AOOS Project Selection criteria061407

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Information about AOOS Project Selection criteria061407

Published on September 25, 2007

Author: Nickel



Alaska Ocean Observing System:  Alaska Ocean Observing System Conceptual Design Development June 18, 2007 Ginny Fay Meghan Wilson Slide2:  Realism Capability Flexibility Ease of use Cost Easy Computerization Criteria for Project Ranking Systems Slide3:  Realism Reflect the reality of the decision making circumstances, including the multiple objectives of agencies and stakeholders. Common measurement system to evaluate projects. Take into account limitations on facilities, capital, personnel. Include a risk factor. Capability Address multiple timeframes Simulate various situations Internal situations (e.g., changes in staff) External situations (e.g., interest rates, appropriations) Optimize the decision Criteria for Project Ranking Systems Criteria for Project Ranking Systems :  Criteria for Project Ranking Systems Flexibility Provide valid results within a range of conditions. Easily modified or self-adjusting to changes in circumstances. Ease of Use Convenient, low execution time, easy to use and understand. Needs no special interpretation, no hard-to-acquire data, no excessive personnel or unavailable equipment. Parameters match one to one with real world variables that are significant to the project. Expected outcomes should be easily simulated. Criteria for Project Ranking Systems:  Criteria for Project Ranking Systems Cost Data gathering and modeling costs should be low relative to the project cost. All costs should be considered, and their total should definitely not be greater than the potential benefits of the project. Easy Computerization Convenient to gather and store information in a computer database. Easy to manipulate the data in the model such as through a spreadsheet. The Nature of the Project Ranking Systems:  Two basic model or ranking types Numeric - use numbers as inputs. Nonnumeric - don't use numbers as inputs. Important model facts to remember: Models don't make decisions; people do. Models only partially reflect reality. The Nature of the Project Ranking Systems Slide7:  First step -- develop model categories based on program goals and objectives. Categories generated at arms length (blind to potential projects) by agencies and stakeholders. Categories should be weighted to represent their relative importance to achieving program goals. Resulting project scores reflect how much their predicted outcomes contribute to goals achievement. Some AOOS identified categories: Ecosystem health Marine operations Public health Living resources Homeland security Weather andamp; climate Natural hazards Project Ranking Systems Project Ranking Systems:  Project Ranking Systems Some factors are recurring, others have one-time impact. Ranges of uncertainty are helpful for hard-to-estimate factors. Thresholds or critical values of acceptance or rejection can be assigned. Items will contain differing levels of specificity. Avoid category overlap to avoid double counting. Type of Project Ranking Systems:  Type of Project Ranking Systems Nonnumeric Models The Sacred Cow Suggested by a senior and powerful official Stopped at successful conclusion or when suggesting official realizes it was a mistake The Operating Necessity Project is required to keep the system going Ask if system is still worth operating The Competitive Necessity Needed to compete Operating necessity projects take precedence over competitive necessity projects Slide10:  Program Leverage Judged on potential match or partnerships, filling a gap, strengthening a weak link or extending into a new desirable direction. Comparative Benefit Model Sort Divide a list of projects into groups of good - fair - poor. Rank projects within groups. One person or a selection committee picks projects. Models widely used. Peer review - outside referee picks projects. Type of Project Ranking Systems Slide11:  Unweighted 0-1 Factor Model List relevant factors on a preprinted form and have top management evaluators determine whether or not the project qualifies for each of the factors. Sum the responses and see if the project qualifies for enough factors to accept. Weighs all factors as equally important. No gradation of degree to which factor is met. Unweighted Factor Scoring Model Grade how well a factor is met by a project on a scale, usually 5 pt. (e.g., 5 - very good, 1 - very bad). All factors still weighted equally Numeric Systems - Scoring Slide12:  Weighted Factor Scoring Model Add weights reflecting the relative importance of the factors and multiply the weight of each factor by its score. Sum these values and compare to a threshold value. This method can provide a sensitivity analysis to identify an component for project improvement. Don't include marginally relevant factors Constrained Weighted Factor Model Similar to above except additional criteria enter the model as constraints (things that must be present or absent in order for the project to be acceptable) rather than as weighted factors Numeric Systems - Scoring Slide13:  Advantages of Numeric Scoring Models Allow multiple criteria to be used for evaluation and decision. Structurally simple and easy to use. A direct reflection of management policy. Easily altered to meet changes in the environment and in management policy. Weighted scoring models acknowledge that some factors are more important than others. Easy sensitivity analysis to see trade-offs between several criteria. Numeric Systems - Scoring Slide14:  Disadvantages of Numeric Scoring Models Output is a relative measure, no utility is reflected, thus no direct indication of project support. Generally linear, elements are assumed to be independent. Tendency to include too many criteria. Unweighted models assume equal importance of all criteria. Numeric Systems - Scoring Analysis Under High Uncertainty:  Analysis Under High Uncertainty Uncertainty in Organizations Most uncertainty is about the timing and the costs. Three areas of uncertainty Timing of project and cash flows. What the project will accomplish. Side effects or unforeseen consequences. Pro-forma documents help reduce uncertainty. Slide16:  Focuses decision maker's attention on the nature and the extent of the uncertainty of some variables in the decision-making process. Uses probability distributions for each of the uncertain variables, instead of the point estimates that financial analysis utilizes. Decision Analysis. General Simulation Analysis Only count costs and times that result from new proposed project. Risk Analysis Slide17:  Subjective vs. Objective Objective - Measurement taken by reference to an external system. Subjective - Reference to a standard that is internal to the system. Quantitative vs. Qualitative Difference is that one may apply the law of addition to quantitative data and not to qualitative. Reliable vs. Unreliable Data source is reliable if repetitions of a measurement vary by less than a pre-specified amount. Comments on Measurements Slide18:  Suggestions on Ranking Systems-Examples 2006 – 2008 Project Evaluation Criteria (Alaska Department of Transportation) Puget Sound Near Shore PartnershipEstuary and Salmon Restoration Program  Puget Sound Near Shore Partnership Estuary and Salmon Restoration Program Center for Neighborhood Technology – Project Scoring Criteria (multiple projects)  Center for Neighborhood Technology – Project Scoring Criteria (multiple projects) Belize – National Protected Areas System (bio-physical and land use characteristics)  Belize – National Protected Areas System (bio-physical and land use characteristics) NOAA – Risk and Vulnerability Assessment Tool (RVAT):  NOAA – Risk and Vulnerability Assessment Tool (RVAT) (Frequency + Area Impact) x Magnitude = Total Score Slide23:  Ecosystem Health Ocean conditions 3-D models of oil spill trajectories Maritime Operations Collision avoidance system Uncharted rocks, reefs and shoals Weather and wave conditions Surface current velocities and direction Improve vessel safety and maximize transportation efficiency Vessel icing conditions Ocean forecast models for currents and shears Tidal predictions in time and space Hazardous icing conditions Slide24:  Public Health Harmful plankton blooms Contamination of cultured shellfish Living Resources Effects of eddies on mixing Fish survival as a function of ocean conditions Effects of large eddies on fish migration Fish habitat State of the ocean data Fisheries variability Climate variability Ocean circulation and mixing Ocean productivity estimates Fish habitat locations and extents Forecast of important food web components Forage fish abundances and distributions Inventory of marine habitats Slide25:  Homeland Security Vessel tracking Search and rescue times are lengthy Vessel locations Weather and Climate Satellite: SAR and QuikSCAT Weather conditions Limited weather observations in Alaska Surface observations of wind and waves in real time Climate variability Wind and wave observations Natural Hazards Storm surge models Vessel icing Vessel safety in winter Coastal flowing Coastal flooding Coastal erosion Prediction of erosion events Contact us:  Contact us Steve Colt, ISER 786-1753 Ginny Fay, EcoSystems 333-3568 Meghan Wilson, ISER - - 786-5408 website:

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