chelst & canbolat value added decision making 02/28/12 1 chapter 11 chapter 11– structured risk...

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Chapter 11 Structured Risk ManagementRisk ManagementExpected Value of Perfect ControlSensitivity Analysis Robustness easy to do with Precision TreeValue Added structured approach - Individual random eventsRole of InformationExpected Value of Imperfect InformationBayes Rule and EVIIOptimal Conditional decisionSequential decisions with information delayReal Options

Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Risk Management ThemeYou cannot manage risk if you do not admit there is uncertaintyManaging uncertainty also includes unrealized upside potential and not just downside lossesYou cannot allocate appropriate resources if you do not quantify the risk or uncertaintyChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 112Figure 11.1: Decision tree Boss Controls automation investment40.0%09.91.9FALSETake Rate-85.8660.0%016.58.5How Much6.3240.0%0.413.80.8TRUETake Rate-136.3260.0%0.62310Automation InvestmentLowHigh30% Take50% Take50% Take30% TakeChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Investment in Automation -Question: Robustness of Optimal SolutionThe High investment alternative involves a new technology. Management is concerned that the capital equipment estimate could be off by + 7%.There is even more concern regarding the variable cost estimate that could be off by + 10%The Low investment alternative is well tested and there is hope that continuous improvement could reduce the variable cost by 5%.Because they did not know, they set the take rate probabilities at 0.6 and 0.4 respectively. However, there is a lot of uncertainty regarding this estimated probability.Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1145.8 5.2 = 0.6M50-50 chance of 30% and 50% take rate averages out 400,000 sales per year. Each dollar decrease is worth $400,00. A $1.50 decrease for Low investment variable cost makes it equal to High alternativeIncreases in probability of Low take rate decreases the expected volume. The higher variable cost for Low Investment means it will get worse and worse. Increases in probability every 10% probability shift causes expected sales to decrease by 20,000. The difference in variable costs is (27-13) or 14. Thus a 10% shift reduces the HIGH advantage by $14(20,000) $280,000 , 20% shift $560,000 and a 22% $616,000Price May be cut? DOES NOT MAKE A DIFFERENCE in preference Every dollar affects both equally. It will change the ROI Return on Investment.Volume OEM forecast overly optimistic every 100,000 is 40,000 options Divide $600,000/14 = 42,571 options 106427 sales.High Take rate too conservative maybe 60% more sales make best alternative betterLow Take rate too optimistic maybe 20% reduces expected sales of options by 50,000 no longer optimal Investment in AutomationRobustness of Optimal SolutionMagnitude of Difference between two solutions ($6.32M-$5.86M) = $460,000Investment(s) How much increase in HIGH Investment fixed cost results in change in best decision?Variable Cost(s)How much would the variable cost for Low Investment have to decline to make it preferred?Probability of 30% take rate: Increases? Decreases?What else and why?Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1155.8 5.2 = 0.6M50-50 chance of 30% and 50% take rate averages out 400,000 sales per year. Each dollar decrease is worth $400,00. A $1.50 decrease for Low investment variable cost makes it equal to High alternativeIncreases in probability of high take rate increases the expected volume. The lower variable cost for High Investment means it will get better and better. Decreases in probability every 10% probability shift causes expected sales to decreases by 20,000. The difference in variable costs is (27-13) or 14. Thus a 10% shift reduces the HIGH advantage by $14(20,000) $280,000 , 20% shift $560,000 and a 22% $616,000Price May be cut? DOES NOT MAKE A DIFFERENCE in preference Every dollar affects both equally. It will change the ROI Return on Investment.Volume OEM forecast overly optimistic every 100,000 is 40,000 options Divide $600,000/14 = 42,571 options 106427 sales.High Take rate too conservative maybe 60% more sales make best alternative betterLow Take rate too optimistic maybe 20% reduces expected sales of options by 50,000 no longer optimal Activate: Precision Tree & Sensitivity AnalysisOutput Separate WorksheetsSensitivity one parameter at a timeOne line Objective function for optimal strategy: A change in optimal decision is usually bend in line Multiple lines Objective function for each decision. Crossing lines change in optimal decisionTornado diagram more variables but less infoSpider Plot more variables, more info, but limited to no more than 3 or 4 variables too cluttered and confusing


02/28/12#Chapter 116Review: Figure 10.23: Sensitivity analysis automation investment fixed cost of high investment$13.48 million X axis Fixed cost input as negative value (-13) Axis would be reversed if cost was stored as (13)Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Review: Figure 10.24: Expected value of the optimal decision for each value of fixed cost of high investmentChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Review: Figure 10.25: Sensitivity analysis automation investment low take rate probabilityDecision changes when probability approaches 0.6 (a 50% increase)Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11List of Variable RangesFixed investment: High Investment: 7% of basePrice: 0 to 10% of baseVariable Cost of Low investment: 10 % of baseVariable Cost of High investment: 0 to 5 % of baseProbability of Low Take rate: 0.2 absoluteLow take rate (30%): 0 to 10% absoluteVolume: 0 to 15% of baseChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1110 Precision Tree Sensitivity Analysis Tornado Diagram Many parameters: unlimited Uses Min & Max values specified in the range and calculates Objective function.Ranks the analysis in order of their range of impact on the objective looks like tornadoDoes NOT show changed decisions!!Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1111Figure 11.2: Tornado diagram Boss Controls automation investmentRange of parameter

Prob of Low Take (0.2 to 0.6)

Vehicles (850 K to 1 million)

Price ($54 to $60)

Low take rate (20% to 30%)

High Invest. ($13 m + 910K)Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11 Precision Tree Sensitivity Analysis Spider Diagram practical limit of 4 parametersMore detailed than Tornado but harder to include many variables.X axis change input (percent)Y axis change in expected valueAggregation of many one-way sensitivity analyses but scaled to a common percentage.Shows the slope of the impact on the objective function and non-linearities.Shows changes in decisions bends in line graphChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1113List of Variable RangesFixed investment: High Investment: 7% of basePrice: 0 to 10% of base one sided (lower value)Range of Change in input % from a negative % to 0%Probability of Low Take rate: 0.2 absoluteDecision does not change except at the very highest value slight bend in line at endVolume: 0 to 15% of base one sided (lower value)Range of Change in input % from a negative % to 0%


02/28/12#Chapter 1114Figure 11.3: Spider plot for Boss Controls automation investment3.544.555.566.577.588.5-60%-40%-20%0%20%40%60% Expected ValueChange in Input (%)Spider Graph of Decision Tree 'Automation Investment'Expected Value of Entire Model Prob. (D13)Vehicles (Mil.) (C10)Price (C4)High_Investment (D6)Decision changes: bendChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Manage RiskImpact of Strategies to Change Risk ProfileShift the risk profile to the right Figure 11-4b. add value to all possible outcomes eliminate altogether an operating cost in a project. Cut off the downside risk Figure 11-4cMove outcomes to some guaranteed level. Minimum purchase quantity in a contractincrease the mean and remove the most disastrous possibilities. Insurance cuts off the downside risk (costs money) leftward shift in the whole risk profile but reduce the overall expected valueChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Figure 11.4: Impact of risk management actions on risk profileFigure a: BaselineF Figure b: Shift to right by adding net value (cost elimination) Figure c: Chop off left eliminate downside risk


02/28/12#Chapter 11Change Risk Profile Manage RiskCentrally concentrate uncertainty: Figure 11-4d Risk sharing: sell half of a risky opportunity for a price equal to half of its expected value

Reduce but not eliminate extremely negative outcomes: Figure 11-4e Magnitude reduction consistent with the way managers view riskProbability reduction not as well understood


02/28/12#Chapter 11Figure 11.4: Impact of risk management actions on risk profileFigure a: Baseline Figure d: Centralize through risk sharing

Figure e: reduce magnitude of negative outcome


02/28/12#Chapter 11Make or Buy Decision: Non-strategic (strictly cost)Decision Context: Manufacture a component yourself or contract with a supplier to manufacture it. There is a design for a component but you are not sure when it comes time to manufacture, that the design will be feasible as is. If not, there will need to be a quick major redesign of the component. If you manufacture it, you expect that with the redesign it will cost 8% more than the original estimate. The decision to make or buy must be made now before you have time to fully check out the design. The demand for the product is also uncertain. If you sign a contract with the supplier for a specific piece price, if the current design turns out to be infeasible, you know the supplier will use the design change as an excuse to increase the price 15%. Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1120Make or Buy Decision: Construct Influence Diagram (Ignore data)

Make or Buy Data: Random EventsRandom Events1. Design Feasibility Prob.Current Design will Work 0.4Need a Major Redesign 0.6

2. Demand Volume Prob. Low 1.0 million0.3 Medium 1.25 million0.5 High 1.5 million0.2 Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1121Make or Buy Data: Cost DataCosts: Make In-House Facility investment fixed Cost -$55M Variable Cost/ per partIf current design works - $100/partIf new Design is needed - $108/partCosts: Buy from SupplierFacility investment fixed Cost - $0 Variable Cost/ per partIf current design works - $140/partIf new Design is needed - $161/partChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1122Figure 11.8: Western Co. make or buy decisionDesign FeasibilityProbabilityMake CostsBuy CostsWorks0.4100140Does NOT0.6108161Premium8%15%DemandProb.30.0%0.1210.311551.250.540.0%Demand1.50.2100177.550.0%0.21.25180Make5520.0%0.08Buy01.5205TRUECurrent Design55183.3830.0%0.18116360.0%Demand108187.350.0%0.31.2519020.0%0.121.5217Decision183.3830.0%0114040.0%Demand140171.550.0%01.2517520.0%01.5210FALSECurrent Design0186.93530.0%0116160.0%Demand161197.22550.0%01.25201.2520.0%01.5241.5Fixed CostsLowMediumHighHighMediumLowHighMediumLowHighMediumLowMake or BuyMakeBuyWorksDoes NOT workDoes NOT workWorksMinimize CostE(X) E(X) Complex calculation & NOT sum of values on branchesChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Structured Risk Management Step: SummaryWithin optimal decisionIdentify random paths with large downside riskLarge values that are negative or poor relative to the best pathsProbability associated with this sequence is not insignificantAssess impact of Increasing relative value of that pathDecreasing the probability of that pathBrainstorm strategies for making the above happenQuantify these alternativesRepeat for 2nd best decisionChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1124Summary of Risk Management Alternatives: Table 11.4


02/28/12#Chapter 1125Summary of Risk Management Alternatives Table 11.4 Continued


02/28/12#Chapter 1126New topic: Information ValuePerfect InformationImperfect InformationSample InformationExpert InformationAccuracy of test (medical or engineering)Delay decision until information unfolds Options


02/28/12#Chapter 11Information GatheringTraditional Approach Gather information (surveys, tests, pilot plant, prototypes) until time or the budget runs out. Most information is gathered to validate already made decision.New Approach - Gather information if the cost of gathering it is less than the gain in expected value.Process Restructure the decision tree to determine the expected value with the informationCounterintuitive How can you determine the value of information before you have even gathered the information?Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1128Expected Value of Perfect Information: EVPIGoal: Determine the expected value of perfect information regarding an Uncertainty or Risk Hire a Clairvoyant Prophet Isaiah (Thomas)This provides an upper bound on the value of all information including imperfect information.If the information never changes the optimal decision then EVPI = 0.Decision Tree Process: Move the random event in question to the front of the tree before the first decision is to be made.Recalculate the overall expected value.The NET Improvement is the EVPI.Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1129Original Decision Tree Automation InvestmentBoss Controls Base Tree60.0%0.62310.0TRUETake Rate-136.32 (=10*0.6 + 0.8*0.4)40.0%0.413.80.8Decision6.32 (MAX{6.32, 5.86})60.0%016.58.5FALSETake Rate-85.86 (=8.5*0.6 + 1.9*0.4)40.0%09.91.9Automation InvestmentHighLow50% Take Rate30% Take Rate50% Take Rate30% Take RateChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11I have no idea how to remove the chapter 10 in footer30Figure 11.7: EVPI tree for Boss Controls InvestmentTake rate event moved to before decisionTRUE0.41.91.9EVPI = 6.76 - 6.32 = 0.4440.0%How Much1.9FALSE00.80.8Take Rate6.76FALSE08.58.560.0%How Much10TRUE0.61010Perfect InformationHighHighLowLow30% Take50% TakeOptimal decision depends on outcome of random eventChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Expected Value of Perfect Information: BossBase strategy High Investment & E(X) = $6.32MIf information indicates 30% take rate then shift to Low Investment with profit = $1.9MIf information indicates 50% take rate then stay with High Investment with profit = $10MWhat is the probability the information will indicate a 30% take rate? Answer 0.4E(X) with perfect information = 1.9(.4) + 10 (.6) = 6.76EVPI = 6.76 6.32 = $0.44M E(Perfect Control) = 10 6.32 = $3.86 M much more valuable to exert control over uncertaintyChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1132Review to contrast EVPC with EVPIMaximum value of risk management Expected Value of Perfect Control: Not about obtaining information but rather exerting control over destiny Goal: Determine the value of eliminating Uncertainty or RiskThis provides an upper bound on the value of risk management with regard to that uncertainty.Process: Assign probability of 1 to the best outcome of an uncertain event.Recalculate the overall expected value.The NET Improvement in expected value is the EVPC.Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1133Review: Expected Value of Perfect Control: Automation Investment Assign probability of 1 to best outcome Net Change: $10 6.32 = $3.68 million100%1.02310.0TRUETake Rate-1310 =10*1 + 0.8*0)0%013.80.8Decision10 (MAX(10, 8.5))100%016.58.5FALSETake Rate-88.5=8.5*1.0 + 1.9*0)0%09.91.950%30%50%30%Automation InvestmentHighLowChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 112nd example: EVPI Western Make or BuyBase strategy Make: E(X) = $183.38MUncertaintiesDesign works or not Bound on Testing Design (Imperfect)EVPI = $2.41 M Demand Bound on value of surveys (Imperfect)EVPI = $2.16 M Both uncertaintiesEVPI = $3.16 M


02/28/12#Chapter 1135Make or BuyDesign Feasibility30.0%0.12Prob.Make CostsBuy Costs1155Works0.410014040.0%DemandDoes NOT0.6108161100177.5Premium8%15%50.0%0.21.2518020.0%0.08DemandProb.1.520510.3TRUECurrent Design1.250.555183.381.50.230.0%0.181163 Fixed Costs60.0%DemandMake55108187.3Buy050.0%0.31.2519020.0%0.121.5217Decision183.3830.0%0114040.0%Demand140171.550.0%01.2517520.0%01.5210FALSECurrent Design0186.93530.0%0116160.0%Demand161197.22550.0%01.25201.2520.0%01.5241.5Make or BuyMakeBuyWorksDoes NOT workLowMediumHighWorksDoes NOT workLowMediumHighLowMediumHighLowMediumHighChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 113630.0%0155155FALSEDemand177.550.0%018018020.0%020520540.0%Decision171.530.0%0.12140140TRUEDemand0171.550.0%0.217517520.0%0.08210210Current Design180.9830.0%0.18163163TRUEDemand0187.350.0%0.319019020.0%0.1221721760.0%Decision0187.330.0%0161161FALSEDemand0197.22550.0%0201.25201.2520.0%0241.5241.5Info DesignWorksDoes NOT workMakeBuyLowMediumHighLowMediumHighMakeBuyLowMediumHighLowMediumHighFigure 11.9: Make-BuyEVPI: Design FeasibilityNet Improvement183.38-180.98 =$2.40MDesign uncertainty resolved before decisionChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 113740.0%0155155FALSECurrent Design0159.860.0%016316330.0%Decision0152.640.0%0.12140140TRUECurrent Design0152.660.0%0.18161161Demand181.2240.0%0.2180180TRUECurrent Design018660.0%0.319019050.0%Decision018640.0%0175175FALSECurrent Design0190.7560.0%0201201.2540.0%0.08205205TRUECurrent Design0212.260.0%0.1221721720.0%Decision0212.240.0%0210210FALSECurrent Design0228.960.0%0242241.5Info DemandLowMediumHighMakeBuyMakeBuyMakeBuyWorksDoes NOT workWorksDoes NOT workWorksDoes NOT workWorksDoes NOT workWorksDoes NOT workWorksDoes NOT workFigure 11.10: Make-BuyEVPI on DemandNet Improvement183.38-181.22 =$2.16MDemand uncertainty resolved before decisionChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1138Figure 11.11: Make-Buy Decision EVPI on Feasibility & Demand CombinedNet Improvement183.38-180.22 = $3.16MLess than the SUM of $2.16 (Demand EVPI) + $2.40 (Feasibility EVPI)

Next slide: Schematic TreesChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1139Make or BuyDemandDesignMake or BuyDemandDesignMake or BuyDemandDesignMake or BuyDemandDesignOriginalEVPI Demand = $2.4 MEVPI Design = $2.16MEVPI Combined: Design & Demand =$3.16MMake or Buy Schematic Trees: EVPIChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Imperfect Information Conditional Decision/ ProbabilitiesP (High | Positive)P(Positive)InvestDownstream values and/or probabilities are affected by an upstream random event Decision made AFTER resolution of random eventOptimal decision path differs depending upon the outcome of a random eventChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1141Expected Value of Imperfect InformationImperfect info partial resolution of uncertaintyTest or Sample InformationFew tests, experiments or surveys are perfect. EVPI is an upper bound on the value of imperfect information.EVII without well documented test reliability: Conditional probabilities based on judgment EVII with Bayes Rule is used primarily in environments with extensive data on the reliability of tests both false positives and false negatives.Oil industry Seismographic data. Test wellsMedical Applications Weather forecastsChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1142EVII without well documented test reliabilityConditional probabilities based on judgment Expert understands the uncertain relationship between test data (performance, throughput, etc.) or market surveys and subsequent outcome.Can the expert provide a probabilistic range of outcomes that have accompanied similar test results?Understand concept of conditional probability experience with both possible outcomes.Need stable process environment A priori probabilities are always in a narrow range, for example, of 0.40 to 0.60.Not used to forecast rare eventsProblem people have invalid intuition. Cannot factor in a priori estimates that are updated with imperfect information.Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1143Boss Controls (BC) is gearing up to manufacture an option to be made available on 1 million new cars world-wide. Initial estimates are that the take rate for the option could be as low as 30% or as high as 50%. Assume for simplicity sake, these two take rates are equally likely. Experience with focus groups indicates that for options such as the one BC is considering, the results will either be Enthusiastic (E) or Good (G). In the past if the focus groups were Enthusiastic, the take rate ended up being at the HIGH end 70% time. However, if the focus groups reactions were just good, then 80% of the time the take rate was at the LOW end. Focus groups have an optimistic bias and tend to be enthusiastic 80% of the time. Boss Controls: Focus Groups & Imperfect Information based on ExperienceChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1144EVII & Decision Trees - ExperienceAdd an uncertain node at the front of tree to represent uncertain outcome of focus groupInsert the probabilities that reflect the likelihood of different responses: Here P(E) = .8 and P(G) = .2Probability of outcomes (Take rates) are now Conditional probabilities based on past experience (or Bayes Rule)Insert the conditional probabilities into tree and calculate expected value.Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1145

Figure 11.14: Decision tree of EVII for BC automation investment Expert estimates conditional probabilitiesEVII = 6.436 6.320 = 0.116Less than one third ofEVPI was $400,000Conditional ProbabilitiesChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Conditional Probabilities Consistent with Original EstimatesA Priori Probability that Take Rate is 30% - Use Partition Formula P(A) = P(A|B)P(B) + P(A|B)P(B) P(T=30%) = P(T=30% | G) P(G) + P(T=30% | E) P(E)P(T=30%) = (3/4)(.4) + (1/3) (.6) = .5 original estimateP(T=50%) = P(T=50% | G) P(G) + P(T=50% | E) P(E)P(T=50%) = (1/4)(.4) + (2/3) (.6) = .5 original estimateChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1147Boss Control: Conditional DecisionIf focus groups reaction is ENTHUSIASTIC then HIGH investment in automationIf focus groups reaction is GOOD then Low investment in automation


02/28/12#Chapter 1148INTUITION?Bayes Rule & Reliable TestRare Disease How Rare: 1 in 1,000Probability of positive reading for a person with the disease test is very reliable P(Pos.| Disease) = P(P|D) = .99Probability of negative reading for a person without the disease 4% false positives P(Neg. | No Disease) = P(N|Dc) = .96Key Question: P(Disease | Pos) = P(D|P) = ??Let Dc = D complement, or D , or No diseaseChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1149Next slide provides intuitive explanation.Bayes Rule & Reliable Test - ResultsBayes Rule (General Formula): with Bc = B complement or NOT BDenominator uses partitioning (all ways that A can occur) to determine P(A)Bayes Rule (Reliable Test): (Pos = Positive test result)

Intuitive 1000 tested yields 40 false positives (4% error rate) and 1 true positive


02/28/12#Chapter 1150Have them guess with no calculations Most answers will be above .90Intuitive explanation1,000 take test and 1 has disease1 Disease 1 positive999 No disease but 5% false positive (1-.96) 40 false positivesP(Disease | Pos.) = 1/(40+1) = .002

Probability MisunderstandingPeople do NOT know how to integrate prior knowledge and data accuracy.Especially problematic withLow probability events and highly accurate testWeakly reliable testsChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1151Bayesian Posterior (after positive result) ProbabilitiesInitial Probability of Success.7.8.9.95.10.210.310.500.68.30.500.630.790.89.40.610.730.86 0.93.450.660.770.880.94.50.700.800.900.95.60.780.860.930.97.70.840.900.950.98Test Accuracy Assume Positive = NegativeFor 0.5, .45, and even 0.40, the final estimates are close to test accuracy.Column heading close to cell value. For initial low probability events, test accuracy and final probability are far apart.Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1152Assume, for example, all of the experience with imperfect data involved predicting events with an initial probability of approximately 0.3.If in the past when the results of a survey indicated success, success followed 80% of the time, you would not use BAYES Rule. The post survey results reflect the actual conditional probabilities.

Bayes rule is appropriate for a standardized testing procedure that is used over a wide range of initial (a priori) probabilities. The testing procedures accuracy is known.EVII: Make or Buy DecisionDecision Context: Manufacture a component yourself or contract with a supplier to manufacture it.Design Reliability is a key concern. Experts initially estimate that the current design will work with probability of only 0.4.However there is a complex test that can be used to ALMOST validate or invalidate the design. This testing procedure is used in a wide range of situations.Looking back at past data over a wide range of initial success estimatesIf the design worked, how often were the test results GOOD?Test results GOOD almost validates P(Test results Good | Design Works) = 0.98If the design failed, how often were the test results BAD?Test Results BAD almost Invalidates P(Test results Bad | Design Fails) = 0.94Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1153EVII - Bayes Rule & Decision TreesAdd an uncertain node at front of tree to represent uncertain outcome of testUse Bayes rule to calculate conditional probabilities.Use partition rule to calculate the probabilities of the test results. (These appear in the denominator of the Bayes Rule equation.)Green on the next page highlights the test result probabilitiesYellow on the next page highlights the conditional probabilities. These vary because they depend upon the results of the tests.Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1154EVII - Bayes Rule & Decision TreesActivity: calculate conditional probabilitiesDataP(Design Works) = P(W)= 0.4P(Design Fails)= P(F) = 0.6P(Test Results Good | Design Works) = P(G|W)= 0.98P(Test Results Bad | Design Fails)=P(B/F) =0.94Activity: Use Bayes Rule to calculateP(Design Works | Test Results Good) = P(W|G)= ??P(G) = ??P(F/ G)= ??Precision Tree Calculate Bayesian Probabilities by hand and Insert all of the initial probabilities upfrontInsert conditional probabilities downstream. Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 115591.6%Demand100177.5FALSEDesign55178.328.4%Demand108187.342.8%Decision0173.660991.6%Demand140171.5TRUEDesign0173.668.4%Demand161197.23Test Design181.381.4%Demand100177.5TRUEDesign55187.1698.6%Demand108187.357.2%Decision0187.161.4%Demand140171.5FALSEDesign0196.8798.6%Demand161197.23EVII Make-BuyGoodBadMakeBuyWorksFailsWorksFailsMakeBuyWorksFailsWorksFails++++++++Figure 11.12EVII for Make/Buy: Test Design183.38 181.38=2.0 EVII = $2M and EVPI =$2.4M+ means collapsed nodeRed Demand values are expected valuesChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1156Conditional DecisionIf test results are GOOD then buy from supplierLess fear of 15% price increaseIf test results are BAD then make it yourselfConcerned over suppliers opportunity for significant price increaseChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1157Activity: Concrete examples of IMPERFECT InformationDescribe a context in which a decision can be made after gathering imperfect information and there is still related uncertainty.Product DevelopmentExample____________________________________________Imperfect Information ________________________Decision AFTER ____________________________Updated future uncertainties _________________________Can you quantify accuracy? _______________________ManufacturingExample____________________________________________Imperfect Information ________________________Decision AFTER ____________________________Updated future uncertainties _________________________Can you quantify accuracy? _____________________________Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1158Sequential Decisions with Information in betweenInability to predict future accurately Must make decisions under uncertaintyA firm unable to determine level of demand in future or predict rivals reactionsUnderstate some perceived risks in order to obtain approvalCan management delay high cost PART of decision until more knowledge is availablePartial Investment Gain Information Broader scope of subsequent investmentChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1159Investment DecisionsThree important characteristics of Investment DecisionsPartially or completely irreversibleUncertainty over future rewards from the investmentAssess the probabilities of alternative outcomesLeeway about timing of your investmentPostpone action to get more information about the futureHow should a firm decide on an investing on a project or a new facility?Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1160Real OptionsAn option represents a Right, but not an Obligation, to do something under predefined arrangementsBuy Option (expand or substitute) Put Option (contract or cut back)Flexibility to adapt in response to new information enhances the investment opportunitys value by improving its upside potentialAn approach that offers a positive and radical reassessment of risk and explorationThe opportunities to acquire real assetsReal OptionsReal Options term coined by Stewart Myers (1977)Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1161Options AnalysisFinancial options Data Availability Precise modelsTechnical optionsData are less accurateOne time decisionsEstimates of values are approximates within bands described by sensitivity analysisAnalytical niceties that might lead to greater precision might be a waste of effortTo decide whether to do the R&D that will lead to a real option on a launch of a new product, managers only need to know if the value of option is greater than the cost to acquire itChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1162Real Options UncertaintyConventional ApproachMinimize RiskReact to uncertaintiesWhat is the best choice under the given circumstances?Work with predetermined set of decisionsReal OptionsProactive towards uncertainties Prepare plans to manage the risksIdentify parts of the system that have most uncertainty, and try to see how these situations can be exploitedIdentify new possible paths: change decision tree by adding flexibilityChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1163Table 11.10 Common Real OptionsOptionDescriptionRelevant Application IndustriesDeferProject that can be postponed allows learning more about project outcomes before making a commitment. Real estate development, farming, paper products, offshore oil leaseStageA multi-stage project whose construction involves a series of cost outlays could be delayed or killed in a midstream. R&D intensive industry such as pharmaceuticals or other long development capital intensive projectsAlter Operating ScaleA project whose operating scale can be expanded or contracted according to market conditions. Mining, facilities planning, fashion apparel, consumer goodsAbandonProject can be abandoned permanently when market conditions are worsen severely and project resources could be sold or put to other more valuable uses.Capital intensive industries (airline, railroad), new product introduction, financial servicesSwitchThe project permits changing its output mix or producing the same outputs using different inputs in response to changes in the price of inputs and outputs.Any good sought in small batches or subject to volatile demand (e.g., consumer electronics, toys, machine parts)ExploreStart with a pilot or prototype project and follow-up with a full-scale project if the pilot or prototype succeeds. High production cost areasChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Options Manufacturing and Product ExamplesDesign all vehicles to facilitate pricey add-ons for specific market segments. (Vehicle personalization) Design a truck such that four-wheel steering is a later option that can be designed into it. Production system that can change easily Inputs: Dual fuel burners (oil and gas)Production lines designed to switch equipment so that they can produce different productsFlexible machines rapid tool changeoverModular Design: Option to upgrade a computer systemEngines? _______________Labor Contract pay premium for option to reduce workforce or close plants if necessary (Put Option)Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 1165Remaining Text Examples and FiguresEVII and Oil DrillingTechnology ChoiceSchematic treeDecision treeRisk ProfileContingent ContractNegotiations 2 perspectivesMercks options


02/28/12#Chapter 11

Figure 11.13: Decision tree for oil drilling case with imperfect informationChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Figure 11.15: Schematic tree for technology development example


02/28/12#Chapter 11

Figure 11.16: Decision Tree for Omega case Chelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Figure 11.17: Cumulative risk profile for technology development case - Omega


02/28/12#Chapter 11Figure 11.18: Contingent Contract Total sales from perspectives of Biotech and BSG

a) BioTech perspectiveb) BSG perspectiveChelst & Canbolat Value Added Decision Making

02/28/12#Chapter 11Figure 11.19: Mercks options and major uncertainties in Project Gama


02/28/12#Chapter 11FactorChangeOptimalComments

Reduce Cost Increase Linked to RedesignFrom $8 to $7

From $8 to $3$730,000

$3.65 MIf redesign is needed try to contain added cost of manufacturing.

Reduce Risk that Design will not WorkFrom 0.6 to 0.5

From 0.6 to 0.3

From 0.6 to 0.0$980,000

$4.16 M

$11.9 MModify design quickly to reduce need for major redesign later.

New Optimal: Use Supplier

Value of Perfect Control

Manage Uncertainty of DemandNot appropriateDoes not make sense to reduce total demand to lower total cost.

FactorChangeOptimalComments

Percentage Price increase by Supplier if design does not workFrom 15% to 14%

From 15% to 8%$0

$3.5 MObtain commitment from supplier not to take advantage of redesign to raise prices disproportionately.

Supplier Price Reduction if Volumes are HighUp to $8 reduction in priceNo ImpactNegotiate major price reduction for high volumes.

EVPI of Design Feasibility$2.4 MTest feasibility of current design

treeCalc_1NameAutomation InvestmentPtree1 Compatibility3Output LabelR-Value Ref.6SheetRef0Eval. Function592147GenInfo0,1,1,0,0,Exponential, 0,0,-1,0,-1,0,.0001Creation Version1.0.?Output Value NFDef. Link=Required Version5.0.0Output Prob NFEXT REFS0Recommended Version5.0.0Input Value NFDef. FormLast Modified By Version5.7.0Input Prob NFCalc MacroHighest#7Anchor CellBranch NamebformtypevalformulapbformuladistributioncumPayoffFunctionlinkENDNODEFORMULAVALPBGenInfoIntRefsRefRefsNodeNamesCollapsed6.32Automation Investment0002,0,0,2,2,3,0,0,00How Much5.86Low0DEFAULT-81,0,0,2,4,5,1,0,00Take Rate6.32High0DEFAULT-131,0,0,2,7,6,1,0,00Take Rate1.930% Take0DEFAULTDEFAULT9.90.44,0,0,0,2,0,008.550% Take0DEFAULTDEFAULT16.50.64,0,0,0,2,0,001050% Take0DEFAULTDEFAULT230.64,0,0,0,3,0,000.830% Take0DEFAULTDEFAULT13.80.44,0,0,0,3,0,00

PTModuleNamePerfect ControlPtree1 Compatibility3Output LabelR-Value Ref.0SheetRef0Eval. Function514964GenInfo0,2,1,0,0,Exponential, 0,0,-1,0,-1,0,.0001Creation Version1.0.?Output Value NFDef. Link=Required Version5.0.0Output Prob NFEXT REFS0Recommended Version5.0.0Input Value NFDef. FormLast Modified By Version5.7.0Input Prob NFCalc MacroHighest#7Anchor CellBranch NamebformtypevalformulapbformuladistributioncumPayoffFunctionlinkENDNODEFORMULAVALPBGenInfoIntRefsRefRefsNodeNamesCollapsed10Perfect Control0002,0,0,2,5,2,0,0,00How Much10High0DEFAULT-131,0,0,2,4,3,1,0,00Take Rate1050% Take0DEFAULTDEFAULT2314,0,0,0,2,0,000.830% Take0DEFAULTDEFAULT13.804,0,0,0,2,0,008.5Low0DEFAULT-81,0,0,2,7,6,1,0,00Take Rate8.550% Take0DEFAULTDEFAULT16.514,0,0,0,5,0,001.930% Take0DEFAULTDEFAULT9.904,0,0,0,5,0,00

treeCalc_2NamePerfect InformationPtree1 Compatibility3Output LabelR-Value Ref.0SheetRef0Eval. Function566421GenInfo0,3,1,0,0,Exponential, 0,0,-1,0,-1,0,.0001Creation Version1.0.?Output Value NFDef. Link=Required Version5.0.0Output Prob NFEXT REFS0Recommended Version5.0.0Input Value NFDef. FormLast Modified By Version5.7.0Input Prob NFCalc MacroHighest#9Anchor CellBranch NamebformtypevalformulapbformuladistributioncumPayoffFunctionlinkENDNODEFORMULAVALPBGenInfoIntRefsRefRefsNodeNamesCollapsed6.76Perfect Information0DEFAULT001,0,0,2,3,2,0,0,00Take Rate1050% Take000.62,0,0,2,6,5,1,0,00How Much1.930% Take000.42,0,0,2,7,4,1,0,00How Much0.8High0DEFAULT0.84,0,0,0,3,0,0010High0DEFAULT104,0,0,0,2,0,008.5Low0DEFAULT8.54,0,0,0,2,0,001.9Low0DEFAULT1.94,0,0,0,3,0,00

treeCalc_3NameImperfect InfoPtree1 Compatibility3Output LabelR-Value Ref.0SheetRef0Eval. Function489238GenInfo0,4,1,0,0,Exponential, 0,0,-1,0,-1,0,.0001Creation Version1.0.?Output Value NFDef. Link=Required Version5.0.0Output Prob NFEXT REFS0Recommended Version5.0.0Input Value NFDef. FormLast Modified By Version5.7.0Input Prob NFCalc MacroHighest#15Anchor CellBranch NamebformtypevalformulapbformuladistributioncumPayoffFunctionlinkENDNODEFORMULAVALPBGenInfoIntRefsRefRefsNodeNamesCollapsed6.436Imperfect Info0001,0,0,2,2,3,0,0,00Focus Group7.24Enthusiastic000.82,0,0,2,7,4,1,0,00How Much3.22Good000.22,0,0,2,13,10,1,0,00How Much7.24High0DEFAULT-131,0,0,2,6,5,2,0,00Take Rate1050% Take0DEFAULTDEFAULT230.74,0,0,0,4,0,000.830% Take0DEFAULTDEFAULT13.80.34,0,0,0,4,0,006.52Low0DEFAULT-81,0,0,2,9,8,2,0,00Take Rate8.550% Take0DEFAULTDEFAULT16.50.74,0,0,0,7,0,001.930% Take0DEFAULTDEFAULT9.90.34,0,0,0,7,0,002.64High0DEFAULT-131,0,0,2,12,11,3,0,00Take Rate1050% Take0DEFAULTDEFAULT230.24,0,0,0,10,0,000.830% Take0DEFAULTDEFAULT13.80.84,0,0,0,10,0,003.22Low0DEFAULT-81,0,0,2,15,14,3,0,00Take Rate8.550% Take0DEFAULTDEFAULT16.50.24,0,0,0,13,0,001.930% Take0DEFAULTDEFAULT9.90.84,0,0,0,13,0,00

treeCalc_4NameReturn on InvestmentPtree1 Compatibility3Output LabelR-Value Ref.0SheetRef0Eval. Function617879GenInfo1,5,1,0,0,Exponential, 0,0,-1,0,-1,0,.0001Creation Version1.0.?Output Value NFDef. Link=Required Version5.0.0Output Prob NFEXT REFS0Recommended Version5.0.0Input Value NFDef. Form-2Last Modified By Version5.7.0Input Prob NFCalc MacroHighest#7Anchor CellBranch NamebformtypevalformulapbformuladistributioncumPayoffFunctionlinkENDNODEFORMULAVALPBGenInfoIntRefsRefRefsNodeNamesCollapsed0.7325Return on Investment0002,0,0,2,5,2,0,0,00How Much0.4861538462High0DEFAULT-131,0,0,2,4,3,1,0,00Take Rate0.769230769250% Take0DEFAULTDEFAULT230.64,0,0,0,2,0,000.061538461530% Take0DEFAULTDEFAULT13.80.44,0,0,0,2,0,000.7325Low0DEFAULT-81,0,0,2,7,6,1,0,00Take Rate1.062550% Take0DEFAULTDEFAULT16.50.64,0,0,0,5,0,000.237530% Take0DEFAULTDEFAULT9.90.44,0,0,0,5,0,00

treeCalc_5Automation Invesment Decision TreesPrice60LowHigh40.0%0Investment8139.91.9Variable Cost27140Take Rate-85.8660.0%0Vehicles (Mil.)116.58.5How MuchTake RateProb.6.32Low30%0.440.0%0.4High50%0.613.80.80Take Rate-136.3260.0%0.62310To see utility scores click on "Automation Investment."In the upper right hand corner, check "use utility function."Change the display to "expected utility" or "certainty equivalent."0.0%0EVPC = 10 - 6.32 = 3.689.91.90Take Rate-88.5100.0%016.58.5How Much100.0%013.80.80Take Rate-1310100.0%1231000.41.91.9EVPI = 6.76 - 6.32 = 0.4440.0%How Much1.9000.80.8Take Rate6.76008.58.560.0%How Much1000.6101030.0%09.91.90Take Rate-86.5270.0%016.58.580.0%How Much07.2430.0%0.2413.80.8EVII = 6.436 - 6.320= 0.1160Take Rate-137.2470.0%0.562310Focus Group6.43680.0%0.169.91.90Take Rate-83.2220.0%0.0416.58.520.0%How Much03.2280.0%013.80.80Take Rate-132.6420.0%0231040.0%0.49.90.23750Take Rate-80.732560.0%0.616.51.0625How Much0.732540.0%013.80.06153846150Take Rate-130.486153846260.0%0230.7692307692

Automation InvestmentLowHigh30% Take50% Take50% Take30% TakePerfect ControlHigh50% Take30% TakeLow50% Take30% TakePerfect Information50% Take30% TakeHighHighLowLowImperfect InfoEnthusiasticGoodHigh50% Take30% TakeLow50% Take30% TakeHigh50% Take30% TakeLow50% Take30% TakeReturn on InvestmentHigh50% Take30% TakeLow50% Take30% Take

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chelst & canbolat value added decision making 02/28/12 1 chapter 11 chapter 11– structured risk...

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