Dear Decision Maker,

PALISADE SOFTWARE TRAININGS, SEMINARS & CONFERENCES

“Good decisions start with quality training”. Make better decisions with @RISK and the DecisionTools Suite.

There is enough uncertainty in the world. These tools are used to gain insight into opportunities and risks.

For more than 25 years, Palisade software and solutions have been used to make better decisions. Cost estimation, NPV analysis, portfolio analysis, operational risk registers, insurance loss modeling, reserves estimation, schedule risk analysis, budgeting, sales forecasting, and demand forecasting are just some of the ways in which Palisade software tools @RISK and the DecisionTools Suite are applied.

Our programmes are designed to introduce you on how to apply powerful quantitative risk and statistical analysis techniques to mitigating/solving complex problems .

Your participation in our Regional Training Seminar & conferences, can ultimately teach you how to apply @RISK and the DecisionTools Suite to real-life problems. Professional development credits are available, including CPE, PMI, and AACEI.

Attendees should be able to perform defensible risk assessments using @RISK software. The course covers other products in the DecisionTools Suite, including PrecisionTree, RISK Optimizer, Evolver, TopRank, StatTools, and NeuralTools.

NESTA GARBA ROBERT

Exec. Dir./CEO

+234 8034639000 DL

+234 7034632467

+234 7051020393

+234 8182477112

Website: www.fixconnetwork.com

Email: ceo@fixconnetwork.com

Palisade tools are currently helping 93% of Fortune 100 and 76% of the Fortune 500 to make better decisions. The corporate applications of the products vary greatly from industry but some of the most common uses are operational risk registers, budgeting and cost estimation, NPV/DCF/ROI analysis and sales forecasting. Palisade tools are currently helping 93% of Fortune 100 and 76% of the Fortune 500 to make better decisions. The corporate applications of the products vary greatly from industry but some of the most common uses are operational risk registers, budgeting and cost estimation, NPV/DCF/ROI analysis and sales forecasting. @RISK IS USED TO CONDUCT INDUSTRY PEFORMANCE.

Risk Management is the process of identifying, analyzing and mitigating risk. This has often

been conducted in a fairly haphazard way, with no real structure to the procedure. A more

forward-looking approach was taken by the steel giant Corus when they conducted a large-

scale intermediate repair to the largest blast furnace in Europe, located at the Teesside Works

at Redcar, England. The programme of work was high profile as the furnace is the only

ironmaking unit for the whole Teesside Works. During the outage period all steel making and

casting operations at Lackenby and ore sintering at the Redcar site ceased.

Corus enlisted the help of HVR Consulting Services Ltd, recognized experts in the field of

Risk Management, to conduct a series of project and business risk assessments. The project

risk assessments aimed to ensure all risks to the project were identified and analysed, and

@RISK was used as the tool to analyse the project timescales. Variability surrounding the

duration of activities in the project plan was combined with risks from the Risk Register to

create a risk model. This was then analysed using @RISK, not only to predict the likely

project outcomes but also to identify key areas requiring management attention and

opportunities for schedule saving.

The business assessments were conducted on the entire Commercial and Industrial (C&I)

section of Corus, to assess the impact of the blast furnace outage on the company. These

business assessments again identified key risks to the Corus business, encompassing

commercial, financial, technical and logistical risks. A process model was designed using

@RISK to model and forecast production throughput at the C&I business sites, incorporating

the effect of the identified risks. This was then used to provide the blast furnace Steering

Committee with guidance on steel stock build requirements, allowing commercial and

production decisions to be made and tested.

The project was, within Corus, heralded as a major success. The furnace reline was completed

in a total outage of 68 days 9 hours, which was within the (extremely aggressive) 70 day

window targeted. Each day of lost production would have cost Corus a significant amount in

lost production, so minimal reline duration was paramount.

The key issue of slab stocking to feed the steel mills whilst the furnace was off line was also

successful. The business operates with a set 'irreducible minimum' of stock, to cater for

unforeseen fluctuations in furnace output, steel caster performance and in steel requirements.

At the end of the outage the business had a slight surplus over the irreducible minimum,

caused mainly by a downturn in orders for one of the mills during the outage period.

A subsequent 'wash up' meeting of the members of the furnace Steering Committee was held

to identify the items which had been critical to the project's success and to identify any

lessons to be learned. The timescale risk analysis techniques (using @RISK) were considered

to be a useful disciplined tool, giving some idea of the outage duration and dovetailing into

the business risk assessment. The fact that the length of the outage duration was key to the

success of the project meant that the schedule risk analysis was of immediate use as it helped

the business plan for realistic outage duration.

The business risk assessment, and in particular the @RISK process model, was seen as one of the

critical success factors. The process model enabled the iron make levels prior to the furnace outage

and the recommissioning rate to be realistically analyzed, including the effects of the risks to the

business and the uncertainty in the reline outage duration.

First estimates suggested that they would have to wait around five months to be brought back to the surface, although this was then revised to three or four months. However, 65 days after the first rock fall, one of the rescue drills broke through to the underground chamber, and the first miner was brought to the surface on October 13, 2010. The rescue operation was completed 22 hours later.

Defining risk with Precision Tree

During the crisis, mining expert Manuel Viera, the CEO and managing partner of engineering consultancy Metaproject, was asked by the Chilean government to advice on the best way to rescue the miners.

Mr. Viera developed a new model to predict the best way to rescue the miners. This calculated the method that would subject them to the least risk.

The magnitude of risk can be defined as a multiple of exposure, probability and severity, where:

Exposure is the time that the miners remain underground (including the rescue process)

Probability is the chance of a (further) hazardous event occurring Severity represents the impact of any further events

Metaproject used Palisade's decision tree analysis tool, PrecisionTree, to evaluate the various rescue alternatives from a technical and economical perspective. This enables an informed decision to be made with regard to selecting the option that is the least risky to

the miners.

Rescue options used in PrecisionTree model

A key decision was whether to raise the miners to 300 meters below the surface, or to keep them in their current location near the refuge at 700 meters. There were also several

drilling options for reaching the trapped miners:

A: Use of a Strata 950 probe to drill a ‘well’ 66 centimeters (cm) in diameter through which the miners could be lifted. The greatest risk was that the well would

collapse when the cage containing the miners was raised to the surface.

This rescue operation would take around three or four months, depending on the quality of rock, and other obstructions which were, at the time, unknown.

B: Because as many rescue alternatives as possible needed to be considered, the use of the larger Schramm T-130 drilling probe was also explored. This would enable a wider

well (between 66cm to 80cm diameter) to be drilled. The risks were similar to those in A but, because the larger well meant that the rescue would be quicker and therefore

reduce the exposure of the miners, the magnitude of risk was less. The timescale was similar.

C: Use of the proven DTH (Down The Hole) 26” probe or oil drilling RIG-421. Fast and powerful, this could potentially reduce the rescue time to one month, which would have

decreased the miners’ exposure to risks.

D: A vertical tunnel to the miners could have been built. This would have required construction and was therefore more expensive and would take longer – however, it was an effective option. The key risks were ‘slabs’ (ventilation problems during the building process as the tunneling would have been carried out 'blind'), general ventilation issues

in the confined space, and the potential lack of patience of the miners.

E: An alternative (horizontal) tunnel could have been excavated from an already-drilled well under the collapsed site to reach the miners directly in the refuge. From here they

could walk along the tunnel and be brought to the surface

Key risks input into PrecisionTree model

The main issues and risks that had to be factored in to any calculation are as follows:

Further landslides in the pit encroaching on the well being drilled (i.e. making the well unstable)

Landslides in the well itself Lack of detailed information (topographical maps, structural geology, location of

major fault lines, etc) making it difficult to know where to drill the well Repeated failures of the drilling machines The amount of water that can be used in the drilling process was limited as it

could fall on the miners Ensuring that there was adequate ventilation and air extraction during the

drilling process Getting it right first time – there was no room for error A failure in the lifting mechanism bringing the miners to the surface The physical health of the miners, who were exposed to a variety of diseases as a

result of extreme humidity, high temperatures, poor ventilation, bacteria and fungi. These could result in broncho-pulmonary diseases, hepatitis, gastric

infections, viruses, etc. The mental health of the miners, whose situation made them prone to

claustrophobia, stress, depression, etc.

PrecisionTree shows viability of each option

PrecisionTree presented a matrix of statistical results for each branch tree (i.e. rescue

option).

This made it is possible to ascertain, for example, that for some of the drilling options it was feasible to move the miners in two stages, but for others it was not, due to logistical

problems.

The PrecisionTree analysis showed that the best option for rescuing the miners was to use the Schramm T-130 (Option B) followed by Option C, the DTH QL 200 (which was replaced by the drilling RIG-421). In addition, it was recommended that both options

were used at the same time. Using two techniques would diminish the risk and increase the reliability of the rescue mission. The option of bringing the miners to 300 meters

first was rejected.

Manuel Viera explains: “Palisade's PrecisionTree is an excellent tool for modelling and conceptualizing real-life problems and analyzes alternatives that are technically feasible

and economically viable in an Excel format. This can be applied to complex problems that have a big impact, and was therefore ideal for a major disaster such as the trapped

miners.”

Rescue operation

The actual rescue operation used three drills at the same time: Drill A, the Strata 950

raise bore machine; Drill B, the Schramm T-130 machine; and Drill C, the RIG 442

machine. As predicted by the Metaproject PrecisionTree analysis, Drill B was the first to

reach the miners.

NeuralTools allows you to create neural networks within Excel. The idea behind neural nets is that they imitate brain functions in looking for patterns in existing data sets – and once they’re trained on historic data, they can take new data and make intelligent predictions about the outcome. Sample applications include: oil reservoir properties estimation, crude oil blending, oil field development, water injection and production predictions.

RISKOptimizer and Evolver are both optimisation programs which are based on genetic algorithms. They are both designed to solve non-linear, complex problems and they way they work is by constantly trying to find new solutions to problems – and then building on those solutions to find even better solutions. The difference between the two products is that RISKOptimizer allows you to also build in uncertainty to your optimisation model by using Monte Carlo Simulation. Sample applications include: production optimization of water flood, production optimization of gas lift wells, distribution, scheduling, budgeting, portfolio and product mix optimisation, and resource allocation.

StatTools replaces Excel’s statistics functions with it’s own more robust and accurate functions and enables you to perform time-series forecasting and statistical analysis. Containing the most common statistical analyses StatTools also allows you to build your own custom statistical procedures. Sample applications include: sales forecasting, real options analysis, Six Sigma and quality control, life cycle analysis.

@RISK IS USED FOR THE ANALYSIS OF AGRICULTURAL

PORTFOLIOS/AGRICULTURAL INVESTMENT.

With the advent of corporate farming, large agricultural companies have begun to apply the same kinds of risk analysis techniques to the powerful uncertainties of the natural world and market prices that their counterparts in the manufacturing and finance sectors use. And Prof. Ariadna Berger, professor of Farm Management at the University of Buenos Aires, has gone so far as to introduce a portfolio approach to assessing and balancing the risks and opportunities of large cash cropping operations in Argentina. She began using @RISK when she was a graduate student at Cornell University and reports that it is the perfect tool to show her clients how to manage the risks in their large-scale farming operations.

@RISK and a Different Kind of Investment Portfolio

In her consulting for farming corporations, Prof. Berger creates portfolios analogous to the portfolios of stocks and bonds used by financial analysts. Just like investment portfolios, the idea is to spread risk via diversification, except that in agricultural portfolios she is creating a mix of climate regions, soils, crops, and cultivation practices. By planting different crops, both market and yield risks are reduced; by planting in different regions and with different cultivation practices, yield risk is reduced even more. Like so many of her counterparts in finance, Prof. Berger uses @RISK to simulate risks and rewards throughout her clients’ potential agricultural “holdings” and to help her clients compare those possible outcomes with potential results from other possible portfolios with different diversification schemes.

Farming operations are conditioned by extreme fluctuations of weather and market price. Fortunately, Argentina has a number of climate zones, and farmers can choose from a number of crops to plant: mainly wheat, soybeans, corn or sunflowers. This situation allows farmers to spread the risk strategically.

Agronomic Simulation Models (Provide Data for @RISK Yield Distributions) According to Prof. Berger, the big challenge in creating her simulations is that, due to continually changing agricultural practices, it has been difficult to get data based on similar practices and technology over a long enough period of time to generate distributions. The way she compensates for the lack of historical data is by using agronomic simulation models to generate yield data to enter as @RISK distributions. These models are based on approximately 30 years of data on soil, water, nutrients, plant variety, and planting methods, and generate simulated yields that can be used to create a distribution. However, agronomic simulation models predict yields based only on restrictions for water and nutrients, while in the field there are other factors reducing yields, such as hail, frost, pests or diseases. Prof. Berger accommodates this limitation by complementing the yield data generated by the agronomic simulation models with other distributions for weather and weather-related random variables, which are based on historical data. In this way, yields simulated in each @RISK iteration are a combination of the distribution generated by the agronomic simulation models and of other random variables that may affect yield.

Large-Scale Farming = Large-Scale Uncertainty

Finally, she rolls land and crop costs into this complex mix, and @RISK suggests answers to such questions as: How much land to assign to each crop? How much land to lease? And how many climate zones to include in the portfolio? This information is crucial to her clients because farming has always been a chancy business, and large-scale farming involves large-scale downturns and upswings with large-scale investments at stake. Investment in crop costs and land rent depends on crops and regions, but on average it can be estimated around US $125 per hectare. Some portfolios can be as big as 100,000 hectares, and more. Because of the unpredictable nature of agriculture, serious planning can only happen if yield and market risks are considered. “Decisions made exclusively on the grounds of expected returns can be misleading and generate an unbearable risk exposure,” says Prof. Berger. “By accounting for a nearly limitless variety of uncertainties in our work, @RISK adds tremendous value. It easily integrates in the Excel programs with which we evaluate portfolios and helps us to make far better decisions on how to structure our agricultural portfolios.” @RISK IS USED TO ASSESS US AGRICULTURE POLICY ASSESSMENT.

The US Government spent $24 billion on farm programs in 2000. The benefits were distributed through a variety of income-enhancing and risk-reducing policies. For instance, some programs provided price protection, others a direct subsidy, and others subsidized the purchase of crop insurance. How do these programs individually and collectively reduce agricultural risk? Are these programs having the desired effects? Is the money helping producers reduce risk and thereby providing a disincentive to purchase crop insurance? And are there better ways to address agricultural risk? Researchers at Cornell University and Purdue University recently completed a study that assesses the impacts of US farm programs on farm returns and answers some of the above questions. The researchers used Palisade’s @RISK to create a simulation model that illustrates the effects of government policy tools on farm incomes. The @RISK Simulation Model

To assess the impacts of government policy tools on farmland risk and return the researchers developed an economic model for farm income and expenses based on a representative parcel of land and crop mix. @RISK allowed the researchers to model the uncertainties associated with crop yield and price. After running base-line simulations, the researchers added the individual farm programs into the model to determine their impacts. Finally they combined all the programs and crop insurance into the model. They compared the simulation outcomes to determine the impact of the various payment/subsidy mechanisms.

Study Results

The @RISK simulation model demonstrated that a combination of all government programs would raise average farm incomes by almost 45%. Additionally, the programs would reduce the economic risks associated with farming by half. Most importantly, the model allowed researchers to examine how the programs interact with one another to alter the return distribution.

@RISK’s Contribution

Producers must make decisions regarding cash rental bids, crop mixes, and even farmland purchases based upon expected returns. These decisions are based on the expectations for market prices, yields and costs – all uncertain elements.

Assistant Professor Brent Gloy of Cornell University’s Applied Economics and Management Department was one of the researchers. “@RISK was vital to the simulation model. It allowed us to incorporate uncertainties and run random simulations on the various scenarios.” He adds, “@RISK’s ability to correlate distributions of random variables was essential to the model. Additionally, we used @RISK’s output statistics to compare the various model scenarios.”

Policy Impacts

The study quantifies how government programs impact each other and subsidized crop insurance. According to Dr. Gloy, “Our results indicate that the risk reduction provided by the standard programs significantly reduce the value that risk averse producers derive from crop insurance programs.” He adds, “@RISK was instrumental to the simulation model. It allowed us to incorporate uncertainties and correlations, and to systematically evaluate each of the farm policy tools.”

PLEASE LOG ONTO BELOW SITE FOR “CASE STUDIES/ TESTIMONIES OF DIFERENT SECTORS USING @ RISK AND DECISION TOOL SOFTWARE.”

www.palisade/casestudy.com

@ RISK USED IN THE AGRICULTURAL SECTOR

@RISK HELPS PREDICTS DISASTER

@RISK USED FOR PROJECT/ BUDGET PLANS

@RISK USED IN FINANCIAL AND BANKING SECTOR

@RISK USED IN THE TRANSPORTATION SECTOR

@RISK USED IN THE MEDICALS AND PHARMACEUTICALS SECTOR

@RISK USED IN THE VERTINARY SECTOR

@RISK USED IN PRODUCTION COMPANIES

@RISK USED IN THE MINING SECTOR

@RISK USED IN THE EDUCATIONAL SECTOR

OIL AND GAS (PETROBRAS) USES @RISK FOR E&P ANALYSIS

@RISK USED IN THE ENVIRONMENTAL SECTOR

@RISK USED IN THE INSURANCE/ REINSURANCE SECTOR

@RISK USED IN THE CONSTRUCTION SECTOR

@RISK USED FOR STANDARD SIX SIGMA.

@RISK USED IN GOVERNMENT AND DEFENCE

MAJOR UNIVERSITIES/INSTITUTIONS USING @RISK

London School of Economics (UK) MIT NARO - Division of Upland Farming (Japan) National Institute of Health Science (Japan) National Institute of Infectious Disease (Japan) New Mexico State University Ohio State University Panamerican School of Agriculture Penn State University Princeton Queensland University of Technology Stanford Texas A&M University University of Alberta (Canada) University of Buenos Aires University of California at Berkeley University of Canberra (Australia) University of Central Florida University of Chicago University Federal de Rio De Janeiro University of Illinois at Urbana-Champagne

University of Marketing and Dist. Sciences (Japan) University of Maryland University of Michigan University of New South Wales (Australia) Universite du Quebec a Rimouski University of Texas – Austin University of Tokyo University of Twente (Netherlands) University of Vermont University of Western Ontario – Richard Ivey School of Business Virginia Commonwealth University Virginia Tech University of Wisconsin Warwick Business School (UK) Wharton School of Business Yale School of Management

INSURANCE/ACTUARIAL CUSTOMERS

ACE Bermuda Insurance Co. Ltd. ACE USA AEGIS Insurance Services, Inc Aetna Chile Seguros de Vida AFLAC Allianz Insurance Company Allstate Insurance Company American Re-Insurance Company American Family Insurance Aon Non-Marine Reinsurance Ltd Aon Re Ireland Aon Risk Services Japan Ltd. Arab-Malaysian Assurance Berhad (Malaysia) Arkwright Mutual Insurance Aspinall & Associates Chaucer Insurance Plc (UK) C.G.U. Insurance S.A. (South Africa) CNA Reinsurance Europe Ltd

Colonial Insurance (Bermuda) Erc Frankona Reinsurance AS (Denmark) General Reinsurance G.I. Actuarial (UK) ING Reinsurance Co Intl Ltd (Ireland) Insurance Corporation of Barbados (Barbados) Kemper Insurance K.L.P. Insurance (Norway) PMA Re London Life & Casualty Manulife Financial Maritime Life Insurance MMI Insurance (Australia) NAC Reinsurance National Grange Mutual Ins. Co New York Life No. Carolina Dept of Insurance

Northwestern MutualLife Partner Reinsurance CompanyPearl Assurance Plc (UK) Penn National Insurance Co. PMA Re Progressive Renaissance Reinsurance

St. Paul Reinsurance Co. Ltd State Farm Insurance Standard & Poor's Insurance Swiss Reinsurance Texas Builders Insurance, Co. Renaissance Reinsurance Transatlantic Reinsurance Comp (UK) Willis Faber Zurich Insurance Group (Switzerland)

OTHER USERS OF DECISION TOOL CUSTOMERS

Abacus Technology ADM AMD AT&T Bank of America Bankers Trust BC Hydro Bechtel Becton Dickinson Black and Decker Blue Cross Blue Shield of Florida Boeing Bombardier Borden Foods Co. Borg Warner Bristol-Myers Squibb Center for Disease Control

Goodyear GRE Insurance Group Halcrow Group Ltd Harley-Davidson Hatch Associates Her Majesty’s Prison Service (UK) Hewlett-Packard Hitachi Honeywell International Hospital Clinic Barcelona Hughes Aircraft IBM Impex Corporation (Japan) ING - Equitable Life IHS Energy Istria Ltd

Ontario Hydro Oracle Owens Corning Pantektor AB Pareto Solutions PepsiCo PCA Life (Japan) Pfizer Post Denmark Price Waterhouse Coopers Procter & Gamble Raytheon RiskAdvisory (Canada) SAIC Sara Lee Shimadzu Corporation Starbucks Sumisho Electronics Co., Ltd. (Japan)

Centre for Traffic and Transport Chase Manhattan Bank Cinergy Citibank Coca-Cola Colgate Palmolive Coopers & Lybrand Corning Corus Cummins Engine Corp. CVRD Brasil Deloitte Tohmatsu Corp. Finance Co. (Japan) Disney Dow Chemical Eastman Chemical eBay Employer's Reinsurance Corp. Ernst & Young Federal Highway Administration Fluor Daniel Ford Motor Company Gannett Fleming

Japan Food Research Labs (Japan) John Deere Johnson & Johnson Katrina Disaster Response Center Kennecott Utah Copper

Kirin Brewery Company, Ltd. (Japan) KPMG Peat Marwick Logion (Netherlands) Los Alamos National Laboratories Louis Berger Group Lucent Technologies Marsh Japan Inc. McKinsey & Co. Met-Mex Peñoles (Mexico) Mettler Consulting Mercantile Co. Ltd (Japan) Merck Microsoft’s Treasury

Sun Life Reinsurance Group Society of Actuaries / Casualty Actuary Society TAP Pharmaceuticals Thales Tillinghast Towers & Perrin Toyota Triangle Economic Research TRW TVA (Tennessee Valley Authority) Unilever US Dept. of Agriculture US Army Corps of Engineers US Steel Verizon Western Australia Department of Agriculture Wal-Mart Stores Westinghouse The World Bank World Conservation Union Whirlpool Xerox

COMPANIES RECEIVING PALISADE TRAINING

Aetna BP Amoco Anadarko Petroleum Atlantic Richfield AT&T BHP Boeing Bristol-Myers Squibb Capital One Centers for Disease Control ChevronTexaco Citigroup Conoco Coopers & Lybrand Eli Lilly Ernst & Young ExxonMobil

FirstEnergy Fluor Daniel Ford Motors General Dynamics General Motors Georgia Pacific GlaxoSmithKline Hewlett-Packard KPMG Lockheed Martin Manulife Financial Marathon Oil Merck & Co. Motorola NationsBank Nokia Northwest Airlines Pennzoil

PetroCanada Phillips Petroleum PriceWaterhouseCoopers Procter & Gamble Prudential Royal Bank of Canada Shell SmithKline Beecham Swiss Re Texas Instruments TRW Unocal US Bancorp VISA International Westinghouse Whirlpool

OIL AND GAS CORPORATIONS

Amoco Anadarko Petroleum BHP BP Blade Energy CDX Gas ChevronTexaco Cinergy Conoco

Duke Energy ExxonMobil Fluor Daniel IHS Energy National Oil well Varco NIPSCO RiskAdvisory (Canada) Petrobras (Brasil) PetroCanada

Phillips Petroleum Schlumberger Shell Statoil Teikoku Oil Co, Ltd. (Japan) Unocal

PALISADE/FIXCON organizes in-house/Onsite and off-shore training courses tailored to help companies maximize their return on the software investment.

We also offer various licensing options servers of our software which could be deployed at your location post-training knowledge enhancement Practical.

Quotations for our Software licensing Options and Trainings are readily available on request.

CATEGORIES OF OUR LICENSING OPTIONS

1. Stand Alone Licenses (Intended for use by one Person.)

2. Concurrent Network License (Serves multiple users and Mostly used in Establishments)

3. Enterprise Server Licenses (Enables you to subscribe to others Subsidiaries.

PALISADE UPCOMING WORLD WIDE TRAININGS AND SEMINARS FOR YEAR 2012

London: 20-21 March 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

London: 20-22 March 2012

Decision-Making & Quantitative Risk Analysis

using the DecisionTools Suite (8023/AACEI Three)

Hamburg: 3-4 April 2012

Entscheidungsfindungs- und Unbestimmtheitsanalyse unter

Verwendung von @RISK

London: 18-19 April 2012

Palisade 2012 EMEA Risk Conference

Abu Dhabi: 14-16 May 2012

Decision-Making and Quantitative Risk Analysis

using the DecisionTools Suite

London: 22-23 May 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

London: 19-20 June 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

London: 17-18 July 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

London: 17-19 July 2012

Decision-Making & Quantitative Risk Analysis

using the DecisionTools Suite (8023/AACEI Three)

London: 21-22 August 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

München: 4-5 September 2012

Entscheidungsfindungs- und Unbestimmtheitsanalyse unter

Verwendung von @RISK

London: 25-26 September 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

London: 23-24 October 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

London: 23-25 October 2012

Decision-Making & Quantitative Risk Analysis

using the DecisionTools Suite (8023/AACEI Three)

London: 20-21 November 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

Brisbane: 6-7 March 2012

Decision-Making and Quantitative Risk Analysis using @RISK and the

DecisionTools Suite (8023/AACEI Two)

Kuala Lumpur: 3-4 April 2012

Decision-Making and Quantitative Risk Analysis using @RISK and the

DecisionTools Suite (8023/AACEI Two)

Beijing: 16-17 May 2012

Decision-Making and Quantitative Risk Analysis using @RISK and

the DecisionTools Suite

(8023/AACEI Two)

Sydney: 29-30 May 2012

2012 Palisade Risk Conference

Singapore: 13-14 June 2012

Decision-Making and Quantitative Risk Analysis using @RISK and

the DecisionTools Suite (8023/AACEI Two)

Melbourne: 19-20 June 2012

Decision-Making and Quantitative Risk Analysis using @RISK and

the DecisionTools Suite (8023/AACEI Two)

Sydney: 10-11 July 2012

Decision-Making and Quantitative Risk Analysis using @RISK and

the DecisionTools Suite (8023/AACEI Two)

Bangkok: 25-26 July 2012

Decision-Making and Quantitative Risk Analysis using @RISK and

the DecisionTools Suite (8023/AACEI Two)

Brisbane: 8-9 August 2012

Decision-Making and Quantitative Risk Analysis using @RISK and the

DecisionTools Suite (8023/AACEI Two)

Singapore: 21-22 August 2012

Decision-Making and Quantitative Risk Analysis using @RISK and the

DecisionTools Suite (8023/AACEI Two)

Orlando, FL: 6-7 March 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

Washington, DC: 27-28 March 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

Washington, DC: 27-29 March 2012

Decision-Making & Quantitative Risk Analysis using the DecisionTools

Suite (8023/AACEI Three)

Phoenix, AZ: 10-11 April 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

Chicago, IL: 24-25 April 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

Chicago, IL: 24-26 April 2012

Decision-Making & Quantitative Risk Analysis using the DecisionTools

Suite (8023/AACEI Three)

Jersey City, NJ: 8-9 May 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

Houston, TX: 15-16 May 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

Houston, TX: 15-17 May 2012

Decision-Making & Quantitative Risk Analysis using the DecisionTools

Suite (8023/AACEI Three)

Minneapolis, MN: 22-23 May 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

Seattle, WA: 12-13 June 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

Seattle, WA: 12-14 June 2012

Decision-Making & Quantitative Risk Analysis using

the DecisionTools Suite (8023/AACEI Three)

Toronto, ON: 27-28 June 2012

Decision-Making and Quantitative Risk Analysis using

@RISK (8023/AACEI Two)

Boston, MA: 17-18 July 2012

Decision-Making and Quantitative Risk Analysis using

@RISK (8023/AACEI Two)

Boston, MA: 17-19 July 2012

Decision-Making & Quantitative Risk Analysis using the

DecisionTools Suite (8023/AACEI Three)

Calgary, AB: 31 July - 1 Aug 2012

Decision-Making and Quantitative Risk Analysis using

@RISK (8023/AACEI Two)

Denver, CO: 11-12 September 2012

Decision-Making and Quantitative Risk Analysis using

@RISK (8023/AACEI Two)

Denver, CO: 11-13 September 2012

Decision-Making & Quantitative Risk Analysis using the

DecisionTools Suite (8023/AACEI Three)

Hartford, CT: 25-26 September 2012

Decision-Making and Quantitative Risk Analysis using @RISK

(8023/AACEI Two)

PAST YEAR’s (2010) ORGANIZED TRAININGS, SEMINARS AND CONFERENCES

PRESENTERS:

Dr. S. Chris Albright Professor of Operations & Decision Technologies, Kelley School of Business, Indiana University

Dr. Albright’s professional interests and expertise are in Spreadsheet Modeling for Optimization, Statistical Analysis and Simulation, Developing Excel Add-ins for Statistics, Statistics for Research, Stochastic Models in Management Science, Quantitative Methods for MBA Core, Web Page Development and Database Access with .NET, Data Mining, and VBA for Excel. He is the author and co-author of many books, including VBA for Modelers; Data Analysis and Decision Making (with Wayne Winston and Christopher Zappe) ; and Practical Management Science (with Wayne Winston). Also, Dr. Albright developed the statistical add-in StatsPro for Excel, later developed as StatTools by Palisade Corporation. Dr. Howard J. Axelrod President, Energy Strategies, Inc.

Dr. Howard J. Axelrod - BSEE, MSEE, MBA, Ph.D. - has more than 40 years of experience in management consulting, strategic planning and marketing for electric and gas utilities. With proficiencies in economics, marketing, and power systems planning, he provides a multi-disciplinary approach to resolving complex business and regulatory issues. He has performed numerous studies and led in the development of strategies addressing such issues as competitive restructuring, strategic business and market planning, organizational development, and business risk analysis. Dr. Axelrod also provides training seminars on risk and uncertainty assessment for such applications as power contract management, generation portfolio assessment, energy trading and derivative valuation and new business ventures. Dr. Axelrod has testified before numerous state regulatory agencies and FERC on such topics as resource planning, power contract management, utility operations and management and productivity. Curtis Beaty Research Engineer, Texas Transportation Institute

Curtis Beaty is the Program Manager for the Dallas Research and Implementation office as well as an Associate Research Engineer. Mr. Beaty has more than 12 years experience in transportation, construction, and business management both in the public and private sectors and holds a Bachelor’s degree in Civil Engineering from Texas A&M University and a Masters in Business Administration from Baylor University.

His professional interests include construction finance, analysis of business aspects of the transportation industry, assessment of traffic and revenue studies for proposed tolling projects, and transportation system operations.

Mr. Beaty’s activities include independent analysis of traffic and revenue studies prepared by others relating to proposed tolling projects by evaluating the reasonableness of assumptions, traffic forecasts, and revenue projections using modeling tool developed within TTI. In addition, Mr. Beaty is leading an effort to assess the state-of-the-practice in market valuation of transportation infrastructure projects being developed through public-private partnerships. Mr. Beaty was also involved in the design, implementation, and testing of intelligent parking and transportation management systems at airports, medical centers, and university throughout North America.

These system replacements also included the evaluation and creation of business processes, operational procedures, performance measurements and monitoring, and conceptual marketing plans

Fixcon Network Limited supplies installs and maintain on request these software amongst other things

The Decision Tool Suites 5.5, 5.7 and 6.0 STATA on (Statistic Graphics Data Management) ARC View GIS Package MedCalc

Palisade tools such as @RISK, Precision Tree and TopRank gives companies the ability to view the things in a different way and allow for the proactive identification and quantification of risks and opportunities in decisions. They help companies understand the implications of taking different courses of action and provide visibility about which risk may be worth taking and which risks should definitely be avoided. They also help companies to see opportunities as well as risks. PALISADE/FIXCON trainers have a wealth of experience and knowledge delivering tailored courses to clients in all fields including the oil and gas industry such as shell, Statoil, GASCO, BG ExxonMobil, TransCanada Pipelines, Suncor, Petro bras of Brazil and many more.

@RISK for Cost-Benefit Analysis

The CTT now uses @RISK on an increasing number of projects. Excel-compatible, @RISK uses Monte Carlo analysis to show all potential scenarios, as well as the likelihood that each will occur, thereby providing the decision-maker with the most complete picture possible.

It allows you to incorporate uncertainties and correlations, and to systematically evaluate each of the policy tools.”

Communication Key to Acceptance According to Dr. Willoughby “We investigated five distinct alternatives and incorporated a variety of costs and future outcomes. The process incorporated opinions of senior Administrators and staff members in the decision analysis model to improve the likelihood of acceptance. Very simply, this entails weighing up if the benefits of a venture justify its economic, social and environmental cost.

Construction companies will provide the initial estimate, because this is usually based on best/worst case predictions, it is not necessarily accurate. It also often increases over the course of the project.

FIXCON’S EXEC. DIR. & ‘’SOME PARTICIPANT’S’’ IN THE TRAINING/ SEMINAR ON PALISADE’S RISK

ANALYSIS AT HOLIDAY INN ON RIVONIA ROAD IN SANDTON, SOUTH AFRICA, 6th–11th MARCH 2011

ONSITE WEBCAST TRAININGS VIA SKYPE & OFFSHORE SEMINARS/CONFERENCES.

SUMMARY On this note we do highly recommend your consideration for :

i. Contacting us (Fixcon Network Limited of Murtala Mohammed way, Opposite Glo/Conoil office, Jos, Plateau State, Nigeria) to facilitate your participation and organizing batch by batch in-house or off-shore trainings of your key and middle workforce in our lined-up ‘’worldwide training programmes’’.

ii. Participants meet other professionals during these sessions iii. Procurement of our licenses would ultimately be very vital.

CORPORATE/CONCURRENT NETWORK LICENSES:

It would be installed to serve multiple users (Computers) from a Network Server, and could be deployed/connected to your departmental computers.

Concurrent network licenses are a very cost – effective way to serve many people who need to use the software independently/occasionally and log off when finished.

Concurrent Network licenses utilize FLEX net technology and come with one serial number and activation ID, regardless of the number of users.

Activation can be done with a single click over the Internet or manually via e-mail. The same installer may be used on all client computers, so scripting is very straightforward. In addition, concurrent network licenses support Terminal Services and Citrix environments. Concurrent network licenses also allow “borrowing,” or temporarily taking one license off the network server and putting it on the client computer. This lets someone use the software even if their computer is disconnected from the server (e.g., taking a laptop on a trip). When a license is borrowed, an expiration date must be set by the user. At expiration, the license is automatically returned to the server license pool and ceases to function on the disconnected client. These Concurrent licenses can also be formatted into Corporate Licenses. Corporate Licenses: They enable you to get the most software for your money, deployed in the most efficient manner possible using Concurrent Network activation methods (though may be customized however you wish) and are structured according to your Organizational needs.

Investing in Palisade/Fixcon corporate license is the most cost-effective path to better decisions enterprise-wide by switching from haphazardly purchasing individual copies to adopting a corporate license

Many companies have found that, the return on their software investment dramatically improved. Not only was their investment outlay reduced compared to buying individual copies, but widespread access to the software encouraged better decision-making throughout organizations.

Benefits of ‘’Concurrent corporate licenses’’ include:

1. The latest software version - for everyone! With current maintenance, all users work with the same current version, and upgrades are handled on the same corporate level to maintain this consistency and avoid compatibility issues.

2. Encourages better decisions throughout the organization regardless of department.

3. Corporate licenses encourage experimentation with powerful Palisade software by all employees. The more people performing quantitative decision analysis, the better your decision becomes.

4. By procuring our software through a single purchasing contract, you can avoid departmental budget constraints, differing purchasing procedures, and other administrative hurdles.

Our trainers have a wealth of experience and knowledge delivering tailored courses to clients in all fields including the oil and gas and other industries. Who should attend?

All key decision and policy makers, researchers, heads of various units, students .

Epidemiology, environmental sciences, public health, medical practitioners, laboratory scientists, biostatistics, economics, engineering, occupational medicine, hygiene officials and several other business executives of other disciplines have benefited immensely from these programmes.

NESTA GARBA ROBERT

Exec. Dir./CEO

+234 8034639000 DL

+234 7034632467

+234 7051020393

+234 8182477112

Website: www.fixconnetwork.com

Email: ceo@fixconnetwork.com