1. Case Study
Timothy J. Havranek, MBA, PMP
Leigh A. Hostetter, PMP
November 1, 2016
Forecasting Portfolio Environmental Liabilities

2. Forecast Model Objectives
Identify Spending
Patterns
• Inform regional
business units
• Estimate annual
costs and net
present value
Estimate Active
Projects by Year
• Forecast human
resource needs
• Identify when
majority of projects
will be complete
Estimate Cost
Efficiency
• Demonstrate value
provided by
environmental
department
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3. Portfolio Elements
Four Business Units & Facility Types
3
Portfolio
Service Stations
Pipelines
Refineries Terminals
North South East West
North
South
East
West
North South East West
North
South
East
West
Retail
Distribution
Production Central Facility

4. Model Considerations
4
Remediation
Project Phases
Initial Investigation
Detailed
Investigation
Feasibility Study
Design
Implement
Operate an
Monitor
Significant Site
Differences
Volume of
Contaminant
Subsurface
Geology
Regulatory
Requirements
External
Stakeholders
Surrounding Land
Use
Business Unit
Differences
Number of Active
Projects
Number of
Projects in Each
Phase
Required Data
Cuts
Business Unit
Facility Type
Total Portfolio

5. Modeling Approach
Two Tier
Structure
• Logistics Modeling Template
• Portfolio Summary Model
Logistics
Template
Features
• Projects dynamically move through phases
• Accounts for new projects entering portfolio
• Allows for recycling, short circuiting, transferring
• Includes risk register for systemic risks
Logistics
Template
Outputs
• Net present value
• Mean inflated annual costs
• Mean active sites by year
5

6. Lifecycle Process
6
A B C D E F
A B C D E F
A B C D E F
A B C D E F
Normal Lifecycle
Recycling through
Phases
Short Circuiting through
Phases
Transferring out of
Portfolio

7. Portfolio Model Features
Inputs • NPV Distributions from Logistics Templates
• Utilizes RiskDuniform Function
Output
• Total Portfolio NPV
• Results with and without Risk Register
• Sensitivity Tornado
7

8. Example Sites Moving through Project Phases
8

9. Logistics Modeling Template Review
9

10. North Retail – Cumulative Distribution Function
10
0%
20%
40%
60%
80%
100%
CumulativeProbability
$ Millions
NPV Without Risk Register NPV with Risk Register
Mean without Risk Register = $X
Mean with Risk Register = $Y

11. North Retail – Mean Annual Costs
11
$0
$5
$10
$15
$20
$25
$30
$35
$40
$45
$50
2016 2026 2036 2046 2056 2066 2076 2086 2096
$Millions
Year
All Sites & Overhead, Inflated
Costs without Risk Register Costs with Risk Register
Last Model Year = 2115
Costs without Risk Register = $X
Costs with Risk Register = $Y

12. North Retail – Cumulative Costs
12
2016 2026 2036 2046 2056 2066 2076 2086 2096
$Millions
Year
All Sites & Overhead, Inflated
Costs without Risk Register Costs with Risk Register

13. North Retail– Sensitivity Tornado
13
Mean = $Y
Threat
Threat
Threat
Opportunity
Opportunity
Threat
Threat
Threat
Opportunity
Threat
$ Millions
NPV with Risk Register
Inputs Ranked By Effect on Output Mean

14. Portfolio – Mean Number of Sites Over Time
14
2016 2022 2028 2034 2040 2046 2052 2058 2064 2070 2076 2082 2088 2094 2100 2106 2112
NumberofSites
Year

15. Liability Reduction Cost Efficiency
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$100 Million Year over Year Change in NPV
$40 Million Actual Expenditure
$2.5 of Liability Discharged per Dollar Spent

16. Summary
Model is representative
• Testing indicates that model is predicting within acceptable levels
• Model is improving with each update
Factors contributing to model being representative
• 20 year historical database
• Database required significant scrubbing and adjusting to make accurate
• Logistics modeling approach
Meets owner’s needs
• Provides data needed to manage business
• Helps prevent surprises
• Establishes proper benchmark for evaluating performance
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