Mazda Omintro
Upcoming SlideShare
Loading in...5
×
 

Mazda Omintro

on

  • 874 views

Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special ...

Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special offers for Mazda Community Members. If you live in Arizona, check out CardinaleWay Mazda's eCommerce website at http://www.Cardinale-Way-Mazda.com

Statistics

Views

Total Views
874
Views on SlideShare
874
Embed Views
0

Actions

Likes
0
Downloads
12
Comments
0

0 Embeds 0

No embeds

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • 1 1
  • 1 1
  • 2
  • 2
  • 3
  • 3
  • 4
  • 5
  • 7
  • 7
  • 7
  • 7
  • 7
  • 9
  • 9
  • 12
  • 12
  • 12
  • 12
  • 12
  • 17
  • 17
  • 17
  • 17
  • 17
  • 17
  • 17
  • 17
  • 18
  • 18
  • 19
  • 19
  • 19
  • 19
  • 19
  • 19
  • 19
  • 20
  • 20
  • 20
  • 22
  • 22
  • 22
  • 22
  • 22
  • 20
  • 22
  • 22
  • 22
  • 22
  • 22
  • 22

Mazda Omintro Mazda Omintro Presentation Transcript

  • DIT 1141: OPERATIONS MANAGEMENT DEPARTMENT OF DECISION AND INFORMATION TECHNOLOGIES COLLEGE OF COMMERCE AND FINANCE VILLANOVA UNIVERSITY
  • INTRODUCTION
  • INTRODUCTION
    • Operations management is the process of obtaining and utilizing resources to produce useful goods and services so as to meet the goals of the organization.
  • INTRODUCTION
    • Production management is concerned with the manufacturing of goods:
    • Examples of goods:
    • cars
    • books
    • chairs
    • computers
    • houses
    • etc.
  • INTRODUCTION
    • Operations management is also concerned with the management of service industries as well as the manufacturing of goods.
  • INTRODUCTION
    • Examples of services:
    • retailing/food
    • banking
    • education
    • health care
    • utilities
    • insurance
    • government agencies
    • etc.
  • OVERVIEW OF OPERATIONS MANAGEMENT MODEL Transformation Process Output Goods or Services Control Input: resources raw materials machines personnel capital land/buildings utilities information etc.
  • OVERVIEW OF OPERATIONS MANAGEMENT MODEL
    • Operations management considers how the input are transformed into goods or services.
    • Control is when something is learned about the goods or services that is used to more effectively transform future goods or services.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Automobile factory
    • Input
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Automobile factory
    • Input
    • steel, plastic
    • glass, paint
    • tools
    • equipment
    • machines
    • personnel, buildings
    • utilities, etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Automobile factory
    • Input
    • steel, plastic
    • glass, paint
    • tools Transformation
    • equipment process
    • machines
    • personnel, buildings
    • utilities, etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Automobile factory
    • Input Output
    • steel, plastic
    • glass, paint
    • tools Transformation
    • equipment process
    • machines
    • personnel, buildings
    • utilities, etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Automobile factory
    • Input Output
    • steel, plastic Car
    • glass, paint
    • tools Transformation
    • equipment process
    • machines
    • personnel, buildings
    • utilities, etc.
  • OPERATIONS MANAGEMENT QUESTIONS
    • 1. How many items will be demanded next month?
    • 2. How many items should be produced next month?
    • 3. How many workers are needed to satisfy the proposed production level?
  • OPERATIONS MANAGEMENT QUESTIONS
    • 4. If a plant is built, how should the activities be scheduled so that the project is completed on time, within budget, and with acceptable quality?
    • 5. How is the quality of our output measured and how is it improved?
    • 6. If tires are needed, how many should be ordered?
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Hospital
    • Input
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Hospital
    • Input
    • patients, doctors
    • nurses, drugs
    • beds
    • building
    • medical equipment
    • support staff, computers
    • utilities, etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Hospital
    • Input
    • patients, doctors
    • nurses, drugs Transformation
    • beds Process
    • building
    • medical equipment
    • support staff, computers
    • utilities, etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Hospital
    • Input Output
    • patients, doctors
    • nurses, drugs Transformation
    • beds Process
    • building
    • medical equipment
    • support staff, computers
    • utilities, etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • Hospital
    • Input Output
    • patients, doctors A treated patient
    • nurses, drugs Transformation
    • beds Process
    • building
    • medical equipment
    • support staff, computers
    • utilities, etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • University
    • Input
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • University
    • Input
    • students, professors
    • secretaries
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • University
    • Input
    • students, professors
    • secretaries, drugs
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • University
    • Input
    • students, professors
    • secretaries, drugs
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • University
    • Input
    • students, professors
    • secretaries, lab equipment
    • dormitories
    • staff, computers
    • buildings
    • etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • University
    • Input
    • students, professors
    • secretaries, lab equipment
    • dormitories
    • staff, computers Transformation
    • buildings process
    • etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • University
    • Input Output
    • students, professors
    • secretaries, lab equipment
    • dormitories
    • staff, computers Transformation
    • buildings process
    • etc.
  • EXAMPLE OF OPERATIONS MANAGEMENT PROCESS
    • University
    • Input Output
    • students, professors A more highly
    • secretaries, lab equipment educated
    • dormitories student
    • staff, computers Transformation
    • buildings process
    • etc.
  • DECISION MAKING IN OPERATIONS: THE ANALYTIC HIERARCHY PROCESS
    • What is the Analytic Hierarchy Process (AHP)?
    • The AHP, developed by Tom Saaty, is a decision-making method for prioritizing alternatives when multi-criteria must be considered.
    • An approach for structuring a problem as a hierarchy or set of integrated levels.
    INTRODUCTION
    • AHP problems are structured in at least three levels:
    • The goal , such as selecting the best car to purchase,
    • The criteria , such as cost, safety, and appearance,
    • The alternatives , namely the cars themselves.
    INTRODUCTION
    • The decision-maker:
    • measures the extent to which each alternative achieves each criterion, and
    • determines the relative importance of the criteria in meeting the goal, and
    • synthesizes the results to determine the relative importance of the alternatives in meeting the goal.
    INTRODUCTION
  • APPROACH
    • How does AHP capture human judgments?
    • AHP never requires you to make an absolute judgment or assessment. You would never be asked to directly estimate the weight of a stone in kilograms.
    • AHP does require you to make a relative assessment between two items at a time. AHP uses a ratio scale of measurement.
  • APPROACH
    • Suppose the weights of two stones are being assessed. AHP would ask: How much heavier (or lighter) is stone A compared to stone B?
    • AHP might tell us that, of the total weight of stones A and B, stone A has 65% of the total weight, whereas, stone B has 35% of the total weight.
  • APPROACH
    • Individual AHP judgments are called pairwise comparisons .
    • These judgments can be based on objective or subjective information.
    • For example, smoothness might be a subjective criterion used to compare two stones. Pairwise comparisons could be based on touch.
  • APPROACH
    • However, suppose stone A is a diamond worth $1,000.00 and stone B is a ruby worth $300.00.
    • This objective information could be used as a basis for a pairwise comparison based on the value of the stones.
  • APPROACH
    • Consistency of judgments can also be measured. Consistency is important when three or more items are being compared.
    • Suppose we judge a basketball to be twice as large as a soccer ball and a soccer ball to be three times as large as a softball.
    • To be perfectly consistent, a basketball must be six times as large as a softball.
  • APPROACH
    • AHP does not require perfect consistency, however, it does provide a measure of consistency.
    • We will discuss consistency in more detail later.
  • AHP APPLICATIONS
    • AHP has been successfully applied to a variety of problems.
    • 1. R&D projects and research papers;
    • 2. vendors, transport carriers, and site locations;
    • 3. employee appraisal and salary increases;
    • 4. product formulation and pharmaceutical licensing;
    • 5. capital budgeting and strategic planning;
    • 6. surgical residents, medical treatment, and diagnostic testing.
  • AHP APPLICATIONS
    • The product and service evaluations prepared by consumer testing services is another potential application.
    • Products and services, such as self propelled lawn mowers are evaluated.
    • Factors include: bagging, mulching, discharging, handling, and ease of use.
    • An overall score for each mower is determined.
  • AHP APPLICATIONS
    • Would you make your purchasing decision based solely on this score?
    • Probably not! Some of the information will be helpful.
    • Some additional questions are:
    • How important is each criterion?
    • Would you weigh the criteria the same way?
    • Are all of the criteria considered important to you?
    • Are there other criteria that are important to you?
    • Have you ever thought about these issues?
  • RANKING SPORTS RECORDS
    • The AHP has been used to rank outstanding season, career, and single event records across sports.
    • Season
    • 1. Babe Ruth, 1920: .847 slugging average
    • 2. Joe DiMaggio, 1944: 56 game hitting streak
    • 3. Wilt Chamberlain, 1961-62: 50.4 points per game scoring average
  • RANKING SPORTS RECORDS
    • Career
    • 1. Johnny Unitas, 1956-70: touchdown passes in 47 consecutive games
    • 2. Babe Ruth, 1914-35: .690 slugging average
    • 3. Walter Payton, 1975-86: 16,193 rushing yardage
    • Single event
    • 1. Wilt Chamberlain, 1962: 100 points scored
    • 2. Norm Van Brocklin, 1951: 554 passing yards
    • 3. Bob Beamon, 1968: 29' 2.5" long jump
  • RANKING SPORTS RECORDS
    • How do we compare records from different sports?
    • It all depends on the criteria that you select!
    • Golden and Wasil (1987) used the following criteria:
    • 1. Duration of record - years record has stood, years expected to stand
    • 2. Incremental improvement - % better than previous record
    • 3. Other record characteristics - glamour, purity (single person vs. team)
  • RANKING SPORTS RECORDS
    • Did this article end all arguments about sports records?
    • Absolutely not!
    • In bars and living rooms across the country, people still argue about sports.
    • AHP provides a methodology to structure the debate.
    • Different criteria and different judgments could produce different results.
  • A FINAL POINT ABOUT SPORTS
    • In reading the sports pages we often see discussion of how well teams match up across different positions.
    • These match-ups are often used to predict a winner.
    • Match-ups is a pairwise comparison concept!
  • AHP APPLICATIONS
    • Our culture is obsessed with quantitative rankings of all sorts of things.
    • There are many measurement problems associated with rankings of products, sports teams, universities, and the like.
    • Many of these issues are discussed on a web site at:
    • http://www.expertchoice.com/annie.person .
    • The discussion of how to compare records from different sports recalls a saying from childhood:
    APPLES AND ORANGES
    • The discussion of how to compare records from different sports recalls a saying from childhood:
    • You can’t compare apples and oranges. All you get is mixed fruit!
    APPLES AND ORANGES
    • The discussion of how to compare records from different sports recalls a saying from childhood:
    • You can’t compare apples and oranges. All you get is mixed fruit!
    • After the discussion about sports, do you still believe this statement?
    APPLES AND ORANGES
  • APPLES AND ORANGES
    • The discussion of how to compare records from different sports recalls a saying from childhood:
    • You can’t compare apples and oranges. All you get is mixed fruit!
    • After the discussion about sports, do you still believe this statement?
    • We hope not!!!
    • What criteria might you use when comparing apples and oranges?
    • There are a vast set of criteria that may change depending upon time of day or season of year:
    • taste, texture, smell,
    • ripeness, juiciness, nutrition,
    • shape, weight, color, and
    • cost.
    • Can you think of others?
    APPLES AND ORANGES
    • The point is that people are often confronted with the choice between apples and oranges.
    • Their choice is based on some psychological assessment of:
    • relevant criteria,
    • their importance, and
    • how well the alternatives achieve the criteria.
    APPLES AND ORANGES
  • CAR PURCHASE EXAMPLE
    • We now consider a motivating example.
    • After completing this example, you will have an understanding of the basics of AHP and its application through Expert Choice (www.expertchoice.com).
    • We want to apply the AHP to help a couple decide which car they should purchase.
  • CAR PURCHASE EXAMPLE
    • The couple is considering three criteria: cost, safety, and appearance.
    • They have narrowed their alternatives to three specific cars: Honda, Mazda, and Volvo.
    • We demonstrate how to build the AHP hierarchy in Expert Choice.
    • Select the F ile, N ew option and enter a file name such as CARS.EC1. (You must use the EC1 file extension.)
    • Choose the D irect option to create the model. Next, specify the description of the goal, such as, “Select the best car.”
    EXPERT CHOICE: FILE SETUP
    • To enter the criteria, use the E dit, I nsert command. Use the Esc key when finished entering the criteria.
    • To add the alternative cars under the cost node, simply highlight the cost node and again use the E dit, I nsert command. Use the Esc key when finished.
    EXPERT CHOICE: FILE SETUP
    • To include the same alternatives under the other criteria nodes, first highlight the cost node, then select E dit, R eplicate children of current node, To P eers, Y es .
    • Double-click on the goal node to display the complete hierarchy.
    • Additional details can be found in the Expert Choice tutorial provided with the software.
    EXPERT CHOICE: FILE SETUP
  • ANALYZING THE HIERARCHY
    • 1. Determine the weights of the alternatives for each criterion.
    • 2. Determine the priorities or weights of the criteria in achieving the goal.
    • 3. Determine the overall weight of each alternative in achieving the goal. This is accomplished by combining the results of the first two stages and is called synthesis.
  • HYPOTHETICAL DATA FOR CAR PURCHASE EXAMPLE Car Cost Safety* Appearance Honda $22,000 28 Sporty Mazda 28,500 39 Slick Volvo 33,000 52 Dull * Safety Rating from a consumer testing service - the higher the number, the safer the car.
  • DETERMINING PRIORITIES
    • The couple begins by making pairwise comparison judgments between each pair of cars for the cost criterion.
    • In our example, three judgments are needed: Honda to Mazda, Mazda to Volvo, and Honda to Volvo.
  • STANDARD 1 - 9 MEASUREMENT SCALE
    • Intensity of Importance Definition Explanation
    • 1 Equal importance Two activities contribute equally
    • 3 Moderate importance Experience and judgment slightly favor one
    • activity over another
    • 5 Strong importance Experience and judgment strongly favor one
    • activity over another
    • 7 Very strong An activity is favored very strongly over
    • another
    • 9 Extreme importance The evidence favoring one activity over
    • another is of the highest possible order
    • of affirmation
    • 2, 4, 6, 8 For compromise Sometimes one needs to interpolate a
    • values compromise between the above judgment
    • numerically because there is no good
    • word to describe it
    • 1.1 - 1.9 For tied activities When elements are close and nearly
    • indistinguishable; moderate is 1.3 and
    • extreme is 1.9
    • Reciprocals of above If activity A has For example, if the pairwise comparison of
    • one of the above A to B is 3.0, then the pairwise comparison
    • numbers assigned of B to A is 1/3
    • to it when compared
    • with activity B,
    • then B has the
    • reciprocal value
    • when compared to A.
  • COST PAIRWISE COMPARISONS
    • The pairwise comparisons are represented in the form of pairwise comparison matrices.
    • The computation of the weights are also shown.
    • Consider the pairwise comparison matrix to compare the cars for the cost criterion.
    • Remember that the costs of the three cars are: $22000, $28500, and $33000, respectively.
    • If we compare the Honda to the Honda, obviously they are equal.
    • Therefore, a 1 (equal preferred) is placed in the first row, first column entry of the matrix.
    COST PAIRWISE COMPARISONS
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1
    • 28.5K Mazda
    • 33K Volvo
    COST PAIRWISE COMPARISONS
    • The other entries along the main diagonal of the matrix are also 1.
    • This simply means that everything is equally preferred to itself.
    COST PAIRWISE COMPARISONS
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1
    • 28.5K Mazda 1
    • 33K Volvo 1
    COST PAIRWISE COMPARISONS
    • Suppose we believe the Honda ($22000) is equally to moderately preferred to the Mazda ($28500). Place a 2 in the row 1, column 2 entry.
    • Some might argue that the Honda should be 1.295 times better than the Mazda (28,500/22,000).
    COST PAIRWISE COMPARISONS
    • Do you agree?
    • It depends!
    • For some, $28,500 is significantly greater than $22,000, implying a judgments greater than 1.295.
    • Others with a lot of money may perceive virtually no difference between the two costs, implying a judgment somewhere between 1 and 1.295.
    COST PAIRWISE COMPARISONS
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2
    • 28.5K Mazda 1
    • 33K Volvo 1
    COST PAIRWISE COMPARISONS
    • If the Honda is 2 times better than the Mazda, this implies that the Mazda ($28500) is one half as good as the Honda ($22000).
    • The reciprocal judgment, (1/2), should be placed in the row 2, column 1 entry of the matrix.
    COST PAIRWISE COMPARISONS
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2
    • 28.5K Mazda 1/2 1
    • 33K Volvo 1
    COST PAIRWISE COMPARISONS
    • Suppose that we judge the Mazda ($28500) to be equally to moderately preferred to the Volvo ($33000).
    • The following judgments would be entered in the matrix.
    COST PAIRWISE COMPARISONS
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2
    • 28.5K Mazda 1/2 1 2
    • 33K Volvo 1/2 1
    COST PAIRWISE COMPARISONS
    • Assuming perfect consistency of judgments, we would expect that the Honda ($22000) is 4 times (that is, moderately to strongly) preferred to the Volvo ($33000).
    • We will relax this assumption later.
    COST PAIRWISE COMPARISONS
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 2
    • 33K Volvo 1/4 1/2 1
    COST PAIRWISE COMPARISONS
    • The matrix is now complete and the weights for each car (for the cost criterion) can be computed.
    • The exact computational procedure is implemented in Expert Choice. For details see Expert Choice homepage and download AHPDEMO.EXE.
    COST PAIRWISE COMPARISONS
    • A simple three step procedure can be used to approximate the weights for each alternative.
    • Essentially, this procedure normalizes the ratios of the judgments between any pair of alternatives.
    COST PAIRWISE COMPARISONS
    • 1. SUM THE ELEMENTS IN EACH COLUMN OF THE ORIGINAL MATRIX.
    • 2. DIVIDE EACH ELEMENT IN THE ORIGINAL MATRIX BY ITS COLUMN SUM.
    • THIS RESULTS IN THE ADJUSTED MATRIX.
    • 3. COMPUTE THE ROW AVERAGES - THESE ARE THE WEIGHTS.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 2
    • 33K Volvo 1/4 1/2 1
    • ------- ------- -------
    • COLUMN TOTALS
    COST PAIRWISE COMPARISONS
    • 1. SUM THE ELEMENTS IN EACH COLUMN OF THE ORIGINAL MATRIX.
    • 2. DIVIDE EACH ELEMENT IN THE ORIGINAL MATRIX BY ITS COLUMN SUM.
    • THIS RESULTS IN THE ADJUSTED MATRIX.
    • 3. COMPUTE THE ROW AVERAGES - THESE ARE THE WEIGHTS.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 2
    • 33K Volvo 1/4 1/2 1
    • ------- ------- -------
    • COLUMN TOTALS 7/4 7/2 7
    COST PAIRWISE COMPARISONS
    • 1. SUM THE ELEMENTS IN EACH COLUMN OF THE ORIGINAL MATRIX.
    • 2. DIVIDE EACH ELEMENT IN THE ORIGINAL MATRIX BY ITS COLUMN SUM.
    • THIS RESULTS IN THE ADJUSTED MATRIX.
    • 3. COMPUTE THE ROW AVERAGES - THESE ARE THE WEIGHTS.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 2
    • 33K Volvo 1/4 1/2 1
    • ------- ------- -------
    • COLUMN TOTALS 7/4 7/2 7
    COST PAIRWISE COMPARISONS
    • 1. SUM THE ELEMENTS IN EACH COLUMN OF THE ORIGINAL MATRIX.
    • 2 . DIVIDE EACH ELEMENT IN THE ORIGINAL MATRIX BY ITS COLUMN SUM.
    • THIS RESULTS IN THE ADJUSTED MATRIX.
    • 3. COMPUTE THE ROW AVERAGES - THESE ARE THE WEIGHTS.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 2
    • 33K Volvo 1/4 1/2 1
    • ------- ------- -------
    • COLUMN TOTALS 7/4 7/2 7
    • B. ADJUSTED COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • Honda 4/7* 4/7 4/7
    • Mazda 2/7 2/7 2/7
    • Volvo 1/7 1/7 1/7
    • * This entry is obtained by dividing the Honda entry in the original matrix (1) by the Honda column total (7/4).
    COST PAIRWISE COMPARISONS
    • Notice that no variation is seen across the rows because the judgments are perfectly consistent.
    • For the third column, judgments totaling 7 were awarded. The Honda received 4 of 7 (57.1%), the Mazda 2 of 7 (28.6%), and the Volvo 1 of 7 (14.3%) of the weight.
    • Similar comparisons can be made for the other two columns.
    COST PAIRWISE COMPARISONS
    • 1. SUM THE ELEMENTS IN EACH COLUMN OF THE ORIGINAL MATRIX.
    • 2. DIVIDE EACH ELEMENT IN THE ORIGINAL MATRIX BY ITS COLUMN SUM.
    • THIS RESULTS IN THE ADJUSTED MATRIX.
    • 3. COMPUTE THE ROW AVERAGES - THESE ARE THE WEIGHTS.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 2
    • 33K Volvo 1/4 1/2 1
    • ------- ------- -------
    • COLUMN TOTALS 7/4 7/2 7
    • B. ADJUSTED COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • Honda 4/7* 4/7 4/7
    • Mazda 2/7 2/7 2/7
    • Volvo 1/7 1/7 1/7
    • * This entry is obtained by dividing the Honda entry in the original matrix (1) by the Honda column total (7/4).
    COST PAIRWISE COMPARISONS
    • 1. SUM THE ELEMENTS IN EACH COLUMN OF THE ORIGINAL MATRIX.
    • 2. DIVIDE EACH ELEMENT IN THE ORIGINAL MATRIX BY ITS COLUMN SUM.
    • THIS RESULTS IN THE ADJUSTED MATRIX.
    • 3. COMPUTE THE ROW AVERAGES - THESE ARE THE WEIGHTS.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 2
    • 33K Volvo 1/4 1/2 1
    • ------- ------- -------
    • COLUMN TOTALS 7/4 7/2 7
    • B. ADJUSTED COST PAIRWISE COMPARISON MATRIX WEIGHTS
    • Honda Mazda Volvo (ROW AVG.)
    • Honda 4/7* 4/7 4/7 0.571
    • Mazda 2/7 2/7 2/7 0.286
    • Volvo 1/7 1/7 1/7 0.143
    • ---------
    • TOTAL 1.000
    • * This entry is obtained by dividing the Honda entry in the original matrix (1) by the Honda column total (7/4).
    COST PAIRWISE COMPARISONS
    • Expert Choice offers a variety of modes for entering the judgments.
    • Highlight the cost node, select A ssessment.
    • There are three options: P airwise, D ata , and R atings .
    • Ratings will be discussed later.
    EXPERT CHOICE: Entering Judgments
    • The D ata option allows the user to enter data items for each alternative, for example, costs, miles per gallon, and number of defects.
    • Expert Choice takes the ratio of these data items and converts them into pairwise comparisons.
    • What assumption are you making if you use the Data option?
    • The data items have a linear preference scale, that is, a $20,000 car is twice as good as a $40,000 car.
    EXPERT CHOICE: Entering Judgments
    • To enter our cost judgments choose P airwise.
    • When comparing alternatives select P reference for T ype ; for criteria select I mportance .
    • Modes options are: V erbal, M atrix (numerical), Q uestionnaire, and G raphic .
    • A ssessment, P airwise, M atrix is demonstrated.
    • Enter judgments, C alculate and R ecord .
    EXPERT CHOICE: Entering Judgments
  • INCONSISTENCY OF JUDGMENTS
    • Since our pairwise comparisons were perfectly consistent, Expert Choice reports INCONSISTENCY RATIO = 0.0.
    • If this ratio is greater than 0.1 some revision of judgments is required.
    • Select Inconsis t ency (within A ssessment , P airwise ) to identify the most inconsistent judgments.
  • INCONSISTENCY OF JUDGMENTS
    • Inconsistency of judgments may result from:
    • problems of estimation;
    • errors between the comparisons;
    • or, the comparisons may be naturally inconsistent.
  • INCONSISTENCY OF JUDGMENTS
    • One example of natural inconsistency is in a sporting contest.
    • If team A is twice as likely to beat team B, and if team B is three times as likely to beat team C, this does not necessarily imply that team A is six times as likely to beat team C.
    • This inconsistency may result because of the way that the teams “match-up” overall.
  • INCONSISTENCY OF JUDGMENTS
    • The point is not to stop inconsistency from occurring.
    • Make sure that the level of inconsistency remains within some reasonable limit.
  • INCONSISTENCY OF JUDGMENTS
    • How does a judgment change affect the car weights?
    • Suppose the Mazda to Volvo changes from 2 to 3.
    • This obviously changes the comparison for Volvo to Mazda from (1/2) to (1/3).
    • The judgments are now somewhat inconsistent.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 3
    • 33K Volvo 1/4 1/3 1
    COST PAIRWISE COMPARISONS
    • 1. SUM THE ELEMENTS IN EACH COLUMN OF THE ORIGINAL MATRIX.
    • 2. DIVIDE EACH ELEMENT IN THE ORIGINAL MATRIX BY ITS COLUMN SUM.
    • THIS RESULTS IN THE ADJUSTED MATRIX.
    • 3. COMPUTE THE ROW AVERAGES - THESE ARE THE WEIGHTS.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 3
    • 33K Volvo 1/4 1/3 1
    • ------- ------- -------
    • COLUMN TOTALS 7/4 10/3 8
    COST PAIRWISE COMPARISONS
    • 1. SUM THE ELEMENTS IN EACH COLUMN OF THE ORIGINAL MATRIX.
    • 2 . DIVIDE EACH ELEMENT IN THE ORIGINAL MATRIX BY ITS COLUMN SUM.
    • THIS RESULTS IN THE ADJUSTED MATRIX.
    • 3. COMPUTE THE ROW AVERAGES - THESE ARE THE WEIGHTS.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 3
    • 33K Volvo 1/4 1/3 1
    • ------- ------- -------
    • COLUMN TOTALS 7/4 10/3 8
    • B. ADJUSTED COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • Honda 4/7* 6/10 4/8
    • Mazda 2/7 3/10 3/8
    • Volvo 1/7 1/10 1/8
    • * This entry is obtained by dividing the Honda entry in the original matrix (1) by the Honda column total (7/4).
    COST PAIRWISE COMPARISONS
    • 1. SUM THE ELEMENTS IN EACH COLUMN OF THE ORIGINAL MATRIX.
    • 2. DIVIDE EACH ELEMENT IN THE ORIGINAL MATRIX BY ITS COLUMN SUM.
    • THIS RESULTS IN THE ADJUSTED MATRIX.
    • 3. COMPUTE THE ROW AVERAGES - THESE ARE THE WEIGHTS.
    • A. ORIGINAL COST PAIRWISE COMPARISON MATRIX
    • Honda Mazda Volvo
    • 22K Honda 1 2 4
    • 28.5K Mazda 1/2 1 3
    • 33K Volvo 1/4 1/3 1
    • ------- ------- -------
    • COLUMN TOTALS 7/4 10/3 8
    • B. ADJUSTED COST PAIRWISE COMPARISON MATRIX WEIGHTS
    • Honda Mazda Volvo (ROW AVG.)
    • Honda 4/7* 6/10 4/8 0.557
    • Mazda 2/7 3/10 3/8 0.320
    • Volvo 1/7 1/10 1/8 0.123
    • --------
    • TOTAL 1.000
    • * This entry is obtained by dividing the Honda entry in the original matrix (1) by the Honda column total (7/4).
    COST PAIRWISE COMPARISONS
  • INCONSISTENCY OF JUDGMENTS
    • The new weights are: 0.557, 0.320, and 0.123. The inconsistency resulted in some change in the original weights of 0.571, 0.286, and 0.143.
    • As expected, the weight for the Mazda increased while the weight for the Volvo decreased.
    • The weights now vary across each row. Essentially, inconsistency measures the degree of variation across the rows.
    • Highlight cost node, select A ssessment, P airwise.
    • Enter a 3 in the Mazda to Volvo cell then C alculate .
    • The weights of 0.558, 0.320, and 0.122 are slightly different from the three-step procedure weights.
    • This is not due to rounding -- Expert Choice gives the exact results.
    • The INCONSISTENCY RATIO is now 0.02.
    EXPERT CHOICE: Revising Judgments
  • INCONSISTENCY OF JUDGMENTS
    • The weights can also be used to measure the effectiveness of the alternatives.
    • For example, based on all pairwise comparisons, we determined that the Honda is 1.74 (0.558/0.320) times better than the Mazda.
    • Why is this ratio 1.74 and not the pairwise comparison of 2?
    • Inconsistency in the judgments!
  • REMAINING COMPUTATIONS
    • Next, the cars must be pairwise compared for the safety criterion and then for the appearance criterion.
    • These judgments are shown on the next page.
    • Since the Mazda to Honda safety comparison is 2, highlight the Honda to Mazda cell, click I nvert , and enter 2.
    • This judgment now appears in red.
  • SAFETY & APPEARANCE JUDGMENTS
    • Safety Pairwise Comparison Matrix
    • Honda Mazda Volvo
    • 28 Honda 1 1/2 1/5
    • 39 Mazda 2 1 1/4
    • 52 Volvo 5 4 1
    • Appearance Pairwise Comparison Matrix
    • Honda Mazda Volvo
    • Sporty Honda 1 5 9
    • Slick Mazda 1/5 1 2
    • Dull Volvo 1/9 1/2 1
  • REMAINING COMPUTATIONS
    • Next, the criteria must be pairwise compared.
    • These judgments are shown on the next page.
    • There are no data to support these judgments since they are purely a reflection of your preferences.
  • CRITERIA JUDGMENTS
    • Original Criteria Pairwise Comparison Matrix
    • Cost Safety Appearance
    • Cost 1 1/2 3
    • Safety 2 1 5
    • Appearance 1/3 1/5 1
  • REMAINING COMPUTATIONS
    • The last stage computes the final weights for each car.
    • Multiply the criteria weight by the car weight for each criterion and then sum over all criteria.
    • This is nothing more than a weighted average.
    • The computational results are shown next.
  • FINAL CAR WEIGHTS
    • CRITERIA WEIGHTS
    • COST SAFETY APPEARANCE
    • 0.309 0.582 0.109
    • CARS FINAL WEIGHTS
    • Honda 0.558 0.117 0.761
    • Mazda 0.320 0.200 0.158
    • Volvo 0.122 0.683 0.082
  • FINAL CAR WEIGHTS
    • CRITERIA WEIGHTS
    • COST SAFETY APPEARANCE
    • 0.309 0.582 0.109
    • CARS FINAL WEIGHTS
    • Honda 0.558 0.117 0.761 0.324
    • Mazda 0.320 0.200 0.158
    • Volvo 0.122 0.683 0.082
    • Honda: (0.558)(0.309) + (0.117)(0.582) + (0.761)(0.109) = 0.324
    • 0.173 0.068 0.083
  • FINAL CAR WEIGHTS
    • CRITERIA WEIGHTS
    • COST SAFETY APPEARANCE
    • 0.309 0.582 0.109
    • CARS FINAL WEIGHTS
    • Honda 0.558 0.117 0.761 0.324
    • Mazda 0.320 0.200 0.158 0.232
    • Volvo 0.122 0.683 0.082
    • Honda: (0.558)(0.309) + (0.117)(0.582) + (0.761)(0.109) = 0.324
    • 0.173 0.068 0.083
    • Mazda: (0.320)(0.309) + (0.200)(0.582) + (0.158)(0.109) = 0.232
    • 0.099 0.116 0.017
  • FINAL CAR WEIGHTS
    • CRITERIA WEIGHTS
    • COST SAFETY APPEARANCE
    • 0.309 0.582 0.109
    • CARS FINAL WEIGHTS
    • Honda 0.558 0.117 0.761 0.324
    • Mazda 0.320 0.200 0.158 0.232
    • Volvo 0.122 0.683 0.082 0.444
    • Honda: (0.558)(0.309) + (0.117)(0.582) + (0.761)(0.109) = 0.324
    • 0.173 0.068 0.083
    • Mazda: (0.320)(0.309) + (0.200)(0.582) + (0.158)(0.109) = 0.232
    • 0.099 0.116 0.017
    • Volvo: (0.122)(0.309) + (0.683)(0.582) + (0.082)(0.109) = 0.444
    • 0.038 0.397 0.009
  • LOCAL VS GLOBAL WEIGHTS
    • For cost, the local weights for the cars are 0.558, 0.320, and 0.122 and sum to 1.000.
    • The global weights are computed by multiplying the cost criterion weight by the local car weights.
    • The global weights are 0.173, 0.099, and 0.038 and sum to the cost criterion weight of 0.309.
    • To compute the final weights select S ynthesis ( from G OAL ).
    • Choose Dis t ributive Mode and Display S ummary .
    • D etails provides the global weights.
    • The output can also be exported to a spreadsheet using the U tilities , Export Model(s) to Spreadsheet commands.
    EXPERT CHOICE: Synthesis
    • The Print icon can be used to select certain options.
    • The recommended print options are: E ntire Tree, T ree Views, J udgments/Data, and S y nthesis .
    EXPERT CHOICE: Printing
  • INTERPRETING THE RESULTS
    • The final weights provide a measure of the relative performance of each alternative.
    • It is important to properly interpret the meaning of these numbers.
    • The Volvo is ranked first, the Honda second, and Mazda third.
    • The Volvo is preferred 1.37 (0.444/0.324) times more than the Honda.
  • INTERPRETING THE RESULTS
    • Should we buy the Volvo?
    • The output is a decision-making aid and cannot replace the decision-maker.
    • The results can be used to support discussion and possibly the judgments will be revised.
    • This iterative process is quite normal.
    • AHP can help to facilitate communication and generate consensus between different groups.
  • SENSITIVITY ANALYSIS
    • Sensitivity analysis is an important aspect of any decision-making process.
    • Sensitivity analysis determines whether small changes in judgments affects the final weights and rankings of the alternatives.
    • If so, the decision-maker may want to review the sensitive judgments.
  • EXPERT CHOICE: Sensitivity Analysis
    • In Expert Choice sensitivity analysis from the GOAL shows how the weights and the rankings of the alternatives change if some or all of the criteria weights change.
    • There are five graphical sensitivity analysis modes available: Performance, Dynamic, Gradient, Two-Dimensional, and Difference.
    • The first three show how a change in a criterion weight affects the final weights of the alternatives.
    • The last two show how the alternatives perform with respect to any two criteria.
    • Performance : places all sensitivity information on a single chart with horizontal line graphs for the alternatives linked to vertical bars for the criteria.
    • Dynamic : two sets of dynamically linked horizontal bar graphs: one for criteria and one for alternatives.
    EXPERT CHOICE: Sensitivity Analysis
    • Gradient : a line graph that shows how the weights of the alternatives vary according to the weight assigned to a specific criterion. (Use the X -Axis to change the selected criterion.)
    • Two-Dimensional : shows how well the alternatives perform with respect to any two criteria.
    • Difference : a graph that shows the differences between any two alternatives for any criterion.
    EXPERT CHOICE: Sensitivity Analysis
    • An important use of sensitivity analysis is to determine how much a given criterion weight must change before there is a change in the rankings of the two highest alternatives.
    • This type of breakeven analysis can be easily done in Expert Choice.
    EXPERT CHOICE: Sensitivity Analysis
    • Choose D ynamic from the Sensitivity- G raphs option.
    • Drag the cost criterion bar 30.9% to approximately 45.9%, and see that the Volvo and Honda have the same highest final weight.
    • The final rankings are relatively insensitive to a change in the cost criterion weight because the cost weight had to be increased by almost 50% to get a change in the rankings.
    EXPERT CHOICE: Sensitivity Analysis
  • NEW PRODUCT INTRODUCTION
    • CHOCK-FUL-O-CHIPS developed the following hierarchy and data that can be used to help decide which chocolate chip recipe they should use.
    Select the best recipe Taste Cost Fat Content Recipe 1 Recipe 2 Recipe 3 Recipe 4 Recipe 1 Recipe 2 Recipe 3 Recipe 4 Recipe 1 Recipe 2 Recipe 3 Recipe 4
  • RECIPE DATA
    • Taste Fat Content
    • Recipe Cost* Rating** (Grams)*
    • 1 $0.166 54% 8.0
    • 2 0.099 24% 7.0
    • 3 0.265 20% 3.5
    • 4 0.224 43% 6.0
    • * Per one ounce cookie
    • ** Percentage of people who rated a cookie either an 8 or 9 on a 9-point scale, where 9 means extremely liked, 8 means liked very much, and down to one which means extremely disliked.
  • TASTE PAIRWISE COMPARISON MATRIX
    • 54% 24% 20% 43%
    • Recipe 1 Recipe 2 Recipe 3 Recipe 4
    • Recipe 1 1
    • Recipe 2 1
    • Recipe 3 1
    • Recipe 4 1
  • COST PAIRWISE COMPARISON MATRIX
    • 0.166 0.099 0.265 0.224
    • Recipe 1 Recipe 2 Recipe 3 Recipe 4
    • Recipe 1 1
    • Recipe 2 1
    • Recipe 3 1
    • Recipe 4 1
  • FAT CONTENT PAIRWISE COMPARISON MATRIX
    • 8.0 7.0 3.5 6.0
    • Recipe 1 Recipe 2 Recipe 3 Recipe 4
    • Recipe 1 1
    • Recipe 2 1
    • Recipe 3 1
    • Recipe 4 1
  • CRITERIA PAIRWISE COMPARISON MATRIX
    • Taste Cost Fat Content
    • Taste 1
    • Cost 1
    • Fat Content 1
  • FINAL WEIGHTS FROM EXPERT CHOICE
    • Criteria Weights
    • Taste Cost Fat Content
    • Final
    • Weights
    • Recipe 1
    • Recipe 2
    • Recipe 3
    • Recipe 4
  • SUMMARY
    • In this chapter:
    • we provided an overview of operations management; and
    • offered the AHP as a decision-making process with application in operations management.
  • SUMMARY
    • AHP benefits include:
    • natural way to elicit judgments;
    • measure degree of inconsistency;
    • easy to use;
    • allows broad participation; and
    • fully supported by Expert Choice.