Practical Use of Asset Management and Structured

Decision Making

Case Study: Seattle Public Utilities

Solid Waste Facilities Master Plan

Presented by: Jenny Bagby

Principal Economist

Seattle Public UtilitiesMay 2, 2006

Outline of Presentation

• Overview of Seattle’s waste management system• Description of problem of planning for new

facilities• Use of three types of analysis to help choose

among options– Benefit cost analysis– Value modeling– Decision analysis for modeling risk and uncertainty

• Conclusion

What is Seattle Public Utilities?

• City Department (our director reports to Mayor) 1200 employees including office professional folks as well as field staff

• Solid Waste

• Wastewater

• Drinking Water

• Surface Water (Drainage)

Project Background

• Long-range planning (30 + years)

• Involves collection, transfer, and disposal of municipal solid waste - Garbage, Yardwaste and Recyclables

• Primary customers affected are the self-haul customers at the recycling and disposal stations (RDS) and adjacent neighbors

The Problem

• The City’s two transfer stations are old and outdated

• Transfer station reliability decreasing

• Transfer system inefficiencies

• Quality of customer service is decreasing

• Existing facilities lack flexibility

Many Existing Problems

• Safety concerns• Old wiring• Seismic retrofit needed• High Maintenance

(floors, compactor)• Too many “band-aid”

fixes

System Overview Facilities

• Two city-owned transfer stations

• Two privately-owned transfer stations

• Two intermodal rail yards

• Two private processing facilities for recyclables

• Private processing facility for organics composting

• Private landfills

C R SH LT

Municipal Garbage Organics Recyclables

NRDS SRDS

IM

Private Transfer

C R SH SH

LandfillOrganics Processing

Recycle Processing

Current Waste Flow Diagram

R

System Overview Materials flow

• City-contracted collection and transfer of residential Garbage, Yardwaste and Recyclables

• City-contracted collection and transfer of commercial Garbage and organics

• Private collection of commercial recyclables

• Individual business and residential self-haul

Solid Waste Facilities

Rail Landfill Connection

• Two Railroad Companies Serve Seattle

• Most Large Regional Landfills are Linked by Rail

• Access to more than one rail line opens access to different landfills creating more competition

Rabanco - Burlington Northern/Santa Fe - Roosevelt Landfill

Waste Management Inc. - Union Pacific - Columbia Ridge Landfill

Understanding the System

• Public & private facilities work in conjunction with each other

• Waste flows to different facilities can change over time

• A flow change to one facility affects the others

Vertical Integration of Solid Waste Business

• Industry consolidation (fewer solid waste service companies than before)

• Companies strive to control all aspects of the market (collection, transfer, long-haul, and disposal)

• An integrated company can reduce operation costs, but may also reduce competition

Project Objectives

• Improve transfer efficiency of solid waste and recyclables

• Improve self-haul customer service

• Minimize neighborhood impacts from transfer stations

• Increase reuse and recycling opportunities

• Provide long-term system flexibility

Primary Questions

• What is the appropriate mix of public and private facilities?

• Remodel or rebuild city stations?

• Do we need additional property at the city stations?

• Does a city-owned intermodal transfer station make economic sense?

Initial Assessment

• A city-owned facility is needed in north and south Seattle

• Siting options are limited; no substantially better sites were found for the City stations

• A third City-owned intermodal transfer facility needs to be evaluated

Enter Asset Management

• AKA Full Employment for Economists• C/B Analysis on all decisions (especially

ones this large)• Emphasis on quantifying in $ terms

everything we possibly can• Challenging!• CH2MHill to the rescue - Value Model and

Decision Framework

Required Elements of an Effective Decision Framework

Develop Value Modeland FormulateAlternatives

DevelopImplementation

Plan

Organizational

Analytical

• Solve the right problem • Put interests & values first • Avoid advocacy & positions• Avoid useless data• Find lowest cost solution• Manage risk and liability• Track progress

CollectMeaningful,Reliable Data

EvaluateAlternativesand MakeDecision

Frame the Problem

Ensure Leadership andCommitment

The OptionsKey elements

• No action (required for EIS) - maintain operation and legal compliance

• Modifications to RDS - retain tipping sheds• Total rebuild of RDS - including additional

reuse and recycling facilities• Add property to NRDS and/or SRDS• Develop a City-owned transfer/intermodal

facility

Options Assessment Steps

• Develop options

• Identify Quality of Service goals & criteria

• Prepare conceptual layout designs for preferred options

• Model Costs, Risk and Quality of Service performance for preferred options

• Revise options based on results

Intermodal Site

South Recycling and Disposal Station Option 11

Asset Management

• We developed a cost model to quantify in dollars everything we could

• Goal was to compare each of the options using benefit-cost analysis

• What we couldn’t quantify we put into a value model to help display the other benefits or values of each option

System Cost ModelCost model calculates total system NET cost

over 30 years of:

• Transfer

• Rail loading and hauling

• Processing

• Disposal

• Collection (IF option results in changes to collection costs)

System Cost Model• Costs include:

– Property Purchase/Lease– Construction Costs – Equipment Capital– Labor and Other O&M – Contractor payments such as Disposal, Private

Transfer, Processing– Long term competitive benefits of partnering– Revenues from partner tons

Example Labor and Equipment CostModel Inputs

Capital EquipmentCalculatedunit price

Tons(Trips) per

hourOperator req't

Useful lifein years

based on2080 hr/yr

Maint peroperating

hour

Scale 1In Scale-70 ft w/Labor $72,240 90 1 63.0 $2.75In Scale-70 ft NO Labor $72,240 90 1 63.0 $2.75Waste CompactionTrack Loader (pit) $340,200 100 1 5.0 $25.00Wheel Loader (push) w/Labor $251,400 100 1 5.0 $22.00Compactor 1 Bale $1,076,160 100 1 14.0 $13.35Compactor 2 Bale $750,000 75 0.5 14.0 $13.35Yard Goat $99,240 200 1 6.0 $12.00Broom Floor Labor Existing $0 15 1 15.0 $0.00HaulingG Tractor N to Argo 25t $117,240 19 1 4.0 $20.00G Tractor S to Argo 25t $117,240 28 1 4.0 $20.00Rail LoadingReach Stacker $576,000 660 2 14.0 $16.00Gantry Crane 75 ft $1,800,000 990 2 29.0 $16.00

Cost Results

Option 4: Cost by Function

$-

$10,000,000

$20,000,000

$30,000,000

$40,000,000

$50,000,000

$60,000,000

$70,000,000

$80,000,000

20

04

20

06

20

08

20

10

20

12

20

14

20

16

20

18

20

20

20

22

20

24

20

26

20

28

20

30

20

32

20

34

20

36

Option 11: NPV Contributions

$0

$100,000,000

$200,000,000

$300,000,000

$400,000,000

$500,000,000

$600,000,000

$700,000,000

Option 11

Partner Revenue

Changes to Collection Costs

Scale

Private Transfer

Argo

Hauling

Existing Facility

Rail Loading

Waste Compaction

General Facility

Recycling Construct and O&M

Prop, Constr & Lease

Disposal and Processing

Cost ResultsComparison of NPV for Options 1-7

-$100,000,000

$0

$100,000,000

$200,000,000

$300,000,000

$400,000,000

$500,000,000

$600,000,000

$700,000,000

Option 0 Option 1 Option2A

Option2B

Option 3 Option4A

Option4B

Option 5 Option 6 Option 7

Quality of ServiceAssessment

Primary Services Provided

• Waste reduction & recycling

• Customer service

• Work environment

• Built environment (community) impacts

• Natural environment impacts

Shaded criteria/ sub-criteria receive performance scales, weights, and option scores.

Natural and Built Environment Impacts are broken out by facility (NRDS, SRDS, Intermodal).

Capita l

O &M

Lim it R isk o fS trande d C osts

Prom oteCom petitio n

Respond to W aste /M arket Change s

Partnerin gO pportunitie s

Room to grow/ M odularity

Long Ru nUncertainty

T o ta l S ys temC o st

Costs

G reenho use ga sF lexibility to respon dto change sin m arkets, tech& waste stream s

% Recyclin g

Reus eO pportunitie s

W aste R edu ctio n &R ec l. G o a ls

W ait T im eDistance to Fac.

Driv in g an dQ ueue T im e

Relia bilit yO ne-stop sho pF lexibility to adap tto change sin waste

Custom erConve nienc e

Fall hazzard sA ir Q ualityVehicle Ac cNois eSeism ic

Health &Safety

Educatio nO pportunitie s

Servic eEquity

C u sto m erS ervice

Facilitie s

Fall hazzard sA ir Q ualityVehicle Ac cNois eM ech SafetySeism ic

Health &Safety

F lexibilit y

W o rkE n viro n m ent

F lexibilit y

Aesthetic s

C onsis tent w /C om p/ N H P lans

Traffic

Nois e

Dust

O dor

Com m unityEquity

B u ilt E n v.(C o m m un ity)

Im pacts

W ater

A irQ uality

O therResource Im p.

N atu ralE n viro n m ent

Im pacts

Quality ofService

Short TermIm pacts

Screening CriteriaSPU Solid W aste Facilities Masterp lan

Importance of Value Model• Facilitated process• Way to get all issues and concerns

identified• Moved discussion from a high level where

things are hard to evaluate• Began discussing what everyone really

meant/valued when they held a certain position

Performance ValueCriterion Measure Rate x Weight = Score

A 3 20 60

B 4 45 180

C 1 10 10

D 2 25 50

Total Score 300

Quantified Evaluation Approach:

Multi-Attribute Utility Theory

Quality of ServiceAssessment

• Non-monetizable Quality of Service benefits were quantified in a variety of ways such as

• Length of time queuing• Square feet of space available for operations• 1-5 scale - best professional judgement• etc.

SPU Solid Waste Facility MasterplanContributions by Criteria - Total Quality of Service Score

Note: Option 5 and 11 score highest on waste reduction. This is the differentiating for its leading score.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Option 5 Option 11 Option 8 Option 0

Customer Service

Work Environment

Waste Reduction

Built Environment

Natural Environment

Contributions to Quality of Service fromLevel:

Used Criterium Decision Plus Software

Insert Cost Risk Profile Graph and Tornado diagram

Cost ($M)

Op

tion

Sco

reOverall Results

Quality of Service vs. Cost

0

0.2

0.4

0.6

0.8

1

480 516 552 588 624 660

Option 5

Option 1

Option 4A

Option 2A Option 3

Option 0

Option 2B

Option 4B

Option 6

Option 7

SPU SW Facilities SPU SW Facilities MasterplanMasterplan

Approach to Capture Cost Risk

Cost Drivers and Uncertainties Affecting NPV of OptionsThe Influence Diagram below illustrates conditional relationships between decisions (yellow rectangles), uncertainties (green ovals), & outcomes (blue boxes).

ResidentialRec. Rate

CommRate

Res.

Com.

Self HaulNet YW

TotalOptionCost

NRDSConst.Costs SRDS

Const.Costs

IMConst.Costs

Growth inCity Waste

Stream

Res/ ComRecycling

Rate

RailPrice

RecyclingRevenues

LaborEfficiency

Factor

ConstructionCosts

KC RailParticipation

DisposalSavings

KC DisposalParticipation

OptionSelected

Intermodal

Step 2: Potential Cost Outcomes and Step 2: Potential Cost Outcomes and Probabilities Tool: Decision TreeProbabilities Tool: Decision Tree

For each possible outcome of a decision,

Decision Trees show:• The Pathway - How did

this happen?

• The Probability - How likely is this?

• The Cost - How much will this outcome cost?

No Additional Facilities

Prob = 100% C = $0 M

Forecast

Prob = 70%

Prob = 20% C = $10M

New Facilities Needed

Prob = 80% C = $30M

AboveForecast

Prob = 30%

Growth in Waste Stream Future Capital Costs

Tools Used– DPL software

Interaction– Workshop and/or

questionnaires to define branch outcomes and estimate probabilities and costs

The influence diagram is actually the top layer of a mathematical model. The underlying model is a series of interconnected decision trees. In our simplified example only possible one tree is shown (above).

Example

Facility Expansion

Calculating the Decision Tree [Example Tree]Calculating the Decision Tree [Example Tree]

Prob of Outcome = 0.7 * 1.0 = 0.7Cost of Outcome = $0

Prob of Outcome = 0.3 * 0.2 = 0.06Cost of Outcome = $10M

Prob of Outcome = 0.3 * 0.8 = 0.24Cost of Outcome = $30M

Costs (NVP):No Additional Facilities = $0MFacility Expansion = $10M New Facilities Needed = $30M

No Additional Facilities

Prob = 100% C = $0 M

Forecast

Prob = 70%

Prob = 20% C = $10M

New Facilities Needed

Prob = 80% C = $30M

AboveForecast

Prob = 30%

Growth in Waste Stream Future Capital Costs

Facility Expansion

The influence diagram is actually the top layer of a mathematical model. The underlying model is a series of interconnected decision trees. In our simplified example only possible one tree is shown (see below).

Decision Trees: Probabilities and Cost OutcomesExample: Rail Savings

Scenario/Probability

Outcome($/ton.)

S1 Merchandise Train

P = 20% 16.80

14.70

Without King County (Rail)

13.40

12.90

P = 60%

With King County (Rail)

P = 40%

S2 - SPU waste w/ others

P = 80%

S3 - SPU Waste w/KC

P = 70%

S4 SPU/KC + shared loading

P = 30%

Intermodal Yes

0.00

Intermodal No

Uncertainty Branch - Disposal Savings with Intermodal

Scenario/Probability

Outcome($savings/ton.)

0

-1

0

-2

Without King County

P = 50%

With King County

P = 50%

No: P = 60%

Yes: P = 40%

No: P = 60%

Yes: P = 40%

Intermodal - Yes

Interface Sheet for Decision AnalysisInputs Value UnitsWaste Stream Generation Rate

Residential 0.03 %Commercial 0.07 %Self-Haul Net of YW 0.07 %Partner Rail Load Only 0.07 %

Percent RecycledResidential 0.70 %Commercial 0.70 %Self-Haul net of YW DO NOT USE THIS ONE SEE EMAIL %

Recycling Revenues 1 %Rail Price 13.70 $/tonDisposal Price Change (for both IM and non-IM options) 0.00 $/ton

Construction Costs (includes recycling construction)NRDS 1 %SRDS 1 %Intermodal 1 %

Equipment Cost 1.2Labor Downtime Factor 1.21 No.Discount Rate 6% %Rail Load Partner Tons 0 tons

OutputTotal NPV 656,983,487 $

COST RISK PROFILE

Probabilistic Range of Option 0 CostC

umul

ativ

e P

roba

bilit

y (%

)

NPV - $M

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

475 500 5250 550 575 600 625 650 675 700 725 750 775 800 825

Base Case = $626M

90th Percentile = $742M

Expected Value = $640M

10th Percentile = $553M

“Tornado” Diagram Relative Impact of Uncertainties: Option 11

NPV - $M

A Tornado Diagram evaluates the impact of each uncertainty by varying it from its best to worst state, while fixing all other uncertainties to their base (most likely) state. The width of the bar shows the impact on total option cost.

Growth in City Waste Stream

Construction Costs

Res/ Com Recycling Rate

Rail Price

KC Rail Participation

Disposal Savings

Recycling Revenues

Labor Efficiency Factor

KC Disposal Participation

620 640 660 680 700 720 740

BASE CASE TORNADO DIAGRAM

Relative Impact of Uncertainties Option 1

NPV - $M

Growth in City Waste Stream

Res/ Com Recycling Rate

Construction Costs

Labor Efficiency Factor

Recycling Revenues

440 460 480 500 520 540 560 580 600

Risk Assessment Results

Impact of Key Uncertainties ($M)

Values shown reflect the impact on total cost when an uncertainty is varied across its range of outcomes. All other uncertainties are held constant at their base states.

Waste StreamGrowth

RecyclingRate

ConstructionCosts

Option 0 189 106 12Option 5 129 73 114Option 8 192 106 39Option 11 135 73 84

Conclusions

• Non-intermodal options (0 and 8) have the greatest cost uncertainty (high spread between their 10th and 90th percentiles).

• Growth in the city’s waste stream and recycling rate changes have the greatest impact on total costs.

• Intermodal options are much less sensitive to variations in city waste and recycling growth rates.

• Construction cost uncertainty is lowest with Options 0 and 8.

• In all options, the expected value of costs is 5-7 percent greater than our baseline cost estimates. This means that there is more upside risk than

downside opportunity in the estimates.

What We LearnedRound 1

• The cost of reuse/recycling facilities is relatively high compared to percent diverted

• Building costs are high at SRDS and intermodal due to soils

• Queue reduction goal was too aggressive; resulted in too large a facility

• Don’t need to purchase property to take advantage of partner tons

Round 2Revised Options

• Modifications to Recycling facilities to increase cost effectiveness

• Less aggressive queue reduction goal

• Alternative construction that does not require pilings at SRDS

Project Status

• Approach and results accepted by SPU Asset Management Committee

• AMC asked us to quantify in $’s some of the benefits from value model

• Plan supported by SPU Director and Mayor• Site for IM announced, property purchases

beginning or underway for all 3 site• Decision to do a DBO for IM

Concluding Remarks• Decisions are likely to be supported if:

– They are rational and compelling– The underlying trade-offs have been clearly

communicated– Discussions and decisions have been

documented for later reference and defensibility– Conflicts have been anticipated, and thus

prevented or well-managed– Participants feel they have been listened to and

that they have had some impact or effect on the final outcome

• No tool replaces human judgment

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