Perfrm Quantitative Risk Analysis Intrductin Quantitative Risk Analysis refers t the thrugh and cmplete numeric analysis f the verall effect f the ttal quantifiable amunt f risks invlved in the prject bjectives. Purpse and Objectives Numeric estimatin f verall effect f risk n prject bjectives based n current plans and infrmatin. Results evaluate the likelihd f success and estimate cntingency reserves fr time and cst that are apprpriate t bth risks and prject stakehlders. Mnte Carl, a quantitative technique, prvides realistic estimatin f prject cst. It is inapprpriate if the qualitative risk analysis prvides enugh infrmatin especially in the case f smaller prjects. The Plan Risk Management prcess shuld ensure the applicatin f quantitative risk analysis in prjects. Calculating estimates f verall prject risk is the fcus f the Perfrm Quantitative Risk Analysis prcess. An verall risk analysis, such as ne that uses quantitative technique, estimates the implicatin f all quantified risks n prject bjectives. The implementatin f verall risk analysis using quantitative methds requires: Cmplete and accurate representatin f the prject bjectives built up frm individual prject elements. e.g., Prject schedule r cst estimate. Identifying risks n individual prject elements such as schedule activities r lineitem csts at a level f detail that lends itself t a specific assessment f individual risks. Including generic risks that have a brader effect than individual prject elements. Applying a quantitative methd (such as Mnte Carl simulatin r decisin tree analysis) that incrprates multiple risks simultaneusly in determining verall impact n the verall prject bjectives. Results f the quantitative risk analysis cmpared t the prject plan gives the verall estimate f the prject risk and answers the fllwing questins: What is the prbability f meeting the prject s bjectives? Hw much cntingency reserve is needed t prvide the rganizatin with the level certainty it requires based upn its risk tlerance? What are thse parts f prject which cntribute mst risk when all risks are cnsidered simultaneusly? Which individual risk cntributes the mst t verall prject risk? 2012 RMstudy.cm Page 1 f 12
Perfrm Quantitative Risk Analysis Estimatin f verall prject risk using quantitative methds helps t distinguish prjects where quantified risks threaten bjectives beynd the tlerance f the stakehlders. Critical Success Factrs fr the Perfrm Quantitative Risk Analysis Prcess The critical success factrs fr the Perfrm Quantitative Risk Analysis prcess are: Prir Risk Identificatin and Quantitative Risk Analysis Apprpriate Prject Mdel Cmmitment t Cllecting High-Quality Risk Data Unbiased Data Overall Prject Risk Derived frm Individual Risks Interrelatinships between the Risks in Quantitative Risk Analysis 1. Prir Risk Identificatin and Quantitative Risk Analysis Perfrm Quantitative Risk Analysis Prcess happens after the Identify Risks and Perfrm Qualitative Risk Analysis Prcesses. Reference t a priritized list f identified risks ensures that Perfrm Quantitative Risk Analysis Prcess will cnsider all the significant risks while analyzing. 2. Apprpriate Prject Mdel Frequently used prject mdels include the prject schedule, line-item cst estimates, decisin tree and ther ttal-prject mdels. Sensitive t the cmpleteness and crrectness f the mdel f the prject that is used. 3. Unbiased Data Successful gathering f data abut risks shuld be dne by interviews, wrkshps, and expert judgment. 4. Overall Prject Risk Derived frm Individual Risks The Perfrm Quantitative Risk Analysis prcess is based n a methdlgy that crrectly derives the verall prject risk frm the individual risks. E.g., Mnte Carl simulatin fr risk analysis f cst and schedule, decisin tree fr making decisins when the future is uncertain. 5. Interrelatinships between the Risks in Quantitative Risk Analysis Cmmn rt cause risks likely t ccur tgether are addressed by crrelating the risks that are related. Using a risk register t list risks r rt cause risks and attaching it t several prject elements. 2012 RMstudy.cm Page 2 f 12
Perfrm Quantitative Risk Analysis Tls and Techniques fr the Perfrm Quantitative Risk Analysis Prcess The characteristics f tls and techniques used fr quantitative risk analysis are as fllws: 1. Cmprehensive Risk Representatin Risk mdels permit representatin f any, if nt all, f the risks, pprtunities, and threats that have impact n an bjective simultaneusly. 2. Risk Impact Calculatin Facilitates the crrect calculatin f the effect f many risks and are described at the level f ttal prject. 3. Quantitative Methd Apprpriate t Analyzing Uncertainty The methds shuld be able t handle the way uncertainty is represented, be it the prbability f ccurrence r prbability f distributins fr a range f utcmes. E.g. Mnte Carl simulatin permitting the cmbinatin f prbability distributins f line-item csts r schedule activity duratins. 4. Data Gathering Tls They include: Assessment f histrical data and wrkshps Interviews r questinnaires 5. Effective Presentatin f Quantitative Analysis Results Results frm quantitative tls are nt available in standard prject management methds such as prject scheduling r cst estimating. E.g. Prbability distributin f prject cmpletin dates r cst estimatin. The results include: Prbability f achieving a prject bjective such as finishing n time r within budget. Amunt f cntingency reserve needed t prvide a required level f cnfidence. Identity r lcatin within the prject mdel f the imprtant risks. 2012 RMstudy.cm Page 3 f 12
Perfrm Quantitative Risk Analysis The elements f the quantitative risk analysis are illustrated in Figure 7.1. 6. Iterative Quantitative Risk Analysis Figure 7.1 Structure f Quantitative Risk Analysis Peridical analysis f individual risks f prject enhances the success f quantitative risk analysis. The frequency f analysis is planned in the Plan Risk Management prcess, and events within the prject als influence it. 7. Infrmatin fr Respnse Planning Overall prject cntingency reserve in time and cst shuld be reflecting in the prject schedule and budget. Quantitative Risk Analysis prvides infrmatin t mdify the prject. Dcumenting the Results f Quantitative Risk Analysis Prcess The cntingency reserves calculated are incrprated int the cst estimates and the schedule t establish a prudent target and a realistic prject. If the cntingency reserves required exceeds the time r resurces, changes in the prject scpe and plan may result. The results f the quantitative risk analysis are recrded and passed n t the persnnel/ grup fr any further actin required t make full use f the results. 2012 RMstudy.cm Page 4 f 12
Perfrm Quantitative Risk Analysis TECHNIQUES The Perfrm Quantitative Risk Analysis seeks t determine the verall risks t prject bjectives when all risks ptentially perate simultaneusly n the prject. It prvides answers t several questins regarding the prject. They are as fllws: Hw likely is the prject t cmplete n the scheduled date r earlier? Hw likely is the prject actual cst t be the budgeted cst and less? Hw reliable will the prduct be that the prject prduces? What is the best decisin t make in the face f uncertain results? Hw much cntingency in time and cst is needed t prvide the rganizatin with its desired degree f cnfidence in the results? Hw shuld the design f the prduct r system be changed mst ecnmically t increase its reliability? What are the individual risks that seem t be the mst imprtant in determining the verall prject risk? 1. Decisin Tree Analysis: Causes the rganizatin t structure the csts and benefits f decisins when the results are determined in part by uncertainty and risk. Slutin f the decisin tree helps select the decisin that prvides the highest Expected Mnetary Value r expected utility t the rganizatin. Critical success factrs: Careful structuring f the decisin tree; all alternative decisins that are materially different shuld be cnsidered; decisin trees shuld be specified cmpletely Access t high-quality data abut prbability, cst, and reward fr the decisins and events specified using histrical infrmatin r judgment f experts. Use f a utility functin that has been validated with the rganizatin s decisin makers. Availability and understanding f the specialized sftware needed t structure and slve the decisin tree. Weaknesses: Smetimes difficult t create the decisin structure. Prbabilities f ccurrences can be difficult t quantify in the absence f histrical data. The best decisin may change with relatively plausible changes n the input data, meaning that the answer may nt be stable. The rganizatin may nt make decisins based n a linear Expected Mnetary Value basis, but rather n a nn-linear utility functin; these functins are difficult t specify. Analysis f cmplicated situatins requires specialized (thrugh available) sftware. There may be sme resistance t using technical appraches t decisin making. 2012 RMstudy.cm Page 5 f 12
Perfrm Quantitative Risk Analysis Specialized and widely available sftware used specifies the structure f the decisin with decisin ndes, chance ndes, csts, benefits, and prbabilities User can evaluate the different decisins using functins based n Expected Mnetary Value r nn-linear utility functins f varius shapes. An example is shwn belw here: Figure 7.2: Example f Decisin Tree fr Chsing between an Experimental Technlgy vs. Cmmercial Off the Sheet (COTS) Technlgy. Surce: Precisin Tree frm Palisade Crpratin The negative numbers represent utflws r investments (e.g. COTS) The percentage represents prbabilities f the event ccurring (e.g. Majr Prblems) The psitive numbers represent rewards r values (e.g., after Fix the prblem ) True indicates the decisin ptin taken frm the square decisin nde, whereas false indicates the decisin ptin nt taken. 2. Expected Mnetary Value (EMV): Allws the user t calculate the weighted average (expected) value f an event that includes uncertain utcmes. It is well-suited t Decisin Tree Analysis. Incrprates bth the prbability and impact f the uncertain events. Simple calculatin that des nt require special sftware. Critical success factrs include: 2012 RMstudy.cm Page 6 f 12
Perfrm Quantitative Risk Analysis Identificatin f all pssible events that need t be included in the EMV calculatin. Access t histrical data r expert pinins n the values f prbability and impact that are needed fr the calculatin f EMV. Understanding f the difference between EMV and the utput f simulatin tls such as Mnte Carl analysis. Weaknesses are: Assessment f prbability f risky events ccurring and f their impact can be difficult t make. EMV prvides nly the expected value f uncertain events; risk decisins ften require mre infrmatin than EMV can prvide. Smetimes used in situatins where Mnte Carl simulatin wuld be mre apprpriate and prvide additinal infrmatin abut risk. The EMV calculatin fr an event by weighting the individual pssible utcmes by their prbabilities f ccurring is shwn in Figure 7.3 belw. Example f an Expected Mnetary Value (EMV) Calculatin fr a Business Strategy that Depends n Uncertain Market Demand Uncertain Outcme Reward ($000) Prbability Cntributin t EMV High Market Demand 800 30% 240.0 Mderate Market Demand 450 45% 202.5 Lw Market Demand 250 25% 62.5 Ttal EMV 505.0 3. Fault Tree Analysis (FMEA): A Fault Tree Analysis is the analysis f a structured diagram which identifies elements that can cause system failure. This technique is based n deductive lgic and can be adapted t risk identificatin t analyze hw risk impacts arise. The effective applicatin f this technique requires a detailed descriptin f the area being discussed. The undesired utcme is first identified and then all pssible cnditins/failures which lead t that event are identified. This reveals ptentially dangerus elements at each phase f the prject. Disadvantage: Opprtunities may be missed in this step as emphasis is laid n threats. The tls required in this technique are generally available nly t experts. 2012 RMstudy.cm Page 7 f 12
Perfrm Quantitative Risk Analysis 4. Mnte Carl Simulatin: Figure 7.4 Fault Tree Analysis f the Pssible Causes f a Crash at the Main Rad Junctin Used primarily fr prject schedule and cst risk analysis in strategic decisins. Allws all specified risks t vary simultaneusly. Calculates quantitative estimates f verall prject risk; reflects the reality that several risks may ccur tgether n the prject. Prvides answers t questins such as: Hw likely the base plan t be successful? Hw much cntingency in time and cst d we need t achieve ur desired level f cnfidence? Which activities are imprtant in determining the verall prject risk? 2012 RMstudy.cm Page 8 f 12
Perfrm Quantitative Risk Analysis Critical success factrs include: Creatin f a gd prject mdel and typical mdels include the cst estimate and the schedule. Use summary-level mdels such as prject schedules and cst estimates. Access t high-quality data n risks including the risks impact n prject elements, uncertain activity duratins and uncertain cst elements; the credibility depends n the quality f the data cllected Use f crrect simulatin tls. Weaknesses include: Schedules are nt simple and ften cannt be used in simulatin withut significant debugging by an expert scheduler. The quality f the input data depends heavily n the expert judgment and the effrt and expertise f the risk analyst. Simulatin is smetimes resisted by management as being unnecessary r t sphisticated cmpared t traditinal prject management tls. Requires specialized sftware which must be acquired and learned, causing a barrier t its use. Prduces unrealistic results unless input data include bth threats and pprtunities. Examples f the utput f schedule and cst risk results are shwn in Figures 7.5 and 7.6. Figure 7.5: Example Histgram frm Mnte Carl Simulatin f a Prject Schedule Surce: Pertmaster v 8.0 Primavera Pertmaster 2012 RMstudy.cm Page 9 f 12
Perfrm Quantitative Risk Analysis Figure 7.6: Example Histgram frm Mnte Carl Simulatin f a Prject Estimate. Surce: Crystal Ball v. 7.3.8 frm Oracle Hyperin (Decisineering) 5. Pst-prject reviews/ Lessns Learned/Histrical Infrmatin: The review f risk databases f previus prjects, such as thse that arise frm pstprject reviews r lessns learned exercises r histrical infrmatin within an rganizatin r industry can reveal infrmatin relevant fr a current prject. This technique leverages previus experience, and prevents the ccurrence f the same mistakes r missing the same pprtunities again. Participatin f previus prject team members and a well-structured prject lessns database increases the effectiveness f this technique. Disadvantages: Only thse risks that have ccurred previusly can be identified. The infrmatin available may als be incmplete with n details n ineffective strategies, lack f details f successful reslutin etc. 6. System Dynamics: System Dynamics (SD) is a particular applicatin f Influence Diagrams and identifies risks within a prject situatin thrugh the representatin f infrmatin flws and ther entities. 2012 RMstudy.cm Page 10 f 12
Perfrm Quantitative Risk Analysis An analysis f the SD mdel expses unexpected inter-relatins between prject elements (feedback and frward lps) which lead t uncertainty. The technique can als shw the impact f risk events n verall prject results. Successful applicatin f this technique depends upn the quality f the mdel, accuracy f input data cllected fr the prject, understanding f feedback, and cmpetence in applying the tls and understanding their utput. Disadvantages: The building f the SD mdel requires specialized expertise and sftware. The technique fcuses n impacts but it is difficult t include the cncept f prbability. Terms and Cncepts Figure 7.7 Example f a Simple System Dynamics Mdel with Feedback Lps 1. Bias: During infrmatin gathering abut risk, the surce f infrmatin exhibits a preference r an inclinatin that inhibits impartial judgment. 2. Cause: Events r circumstances which currently exist and which might give rise t risks. 3. Decisin Tree Analysis: A diagram that describes a decisin under cnsideratin and the implicatins f chsing ne r anther f the available alternatives. It is used when sme future scenaris r utcmes f actins are uncertain. 4. Mnte Carl Analysis: A technique that cmputers r iterates the prject cst r prject schedule many times using input values, selected at randm frm prbability distributin f pssible csts r duratins, t calculate a distributin f pssible ttal prject cst r cmpletin f prject dates. 5. Overall Prject Risk: It represents the effects f uncertainty n the prject as a whle. 2012 RMstudy.cm Page 11 f 12
Perfrm Quantitative Risk Analysis 6. Perfrm Quantitative Risk Analysis: The prcess f numerically analyzing the effect f identified risks n verall prject bjectives. 7. Prject Management Prcess Grup: The prject management prcess grup refers t specifically the area f lgic riented gruping r arrangement f the numerus prjects. 8. Risk Mdel: A representatin f the prject including data abut prject elements and risks that can be analyzed by quantitative risk analysis. 2012 RMstudy.cm Page 12 f 12