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Case Studies
Academic
Brazilian Petroleum Institute
Product: @RISK
Application: Oil Exploration
Bucknell University
Product: PrecisionTree
Application: Technology Implementation
Cambridge University
Product: @RISK
Application: Climate Change
Columbia University
Product: @RISK
Application: Energy and Public Policy
Cornell University
Product: @RISK
Application: Financial Management
Curtin University of Technology
Product: @RISK
Application: Statistical analysis
DePaul University/Advanced Analytics LLC
Product: DecisionTools Suite
Application: Sports Wagering
Illinois State University
Product: @RISK
Application: Pharmaceutical R&D, Net Present Value, Internal Rate of Return
Illinois State University
Product: @RISK
Application: Real Options and Finance
INCAE Business School
Product: @RISK
Application: Finance and Capital Markets
Johnson Graduate School of Management, Cornell University
Product: @RISK
Application: Election Forecasting
Missouri University of Science and Technology
Product: @RISK
Application: Mining Engineering
Pace University
Product: StatTools, DecisionTools Suite
Application: Operations Research
Shanghai Jiao Tong University
Product: DecisionTools Suite
Application: Engineering system design and optimization
Swinburne University of Technology
Product: @RISK
Application: Venture Capital Management
Technical University of Denmark
Product: @RISK
Application: Transportation research
Tulane University
Product: @RISK
Application: Food Safety
University of Adelaide
Product: @RISK
Application: Prediction of metal corrosion in salt water
University of Houston
Product: DecisionTools Suite
Application: Managerial Decision Making
University of London Royal Veterinary College / EpiX Analytics
Product: @RISK
Application: Health and food safety
University Melbourne
Product: @RISK
Application: Forecasting Dairy Operating Profits
University of New Brunswick
Product: @RISK
Application: Reducing Environmental Impact of Commercial Aquaculture
University of Pennsylvania
Product: DecisionTools Suite
Application: Organizational Dynamics
University of Pretoria
Product: DecisionTools Suite, @RISK, TopRank
Application: Viral Exposure
University of Toronto
Product: DecisionTools Suite
Application: Financial Management
University Of Victoria
Product: @RISK
Application: Lifetime Excess Cancer Risk
University of Witwatersrand
Product: @RISK
Application: Mining Engineering
Virginia Commonwealth University
Product: @RISK
Application: Value at Risk Analysis

READ ALSO
In Agriculture:
Cornell University / Purdue University
Product: @RISK
Application: Agriculture Policy Assessment
University of Buenos Aires
Product: @RISK
Application: Agricultural Investment
University of Tokyo
Product: @RISK
Application: Agriculture
In Environment:
Cornell University /
Lower Colorado River Authority

Product: @RISK
Application: Natural Resource Mgmt
In Energy:
Cranfield University
Product: @RISK
Application: Equipment Reliability
In Finance:
London Business School /
Novartis Pharmaceutical

Product: @RISK, PrecisionTree, RISKOptimizer, DecisionTools Suite
Application: Portfolio Management
In Medical:
Royal Veterinary College
Product: @RISK
Application: Disease Prevention
Recently Added
Kinder Morgan / Riskcore Ltd.
Product: @RISK
Application: Modelling project cost contingency and escalation
Industry: Energy
Federal Home Loan Bank of Indiana
Product: @RISK
Application: Portfolio Credit Risk
Industry: Finance
Red Leaf Resources
Product: @RISK
Application: Developing new oil shale technologies
Industry: Energy & Utilities
Sinopec Engineering (Group) Co., Ltd (SEG)
Product: @RISK
Application: Evaluate petroleum and petrochemical projects valued over US$100m
Industry: Energy
Tesla Consultants
Product: @RISK
Application: Assessing Needs for Equipment Upgrades
Industry: Utilities
University of Adelaide
Product: @RISK
Application: Prediction of metal corrosion in salt water
Industry: Academic
University of Tasmania
Product: @RISK
Application: Evaluating production strategies for salmon farming
Industry: Agriculture/Aquaculture
National Grid UK
Product: @RISK
Application: Modeling electricity network restoration performance
Industry: Energy & Utilities
Solis Financial Forensics LLC
Product: @RISK
Application: Projecting Economic Damages for Attorneys Involved in Complex Lawsuits
Industry: Financial Forensics
CONTI-Group
Product: @RISK
Application: Financial Planning for Shipping Investment Funds
Industry: Shipping/Transport
Major Utility in Eastern US
Product: The DecisionTools Suite
Application: Load Forecasting
Industry: Utilities
Amway
Product: Custom Development
Application: Long-Range Capacity Planning
Industry: Manufacturing
L.E. Peabody & Associates
Product: @RISK
Application: Real Option Valuations of Power Plants
Industry: Energy & Utilities
Louisiana Department of Health
Product: Custom Development
Application: Large-scale Hospital Evacuation Planning
Industry: Government
Pellegrino & Associates
Product: @RISK
Application: Intellectual Property Valuation
Industry: Finance
Integral Consulting
Product: @RISK
Application: Mine Remediation
Industry: Environmental
University Of Victoria
Product: @RISK
Application: Lifetime Excess Cancer Risk
Industry: Academic
Rusnano
Product: The DecisionTools Suite
Application: Private equity investment in nanotechnology
Industry: Finance
MRAG Asia Pacific
Product: @RISK
Application: Quantifying Illegal Fishing
Industry: Environment
Netconomy
Product: @RISK
Application: Budget Forecasting
Industry: Finance
Department of Energy and Climate Change
Product: @RISK
Application: Carbon emission forecasts
Industry: Government
Academic
Brazilian Petroleum Institute
Product: @RISK
Application: Oil Exploration
Bucknell University
Product: PrecisionTree
Application: Technology Implementation
Cambridge University
Product: @RISK
Application: Climate Change
Columbia University
Product: @RISK
Application: Energy and Public Policy
Cornell University
Product: @RISK
Application: Financial Management
Curtin University of Technology
Product: @RISK
Application: Statistical analysis
DePaul University/Advanced Analytics LLC
Product: DecisionTools Suite
Application: Sports Wagering
Illinois State University
Product: @RISK
Application: Pharmaceutical R&D, Net Present Value, Internal Rate of Return
Illinois State University
Product: @RISK
Application: Real Options and Finance
INCAE Business School
Product: @RISK
Application: Finance and Capital Markets
Johnson Graduate School of Management, Cornell University
Product: @RISK
Application: Election Forecasting
Missouri University of Science and Technology
Product: @RISK
Application: Mining Engineering
Pace University
Product: StatTools, DecisionTools Suite
Application: Operations Research
Shanghai Jiao Tong University
Product: DecisionTools Suite
Application: Engineering system design and optimization
Swinburne University of Technology
Product: @RISK
Application: Venture Capital Management
Technical University of Denmark
Product: @RISK
Application: Transportation research
Tulane University
Product: @RISK
Application: Food Safety
University of Adelaide
Product: @RISK
Application: Prediction of metal corrosion in salt water
University of Houston
Product: DecisionTools Suite
Application: Managerial Decision Making
University of London Royal Veterinary College / EpiX Analytics
Product: @RISK
Application: Health and food safety
University Melbourne
Product: @RISK
Application: Forecasting Dairy Operating Profits
University of New Brunswick
Product: @RISK
Application: Reducing Environmental Impact of Commercial Aquaculture
University of Pennsylvania
Product: DecisionTools Suite
Application: Organizational Dynamics
University of Pretoria
Product: DecisionTools Suite, @RISK, TopRank
Application: Viral Exposure
University of Toronto
Product: DecisionTools Suite
Application: Financial Management
University Of Victoria
Product: @RISK
Application: Lifetime Excess Cancer Risk
University of Witwatersrand
Product: @RISK
Application: Mining Engineering
Virginia Commonwealth University
Product: @RISK
Application: Value at Risk Analysis

READ ALSO
In Agriculture:
Cornell University / Purdue University
Product: @RISK
Application: Agriculture Policy Assessment
University of Buenos Aires
Product: @RISK
Application: Agricultural Investment
University of Tokyo
Product: @RISK
Application: Agriculture
In Environment:
Cornell University /
Lower Colorado River Authority

Product: @RISK
Application: Natural Resource Mgmt
In Energy:
Cranfield University
Product: @RISK
Application: Equipment Reliability
In Finance:
London Business School /
Novartis Pharmaceutical

Product: @RISK, PrecisionTree, RISKOptimizer, DecisionTools Suite
Application: Portfolio Management
In Medical:
Royal Veterinary College
Product: @RISK
Application: Disease Prevention
Energy & Utilities
Abu Dhabi Water &
Electricity Company

Product: @RISK
Application: Demand Forecasting
Águas do Douro e Paiva
Product: @RISK
Application: Cost Reduction
Alesco Risk Management
Services Limited

Product: @RISK
Application: Risk modelling to determine optimum risk financing and insurance strategies
BC Hydro
Product: @RISK
Application: Project Analysis
Bigen Africa
Product: @RISK
Application: Demand and Cost Forecasting
Blade Energy
Product: @RISK
Application: Drilling Productivity
Carnegie Mellon University / Pittsburgh Technical
Product: @RISK
Application: Mitigating project schedule and cost overrun risk
Cinergy
Product: @RISK
Application: Acquisitions Analysis
Cranfield University
Product: @RISK
Application: Equipment Reliability
Det Norske Veritas (DNV)
Product: @RISK
Application: ROI of Different Projects
ECN
Product: @RISK
Application: Financial analysis of offshore wind farms
Enex
Product: DecisionTools Suite
Application: Project Planning
Fitness First
Product: @RISK, RISKOptimizer
Application: Energy Budget Resilience Optimization
Fluor Corporation
Product: @RISK
Application: Oil and Gas Estimating
Futuremetrics
Product: @RISK
Application: Price Hedging
Grupo ISA
Product: @RISK
Application: Investment Analysis
Hydroelectric Power in Colombia
Product: @RISK
Application: Cost estimation
IHS Energy Group
Product: @RISK Developer Kit
Application: Exploration, Drilling, and Production Analysis
Integrating Renewables with Electricity Storage
Product: @RISK
Application: Energy supply and Electricity Demand
Kinder Morgan / Riskcore Ltd.
Product: @RISK
Application: Modelling project cost contingency and escalation
L.E. Peabody & Associates
Product: @RISK
Application: Real Option Valuations of Power Plants
Major Utility in Eastern US
Product: The DecisionTools Suite
Application: Load Forecasting
Metaproject
Product: @RISK, PrecisionTree
Application: Rescue Operations
National Grid UK
Product: @RISK
Application: Modeling electricity network restoration
Northern Indiana
Public Service Company

Product: @RISK, Evolver, RISKOptimizer
Application: Pricing, Production Allocation for Regulation Compliance
Petrobras
Product: @RISK
Application: Exploration & Production
Red Leaf Resources
Product: @RISK
Application: Developing new oil shale technologies
RiskAdvisory
Product: @RISK Developer Kit
Application: Energy Production and Pricing
Rudd Asset Management (RAM)
Product: @RISK
Application: Waste disposal and renewable energy
Sark7
Product: Decision Tools Suite (@RISK, Precision Tree and Evolver)
Application: Optimising the business case for sustainable energy projects
Sinopec Engineering (Group) Co., Ltd (SEG)
Product: @RISK
Application: Evaluate petroleum and petrochemical projects valued over US$100m
Tesla Consultants
Product: @RISK
Application: Assessing Needs for Equipment Upgrades
Tioga Energy
Product: @RISK
Application: Solar energy savings
Transelectrica
Product: @RISK
Application: Measuring and Mitigating Open Market Risk
WEnergy Global
Product: @RISK
Application: Return on Investment

SEE ALSO:
Halcrow
Product: @RISK
Application: Flood Protection

SEE ALSO: INDUSTRY MODELS
» Energy Models

Environment
Air and Waste Management Association
Product: @RISK
Application: Estimating vehicles on congested roads
Aspinall & Associates
Product: @RISK, PrecisionTree, DecisionTools Suite
Application: Disaster Planning
AECOM
Product: @RISK
Application: Climate Change and Flooding Risk Assessment
Bristol’s Environmental Risk Research Centre (BRISK)
Product: DecisionTools Suite
Application: Disaster Planning
Cornell University /
Lower Colorado River Authority

Product: @RISK
Application: Natural Resource Management
Greenup Locks and Dam
Product: @RISK
Application: Dam maintenance and repair
Halcrow
Product: @RISK
Application: Flood Protection
Halcrow Group Ltd /
UK Environment Agency

Product: @RISK
Application: Flood Management
Integral Consulting
Product: @RISK
Application: Mine Remediation
Kennecott Utah Copper
Product: @RISK
Application: Pollution Cleanup
MRAG Asia Pacific
Industry: Environment
Application: Quantifying Illegal Fishing
NOAA Fisheries
Product: @RISK
Application: Estimating effects of dams on Atlantic salmon population dynamics
Purdue University
Product: @RISK
Application: Cost-benefit analysis of biofuel production under two government policies
Triangle Economic Research
Product: @RISK, PrecisionTree
Application: Pollution Cleanup
University of California, Berkeley
Product: @RISK
Application: Modeling extinction risk for Endangered Species
World Conservation Union
Product: @RISK, PrecisionTree, DecisionTools Suite
Application: Endangered Species Protection

READ ALSO
In Academic:
Cambridge University
Product: @RISK
Application: Climate Change
Sark7
Product: Decision Tools Suite (@RISK, Precision Tree and Evolver)
Application: Optimising the business case for sustainable energy projects
In Agriculture:
World Aquatic Veterinary Medical Association (WAVMA)
Product: @RISK
Application: Helping aquatic farmers to make informed choices that reduce risk of disease in their animals without extensive and costly testing
Finance
Albert Fensterstock Associates
Product: NeuralTools
Application: Credit and debt risk analysis
Benemerita Universidad Autonoma de Puebla
Product: @RISK
Application: Derivatives Pricing
Cynametrix
Product: DecisionTools Suite
Application: Portfolio Optimization including IDB
Federal Home Loan Bank of Indiana
Product: @RISK
Application: Portfolio Credit Risk
FiduciaryVest
Product: @RISK
Application: Asset Allocation, Value-at-Risk
George Washington University
Product: @RISK & RISKOptimizer
Application: Debt Portfolios, Capital Investments
London Business School /
Novartis Pharmaceutical

Product: @RISK, PrecisionTree, RISKOptimizer, DecisionTools Suite
Application: Portfolio Management
LStar Capital
Product: @RISK
Application: Risk Quantification in Film Financing
Merck
Product: @RISK
Application: Value-at-Risk Exchange Rate
Netconomy
Product: @RISK
Application: Budget Forecasting
Nighthawk Intelligence
Product: DecisionTools Suite
Application: Private label credit card (PLCC) accounts for major electronics retailer
Pellegrino & Associates
Product: @RISK
Application: Intellectual Property Valuation
Performance Thinking & Technologies
Product: @RISK & RISKOptimizer
Application: Hedge Fund management
Procter & Gamble
Product: @RISK, PrecisionTree, DecisionTools Suite
Application: Production Siting, New Product Analysis, Exchange Rate Analysis, Real Options
Risqworx
Product: @RISK
Application: Calculating investment risk and ROI
Rusnano
Industry: Finance/Banking
Application: Private equity investment in nanotechnology
RoseCap Investment Advisors
Product: @RISK
Application: Valuation of Direct Investment
Solis Financial Forensics LLC
Product: @RISK
Application: Projecting Economic Damages for Attorneys Involved in Complex Lawsuits

READ ALSO
In Academic:
Swinburne University of Technology
Product: @RISK
Application: Venture Capital Management
In Energy:
Cinergy
Product: @RISK
Application: Acquisitions Analysis
Grupo ISA
Product: @RISK
Application: Investment Analysis
In Government:
Her Majesty's Prison Service
Product: @RISK
Application: Cash Flow Analysis
In Insurance/Reinsurance:
Society of Actuaries /
Casualty Actuary Society

Product: @RISK
Application: Pension and Insurance Planning

SEE ALSO: INDUSTRY MODELS
» Finance/Banking Models
Recently Added
Kinder Morgan / Riskcore Ltd.
Product: @RISK
Application: Modelling project cost contingency and escalation
Industry: Energy
Federal Home Loan Bank of Indiana
Product: @RISK
Application: Portfolio Credit Risk
Industry: Finance
Red Leaf Resources
Product: @RISK
Application: Developing new oil shale technologies
Industry: Energy & Utilities
Sinopec Engineering (Group) Co., Ltd (SEG)
Product: @RISK
Application: Evaluate petroleum and petrochemical projects valued over US$100m
Industry: Energy
Tesla Consultants
Product: @RISK
Application: Assessing Needs for Equipment Upgrades
Industry: Utilities
University of Adelaide
Product: @RISK
Application: Prediction of metal corrosion in salt water
Industry: Academic
University of Tasmania
Product: @RISK
Application: Evaluating production strategies for salmon farming
Industry: Agriculture/Aquaculture
National Grid UK
Product: @RISK
Application: Modeling electricity network restoration performance
Industry: Energy & Utilities
Solis Financial Forensics LLC
Product: @RISK
Application: Projecting Economic Damages for Attorneys Involved in Complex Lawsuits
Industry: Financial Forensics
CONTI-Group
Product: @RISK
Application: Financial Planning for Shipping Investment Funds
Industry: Shipping/Transport
Major Utility in Eastern US
Product: The DecisionTools Suite
Application: Load Forecasting
Industry: Utilities
Amway
Product: Custom Development
Application: Long-Range Capacity Planning
Industry: Manufacturing
L.E. Peabody & Associates
Product: @RISK
Application: Real Option Valuations of Power Plants
Industry: Energy & Utilities
Louisiana Department of Health
Product: Custom Development
Application: Large-scale Hospital Evacuation Planning
Industry: Government
Pellegrino & Associates
Product: @RISK
Application: Intellectual Property Valuation
Industry: Finance
Integral Consulting
Product: @RISK
Application: Mine Remediation
Industry: Environmental
University Of Victoria
Product: @RISK
Application: Lifetime Excess Cancer Risk
Industry: Academic
Rusnano
Product: The DecisionTools Suite
Application: Private equity investment in nanotechnology
Industry: Finance
MRAG Asia Pacific
Product: @RISK
Application: Quantifying Illegal Fishing
Industry: Environment
Netconomy
Product: @RISK
Application: Budget Forecasting
Industry: Finance
Department of Energy and Climate Change
Product: @RISK
Application: Carbon emission forecasts
Industry: Government
DecisionTools Suite Cases
Aspinall & Associates
Industry: Environment
Application: Disaster Planning
Bristol’s Environmental Risk Research Centre (BRISK)
Industry: Environment
Application: Disaster Planning
Cal Poly, San Luis Obispo
Industry: Agriculture
Application: Financial Risk Management in Agriculture
Captum Capital
Industry: Healthcare/Pharmaceutical
Application: rNPV of New Companies and Technologies
Cynametrix
Industry: Finance
Application: Portfolio Optimization including IDB
DePaul University/Advanced Analytics LLC
Industry: Academia
Application: Sports Wagering
DNV GL SE
Industry: Shipping
Application: Risk and cost-benefit analysis to enhance the safety of ships and ship systems
Enex
Industry: Energy
Application: Geothermal Power Plant Equipment Procurement
Linksbridge SPC
Industry: Non-Profit
Application: Evaluating Efficacy of Public Health Initiatives
Logion BV
Industry: Transportation
Application: Transportation, Distribution, and Inventory Management
London Business School /
Novartis Pharmaceutical

Industry: Financing/Banking
Application: Portfolio Management
Major Utility in Eastern US
Industry: Utilities
Application: Load Forecasting
MegaFon
Industry: Telecommunications
Application: Enterprise Risk Management
Met-Mex Peñoles
Industry: Six Sigma
Application: Six Sigma Design of Experiments
Nighthawk Intelligence
Industry: Finance
Application: Private label credit card (PLCC) accounts for major electronics retailer
Novelis
Industry: Manufacturing
Application: Project Evaluation
PragmaRisk
Industry: Engineering, Construction
Application: Project cost and schedule risk analysis
Procter & Gamble
Industry: Academic
Application: Production Siting, New Product Analysis, Exchange Rate Analysis, Real Options
Rusnano
Industry: Finance/Banking
Application: Private equity investment in nanotechnology
Sark7
Industry: Sustainable energy (project consulting)
Application: Optimising the business case for sustainable energy projects
Shanghai Jiao Tong University
Industry: Finance
Application: Engineering system design and optimization
Triangle Economic Research
Industry: Environment
Application: Pollution Cleanup
Unilever
Industry: Manufacturing
Application: Product launch, capex, and many others
University of California San Diego School of Medicine
Industry: Healthcare / Pharmaceutical
Application: Public Health screening
University of Houston
Industry: Energy, Finance, Medical, and more
Application: Managerial Decision Making
University of Toronto
Industry: Academic
Application: Financial Management
University of Pretoria
Industry: Sciences
Application: Viral Exposure
World Conservation Union
Industry: Environment
Application: Endangered Species Protection

SEE ALSO
In StatTools:
Pace University
Industry: Academic
Application: Operations Research

In @RISK:
Northern Indiana
Public Service Company

Industry: Energy
Application: Pricing, Production Allocation for Regulation Compliance


@RISK Cases
Abu Dhabi Water &
Electricity Company

Industry: Energy and Utilities
Application: Demand Forecasting
African Risk Capacity (ARC)
Industry: Insurance/Reinsurance
Application: Assessing Catastrophic Drought Risk
Air and Waste Management Association
Industry: Environment
Application: Estimating vehicles on congested roads
Alesco Risk Management
Services Limited

Industry: Energy
Application: Risk modelling to determine optimum risk financing and insurance strategies
Amway
Services Limited

Industry: Manufacturing
Application: Long-Range Capacity Planning
Analytics
Industry: Academic
Application: Health and food safety
Águas do Douro e Paiva
Industry: Energy
Application: Cost Reduction
AgustaWestland
Industry: Aerospace
Application: New product development, feasibility studies, business initiatives
Antea Group
Industry: Environment
Application: Cost Justification of Water Recycling Systems
ARC of Yates
Industry: Non-Profit
Application: Budget Forecasting
AECOM
Industry: Environment
Application: Climate Change and Flooding Risk Assessment
BC Hydro
Industry: Energy
Application: Project Analysis
Benemerita Universidad Autonoma de Puebla
Industry: Finance
Application: Derivatives Pricing
Bigen Africa
Industry: Energy & Utilities
Application: Demand and Cost Forecasting
Blade Energy
Industry: Energy
Application: Drilling Productivity
Brazilian Petroleum Institute
Industry: Academic
Application: Oil Exploration
Broadleaf Capital International
Industry: Mining
Application: Schedule and Cost Risk, Project Management
Cambridge University
Industry: Academic
Application: Climate Change
Carnegie Mellon University / Pittsburgh Technical
Industry: Nuclear Energy / Energy Infrastructure
Application: Mitigating project schedule and cost overrun risk
Centre for Traffic
& Transport, Denmark

Industry: Transportation
Application: Transportation Planning
Cinergy
Industry: Energy
Application: Acquisitions Analysis
City of Edmonton / SMA Consulting
Industry: Transportation / Government
Application: Project management
Columbia University
Industry: Academic
Application: Energy and Public Policy
CONTI-Group
Industry: Shipping/Transport
Application: Financial Planning for Shipping Investment Funds
Cornell University
Industry: Academic
Application: Financial Management
CP Risk Solutions/
Illinois State University

Industry: Insurance
Application: Reinsurance Strategy
CP Risk Solutions/Illinois State University
Industry: Manufacturing
Application: Factory Shutdown
Cranfield University
Industry: Energy
Application: Equipment Reliability
Cornell University/
Lower Colorado River Authority

Industry: Environment
Application: Natural Resource Mgmt
Cornell University
Industry: Agriculture
Application: Agriculture Policy Assessment
Cummins Inc.
Industry: Six Sigma
Application: Six Sigma Quality Analysis
Curtin University of Technology
Industry: Academic
Application: Statistical analysis
Department of Energy and Climate Change
Industry: Government
Application: Carbon emission forecasts
Deloitte
Industry: Insurance/Reinsurance
Application: Cell Captive Insurance
Deloitte AIS
Industry: Insurance/Reinsurance
Application: Financial Planning for Environmental Rehabilitation
Det Norske Veritas (DNV)
Industry: Energy
Application: ROI of Different Projects
DNV GL
Industry: Energy
Application: Project Risk Management, Enterprise Risk Management, SHE Risk Management, Operational Risk Management
ECN
Industry: Energy research organisation
Application: Financial analysis of offshore wind farms
ENGCOMP
Industry: Government/Defense
Application: Budgeting
Federal Highway Adminstration
Industry: Transportation
Application: Pavement Cost Analysis
Federal Home Loan Bank of Indiana
Industry: Finance
Application: Portfolio Credit Risk
FiduciaryVest
Industry: Finance
Application: Asset Allocation, Value-at-Risk
Fitness First
Industry: Energy / Utilities
Application: Energy Budget Resilience Optimization
Fluor Corporation
Industry: Energy
Application: Oil and Gas Estimating
Fort McMurray Airport Authority (FMAA) / Revay and Associates Ltd.
Industry:Transportation
Application: Enterprise Risk Management (ERM)
Futuremetrics
Industry: Energy
Application: Price Hedging
Gemeente Hoorn
Industry: Government
Application: Urban Planning
Graphic Era University - Dehradun
Industry: Manufacturing
Application: Supply Chain Management
George Washington University
Industry: Finance
Application: Debt Portfolios, Capital Investments
Greenup Locks and Dam
Industry: Energy
Application: Dam maintenance and repair
Grupo ISA
Industry: Energy
Application: Investment Analysis
Halcrow
Industry: Energy & Utilities
Application: Flood Protection
Halcrow Group Ltd /
UK Environment Agency

Industry: Environment
Application: Flood Management
Her Majesty's Prison Service
Industry: Government
Application: Cash Flow Analysis
Hospital Clinic Barcelona
Industry: Medical
Application: Blood Screening
Hydroelectric Power in Colombia
Industry: Energy
Application: Cost estimation
Illinois State University
Industry: Academic / Pharmaceutical
Application: Pharmaceutical R&D, Net Present Value, Internal Rate of Return
Illinois State University
Industry: Academic
Application: Real Options and Finance
INCAE Business School
Industry: Academic
Application: Finance and Capital Markets
Integral Consulting
Industry: Environment
Application: Mine Remediation
Integrating Renewables with Electricity Storage
Industry: Energy & Utilities
Application: Energy Supply and Electricity Demand
JB Scherer Consulting Group LLC
Industry: Manufacturing
Application: Future Net Income Forecasting
Johnson Graduate School of Management, Cornell University
Industry: Academic / Politics
Application: Election Forecasting
Kewpie Corporation and University of Tokyo
Industry: Agriculture
Application: Food Safety
Katrina Disaster Response Center
Industry: Government
Application: Disaster Response Planning
Kennecott Utah Copper
Industry: Environment
Application: Pollution Cleanup
Kinder Morgan / Riskcore Ltd.
Industry: Energy & Utilities
Application: Modelling project cost contingency and escalation
KSU College of Veterinary Medicine
Industry: Agriculture
Application: Biosecurity to Avoid Disease Introduction
Kuwait Environment Public Authority
Industry: Government
Application: Air Quality
Larsen & Toubro Institute of
Project Management

Industry: Manufacturing
Application: Project Management
L.E. Peabody & Associates
Industry: Energy & Utilities
Application: Real Option Valuations of Power Plants
Lockheed Martin / NASA
Industry: Aerospace
Application: Schedule risk assessment
Louisiana Department of Health
Industry: Government
Application: Large-scale Hospital Evacuation Planning
LStar Capital
Industry: Finance
Application: Risk Quantification in Film Financing
Merck
Industry: Finance/Banking
Application: Value-at-Risk Exchange Rate
Missouri University of Science and Technology
Industry: Academic
Application: Mining Engineering
MRAG Asia Pacific
Industry: Environment
Application: Quantifying Illegal Fishing
Murray & Roberts
Industry: Construction
Application: Evaluating enterprise risks using risk modelling for a cash flow based approach
National Grid UK
Industry: Energy & Utilities
Application: Modeling electricity network restoration performance
Netconomy
Industry: Finance
Application: Budget Forecasting
New York City Department
of Health and Mental Hygiene

Industry: Medical
Application: Future Pandemic Planning
Northern Indiana
Public Service Company

Industry: Energy
Application: Pricing, Production Allocation for Regulation Compliance
NOAA Fisheries
Industry: Environment
Application: Estimating effects of dams on Atlantic salmon population dynamics
Pantektor AB
Industry: Construction
Application: Fire Risk Mitigation
Pareto Solutions
Industry: Government
Application: Budget Projection
Patrick Engineering / MBTA
Industry: Construction
Application: Cost and Schedule Risk Analysis
RD&I Consulting
Industry: Construction
Application: Fehmarn Belt Fixed Link
Pellegrino & Associates
Industry: Finance
Application: Intellectual Property Valuation
Petrobras
Industry: Energy
Application: Exploration & Production
Preformed Line Products
Industry: Manufacturing
Application: Tolerance Stacking
ProjectPMO
Industry: Aerospace
Application: Schedule and Cost Risk, Project Management
Post Danmark / PricewaterhouseCoopers
Industry: Government
Application: Insurance Premium Reduction
Purdue University
Industry: Environment, Government
Application: Cost-benefit analysis of biofuel production under two government policies
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Students at Virginia Commonwealth Use @RISK to Analyze Amazon and FedEx’s Financial Risk

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Background
Dr. Etti Baranoff, an Associate Professor of Insurance and Finance at Virginia Commonwealth University in Richmond uses @RISK in her business school class, ‘Managing Financial Risk’ (taught every semester). In this class, students learn how to apply Value at Risk (VaR) analysis to understanding the measures of risks and applying tools for risk management. Dr. Baranoff’s students use @RISK to analyze which accounting data inputs (from both the balance sheet and income statements) can be most damaging to the net income and net worth of a selected company.1 The main analyses are to determine the inputs contributing the most to the VaR of the net income and net worth, the stress analysis and sensitivity analysis. Historical data is used for @RISK to determine the best fitted statistical distribution for each input, and @RISK’s Monte Carlo simulation functionality is used.

While uncovering these quantitative results for each case study of the selected company, the students try to match these “risks” with risks they map using the 10Ks reports of the company. Finding the most damaging risks (proxies by accounting data) and applying them to the risk map provides an overall enterprise risk management view of the cases under study.

Since 2009, many cases have been developed by the students, who major in financial technology, finance, risk management and insurance, and actuarial science. Featured here are two cases from Fall 2014. At the end of the semester, the class creates a large table with inputs from each of the cases studied in the class. Table 2 is used to compare the results of the companies and evaluate the risks. The class acts as a risk committee providing analytical insights and potential solutions.

Analyzing Amazon
For Amazon the group used @RISK to assign a distribution to each input variable collected.2 The data used was annual and quarterly data. Here we feature the quarterly results for the net worth analysis.

Each group is asked to create the statistical distributions of the historical data of each input with and without correlations (as shown in Figure 3). The simulations are run with and without the correlations. The runs are then compared. Dr. Baranoff explains that “without correlation, these results are not appropriate since the size is the most influential. By correlating the inputs, the size affect is mitigated.3 I have them do this first to show the size influence and the importance of the correlation.”

For Amazon, we show the results for the net worth using quarterly data with correlation among the inputs we see the VaR for the net worth in Figure 1.

Figure 1: Value at Risk (VaR) for Amazon net-worth with correlation among the inputs

The quarterly data showed a probability of getting negative net worth at 5% value at risk, with ‘Property, plant, and equipment,’ and ‘Cash and short-term investments’ as the highest influencers of net worth. “So as far as the net worth goes,” says Dr. Baranoff, “Amazon is a strong company.” She also adds, “Interestingly the statistical distributions used for these inputs are Exponential distribution for net ‘Property, plant, and equipment,’ and ExtraValue distribution for ‘Cash and short term investments.’” These are shown in the following two graphs.

Figure 2: Amazon: Examples of statistical distributions for Inputs

Table 1: Correlation among the applicable inputs for Amazon’s net-worth

Figure 3: Sensitivity Analysis for Amazon net-worth with correlation among the inputs

Verifying the VaR results, the sensitivity analysis shows the relationship of the contribution of each of the inputs to the net worth. Again, as expected, ‘Property, plant, and equipment’ have the steepest slope. (As base value changes, ‘Property, plant, and equipment’ will have the biggest impact on net worth.)

Figure 4: Stress Test for Amazon net-worth with correlation among the inputs

For the stress test4 it appears again that the ‘Property, plant, and equipment’ can stress Amazon’s net worth at the 5% VaR level.

While it is not shown here, the project also includes an examination of the inputs impacting the net income.

When the project begins, the students create a qualitative Risk Map for the company. Figure 5 is the Risk Map for Amazon. This is done independently from the @RISK analysis. The students study the company in depth using the 10K including all the risk elements faced by the company. They create a risk map based on qualitative data of ranking the risks by frequency and severity of the potential losses from the risk exposures. After they complete the @RISK analysis, they compare the results for the net worth and net income with the qualitative Risk Map’s inputs.

Figure 5: Risk Map for Amazon – Qualitative Analysis based on 10K Report

The @RISK analysis revealed that ‘Property, plant and equipment’ have the highest possibility to destroy the net worth of Amazon. In terms of the qualitative risk map, the connection would be to the risk of ‘Supply Chain Interruption.’ Any problems with plants’ equipment will lead to supply chain risk. Another connecting input is ‘Goodwill’ as a proxy for ‘Reputational Risk’ in the Risk Map. While it is high severity and high frequency by the students’ qualitative analysis, it is shown to have medium impact on the net worth of Amazon as per Figure 1 for the VaR analysis. Similarly, ’Goodwill’ has impact on the stress analysis in Figure 4, but, not as high as implied from the Risk Map.

For this short article, Dr. Baranoff has not discussed all the analyses done with @RISK and all the relationship to risks (inputs) in the Risk Map. Dr. Baranoff says they were able to draw conclusions about how Amazon should plan for the future: “In order to have high sales revenue, Amazon will need to maintain an excellent reputation and stay at competitive prices to avoid reputational risk and decline in market share,” she says. “Also, to avoid weather disruption risk and supply chain interruption risk, Amazon will need to diversify the locations of its properties and keep the warehouses spread across the country.”

Analyzing FedEx
Dr. Baranoff’s students also analyzed the risk factors of FedEx, with the same objectives as for Amazon. The students gathered financial data from S&P Capital IQ, as well as from the FedEx investor relations website. With the information in hand, the students used @RISK for distribution fitting, stress analysis, and sensitivity analysis.

When analyzing FedEx’s risk factors, the students used the 10K of FedEx and then related them to the quantitative analysis using @RISK. A number of key areas came up, including Market risk, Reputational risk, Information Technology risk, Commodity risk, Projection risk, Competition risk, Acquisition risk, and Regulatory risk. The students created a risk map of all the factors, identifying the severity and frequency of each as shown in Table 6 5 :

Figure 6: Risk Map for FedEx – Qualitative Analysis based on 10K Report

Working with @RISK for FedEx
The distribution fitting for most of the inputs on the FedEx income statement came up as a uniform distribution. This made sense to the students based on data from the last ten years, as FedEx has been a mature company strategically placing itself to confront changing market conditions. Net income was negative at the 45% VaR for the uncorrelated data against a 40% VaR for the correlated data. ‘Revenue’ and ‘Cost of goods sold’ are the two major contributors on net income.

Figure 7: Value at Risk (VaR) for FedEx net-income with correlation among the inputs

The sensitivity analysis also confirms that ‘Revenue’ and ‘Cost of goods sold’ have the most influence. They are very large amounts compared to the other inputs on the income statement.

Figure 8: Sensitivity Analysis for FedEx net-income with correlation among the inputs

For the net worth, once again the most common distribution was the uniform distribution though there were a couple of normal distributions. Pensions had a lognormal distribution which is one of the most common distributions used by actuaries. Running the simulation with and without correlation had ‘Property Plant and Equipment’ (PPE) as the most influential input, but when running the simulation on the correlated balance sheet things evened out to a point where almost all the inputs had equal effects on net worth.

Figure 9: Value at Risk (VaR) for FedEx net-worth with correlation and without correlation among the inputs

Figure on left with Correlation. Figure on right Without correlation

Figure on left with Correlation. Figure on right Without correlation

FedEx Segments
FedEx Corporation has 8 operating companies (FedEx Express, FedEx Ground, FedEx Freight, FedEx Office, FedEx Custom Critical, FedEx Trade Networks, FedEx Supply Chain, and FedEx Services). Since FedEx started with FedEx Express and acquired the rest of the operating companies for the segments, the data for FedEx Custom Critical, FedEx Office, FedEx Trade Networks, FedEx Supply Chain, and FedEx Services are reported in FedEx Express. The segments used by the students are therefore made up of FedEx Express, FedEx Ground and FedEx Freight. With that said, FedEx Express was the highest earner for the segments. FedEx Express had the lowest profit margin followed by freight and then ground. Where freight and ground only operate vehicles, Express operates aircraft as well, so it is logical that their expenses are higher as aircraft operations are much more expensive than vehicles. For segments without correlation, operating profit was negative all the way to the 25% VaR, but segments with correlation had positive operating profit at the 5% VaR. Also, the ranking of the inputs by effects for the segments were different for the correlated and uncorrelated inputs (see Figure 10).

Figure 10: Value at Risk (VaR) for FedEx Operating Profits with correlation

For the stress test analysis of the inputs impacting the output at the 5% VAR level, there wasn’t much of a difference between the correlated and uncorrelated segments even though the deviations from the baseline was more pronounced for the correlated than the uncorrelated segments, as can be seen in Figure 11.

Figure 11: Stress Test for FedEx Operation Profits with correlation among the segments’ inputs

The students concluded, “FedEx has strategically diversified itself to compete effectively in the global market place...Property Plant and Equipment was a big influence on net worth, but the company is constantly evaluating and adjusting this factor, so that there are no shortages or excesses.”

For Dr. Baranoff, @RISK’s ease of use is a major reason for making it the tool of choice for her classroom. Additionally, she lists its ability to provide credible statistical distributions as another major plus. “@RISK also allows me to show students the differences of selecting different statistical distributions, and the importance of correlation among some of the inputs,” she says. “Additionally, it allows us to combine the results for VaR analysis, stress analysis, and sensitivity analysis to discover what inputs can be destructive to a company’s net income and net worth. And, at the end of the day, it gives us good viewpoints to compare the results among the cases. The stories and the results lead to debate, and provide lots of fun in the classroom as the whole class become a risk committee.”

To conduct the comparison and give the tools to the risk committee to debate the risks and ways to mitigate them, the class creates Table 2.7 Table 2 is the foundation for the risk committee work. Each group is asked to provide a comparative evaluation among the cases under study (usually, about 5 cases each semester) as the second part of its case study report. This project concludes the semester. 8

Table 2: Comparing all case studies for the Fall 2014 semester – Managing Financial Risk Course

Footnotes

1 Students first gathered financial statements from financial information provider S&P Capital IQ.

2 Each group attempts to have as many historical data points as possible to generate the statistical distributions. If quarterly data is available, it is used. The minimum number of observations cannot be less than 9 data points.

3 While “Accounts payable” are large in size, the input is no longer the most important variable under the correlation. As shown in Figure 1, it went to the bottom of the tornado.

4 The Stress test that is captured in the graphic takes the individual distributions that are fitted based on historical data, and stresses each inputs distribution for values that are within a specified range. In this example the range tested is the bottom 5% of the variables distribution. The goal is to measure each variable’s individual impact to an output variable when stressed. The box plots observe each stressed variable’s impact to the net worth (Assets-Liabilities) of Amazon. This test shows which variables distribution’s tails have the biggest ability to negatively or positive affect the company’s net worth at the tail of 5% value at risk net worth.

Each variable is stressed in isolation, leaving the rest of the variables distributions untouched when determining the net worth values. When Assets are tested at their bottom 5% of their range, the net worth of the company is found to decrease as the distribution will be focusing on the bottom 5% of the distribution’s range resulting in less assets on the balance sheet. Likewise, when testing the bottom 5% of the liabilities, the distribution is focusing on small values for the liabilities, and due to the decrease in liabilities, net worth will increase when compared to the baseline run.

5 Each group creates its own version of the risk map as one template is not required.

6 See footnote 4.

7 We acknowledge some imperfections in Table 2, but it serves its purpose as a stimulating starting point for dialogue.

8 The students deserve recognition for their excellent work on the case studies presented in the matrix below. They are:

Agack, Adega L., Alhashim, Hashim M., Baxter, Brandon G. Coplan, Thomas P., Couts, Claybourne A., Gabbrielli, Jason A., Ismailova, Railya K., Liu, Jie Chieh, Moumouni, As-Sabour A., Sarquah, Samuel, Togger, Joshua R.

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