2 Learning Objectives Discuss how decision making relates to planning Explain the process of engineering problem solvingSolve problems using three types of decision making toolsDiscuss the differences between decision making undercertainty, risk, and uncertaintyDescribe the basics of other decision making techniques
3 DECISION MAKING Preview Decision making and problem solving are used in all management functions, although usually they are considered a part of the planning phase.
4 Relation to PlanningDecision Making: The Process of making a conscious choice between 2 or more alternatives producing most desirable consequences (benefits) relative to unwanted consequences (costs).If Planning is truly Deciding in advance what to do, how to do it, when to do it and who is to do it, then Decision Making is an essential part of Planning
5 Occasions for Decision Occasions are in 3 distinct fields:1. From Authoritative Communications from superiors2. From Cases Referred for Decision by Subordinates3. From Cases Originating in the Initiative of the executive concerned
6 Types of DecisionsRoutine Decisions (e.g. payroll processing, paying suppliers etc)Recur frequentlyInvolve Standard Decision ProceduresHas a Minimum of UncertaintyStructured SituationsNonroutine DecisionsUnstructured and Novel SituationsNonrecurring NatureHigh Level of Uncertainty
7 Objective versus Bounded Rationality A Decision is objectively rational if it is the correct behavior formaximizing given values in a given situation.Rationality requiresA complete knowledge and anticipations of consequencesafter a choiceImagination since Consequences lie in futureA choice among all possible alternatives.We can only talk about bounded rationality
8 Objective versus Bounded Rationality Objective Rationality looks for the ‘best’ solution whereas Bounded Rationality looks for the ‘good enough’ solution.
9 Management Science Models? Quantitative techniques used in business for many years inapplications such as return on investment, inventory turnover,and statistical sampling theory. Quantitative solutions of complexProblems in operations and management. Today is called managementscience. After the World war II, these techniques were applied to longrange military problems and industrial organizations. Managementscience characteristics:1. A System View of Problem: significant interrelated variablescontained in the problem.2. Team Approach : training work together on specific problems.3. Emphasis on the Use of Formal Mathematical Models andStatistical and Quantitative MethodsModels?
10 Models and Their Analysis Model: Abstraction and Simplification of Reality(Designed to include Essential Features)Simplest Modelnet income = revenue - expenses - taxes
11 5 Steps of Modeling Real World Simulated (Model) World 1. Formulate Problem(Define objectives, variables and constraints)2. Construct a Model (simple butrealistic representation of system)3. Test the Model’s Ability4. Derive a Solution from Model5. Apply the Model’s Solution toReal System, Document its Effectiveness
12 Categories of Decision Making Decision Making Under Certainty: Linear ProgrammingDecision Making Under Risk: expected value, decisiontrees, queuing theory, and simulationDecision Making Under Uncertainty: Game Theory
13 Payoff (Benefit) Table - Decision Matrix N N2 ……… Nj ……… NnP P2 ……… Pj ……… PnA O O12 ……… O1j ……… O1nA O O22 ……… O2j ……… O2n… … … ……… … …… …Ai Oi Oi ……… Oij ……… OinAm Om Om ……… Omj …… OmnAlternativeState of Nature / ProbabilityOutcomeSum of n values of pj must be 1
14 A1, A2,…. Am Decision alternatives N1 , N2 , Nj ……… Nn Decision alternatives, state of natureP P2 ……… Pj ……… Pn Probability of occurrenceOm Om ……… Omj …… Omn Outcomes
15 Decision Making Under Certainty Implies that we are certainof the future state of nature(or assume we are). Linear programming is a tool for thedecision making under certainty.This means:-the probability of pj of future Nj is 1 and allother futures have zero probability
16 Example for linear programming A factory is producing two types of products; X and Y. If you can realize 10$ profit per unit of product X and 14$ per unit of product Y. The factory employs five workers: 3 machinists and 2 assemblers and each works only 40 hours a week. Meanwhile, product X requires 3 hours of machining and one hour of assembly per unit. Product Y requires two hours of machining and two hours of assembly per hour. What is the production level of product X and product Y to maximize the profit ?
17 Decision Making Under Certainty Linear ProgrammingCOMPUTER SOLUTIONIn reality, we have more than 2 variables (dimensions) not like machining and assembling only.Computer solution called Simplex method has been developed to be used with many variables.For example, an AT&T model of current and future telephone demand has got variables taking 4 to 7 computer hours for single run.For example, one model of the domestic long-distance network has variables and would take weeks for one solution.Narendra Karmarkar at AT&T Laboratories has developed a shortcut method * based on “projective geometry” that is 50 to 100 times faster.* “The Startling Discovery Bell Labs Kept in Shadows”, Business Week, September 21, 1987, pp.69, 72, 76
18 Payoff Table This means:- Decision Making Under RiskPayoff TableThis means:-Each Nj has a known (or assumed) probability of pj andthere may not be one state that results best outcome.
19 Payoff Table This means:- Decision Making Under UncertaintyPayoff TableThis means:-Probabilities pj of future states are unknown.
20 Payoff Table Decision Making Under Risk There exist a number of possible future states of Nature Nj.Each Nj has a known (or assumed) probability pj of occurring.There may not be one future state that results in the best outcome for all alternatives Ai.Examples of future states and their probabilitiesAlternative weather (N1=rain, N2=good weather) will affect theprobability of alternative construction schedule; the probabilities P1 ofrain and p2 of good weather can be estimated from historical data2. Alternative economic futures (boom and bust) determine the relativeProfitability of high risk investment strategy.
21 Payoff Table (pjOij) Ei= Decision Making Under Risk n Expected Values (Ei) : given the future states of nature and theirprobabilities, the solution in decision making under risk is thealternatives Ai that provides the highest expected value Ei, which isdefined as the sum of the products of each outcome Oijtimes the probability pj that the associated state of nature Nj occursnj=1(pjOij)Ei=Choose the Alternative Ai givingthe highest expected value
22 Example of Decision Making Under Risk Alternatives Calculate Expected Values (Ei)Example of Decision Making Under RiskNot Firein your houseP1 = P2=0.001Insure house $ $-200Do not Insure house $-100,000AlternativesFireState of NatureProbabilitiesnj=1(pjOij)Ei=Would you insure your house or not?E1=$-200E1=0.999*(-200)+0.001*(-200)E2=$-100E2=0.999* *(-100,000)
23 Example: Consider that you own rights to a plot of land under which there may or may not be oil. You are considering three alternatives:Doing nothing (don’t drill), drilling your own expense of $(Drill alone), and farming out, the opportunity to someone who willdrill the well and give you part of the profit if the well is successful.Drilling your own a small Well will make $ profit and a Big well$.Farm out alternative with Dry hole will not cost at all, but a small WellWill make $ and a Big Well will make $ profit.Constitute the Payoff table and calculate the expected value of eachalternative solve the problem.
24 $720,000 Alternative State of Nature / Probability Decision Making Under RiskCalculate Expected Values (Ei)Well Drilling Example-Decision Making Under RiskN1:Dry Hole N2 :Small Well N3:Big WellP1= P2= P3=0.1A1:Don’t Drill $ $ $0A2:Drill Alone $-500, $300, $9,300,000AlternativeState of Nature / ProbabilityA3:Farm Out $ $125, $1,250,000ExpectedValueE1=0.6*0+0.3*0+ 0.1*0$0E2=0.6*(-500,000)+0.3*(300,000)+ 0.1*(9,300,000)$720,000E3=0.6*0+0.3*(125,000)+ 0.1*(1,250,000)$162,000$720,000A2 is the solution if you are willing to risk $500,000
25 Mathematical solution is identical, visual representation is different Decision Making Under RiskCalculate Expected Values (Ei)Decision TreesDecisionnode AiChancenode NjOutcome(Oij)Probability(Pj)Expected ValueEix=No Fire:(-200)x(0.999)=(-199.8)+= $-200Insure(-200)x(0.001)=(-0.2)Fire:No Fire:(0)x(0.999)=(0)Don’t Insure+=$-100Fire:(-100,000)x(0.001)=(-100)Mathematical solution is identical,visual representation is different
26 Decision Making Under Uncertainty We do not know the probabilities pj of future states of nature Nj
27 Decision Making Under Uncertainty Decision Making Under RiskDecision Making Under UncertaintyN1:Dry Hole N2 :Small Well N3:Big WellA1:Don’t Drill $ $ $0A2:Drill Alone $-500, $300, $9,300,000AlternativeState of Nature / ProbabilityA3:Farm Out $ $125, $1,250,000
28 Decision Making Under Uncertainty Decision Making Under Uncertainty: ExampleCoefficient of optimism(Maximax) (Maximin)Hurwicz EquallyAlternative Maximum Minimum (=0.2) LikelyOptimist PessimistMaximize [(best outcome)+(1-)(worst outcome)]$1,450,000*[0.2(9,300,000)+(1-0.2)(-500,000)]A2:Drill Alone $9,300,000 * $-500,000$3,033,333*E2=-500, ,000+9,300,0003[0.2(1,250,000)+(1-0.2)(0)]$250,000A3:Farm Out $1,250, $0*$458,333E3=0+125,000+1,250,0003
29 Alternative Alternative State of Nature / Probability Decision Making Under RiskDecision Making Under Uncertainty: Maximum RegretN1:Dry Hole N2 :Small Well N3:Big WellA1:Don’t Drill $ $ $0A2:Drill Alone $-500, $300, $9,300,000AlternativeState of Nature / ProbabilityA3:Farm Out $ $125, $1,250,000N1:Dry Hole N2 :Small Well N3:Big WellWe do not know probabilitiesA1:Don’t Drill $ $300, $9,300,000A2:Drill Alone $500,AlternativeState of Nature / ProbabilityA3:Farm Out $ $175, $8,050,000MaximumRegret$9,300,000$500,000$8,050,000
30 Alternative State of Nature / Probability Decision Making Under Uncertainty: Maximum RegretState of Nature / ProbabilityAlternativeN1:Dry Hole N2 :Small Well N3:Big WellMaximumRegretWe do not know probabilitiesA1:Don’t Drill $ $300, $9,300,000$9,300,000A2:Drill Alone $500,$500,000A2 is the solution.We choose the minimum among maximum regrets.A3:Farm Out $ $175, $8,050,000$8,050,000
31 Integrated Data Bases, MIS, DSS and Expert Systems
32 4 Types of Information Systems Transaction Processing Systems (TPS)Management Information Systems (MIS)Decision Support Systems (DSS)Expert Systems (ES)
33 Payroll System Personnel Data Projects Data Tax Data Personnel Data Traditional Approach and Data (User) Oriented ApproachPayrollSystemProjectManagementSystemPersonnelDataProjectsDataTaxDataPersonnelDataTraditional Approach
34 Payroll System Personnel Data Projects Data Tax Data Traditional Approach and Data (User) Oriented ApproachPayrollSystemProjectManagementSystemPersonnelDataProjectsDataTaxDataDatabase Approach
35 4 Types of Information Systems Transaction Processing Systems (TPS)Automate handling of data about business activities (transactions)Management Information Systems (MIS)Converts raw data from transaction processing system into meaningful formDecision Support Systems (DSS)Designed to help decision makersProvides interactive environment for decision making
36 4 Types of Information Systems Expert Systems (ES)Replicates decision making processKnowledge representation describes the way an expert would approach the problem
37 For many companies, information systems Competition needs very fast decisions andrapid development of information systems.Concentrate on what to do rather than how to do.For many companies, information systemscost 40 percent of overall costs.
38 Effect of Management Level on Decisions Management Number of Cost of Making InformationLevel Decisions Poor Decisions NeedsTop Least Highest StrategicMiddle Intermediate Intermediate ImplementationFirst-Line Most Lowest Operational
39 Decisions are useless unless they are put into practice. ImplementationDecisions are useless unless they are put intopractice.Courage is the willingness to submergeoneself in the loneliness, the anxiety, and theguilt of a decision maker.