Strategic Planning Tools

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Transcript Strategic Planning Tools

Joseph Lewis Aguirre 4/26/2020

Decision Making Tools

Strategic Planning Tools

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Affinity Chart

Summary Problem Solving Tools

Force Field Analysis Pareto Chart Bar Chart Group Think Pie Chart Benchmarking Histograms Plan-Do-Check-Act Brainstorming Imagining Provocation Cause-Effect Importance Weighting Run Chart Cause Screening Influence Diagram Requirements Analysis Check Sheets Intuition Scatter Diagram Criteria Matrix Line graph Starbusting Control Chart Metaphorical Thinking Value Analysis Decision Tree Mind Mapping Visualizing Flow Chart Multivoting Following the Rule 4/26/2020 Nominal Group Technique

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SWOTT Planning Models

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GE’s Planning Matrix

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Ansoff’s Product/Market Matrix

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Porters Generic Strategy

Bowman’s Clock

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competitive advantage in relation to cost or differentiation advantage

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Window of Opportunity

Window of Opportunity when Solution remains Valid

Benchmarking

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Benchmarking

…. the process of comparing and measuring an organization’s operations against those of a best-in-class performer from inside or outside its industry. 4/26/2020

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Benchmarking is NOT!

 Cheating  Unethical  Illegal

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Benchmarking process

Obtain management commitment.

Baseline your own processes. Identify your strong and weak processes and document them.

Select processes to be benchmarked.

Form benchmarking teams.

Research the best-in-class Select candidate best-in-class benchmarking partners.

Form agreements with bench-marking partners.

Collect data.

Analyze data and establish the gap.

Plan action to close the gap/surplus.

Implement change.

Monitor.

Update benchmarks: continue the cycle.

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Benchmarking drivers

 Compares processes with those of a best-in-class performer  Major improvements achieved quickly

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Alterantives

“[Benchmarking] is the difference between teaching yourself how to hit a golf ball and taking lessons from Jack Nicklaus.”

-Steven George

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Benchmarking references

American Society for Quality Control Benchmark Application - Medical Field

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Tools and Techniques Decision Matrix

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Introduction

   The Decision matrix prioritizes a list of options It helps make a tough decision based on the criteria chosen It is only used when only one decision can be reached

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Picking the criteria

The criteria must be picked on what is most important. Careful selection of the criteria can help ensure a favorable solution.

4/26/2020 Rating the criteria   The ratings can be assigned by a team or by an individual Guesswork is sometimes involved with rating certain criteria

Conclusion – Everything that has a beginning has an end, even a tough decision!

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Following the Rules

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Introduction

A rule is described as:  A regulation  A principle or condition that customarily governs behavior

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Topics of Discussion

  1.

2.

Description Application Examples Sunday Rules National Association of Realtors® Code of Ethics

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Conclusion

 Critical Thinking provides an excellent framework for clearly and carefully evaluating whether or not we can assume a definite position and follow a rule with reliance.

False Rules Tool

When using the False Rule method, an unrelated rule is used in a new environment.

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Definition

 With the False Rules method of coming up with new ideas, you take a pre-existing, not related rule and attempt to apply it in your own area of business. It requires a connecting thought between the irrelevant rule and the current business.

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Example

   Keep medicine away from kids.

Keep dangerous materials away from irresponsible people.

We’ll confine and lock up our dangerous resources by eliminating irresponsible people from the area through the use of ID cards.

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False Rules/Not Always

 False Rules do not come to distinct problem solving, they are used to generate new ideas.

Analogies

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What is an Analogy

  Analogies involves correlating one problem to other similar problems/solutions In business particularly, analogies are used as descriptors to show employees correlations to how others have solved problems or overcame barriers

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When to use

  Used when you have a person or group that may not understand the exact process you are teaching or describing but has the ability to understand once an inference is made Can be used to clarify ones point of view

When not to use

  For a Analogy to be effective, the receptor must understand or at least partially understand (Gentner, et al, 2003) what is meant by the analogy Not to be overused with any one group or situation 4/26/2020

Metaphorical Thinking

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Overview of Metaphorical Thinking

    Children are not taught to think metaphorically.

Logical thinking cannot efficiently be used to analyze complex thoughts.

Thinking Metaphorically leads to more abstract ideas.

Metaphorical Thinking is comparing a subject to a completely unrelated topic.

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The Metaphorical Thinking Process Consists of Using the Imagination to View Ideas or Objects in a Different Way

The Logically Thinking Business Mind

The Manager The Manager Input Output A Rubber Ball A Rubber Ball The Logically Thinking Mind Sees No Correlation Between the Manager and a Rubber Ball 4/26/2020 and Therefore the Output is the Same as the Input When Asked to Compare Them

The Metaphorically Thinking Business Mind The Manager The Manager A Rubber Ball + Input Output Globally Thinking Well Rounded Flexible A Rubber Ball 4/26/2020 The Metaphorically Thinking Mind Uses Metaphors to Compare the Manager to a Rubber Ball

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Metaphorical Thinking and the English Language

   Thinking metaphorically allows the thinker to use a more extensive tool set to describe a condition Metaphors are used to provide a better understanding of an idea by relating it to a completely unrelated topic.

Metaphors for this reason are used often in the media to more clearly present information to the average reader without losing them in minute details

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Metaphorical Thinking

    Ignites imagination and allows individuals to think beyond the logical and rational.

Metaphors seek to substitute A for B, stating that A woman

is

is B, as in “A a delicate rose” Tetrium comparison: Only similarities in above is that the two are live organisms.

Similies explain(explicit), Metaphors implies (implicit)

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Metaphorical -Types

    Synectics - creates connections by making the unfamiliar seem familiar and vice-versa. Proprietary .

Conceptual Metaphor- “bursting with flavor” Tetrium comparison: Only similarities in above is that the two are live organisms.

Similies explain(explicit), Metaphors implies (implicit)

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Examples of Metaphors

    Fuzzy logic is a term meaning the logic in a statement is intentionally left vague.

There was a scandal involving Iran and the Contras that was dubbed Contra-gate.

Terms in football use metaphors to describe plays such as the “Flea Flicker” or the “Statue of Liberty” Wine tasting uses metaphors such as Fruity, bouquet, baked, dry and thin.

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Improving the Critical Thinking Proces

   Metaphorical Thinking expands the horizon of the thinking realm.

Using shades to describe things allows for a more granular view also expanding the tools used in description. Metaphors can be used during problem solving to describe particular situations in more detail and in ways.

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Summary

    Metaphorical Thinking is a powerful tool that can be used in the decision making process. Metaphors are used in the English language to supplement explanations or to provide more colorful descriptions.

Metaphors are used to compare an entity unknown to the listener to a known entity. Thinking in an abstract way by using metaphoric thinking enables the thinker to identify certain aspects of a problem with an unrelated topic.

Tools and Techniques: The Decision Tree Analysis

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Decision Tree Analysis

 How to draw  Begin with a decision  Draw lines from the decision  Lines represent solutions  At the end of each line  Draw a square or a circle   Squares represent more decisions Circles represent uncertain outcomes  Continue until you can go no further

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Decision Tree Analysis

  How to evaluate  Assign a value to possible outcomes  Assign a probability to each outcome How to calculate  Value of uncertain outcomes  Multiply the value of the outcome by the probability  Value of decisions  Subtract the cost from the outcome value to get benefit

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Decision Tree Analysis

  Benefits  Shows all possible outcomes  Shows risks and rewards  Allows decisions to be made based on what you know Drawbacks  Time consuming

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Decision Tree Analysis

 References  (2006). Decision tree analysis: choosing between April 8, 2006 from html options by projecting likely outcomes. Retrieved http://www.mindtools.com/dectree/

Linda Birnbaum

Influence Diagram: A Decision Making Tool

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What is an Influence Diagram?

  A visual representation of a decision problem Method of identifying and displaying  Decisions  Uncertainties  Objectives  How they influence each other 4/26/2020

Node Shapes  Decision  Variable the decision maker has the power to control  Chance Variable  Variable the decision maker cannot directly control  Objective Variable  Quantitative criterion decision maker is trying to maximize or minimize  General Variable  Determined by the quantities it depends on  Arrow (arc)  Signifies influence 4/26/2020

4/26/2020 Influence Diagrams are best used for:       Sensitivity analysis Mathematical modeling Model fidelity Improvement initiatives Quantifying risk Quantifying uncertainties

Sample Application

Marketing Budget Costs Market Size Market Share Unit Sales Profit Product Price Revenues

• Marketing budget and product price influence expectations associated with market size and market share • Market size and market share influence costs and revenues • Costs and revenues influence profits 4/26/2020

Planning, Tools and Descriptions

It is not the plan that matters, It’s the

planning. General Dwight D. Eisenhower

Graphical Diagrams do not constitute a specification….nothing replaces clear, concise text .

- David A. Ruble

At a recent study, I commented at one point in our deliberations that we had spent more time on wordsmithing

than we had on considering the substance of our report. --

Robert W. Lucky, VP for Applied Research at Telecordia. NJ

It seems to me language by its very nature is imprecise. I think of each word as inhabiting a fuzzy ball of uncertain semantic meaning…. –

Robert W. Lucky

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Decision Making Framework

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GOALS OPPORTUNITIES OBJECTIVES PROBLEMS EVALUATION IMPLEMENTATION OPTIONS RELATIVE TIME SPAN

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Dashboard - PM KBI

03/04/2001 11/28/2001

Decision Tree - Invest or no

1. Alternative 1 Stop $0

Decision

Test market $75K n

Probability

2 3 Test fail 70% n

Terminal

4 Success 30% 5 Expand 4/26/2020 Increment, $100K Large $300K 12 9 6 No expansion, $0 No comp. 60% 7 Comp 40% 11 8 10 No comp. 60% Comp 40%

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To be or not to Be

Suffer the slings and arrows of outrageous fortune

A

Bear whips and scorns of time oppressor’s wrong proud man’s contumely pangs of dispriz’d love law’s delay insolence of office

To Be

spurns that patient merit of the unworthy takes

Not To Be Not Dream B

End the heartache and the thousand natural shocks that flesh is heir to: a consummation devoutly to be wished

Dream C

Ills we know not of

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Mind Mapping

Business Trip

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Mind Mapping

Preparing Business Trip Travel Recreation Business

Travel kit Suit from

Mind Mapping

cleaners Money Photo ID Tickets

Preparing

Clothes Business Trip Business items School items

Business

presentation 4/26/2020 Sales samples Business cards Casino Hotel

Travel

Out of office message on phone & computer Rental Car

Recreation

Food Pool Golf

New razor Travel kit Suit from

Mind Mapping

cleaners Money Photo ID Tickets

Preparing

Tums Clothes Golf shoes Swimsuit Business items Laptop Cell phone School items Phone card

Business

Syllabus & notes presentation 4/26/2020 Sales samples Business cards Business Trip Casino Hotel

Travel

Out of office message on phone & computer Rental Car

Recreation

Food Seafood Pool Golf reservations

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Analytic Hierarchy Process

Joseph Lewis Aguirre

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AHP

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AHP

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AHP

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AHP

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AHP

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AHP

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AHP

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AHP

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AHP

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AHP

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Eigen Vector Summary

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Force Field Analysis

COTO DE CAZA FORCE FIELD ANALYS Vision:

Public Safety, Accountability, Transparency

Driving Forces Varo Mezger Hill Keystone Harkins Consultants ARC/Landscaping Committees Restraining Forces Yocham Zipperman

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Force Field Analysis

CDC COMMUNITY FORCE FIELD ANALYS Vision:

Public Safety, Accountability, Transparency

Driving Forces Varo Mezger Hill Keystone Harkins Consultants ARC/Landscaping Committees Restraining Forces Yocham Zipperman

Radar Charts

Logical Instrumentalism Political Planning Ecological Cultural Visionary •

Allows a visual comparison between several quantitative or qualitative aspects of a situation

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Ishikawa Diagram

Finance Cost of models Quality Penalties Lost sales Training Process Components Training Deliver No time Lack of resources Prototypes Communication Management No credibility Technical

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Criteria Matrix

SOLUTIONS 1 2 3 4 5 "Must" Criteria A

0

B

+

C

+

D

+ + ?

0 + + + + + ?

+ + + + ?

e

+ 0 + +

"Want" Criteria f

0 + 0

g

+ + 0

Weighted Matrix

Criteria

Supports key business objectives Has strong internal sponsor Has strong customer support Realistic level of technology Can be implemented in one year or less Provides positive NPV

Weighted Project Scores Weight Project 1 Project 2 Project 3 Project 4

25% 90 90 50 20 15% 15% 70 50 90 90 50 50 20 20 10% 5% 20%

100%

25 20 50 20

56

90 20 70 50

78.5

50 50 50 50

50

70 90 50 90

41.5

Weighted Score by Project

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Affinity Charts

Cluster qualitative data and come up with a consensus view on a subject.

Aid to stimulate debate.

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Do not sell at discount Decision Sell discount

Yield Management Model

No full fare passenger Full fare passenger arrives = Full fare = zero revenue Price of discount ticket 4/26/2020

Decisions

Influence Diagram & Decision Tree

Uncertainties Final Outcome Decision 4/26/2020 Outcome 1 Outcome 2 Outcome n

Decision Tree

1. Alternative 1 Stop $0 Test market $75K 2

Decision

n

Probability

3 Test fail 70% n

Terminal

4 Success 30% 5 Expand 4/26/2020 Increment, $100K Large $300K 12 9 6 No expansion, $0 No comp. 60% 7 Comp 40% 11 8 10 No comp. 60% Comp 40%

To develop or Not

Nodes New product Decision Consolidate Uncertainty circle 4/26/2020 Keep adding outcomes and probabilities for the decision

To Develop or Not

.25 Low Volume .50 base vol.

.25 high vol Do Seismic .25 low price .50 base price Capital $6 million $8 million Expected Value Vol x price – capital x .25

$312,500 $7.5 million (1) $12 million $15 million .25 high price .

25 good prospect .65 poor prospect Do you spend $800,000 on seismic to clarify how good the prospect is?

.15 inconclusive Dry Hole -1.2 million Sell $400,000 Low vol = 500,000 bbls, base = 1,000,000, high = 2,000,000 low price = 12.50 per bbls, base = 21.00, high = 30.00

(1) Volume x price – capital x .50 = 7.5 million The only guaranteed expected value is the sell price of $400,000 you have been offered.

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What if

What-if analysis

Observing how changes to selected variables affect other variables What if we cut advertising by 10%. What happens to sales?

Sensitivity Analysis:

Observing how repeated changes to a single variable affect other variables Let’s cut advertising by $100 repeatedly so we can see its relationship to sales

Goals Seeking Analysis

Making repeated changes to selected variables until a chosen variable reaches a target value Let’s try to increase stock videos until we reach $3000 in revenue

Optimization Analysis

Finding an optimal value for selected variables given certain constraints 4/26/2020

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Group Decision Support Strategies

    Brainstorming Nominal group technique Delphi technique Computer assisted decision making  GDSS = Group Decision Support System  CSCW = Computer Supported Collaborative Work

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Brainstorming

 Group process for gathering ideas pertaining a solution to a problem  Developed by Alex F Osborne to increase individual’s synthesis capabilities  Panel format  Leader: maintains a rapid flow of ideas   Recorder: lists the ideas as they are presented Variable number of panel members (optimum 12)  30 min sessions ideally

Brainstorming

Step 1: Preliminary notice  Objectives to the participants at least a day before the session  time for individual idea generation Step 2: Introduction  The leader reviews the objectives and the rules of the session Step 3: Ideation     The leader calls for spontaneous ideas Brief responses, no negative ideas or criticism All ideas are listed To stimulate the flow of ideas the leader may    Ask stimulating questions Introduce related areas of discussion Use key words, random inputs Step 4: Review and evaluation 4/26/2020  A list of ideas is sent to the panel members for further study

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Brainstorming

+ Large number of ideas in a short time period + Simple, no special expertise or knowledge required from the facilitator - Credit for another person’s ideas may impede participation Works best when participants come from a wide range of disciplines

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NGT

 Organised group meetings for problem identification, problem solving, program planning  Used to eliminate the problems encountered in small group meetings  Balances interests  Increases participation  2-3 hours sessions  6-12 members  Larger groups divided in subgroups

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NGT

Step 1: Silent generation of ideas  The leader presents questions to the group   Individual responses in written format (5 min) Group work not allowed Step 2: Recorded round-robin listing of ideas  Each member presents an idea in turn  All ideas are listed on a flip chart Step 3: Brief discussion of ideas on the chart  Clarifies the ideas  problem common understanding of the  Max 40 min

NGT

Step 4: Preliminary vote on priorities   Each member ranks 5 to 7 most important ideas from the flip chart and records them on separate cards The leader counts the votes on the cards and writes them on the chart Step 5: Break Step 6: Discussion of the vote  Examination of inconsistent voting patterns Step 7: Final vote  More sophisticated voting procedures may be used here Step 8: Listing and agreement on the prioritised 4/26/2020 items

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NGT

 Best for small group meetings  Fact finding   Idea generation Search of problem or solution  Not suitable for  Routine business    Bargaining Problems with predetermined outcomes Settings where consensus is required

Delphi Technique

    Group process to generate consensus when decisive factors may be subjective Used to produce numerical estimates, forecasts on a given problem Utilises written responses instead of brining people together Developed by RAND Corporation in the late 1950s   First use in military applications Later several applications in a number of areas    Setting environmental standards Technology foresight Project prioritisation  A Delphi forecast by Gordon and Helmer 4/26/2020

Delphi

Characteristics:   Panel of experts Facilitator who leads the process    Anonymous participation  Easier to express and change opinion Iterative processing of the responses in several rounds    Interaction with questionnaires Same arguments are not repeated All opinions and reasoning are presented by the panel Statistical interpretation of the forecasts 4/26/2020

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Delphi

First round    Panel members are asked to list trends and issues that are likely to be important in the future Facilitator organises the responses   Similar opinions are combined Minor, marginal issues are eliminated  Arguments are elaborated  Questionnaire for the second round

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Delphi

Second round    Summary of the predictions is sent to the panel members Members are asked the state the realisation times Facilitator makes a statistical summary of the responses (median, quartiles, medium)

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Delphi

Third round     Results from the second round are sent to the panel members Members are asked for new forecasts  They may change their opinions Reasoning required for the forecasts in upper or lower quartiles A statistical summary of the responses (facilitator)

Delphi

Fourth round    Results from the third round are sent to the panel members Panel members are asked for new forecasts  A reasoning is required if the opinion differs from the general view Facilitator summarises the results 4/26/2020 Forecast = median from the fourth round Uncertainty = difference between the upper and lower quartile

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Delphi

 Most applicable when an expert panel and judgemental data is required      Causal models not possible The problem is complex, large, multidisciplinary Uncertainties due to fast development, or large time scale Opinions required from a large group Anonymity is required

Delphi

+ Maintain attention directly on the issue + Allow diverse background and remote locations + Produce precise documents - Laborious, expensive, time-consuming - Lack of commitment  Partly due the anonymity - Systematic errors       Discounting the future (current happenings seen as more important) Illusory expertise (expert may be poor forecasters) Vague questions and ambiguous responses Simplification urge Desired events are seen as more likely Experts too homogeneous  skewed data 4/26/2020

Groupware

    4/26/2020 A large number software packages available for  Decision analysis   Group decision making Voting Web based applications Interfaces to standard software; Excel, Access Advantages   Graphical support for problem structuring, value and probability elicitation Facilitate changes to models relatively easily    Easy to conduct sensitivity analysis Analysis of complex value and probability structures Allow distributed locations

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Multivoting

   In democracy most decisions are made in groups or by the community Voting is a possible way to make the decisions  Allows large number of decision makers  All DMs are not necessarily satisfied with the result The size of the group doesn’t guarantee the quality of the decision  Suppose 800 randomly selected persons deciding on the materials used in a spacecraft

Multivoting as a social issue

    N alternatives x 1 , x 2 , …, x n K decision makers DM 1 , DM 2 , …, DM k Each DM has preferences for the alternatives Which alternative the group should choose?

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Plurality Voting

   Each voter has one vote The alternative that receives the most votes is the winner Run-off technique  The winner must get over 50% of the votes   If the condition is not met eliminate the alternatives with the lowest number of votes and repeat the voting Continue until the condition is met

Plurality Voting

Suppose, there are three alternatives A, B, C, and 9 voters.

4 states that A > B > C 3 states that B > C > A 2 states that C > B > A Plurality voting 4 votes for A 3 votes for B 2 votes for C A is the winner Run-off 4 votes for A 3+2 = 5 votes for B B is the winner 4/26/2020

Condorcet

   Each pair of alternatives is compared.

The alternative which is the best in most comparisons is the winner.

There may be no solution.

Consider alternatives A, B, C, 33 voters and the following voting result A A B 18,15 C 18,15  C got least votes (15+1=16), thus it cannot be winner  eliminate B 15,18 32,1 C 15,18 1,32  A is better than B by 18:15  A is the Condorcet winner  Similarly, C is the Condorcet loser 4/26/2020

Borda

   Each DM gives n-1 points to the most preferred alternative, n-2 points to the second most preferred, …, and 0 points to the least preferred alternative.

The alternative with the highest total number of points is the winner.

An example: 3 alternatives, 9 voters 4/26/2020 4 states that A > B > C 3 states that B > C > A 2 states that C > B > A B is the winner A : 4·2 + 3·0 + 2·0 = 8 votes B : 4·1 + 3·2 + 2·1 = 12 votes C : 4·0 + 3·1 + 2·2 = 7 votes

Approval Voting

   Each voter cast one vote for each alternative she / he approves of The alternative with the highest number of votes is the winner An example: 3 alternatives, 9 voters 4/26/2020 A DM 1 DM 2 DM 3 DM 4 DM 5 DM 6 DM 7 DM 8 DM 9 X X X X total 4

B

C -

X X X X X X -

-

X

X -

7

X 2 the winner!

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Codorcet Paradox

Consider the following comparison of the three alternatives DM 1 DM 2 DM 3 A B C 1 2 3 3 1 2 2 3 1 Every alternative has a supporter!

Paired comparisons:  A is preferred to B (2-1)   B is preferred to C (2-1) C is preferred to A (2-1)

Condorcet Paradox

Three voting orders: 1) (A-B)  A wins, (A-C)  2) (B-C)  3) (A-C)  B wins, (B-A)  C wins, (C-B)  DM 1

B is the winner

DM 2 DM 3 A

C is the winner

The voting result depends on the voting order!

There is no socially best alternative*. * Irrespective of the choice the majority of voters would prefer another alternative.

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Strategic Voting

4/26/2020  DM voters and the voting order (A-B, B-C, A C) 1 knows the preferences of the other  Her favourite A cannot win*  If she votes for B instead of A in the first round  B is the winner  She avoids the least preferred alternative C * If DM 2 and DM 3 vote according to their preferences

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Coalitions

 If the voting procedure is known voters may form coalitions that serve their purposes  Eliminate an undesired alternative  Support a commonly agreed alternative

Weak Preference Order

The opinion of the DM i about two alternatives is called a weak preference order R i : The DM y i thinks that x is at least as good as y  x R i  How the collective preference R should be determined when there are k decision makers?

 What is the social choice function R=f(R 1 ,…,R k )?

f that gives  Voting procedures are potential choices for social choice functions.

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Social Choice Function Requirements

1) Non trivial There are at least two DMs and three alternatives 2) Complete and transitive R i :s If x  y  opinion) If x R i y  x R i y R i y  z  y R i x R i z x (i.e. all DMs have an 3) f is defined for all R preferences are i :s The group has a well defined preference relation, regardless of what the individual

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Social Choice Function Requirements

4) Independence of irrelevant alternatives The group’s choice doesn’t change if we add an alternative that is   Considered inferior to all other alternatives by all DMs, or Is a copy of an existing alternative 5) Pareto principle If all group members prefer x to y, the group should choose the alternative x 6) Non dictatorship There is no DM i such that x R i y  x R y

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Arrow’s theorem

There is no complete and transitive f satisfying the conditions 1-6

Arrow’s Theorem

Borda criterion: x 1

x 2

x 3 x 4 DM 1 3 2 1 0 DM 2 3 2 1 0 DM 3 1 3 2 0 DM 4 2 1 0 3 DM 5 total 1 10 3

11

0 4 2 5

Alternative x 2 is the winner!

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x 1

x 2 DM 1 1 0 DM 2 1 0 DM 3 0 1 DM 4 1 0 DM 5 total 0

3

1 2

Alternative x is the winner!

1 The fourth criterion is not satisfied!

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Value Aggregation

Theorem (Harsanyi 1955, Keeney 1975): Let v i (·) be a measurable value function describing the preferences of DM i . There exists a k-dimensional differentiable function v g () with positive partial derivatives describing group preferences > g in the definition space such that a > g b  v g [v 1 (a),…,v k (a)]  v g [v 1 (b),…,v k (b)] and conditions 1-6 are satisfied.

Value Aggregation

 In addition to the weak preference order also a scale describing the strength of the preferences is required DM 1 : beer > wine > tea DM 1 : tea > wine > beer Value 1 Value 1 beer wine tea beer wine tea  4/26/2020 Value function describes also the strength of the preferences

Value Aggregation downside

     There is a function describing group preferences but it may be difficult to define in practice Comparing the values of different DMs is not straightforward Solution:   Each DM defines her/his own value function Group preferences are calculated as a weighted sum of the individual preferences Unequal or equal weights?    Should the chairman get a higher weight Group members can weight each others’ expertise Defining the weight is likely to be politically difficult How to ensure that the DMs do not cheat?

See value aggregation with value trees

Problem Identification Process

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· · The problem identification process · Types of thinking used in problem recognition processes.

Examine various forces affecting problem framing.

· Effectiveness of problem solving techniques.

· Resources in terms of their usefulness for problem solving in various organizational scenarios.

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Problem Identification Gap Analysis

Issue Descriptive Issues: What, where, when, how Prescriptive Issues: What should be Conclusion

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Problem Identification

Inference This because of That This

Conclusion That

Support of conclusion

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Problem Identification Inference

We oppose a mandatory retirement age. We believe that age is an inappropriate and unreasonable basis for determining whether an individual can do a job

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Finding Nemo

1: What is the issue?

2. Indicators 3. Look in likely locations 4. A Conclusion is not

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Reasons

Reasons are beliefs, evidence, metaphors, analogies, and other statements offered to support or justify conclusions. Reasons are explanations or rationales for why we should believe a particular conclusion. They are what is offered as a basis for why we should accept the conclusion.

Evidence

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Argument

Reasons + Conclusion = Argument

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Questioning Process

why question

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Questioning Process

Is the cost of hospital care outrageous? A recent survey by the American Association of Retired Persons offers reliable evidence on this issue. Independent audits of the bills of 2,000 patients found that hospitals overcharge their patients by an average of 15 percent. In addition, exit interviews services provided warrant.

with 400 patients revealed high amounts of dismay and anger when the patients were informed about the size of their total hospital bill. In short, the costs of hospital care are higher than the

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Questioning Process

Euthanasia is detrimental to the welfare of society because it destroys man's ideas of sacrifice, loyalty, and courage in bearing pain. Some dying persons accept their suffering as a way of paying for their sins. These people should be permitted to die as they wish—without help from any other person in speeding up the dying process.

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Equity Premium Dilemma

Risky assets (stocks) outperform safe assets (fixed returns) Why do people invest so much in safer stocks?

Loss aversion Myopia Bernatzi/Thaler

Wheel of Logic

2. Conclusion 1. Observation 3. Prediction 4/26/2020 4. Verification

Scientific Method

Groupthink Symptoms

 1. Illusion of Invulnerability:  2. Belief in Inherent Morality of the Group:  3. "Hear no evil, see no evil, speak no evil".

 4. What other people think of the group.

 5. Self-Censorship: Gloss over the bad.  6. Illusion of Unanimity:  7. Direct Pressure on Dissenters:  8. Self-Appointed Mind Guards: Mindguards protect a leader from assault by troublesome ideas 4/26/2020 Source: Irvin Janus

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Groupthink Examples

      Pearl harbor Kennedy’s Bay of Pigs fiasco Johnson’s escalation of the Vietnam war Nixon’s Watergate break in Reagan’s Iran Contra scandal cover ups Clinton’s approval on the Waco  Texas raid.

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Misc terms

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Decision Making Strategy Satisficing

Sensible decision procedures given the constraints

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Bounded Rationality

Because of computational limits and cost of deliberation, we use decision heuristics, rules of thumb.