Risk & Opportunity Management
Outline • • • • • •
Risk & Opportunity Management Process Risk & Opportunity Registry and Metrics Qualitative and Quantitative Assessment Risk & Opportunity Impact Modeling Contingency Assessment Next Steps
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Risk Management is a tool to ensure we are mitigating our highest impact risks • •
Risk Management is required for both DOE and NSF projects It is useful at every stage of the project – NSF: Design and Development Phase and DOE: Pre-CD-0 • Informs the development plan to DOE and NSF. Helps ensure the project is “burning down” the highest risks at the appropriate time with the funds available. • Identifies baseline budget needed for mitigations and “residual risk” contingency
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Risk Registry is a systematic way for the scientific and technical staff to communicate to management what the risks are and how we can mitigate them. – It is a record that is updated regularly – Judgement, subjective ➔ leveling or risk review needed – Review top 10/watch list: • Risk re-assessment may change the top risks – Risk mitigation activities needs an approved and budgeted plan – Track action items and progress related to the top risks
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Opportunities are also included in the registry Note – this is just “one” tool that is helpful at this early stage and does not substitute the need for “alternative analysis - value engineering” (defining & prioritizing requirements and concept tradeoffs). 3
Risk Registry: Identification, Assessment, Mitigation Planning, Status, and Modeling Proposal •
CM-S4 Risk Registry is currently project’s risk tracking repository : – – – –
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Specific Discrete Risks are identified. Qualitative Assessment is recorded/tracked for Current and Post-Mitigation (Residual) levels. Mitigation Planning and Status for each identified risk is tracked directly in the Risk Registry. Registry is fully filter/sortable to aid in tracking / reporting Cost and Schedule risk drivers and associated mitigations, for all identified risks.
Quantitative Assessment and Modeling: – –
Potential cost and schedule impact ranges are calculated and tracked in the registry Cost and Schedule impact ranges are applied directly to schedule and cost Monte Carlo models or other statistical methods could be calculated to develop discrete risk contingency estimates. Code macro for google
Risk Identification & Current Assessment Fields are required. 4
Risk Registry: Identification, Assessment, Mitigation Planning, Status, and Modeling (2)
Data for Monte Carlo Probability > 10%
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Definition of Risk and Risk Analysis • • •
Risk can be defined as an undesirable event during project execution that negatively effects program goals for performance, cost or schedule. Risk management is the ongoing process of comprehensively assessing project risks. Risk Management Plan (TBD) defines the methodology to manage our risks
→Objective qualitative process to assign values to probability of occurrence and impact of occurrence aspects of risk. →Total Impact: (0.33*Cost Impact) + (0.33*Schedule Impact) + (0.33*Performance Impact) →Risk Score: Risk Probability * Total Impact
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Readout Performance, Schedule and Cost Burndown Plan (Preliminary Assessment)
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Example: Risk Analysis – Contingency Assessment • Cost estimate contingency assessed at the lowest WBS level
• Contingency Assessment (Bottoms-up + Discrete Risks + Unknowns) = • $22.17M + $8.64M + $4.34M = $35.15M Contingency (TPC) • Opportunities to generate additional contingency: • CAM-033 Sensor Cost: potential for $5-10M cost reduction. • CAM-010 Exchange Rate: potential for $2.5M cost reduction. 8
Risk management near term support needed from technical collaboration Identify major risks
Define current exposure level
Develop mitigations Perform Post-Mitgation exposure level 9
End of Presentation • Link to CMB-S4 Risk and Opportunity folder – https://drive.google.com/drive/u/1/folders/1U5eCZakq-rj6VJ7Et6A4H__Oq2_Shxq2
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Other available tools - Google form
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Summary of Risks (CDT Example)
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Example: CMB-S4 Current Top Technical Risks (Preliminary) Risk Title
Risk Description (if/then)
Current Exposure
Residual Exposure
Integrated FP Performance
IF end-to-end noise floor in science band exceeds (e.g. >20% of in-band background limit) in yielded packages THEN reduces experiment sensitivity and/or increases cost/schedule
23.3
Critical
12.0
Moderate
Wafer sustained yield
IF decrease in fab or performance yield over production period (define criterion) THEN reduces experiment sensitivity and/or increases cost/schedule
23.3
Critical
9.3
Minor
MUX factor
IF achieved MUX factor < (e.g. 128 or 512) THEN increases qty + complexity of cold MUX and increases cost/schedule
20.0
High
9.0
Minor
MUX Noise
IF integrated noise of readout electronics is greater than 50% of predicted photon noise limit THEN it will reduce system mapping spped by >20%
18.3
High
9.0
Minor
Detector Testbed reliability
IF 1-2 fully characterized test beds doesn't exist THEN throughput reduction and/or increase cost/schedule
18.3
High
6.0
Minor
Readout Testbed reliability
IF 1-2 fully characterized test beds don't exist THEN throughput reduction and/or increase cost/schedule
18.3
High
6.0
Minor
FP I&T Testbed reliability
IF 1-2 fully characterized test beds don't exist THEN throughput reduction and/or increase cost/schedule
18.3
High
6.0
Minor
Sensor Performance
IF wafer performance yield < (eg.80-90%); (yield = Sensor parameter variation (e.g., Psat, Rn, L) meet spec for wafer) THEN reduces experiment sensitivity and/or increases cost/schedule
16.7
High
7.0
Minor
MUX integration yield
IF integration yield < 80% THEN reduces experiment sensitivity and/or increases cost/schedule
16.7
High
8.0
Minor
16.0
High
9.0
Minor
16.0
High
10.7
Moderate
15.0
Moderate
7.0
Minor
FP performance: stability
Wafer Fab Yield Cryostat Assembly complexity
IF end-to-end low-frequency noise in science band exceeds (e.g.>20% of inband background) THEN reduces experiment sensitivity and/or increases cost/schedule IF wafer fabrication yield
