RISK Monte Carlo simulation is no longer adequate to derive large capital project contingency, as empirical data confirmed that many disastrous cost overrunning projects were lack of contingency to cover the covert risks. To show the management complete risk picture on a project, both systemic risks that empirical history indicate the likelihood of occurrence, and specific risks that have discrete probabilistic characteristics, should be included in the overall project risk analysis. Therefore the combination of continuous PDF for project cost estimates and discrete PDF for project risk register may prevail and provide management with more convincing project cost contingency.

This task is easy said than done as many oil companies currently neither use such approach, nor willingly collect empirical data to support such combination, they unfortunately continue with ranging line-items. To reach the climax of best in class quantitative risk analysis, research in theories and trials on pragmatic frontier realities are necessary. The author’s self interested manipulation of a mock cost estimate contingency model using @RISK simulation functions indicated that the integrated qualitative risk assessment and quantitative risk analysis can yield a more realistic project contingency, as the postulated estimate contingency percentages can no longer represent today’s economy. More over, incorporating potential delays of project execution schedule into cost risk analyses reflect the industry reality more often than not, generating a realistic project contingency. Therefore, such a model demonstrates a capability to better mimic project overall risk scenarios in a comprehensive manner, hence deriving a risk contingency that can sustain the contests of project vulnerability over the time. This approach also aligns with the AACEi’s Recommended Practices (RP 2009) for cost risk analyses.


Risk Based Water Distribution Rehabilitation Planning

Alec Yeowell

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The performance of deteriorating water distribution systems can be managed by replacing or rehabilitating pipes which is funded with capital maintenance expenditure.

Capital maintenance investments in UK water company assets are justified on the basis of risk to customer service.

It is important for water companies to achieve good capital efficiency by getting the best return from each £ invested. Halcrow worked with Bournemouth and West Hampshire Water to develop an innovative risk-based approach to targeting capital maintenance investment with economically efficient rehabilitation schemes.

A spatial optimisation tool was developed to identify areas of the pipe network with the highest failure rates. The failure statistics of the ‘clusters’ of events were used to drive quantitative consequential impact models using Palisade @RISK tools to estimate the potential service risk that could mitigated through capital maintenance expenditure.

Potential schemes for targeted investment were benchmarked against the company’s operational experience.

The results suggest the clustering approach has the potential to provide significant improvement in terms of capital efficiency, when compared to the models used for long-term business planning.
Temporal studies using the clustering and risk models will enable several of the underlying assumptions to be verified and the longer-term company periodic review process, undertaken by the industry Regulator, will provide a basis for assessing the effectiveness of the approach.


Risk Sharing in Waste Management Projects

Steven Vaughan-Jones

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The waste management industry in the UK is undergoing a major shift in its approach to managing waste with the introduction of the European Commission’s Landfill Directive. The industry is set for significant expansion in the coming years. A large number of new treatment facilities are required to implement these changes and this will inevitably require huge investments. However, there is some reluctance in investing in the waste sector due to a lack of understanding of the potential risks associated with integrated waste management projects. This reluctance is intensified further by the current economic downturn, which has often had inadequate risk management cited as a key cause. Therefore, a transparent and auditable assessment of the performance, costs and risks of new treatment technologies is required to resolve the bottleneck in financing waste management facilities. In this paper, a risk assessment model for integrated waste management projects is presented. The model assesses the performance and cost related risks and uncertainties of a variety of mechanical, biological and thermal waste treatment technologies. In this paper, the cost and performance related uncertainties that can significantly influence the net cost of treating residual waste in a mechanical biological treatment facility and energy from waste facility are reported and discussed. The bespoke model utilises Palisade’s @RISK software and uses Monte Carlo simulations in order to report the highly sensitive parameters in the form of tornado graphs and net costs as probability distribution graphs.

To date, most decision support models available and applied in the waste management sector assess impacts of uncertainties by modelling best and worst case scenarios. Most of these applications are incapable of handling uncertainties associated with a wide range of parameters as considered in the risk model discussed in this paper and therefore have limited ability to inform risk sharing in waste management projects.

The model and the results discussed here act as a powerful decision support tool to inform and influence the risk sharing process on large integrated waste management projects.


Securing of supply chain in aeronautic industry

Philippe Stollsteiner

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More than in other industry, aeronautic industry needs a particular attention from industrial suppliers regarding the supply in time of the on-board requested equipment. Consequences of a delay may lead to huge penalties from the aircraft manufacturer client, or even worse, order cancellation. On the other hand, for the electronics manufacturer, keeping in stock quantities of parts or equipment may represent a significant cost.
How to determine the optimal stock for each equipment and avoiding any supply shortage?

After analyzing the main cause of delivery delay, 2 causes have been determined:
-    Non punctuality of the  manufacturer's supplier.
-    Non quality of products delivered by the manufacturer's supplier.
The other parameters taken in account were:
-    Delay value in case of late delivery by the supplier in case of non ponctuality.
-    Time to repair and re-deliver in case of non quality.

A model has been developed in Excel using @Risk binomial distribution for determining the number of delayed items and the number of faulty items per week. Then, using @®isk discrete distribution, the delay or time to repair has been entered in order to compute the delayed delivery.
A discussion has been initiated with the client regarding whether to take a standard value of the delay (or mean value), which makes easy the typing of formula in Excel, or to take a more realistic approach by computing a distribution of the possible delay. This second option requires the use of VBA macro-instructions between each iteration of the Monte Carlo simulation. This last point leads to a discussion of the necessity of modelizing extreme values, when no tolerance is accepted regarding the risk impact


A Six Sigma & Simulation Approach to Software Quality Risk Management

Vojo Bubevski

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In today’s competitive business environment, software quality and customer satisfaction are more important than ever. Achieving software quality goals is a major objective for software development organizations as it is a critical constraint on their projects. Software Quality is a phenomenon with significant uncertainty involving a substantial risk. Consequently, managing the software quality risk is an important challenge for software projects. The conventional approach to Software Quality Risk Management is based on analytic models and statistical analysis. The analytic models are static, so they don’t account for the inherent variability and uncertainty of software quality processes, which is an apparent deficiency.

This paper presents an application of Six Sigma and Simulation in Software Quality Risk Management. DMAIC and simulation are applied to a quality process, such as testing, to assess and mitigate the risk in order to deliver the product on time, whilst achieving the quality goals. DMAIC is used to improve the process and achieve required (higher) capability. Simulation is used to predict the quality (reliability) considering the uncertainty and variability, which, in comparison with the analytic models, more accurately models the process.

Presented experiments are applied on a real project using published data. Compared with the actual data, the experimental results are very satisfactorily verified. This enhanced approach is compliant with CMMI® and provides for substantial Software Quality performance-driven improvements.

Simulation and Neural Networks Applications
in Software Reliability Prediction

Vojo Bubevski

Achieving software reliability goals is a major objective for software development organizations as it is a critical constraint on their projects. Predicting the software reliability at some point in the future based on data already available, is an important challenge for software projects. The conventional approach to Software Reliability prediction is based on analytic models. These models don’t account for software processes’ inherent variability and uncertainty, and require estimation of parameters and unrealistic/oversimplified assumptions – apparent deficiencies.

This paper presents applications of Simulation and Neural Networks in Software Reliability prediction from the practitioners’ perspective. Different simulation and neural networks models are used to predict the reliability of a real software system using published data. The predictive capability of the models is evaluated using the actual data.

Comparison of simulation and neural networks models versus analytic models is provided. Simulation and Neural Network models offer a superior alternative to conventional analytic models.


Succeeding in DecisionTools Suite 5 rollout – Unilever’s story

Andrew Evans

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Unilever is present in 150 countries, employs 175,000 people and has an annual turnover in excess of €40 bn. A pivotal question for any organisation of this size is how to embed Decision Analysis and ensure that the right people have the appropriate skills, tools and support to ensure that high quality strategic decisions are made wherever and whenever required.

Software has a key part to play in the solution, but by itself it can never be the entire answer. Unilever has put into place a range of interventions to ensure the successful adoption of DecisionTools Suite (DTS) as part of an overall Decision Analysis embedding programme.

A self-reinforcing cycle of interventions is ensuring the successful adoption of DTS within Unilever. This cycle starts with the use of a flexible licensing system (FLEXnet) which enables company-wide installation of DTS, and effective monitoring of users. A wide range of training interventions - from 20 minute e-learning modules to a 6-month advanced programme - ensures that users have the skills to use the software and other tools to apply Decision Analysis. Following training, users are supported in their use of DTS through a network of global and local communities of practice, a network of Advanced Practitioners, and a rapid response help service known internally as ‘Model Solutions’. Best practice Decision Analysis is recognised and celebrated throughout the organisation by the publishing of a best practice database and an annual awards event which is sponsored by members of the Unilever Finance Leadership Team. The cycle is completed through external validation of training programmes by Stanford University, which includes an examination of course participants to ensure they have the skills needed to analyse complex decisions. The potential to obtain professional certification provides an incentive to practitioners to use DTS as part of the Decision Analysis programme and ultimately to enable Decision Analysis to become ‘the way we do things’ at Unilever.

Use of Monte Carlo Simulations for Risk Management in Pharmaceuticals - A Case Study

Barry (Bir) Gujral
Peter Amanatides

The risk based regulatory approaches recognize the level of scientific understanding of  different factors affecting the product and quality performance and the capability of process control strategies to prevent and mitigate the risk of producing a poor quality product in Pharmaceuticals.  A probability distribution function is assigned to the unknown variables and then the Monte Carlo Simulations using @RISK are run to determine the combined effect of multiple variables. The Simulation envisions process variances, uncertainties and interdependencies for continuous improvement. It also helps to control random and non-random variability for better consistency. The risk analysis approach selects values for independent variables as a function of a probability distribution function for each variable. Thus for a given data the variability in effectiveness is viewed as a probability distribution. A standard sensitivity study shows us the sensitivity of the resulting improvements from the range of outputs from a single variable.


Best Practices in Modeling

Dr Michael Rees
Palisade Corporation

This session covers some ideas in modeling best practices, in both Excel models and @RISK models. Topics include issues in model design, structure, formatting, error-checking and a variety of tools related to sensitivity analysis. We also mention some uses of Palisade’s TopRank for model auditing and checking.


Introduction to DecisionTools Suite 5.5.1
and the new international versions

Dr Michael Rees
Business Best Ltd.

This session will show you how to use the elements of the new DecisionTools Suite 5.5.1 as a comprehensive risk analysis, optimization, and statistical analysis toolkit. Each of the products in the Suite — @RISK, RISKOptimizer, Evolver, PrecisionTree, TopRank, StatTools, and NeuralTools — will be presented, showing how they can be used to solve practical problems in the real-world. Pick up hints and tips for using the products together. Also, we'll take a look at the completely-translated Suite in one of the international versions: Spanish, French, German, Portuguese, or Japanese.


Introduction to @RISK

Dr Michael Rees
Business Best Ltd.

This introduction to @RISK will walk you through a risk analysis using various example models. Key features of @RISK will be highlighted, and new enhancements in version 5.5 will be pointed out along the way. You will experience the intuitive interface of @RISK as you define distributions, correlations, and other model components. During simulation you will be able to see all charts, thumbnails, and reports update in real time. View results with a variety of graphing options, including new cumulative-histogram overlays, scatter plots in scenario analysis, and more. There’s so much to see, we’ll cover as much as time permits.


Intro to RISKOptimizer

Dr Mirek Janusz
Palisade Corporation

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RISKOptimizer uses powerful genetic algorithms to perform optimization in Microsoft Excel. Moreover, RISKOptimizer builds on traditional optimization by adding Monte Carlo simulation to account for uncertain (stochastic), uncontrollable factors in your optimization problem. This session introduces you to this powerful tool, showing you how to set up a model, define constraints within the model, and ultimately arrive at the optimal outcome. Examples of resource allocation, budgeting, and scheduling will be included.


Introduction to Project Risk Management using @RISK for Project

Ian Wallace
Palisade Corporation

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The aim of this seminar is to give people a basic understanding of how @RISK for Microsoft Project works, including hands-on experience for setting up and running simulations, and interpreting the results.

Attendees will learn about the key functionality within @RISK for Project in step-by-step method, enabling them to quickly become familiar with basic concepts and terminology.

In addition to graphing and quantifying the risk in a business plan, you will learn how @RISK for Project, using Monte Carlo simulation, enables you to:

  • Calculate the probability of success
  • Graph the margin of error around the most likely outcome
  • Quantify and prioritize the risk drivers
  • Quantify the amount ‘@RISK’

Introduction to StatTools and NeuralTools

Dr Mirek Janusz
Palisade Corporation

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In this session we will learn how to use Palisade’s two data analysis tools: StatTools and NeuralTools.

StatTools is a Microsoft Excel statistics add-in. This session will cover how to perform the most common statistical tests, and will include topics such as: Statistical Inference, Forecasting, Data Management, Summary Analyses, and Regression Analysis.

NeuralTools imitates brain functions in order to “learn” the structure of your data. Once NeuralTools understands the data, it can take new inputs and make intelligent predictions. The new predictions are based on the patterns in known data, and offer uncanny accuracy. NeuralTools can automatically update predictions when input data changes, and it can even be combined with Palisade’s Evolver or Excel’s Solver to optimize tough decisions and achieve desired goals.


Real Options Modelling with
@RISK and PrecisionTree

Dr Michael Rees
Business Best Ltd.

This session introduces the topic of real options modelling as an extension of risk modelling. The link to general decision making under uncertainty and financial market options is also discussed. A variety of examples using @RISK and PrecisionTree is presented.

Selecting the Right Distribution in @RISK

Dr Javier Ordóñez
Palisade Corporation

This session covers the choice of the appropriate distribution in @RISK. A variety of approaches are presented and compared, including pragmatic, theoretical and data-driven methods. The use of distributions to treat a variety of risk modeling situations is discussed, and some new distributions and features in v5.5 are shown.


Using @RISK in Cost Risk Analysis

Dr Javier Ordóñez
Palisade Corporation

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In this session we will explore how @RISK can be used to model cost uncertainty and risk events that will affect the total project cost. We will show how to model cost ranges and risk registers through the use of probability distributions. We will discuss how to measure correlation between variables, how to add a correlation matrix into a model, and the impact of correlation in a result.

Once our simulation model is run, we will learn how to assess the contingency required. We will also learn how to identify the key drivers that are needed for a mitigation analysis, and learn how to use of multiple simulations to compare the effectiveness of the different mitigation strategies.

If time allows, we will present examples of VBA macros that allow automating the construction and reporting of risk models.



Peter Amanatides
Vice President, QA and QC
Noven Pharmaceuticals, Inc.

Peter Amanatides joined Noven Pharmaceuticals, Inc. in September of 2008 as Vice President of Quality Assurance and Quality Control.  Prior to joining Noven, Peter had over 20 years of diverse pharmaceutical and biotechnology industry experience. He previously served DSM Pharmaceuticals, Inc. (a global contract manufacturer of oral and topical dosage forms, sterile products and active pharmaceutical ingredients) in several senior quality roles. In his most recent position at DSM, Peter held the Senior Director of Quality Operations at the DSM Greenville, NC site with responsibility for all quality operations, including quality assurance, quality control, analytical development and regulatory affairs. Prior to his career at DSM, he was Director of Quality Systems at Celera Genomics, a human genome sequencing company affiliated with Applera Corporation. Peter worked for several pharmaceutical and biotechnology companies holding positions of increasing responsibility in Quality Assurance and Quality Control functions.

Peter holds a Master of Science degree in biotechnology and molecular biology from Hood College and a Bachelor of Science Degree in biology from Regents College. He has authored and co-authored several scientific publications in the areas of genome sequencing and biotoxic compounds for a variety of magazines including Science, Nature and Hybridoma.  In addition, Peter is a Six Sigma black belt trained at the University of Texas- Austin.  He has created and managed several six sigma programs in the pharmaceutical and biotech industry during his career.

Dr Michael Brand
Co-founder and Director
Captum Capital Limited

Michael Brand is a founding director of Captum Capital Limited, an innovative consulting company which provides business development services to early stage life science companies. Captum offers a series of training courses in business development, including the highly successful Valuing Life Science Technology MasterClass, which has been attended by over 350 executives. Michael spent most of his career in the USA holding senior executive level positions in multi-national corporations, including a venture capital company. He has successfully negotiated international joint venture, licensing and investment agreements. He holds a PhD from Imperial College, London, a MBA from the Sloan School, MIT, Boston, and the Investment Management Certificate of the CFA Society of the UK; he is a Fellow of the Royal Society of Chemistry.

Vojo Bubevski
Senior Systems Analyst / Designer
TATA Consulting Services

Vojo Bubevski comes from Berovo, Macedonia. He graduated from the University of Zagreb, Croatia in 1977, with a degree in Electrical Engineering - Computer Science. He started his professional career in 1978 as an Analyst Programmer in Alkaloid Pharmaceuticals, Skopje, Macedonia. At Alkaloid, he worked on applying Operations Research methods to solve commercial and pharmaceutical technology problems from 1982 to 1986.

In 1987 Vojo immigrated to Australia. He worked for IBM™ Australia from 1988 to 1997. For the first five years he worked in IBM™ Australia Programming Center developing systems software. The rest of his IBM™ career was spent working in IBM™ Core Banking Solution Centre.

In 1997, he immigrated to the United Kingdom where his IT consulting career started. As an IT consultant, Vojo has worked for Lloyds TSB Bank in London, Svenska Handelsbanken in Stockholm, and Legal & General Insurance in London. In June 2008, he joined TATA Consultancy Services Ltd.

Vojo has a very strong background in Mathematics, Operations Research, Modeling and Simulation, Risk & Decision Analysis, Six Sigma and Software Engineering, and a proven track record of delivered solutions applying these methodologies in practice. He is also a specialist in Business Systems Analysis & Design (Banking & Insurance) and has delivered major business solutions across several organizations. He has received several formal awards and published a number of written works, including a couple of textbooks. Vojo has also been featured as a guest speaker at several prominent conferences internationally.

Maria Pia D’Ambrosio
Senior Trainer, Owner
SixSigmaIn Team snc

Maria Pia is 51 years old and works as Senior Trainer at her own Company – SixSigmaIn Team - that provides advanced and customized DFSS, Design of Experiments, Tolerance and Reliability Analysis trainings and support in Italy. She studied Chemistry at University of Milan and she worked as Process Engineer for metallurgical Companies for about 15 years. She has been involved in the Six Sigma activities and Statistic since 1997. She is a Certified MBB and her main activities is coaching, tutoring and supporting people and Companies to get a major breakthrough in their processes.

Dr Pierre Delfiner
Petro Decisions

Pierre Delfiner is with PetroDecisions, a consulting company which he formed after taking retirement from Total at the end of 2008. At Total he was Scientific Advisor to the Director of Geosciences in Paris, specializing in Decision & Risk Analysis, probabilistic reserves estimation and geostatistical modeling. His recent work includes prospect evaluation methodology, modeling of new ventures with multiple objects, analysis of gas supply for LNG plants, and value-of-information assessments of 4D seismic data coupled with physical modeling. He served as review chairman for the SPE Reservoir Evaluation and Engineering journal and is currently member of the Management Subcommittee for the 2010 ATCE in Florence, Italy. He is the co-author of Geostatistics: Modeling Spatial Uncertainty, (Wiley & Sons, 1999).

Prior to Total he worked for Schlumberger Wireline, and before that conducted research at the Center for Geostatistics of the Ecole des Mines de Paris. Delfiner holds an engineering degree from the Ecole des Mines and a PhD in Statistics from Princeton University.

Andrea Dickens
Decision Analysis Group Leader
Finance Academy, Unilever

Andrea Dickens joined Unilever in 1988 as a statistician. Since then she has had a number of roles in Unilever, but all with one thing in common: managing and analysing uncertainty.

Andrea now leads the Decision Analysis Group, which has been developing and applying a wide range of decision analysis techniques. These techniques have been deployed on probabilistic business cases and complex decision problems throughout Unilever. The group also leads the development and rollout of training courses for Unilever Managers in Decision Making techniques, and provides coaching to course participants. In addition, the Decision Analysis Group has an internal consultancy role where they provide facilitative leadership to analyse complex business decisions. These tend to be the large, difficult and sensitive problems, high stake one-off decisions, or problems that cross organisational boundaries.

Andrew Evans
Decision Analyst
Decision Analysis Group, Unilever

Andrew Evans is a Decision Analyst within Unilever’s Decision Analysis Group. His role includes training and coaching finance professionals on the Unilever Decision Making Under Uncertainty (DMUU) Programme, and providing internal consultancy on Decision Analysis for project teams. He also works on the embedding of Decision Analysis through the provision of support services to DMUU Practitioners. Prior to joining Unilever, Andrew worked in the Mobile Resource Management (MRM) sector of the IT industry.

Barry Gujral
Associate Director
Quality Engineering

Barry Gujral holds a Master of Chemistry degree from Illinois Institute of Technology, Chicago and PhD in Chemistry from Meerut University. Barry also received Global MBA degree from Fuqua School of Business, Duke University. He has authored several scientific publications in the areas of Risk Analysis, Monte Carlo Simulations and Predictive Modeling. In addition, Barry is a Six Sigma Black Belt trained from the NC State University Raleigh and is author of three textbooks in Chemistry. Barry was awarded a US Patent on Synthesis of new ion-exchangers.
Barry Gujral is working as Associate Director of Quality Engineering at Noven Pharmaceuticals, Inc. He has over 20 years of diverse pharmaceutical and biotechnology industry experience. Barry completed two projects with the US Food and Drugs Administration (FDA) on Risk Analysis, Monte Carlo Simulations, Statistical Modeling and Quality by Design.

Frank Lyhne Hansen
Leader, Enterprise Risk Management

Frank is leader of the economic and financial modelling team in Enterprise Risk Management. He is experienced both within the financial and non-financial sector and is a member of the PwC steering board on Solvency II. Frank is a specialist in Solvency II, Basel II and economic capital.

Frank has extensive experience in operational, financial and strategic risk management. This includes the development, description and analysis of models as well as implementation, discussion and elaboration of economic models for the total economic optimization and preparation of management reporting.

Frank has served as project and program manager on several major projects both in Denmark and abroad. In addition, Frank has a long experience of teaching as an associate professor on CBS (financial) where he teaches risk management. Frank has taught at CBS since 1996 and has a large network and extensive experience in teaching in risk management, statistics and finance.

Randy Heffernan
Vice President, Palisade Corporation

Randy Heffernan started with Palisade in 1997, and helped the company expand with its first overseas office in Plymouth, England, in 1998. Further geographic expansions included London in 2002 and Sydney, Australia in 2005. He has held a variety of roles in sales, marketing, and management, and currently oversees much of the corporate operations. Randy works closely with the sales staff to understand client needs and liaise with software development. Randy holds a Bachelor of Science degree in Business Management and Marketing from Cornell University.

Dr Christian Hofstadler
Institute of Construction Management and Economics, Graz University of Technology, Lessingstraße

Christian has been an Associate Professor at the Institute of Construction Management and Economics at Graz University of Technology since 2006. He teaches, conducts research and cooperates with the industry in the fields of construction management and economics. He’s gained practical experience in building and industrial construction as well as in bridge construction in 3 different construction companies. In the course of his work, he’s participated in the construction of fair-faced concrete components and structures. In the process, he’s gathered experience in formwork works, reinforcing and concreting. In general, his function at the institute is the planning and the optimization of construction works.

The main focus of his work is the optimization of reinforced concrete works from the point of view of construction management and economics.

His areas of expertise are:
• Reinforced concrete works (technology, construction method, operations planning, logistics etc.)
• Formwork works – technological foundations, fair-faced concrete, process comparison, sequence planning, logistics and cost estimation etc.)
• Construction sequence planning and logistics in construction operations
• Fair-faced concrete (technology, announcement, assurance of quality, operational sequence etc.)
• Construction progress malfunctions (construction operational and construction industrial treatment of construction progress malfunctions)

David Inbar
Minet Technologies

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. Minet has developed a supply chain risk management methodology and performed numerous projects in this field. Based on our experience we created a unique supply chain risk management workshop and conducted it in Europe and Israel.

David has a B.Sc. in chemical engineering from the Technion – Israel institute of Technology and a M.Sc. in management from Tel Aviv University. Before founding Minet David was purchasing manager of several divisions of Teva Pharmaceutical Industries, the world's biggest generic pharmaceutical company. He has lectured in several academic institutions in Israel and Europe.

Dr Mirek Janusz
Palisade Corporation

Mirek Janusz is a software engineer at Palisade Corp. He holds a masters degree in computer science from Cornell University. He helped develop Palisade applications for risk analysis, statistical analysis and optimization, and has assisted other companies in integrating Palisade Developer's Kits into their own applications. He is the lead developer for Palisade's Excel add-in for neural networks, NeuralTools.

Ulla Kellner
Research Assistant

Ulla Kellner studied from 2004-2008 Agricultural Economics at Bonn University. After her graduation she works as a research assistant in the farm management group at the Georg-August-University in Goettingen. Her main research areas are risk management instruments especially crop insurances and weather derivatives for farmers in Germany.

Marco Manara
Casappa S.p.A.

Marco Manara is 33 years old and works as Engineering Manager of the gear pumps and motors business unit at Casappa S.p.A. He obtained a Master of Science in Mechanical Engineering from the University of Parma, Italy. He worked as R&D engineer and as Engineering Director Assistant before being appointed to his current position in 2007. In the last years, he started getting interested in statistics and, in particular, in Six Sigma methodology. He was trained by Maria Pia and, at the beginning of 2008, he obtained the BB Certification. He started a process of re-engineering and re-designing several company products, using statistical definition of the process tolerances and Design For Six Sigma concepts.

Sam McLafferty
President and CEO
Palisade Corporation

Sam McLafferty is Palisade's founder, president, and CEO. He started the company in 1984 with the release of PRISM, a stand-alone Monte Carlo simulation package for DOS on the PC. PRISM later evolved into @RISK for Lotus 1-2-3, and then for Excel. Sam is Palisade's lead developer, with over thirty years of programming experience. He works closely with the technical and sales staff, ensuring that customer feedback is heard. He personally oversees the development and evolution of every one of the fifteen software products Palisade sells. Prior to Palisade, he was a risk analysis consultant.

Scott Mongeau

Scott Mongeau is founding director of Biomatica BV (, a consultancy specializing in biotechnology industry risk management. Scott has over a decade of experience in biotech, including key positions at Genentech Inc. related to risk management. He currently consults for several biofuel start-up initiatives and completed his thesis on biofuel project risk management. He is in the Global Executive MBA (OneMBA) program at Erasmus Rotterdam School of Managment, where he also holds a Masters in Financial Management. He has a Certificate in Finance from University of California at Berkeley, a Masters in Communication from University of Texas at Austin, and a Graduate Degree in Applied Information Systems Management from the Royal Melbourne Institute of Technology as a Rotary Ambassadorial Scholar. Having lived and worked in a number of countries, Scott is an American citizen and currently resides in the Netherlands.

Dr Javier Ordóñez
Director of Custom Solutions
Palisade Corporation

Dr Javier Ordóñez holds a BS in Civil Engineering from the Universidad de Cuenca, Ecuador and a MS in Project Management from the University of Maryland. Javier earned his PhD from the University of Maryland performing research on project risk analysis. His current research deals with cost and schedule integration and correlation issues through the use of Bayesian belief networks.

Javier's experience is in the areas of construction and project management, optimal project and capital investments selection, earned value management, engineering and project risk analysis, and operations research applications to engineering and management problems.

Javier has taught as an adjunct professor in the Project Management Program at University of Maryland and provides training in risk and decision analysis. He is also registered as a Project Management Professional (PMP).

Dr Michael Rees
Business Best Ltd.

Michael has 20 years’ of business and finance experience, including roles such as Principal (Partner) at the strategy consultants Mercer Management Consulting (now Oliver Wyman) and Vice-President of Equity Research at J.P. Morgan. He has worked independently for 8 years, for 6 of which he was retained by Palisade Corporation to act as their Director of Training and Consulting. He is the author of Financial Modelling in Practice: A Concise Guide for intermediate and Advanced Level, John Wiley & Sons, 2008).

His academic credentials include a Doctorate in Mathematical Modelling and Numerical Algorithms, and a B.A. with First Class Honours in Mathematics, both from Oxford University. He has an MBA with Distinction from INSEAD in France, as well as holding the Wilmott Certificate of Quantitative Finance, where he graduated top of the course for class work and also received the Wilmott Award for the highest final exam mark. Michael is based in the UK and speaks fluent French and German.

Dr Sven Roden
Decision Analyst
Finance Academy, Unilever

Dr Sven Roden is a senior Decision Analyst within Unilever’s Finance Academy. Dr Roden acts 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.

Stefan Sadnicki
Senior Consultant, Operational Research
Capgemini Consulting

Stefan Sadnicki has worked for 2 years as an Operational Research consultant with Capgemini Consulting. Recent projects include sensitivity analysis of the long-term business plan for a large water utility company, a supply and demand system dynamics model for a central government ministry, and the development of a workforce-planning tool for a local council social work department. Before joining Capgemini, Stefan worked for 3 years as an IT consultant to the asset finance industry, firstly with CHP Consulting in England before contracting for Banco de Banesco in Caracas, Venezuela. Stefan graduated in 2004 from The Queen’s College, Oxford University with a first class degree in Mathematical Sciences and an MSc with distinction in Computer Science. More recently, he has completed a Post Graduate Certificate in Operational Research by distance learning from the University of Strathclyde. His current interests lie in optimisation, Monte Carlo simulation and consulting opportunities within Europe.

Dr Kim Salling
Department of Transport
Technical University of Denmark

Kim Bang Salling is currently employed as an assistant professor at the Department of Transport at the Technical University of Denmark. He defended his PhD thesis entitled: Assessment of Transport Projects: Risk Analysis and Decision Support, November 2008. He furthermore holds a Master’s degree in Engineering within socio-economic evaluation methodologies and decision support systems with special emphasis on cost-benefit analysis and Risk Analysis. The Ph.D. project concerned a decision support model for assessing transport projects. By use of the developed CBA-DK software a new risk-oriented methodology for feasibility risk assessment is developed. Recently, he has been part of the Centre for Logistics and Freight (CLG) project where he co-developed a new Decision Support System applied for large scale infrastructure projects, under the CLG task 9 project. He has also been working on a software program for a composite evaluation model called COSIMA-VEJ for the Danish Road Directory. Furthermore, he has been involved in a large transport appraisal study in Greenland evaluating the overall transport system and a customized decision support model for the Rail Net Denmark in order for them to optimize asset management and project ranking. Currently, he is co-managing a large-scale research project for the Danish Strategic Research Council over the four year period 2009-2012 entitled: Uncertainties in Transport Project Evaluation.

Philippe Stollsteiner
Founder and Manager

Philippe Stollsteiner is the founder and CEO of Projiris, a company providing project management services in France. The company is located near Versailles. Started in 2003, Projiris has evolved since 2006 from project management assistance to companies dealing in the Defence and Security sector to risk management assistance. It provides consulting and training using @Risk to various clients in Defence, traffic forecast, food safety, bank, railway industries.

He has given several lectures about quantitative risk analysis at PMI Paris chapter and AFITEP.

Philippe has a master’s degree (diplôme d’ingénieur) in electronics from CNAM university, and a MBA from IAE St-Charles in Paris. He is Project Management Professional (PMP) certified by the Project Management Institute.

Jan Paul Van Driel

Jan Paul is a co-founder of StrategicFit. He specialises in developing strategies in highly uncertain situations where there are often conflicting perspectives. He has particular expertise in high stake energy investment decisions and designing effective decision processes. He has supported clients in most aspects of the energy life-cycle, in conventional, unconventional and renewable energy sources. He has also consulted in many other industries, such as high tech and manufacturing, where innovation, flexibility, and market differentiation enable value creation.

Steven Vaughan-Jones
Project Consultant, Waste Strategy & Procurement
SLR Consulting

Steven Vaughan-Jones is currently a project consultant within the Waste Strategy & Procurement team at SLR Consulting, a multidisciplinary environmental consultancy. Steven joined SLR two years ago, initially to complete his MSc thesis which involved analysis of the risks and uncertainties associated with waste management technologies. Steven’s main responsibilities include the development of, predominately, excel based models for a diverse range of business development and due diligence purposes, in addition to various waste flow and process flow modelling exercises. Steven also undertakes various sustainability and carbon footprint assessments, using both bespoke models and established Life Cycle Assessment tools, and provides support on both private and public sector procurements.

Prior to joining SLR Steven obtained a BSc in Environmental Risk Management from the University of Wales Institute, Cardiff. This enabled him to secure a Natural Environment Research Council Scholarship for his MSc in Environmental Diagnosis and Management at the Royal Holloway, University of London. Steven is soon to commence an MBA through the University of Wales.

Ian Wallace
Consultant & Trainer
Palisade Corporation

Ian is a qualified accountant (ACMA) and spent the first 10 years of his career working in finance functions within the pharmaceutical and precious metal refining industries. Here he gained valuable experience in managing staff, preparing financial and management accounts, and working closely with business managers to help develop project appraisals, corporate plans and annual budgets.

Ian then decided to move into management consultancy with KPMG and worked on a wide variety of interesting projects throughout Europe and the Middle East, both in the private and public sectors. Most of his work was project management related and involved the selection and implementation of the latest systems and business processes within the finance function. Work included preparing strategy studies and business cases for change, PFI and supplier evaluations, recruiting project teams, leading systems design and working with all the management and stakeholders connected with the change. As a result, Ian has considerable experience of project management methodologies such as PRINCE 2 and others.

It was during the course of this work that Ian came across Monte Carlo Simulation and @RISK. He found it such a useful tool for managing expectations, developing risk-efficient plans and getting adequate resources for his projects that in 2001 he decided to specialise in the technique. Ian has now been working closely with Palisade since 2005, and during this time has delivered numerous on-site customer training sessions, public classroom courses, web-conference training sessions, and consultancy projects.

Ian's wide experience, together with his communication skills, enable him to communicate complex subjects using everyday, easy to understand language.

Hans Waszink
Waszink Actuarial Advisory

Hans Waszink is an expert in quantitative financial risk modelling. He has a Master's degree in Mathematics from the University of Groningen in The Netherlands, a Master's Degree in Actuarial Sciences from the University of Amsterdam, and an MBA from London Business School. He is a Fellow of the Dutch Actuarial Society and the British Institute of Actuaries.

Mr. Waszink is director of Waszink Actuarial Advisory, a niche consulting firm specialising in risk management for the Financial Services Industry.

Erik Westwig
Software Engineer
Palisade Corporation

Erik Westwig received his BS in 1991 and MS in 1994 from the applied and engineering physics department at Cornell University. In 1998 he published the book Mathematical Physics with co-author Bruce Kusse, which was re-released in its second edition in 2006. Since 1995, Erik has worked as a software engineer at Palisade as part of the DecisionTools Suite development team.

Alec Yeowell
Halcrow Group Ltd

Alec is a specialist in physical asset management and has worked on water industry projects for 10 years. He specialises in modelling the behaviour, performance and risk associated with water company assets. After graduating in Civil Engineering in 1999, Alec worked for eight years at WRc Plc delivering water industry focused research projects.
Alec maintains a strong interest in innovation and has been actively involved in Halcrow internal research activities where he has recently been developing spatial optimisation capabilities. The tools have been applied to capital maintenance planning activities to enable clients to meet and improve on agreed performance and spending targets.

Alec is a Chartered Engineer and Member if the Chartered Institute of Water and Environmental Management (CIWEM).

John Zhao
Quality and Risk Manager
StatoilHydro Canada Limited

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




Palisade Corporation
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