a scoring model for job selection

48
1 © 2003 Thomson © 2003 Thomson /South-Western /South-Western Slide A Scoring Model for Job Selection A Scoring Model for Job Selection A graduating college student with a A graduating college student with a double major in Finance and Accounting double major in Finance and Accounting has received the following three job has received the following three job offers: offers: financial analyst for an investment financial analyst for an investment firm in Chicago firm in Chicago accountant for a manufacturing firm in accountant for a manufacturing firm in Denver Denver auditor for a CPA firm in Houston auditor for a CPA firm in Houston

Upload: eleanor-hanson

Post on 01-Jan-2016

28 views

Category:

Documents


3 download

DESCRIPTION

A Scoring Model for Job Selection. A graduating college student with a double major in Finance and Accounting has received the following three job offers: financial analyst for an investment firm in Chicago accountant for a manufacturing firm in Denver auditor for a CPA firm in Houston. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: A Scoring Model for Job Selection

1 1© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

A graduating college student with a double major A graduating college student with a double major in Finance and Accounting has received the in Finance and Accounting has received the following three job offers:following three job offers:

•financial analyst for an investment firm in financial analyst for an investment firm in ChicagoChicago

•accountant for a manufacturing firm in Denveraccountant for a manufacturing firm in Denver

•auditor for a CPA firm in Houstonauditor for a CPA firm in Houston

Page 2: A Scoring Model for Job Selection

2 2© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

The student made the following comments:The student made the following comments:

•““The financial analyst position provides the The financial analyst position provides the best opportunity for my long-run career best opportunity for my long-run career advancement.”advancement.”

•““I would prefer living in Denver rather than in I would prefer living in Denver rather than in Chicago or Houston.”Chicago or Houston.”

•““I like the management style and philosophy I like the management style and philosophy at the Houston CPA firm the best.”at the Houston CPA firm the best.”

Clearly, this is a multicriteria decision problem.Clearly, this is a multicriteria decision problem.

Page 3: A Scoring Model for Job Selection

3 3© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Considering only the Considering only the long-run career long-run career advancementadvancement criterion: criterion:

•The The financial analyst position in Chicagofinancial analyst position in Chicago is the is the best decision alternative.best decision alternative.

Considering only the Considering only the locationlocation criterion: criterion:

•The The accountant position in Denveraccountant position in Denver is the best is the best decision alternative.decision alternative.

Considering only the Considering only the stylestyle criterion: criterion:

•The The auditor position in Houstonauditor position in Houston is the best is the best alternative.alternative.

Page 4: A Scoring Model for Job Selection

4 4© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Steps Required to Develop a Scoring ModelSteps Required to Develop a Scoring Model

Step 1:Step 1: List the decision-making criteria. List the decision-making criteria.

Step 2:Step 2: Assign a weight to each criterion. Assign a weight to each criterion.

Step 3:Step 3: Rate how well each decision Rate how well each decision alternative alternative satisfies each criterion.satisfies each criterion.

Step 4:Step 4: Compute the score for each decision Compute the score for each decision alternative.alternative.

Step 5:Step 5: Order the decision alternatives from Order the decision alternatives from highest highest score to lowest score. The score to lowest score. The alternative with alternative with the highest score is the the highest score is the recommended recommended alternative.alternative.

Page 5: A Scoring Model for Job Selection

5 5© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Mathematical ModelMathematical Model

SSjj = = wwii r rijij

ii

where:where:

rrijij = rating for criterion = rating for criterion ii and decision and decision alternative alternative jj

SSjj = = score for decision alternativescore for decision alternative j j

Page 6: A Scoring Model for Job Selection

6 6© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Step 1: List the criteria (important factors).Step 1: List the criteria (important factors).

•Career advancement Career advancement

•LocationLocation

•ManagementManagement

•SalarySalary

•PrestigePrestige

• Job SecurityJob Security

•Enjoyable workEnjoyable work

Page 7: A Scoring Model for Job Selection

7 7© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Five-Point Scale Chosen for Step 2Five-Point Scale Chosen for Step 2

ImportanceImportance WeightWeight

Very unimportantVery unimportant 11

Somewhat unimportantSomewhat unimportant 22

Average importanceAverage importance 33

Somewhat importantSomewhat important 44

Very importantVery important 55

Page 8: A Scoring Model for Job Selection

8 8© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Step 2: Assign a weight to each criterion.Step 2: Assign a weight to each criterion.

CriterionCriterion ImportanceImportance WeightWeightCareer advancementCareer advancement Very importantVery important 55LocationLocation Average importanceAverage importance 33ManagementManagement Somewhat importantSomewhat important 44SalarySalary Average importanceAverage importance 33PrestigePrestige Somewhat unimportantSomewhat unimportant 22Job securityJob security Somewhat importantSomewhat important 44Enjoyable workEnjoyable work Very importantVery important 55

Page 9: A Scoring Model for Job Selection

9 9© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Nine-Point Scale Chosen for Step 3Nine-Point Scale Chosen for Step 3

Level of SatisfactionLevel of Satisfaction RatingRating Extremely lowExtremely low 11 Very lowVery low 22 LowLow 33 Slightly lowSlightly low 44 AverageAverage 55

Slightly highSlightly high 66 HighHigh 77 Very highVery high 88 Extremely highExtremely high 99

Page 10: A Scoring Model for Job Selection

10 10© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Step 3: Step 3: RateRate how well each decision alternative how well each decision alternative satisfies each criterion.satisfies each criterion.

Decision AlternativeDecision Alternative Analyst AccountantAnalyst Accountant

AuditorAuditor CriterionCriterion ChicagoChicago DenverDenver HoustonHouston

Career advancementCareer advancement 88 66 44LocationLocation 33 88 77ManagementManagement 55 66 99SalarySalary 66 77 55PrestigePrestige 77 55 44Job securityJob security 44 77 66Enjoyable workEnjoyable work 88 66 55

Page 11: A Scoring Model for Job Selection

11 11© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Step 4: Compute the score for each decision Step 4: Compute the score for each decision alternative.alternative.

Decision Alternative 1 - Analyst in ChicagoDecision Alternative 1 - Analyst in Chicago

CriterionCriterion Weight ( Weight (wwi i ) Rating () Rating (rrii11) ) wwiirrii11

Career advancementCareer advancement 5 5 x x 8 8 = = 4040LocationLocation 3 3 3 3 9 9ManagementManagement 4 4 5 5 2020SalarySalary 3 3 6 6 1818PrestigePrestige 2 2 7 7 1414Job securityJob security 4 4 4 4 1616Enjoyable workEnjoyable work 5 5 8 8 4040

ScoreScore 157 157

Page 12: A Scoring Model for Job Selection

12 12© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Step 4: Compute the score for each decision Step 4: Compute the score for each decision alternative.alternative.

SSjj = = wwii r rijij

ii

SS11 = = 5(8)+3(3)+4(5)+3(6)+2(7)+4(4)+5(8) = 1575(8)+3(3)+4(5)+3(6)+2(7)+4(4)+5(8) = 157

SS22 = = 5(6)+3(8)+4(6)+3(7)+2(5)+4(7)+5(6) = 1675(6)+3(8)+4(6)+3(7)+2(5)+4(7)+5(6) = 167

SS33 = = 5(4)+3(7)+4(9)+3(5)+2(4)+4(6)+5(5) = 1495(4)+3(7)+4(9)+3(5)+2(4)+4(6)+5(5) = 149

Page 13: A Scoring Model for Job Selection

13 13© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Step 4: Compute the Step 4: Compute the scorescore for each decision for each decision alternative.alternative.

Decision AlternativeDecision Alternative Analyst AccountantAnalyst Accountant

AuditorAuditor CriterionCriterion ChicagoChicago DenverDenver HoustonHouston

Career advancementCareer advancement 4040 3030 2020LocationLocation 9 9 2424 2121ManagementManagement 2020 2424 3636SalarySalary 1818 2121 1515PrestigePrestige 1414 1010 8 8Job securityJob security 1616 2828 2424Enjoyable workEnjoyable work 4040 3030 2525

ScoreScore 157 157 167 167 149 149

Page 14: A Scoring Model for Job Selection

14 14© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Step 5: Order the decision alternatives from Step 5: Order the decision alternatives from highest highest score to lowest score. The score to lowest score. The alternative with the alternative with the highest score is the highest score is the recommended alternative.recommended alternative.

•The The accountant position in Denveraccountant position in Denver has the has the highest score and is the highest score and is the recommended decision recommended decision alternativealternative..

•Note that the analyst position in Chicago ranks Note that the analyst position in Chicago ranks first in 4 of 7 criteria compared to only 2 of 7 first in 4 of 7 criteria compared to only 2 of 7 for the accountant position in Denver.for the accountant position in Denver.

•But when the weights of the criteria are But when the weights of the criteria are considered, the Denver position is superior to considered, the Denver position is superior to the Chicago job.the Chicago job.

Page 15: A Scoring Model for Job Selection

15 15© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Partial Spreadsheet Showing Steps 1 - 3Partial Spreadsheet Showing Steps 1 - 3

A B C D E1 RATINGS2 Analyst Accountant Auditor3 Criteria Weight Chicago Denver Houston4 Career Advance. 5 8 6 45 Location 3 3 8 76 Management 4 5 6 97 Salary 3 6 7 58 Prestige 2 7 5 49 Job Security 4 4 7 610 Enjoyable Work 5 8 6 5

Page 16: A Scoring Model for Job Selection

16 16© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Partial Spreadsheet Showing Formulas for Step 4Partial Spreadsheet Showing Formulas for Step 4

A B C D E12 SCORING CALCULATIONS1314 Analyst Accountant Auditor15 Criteria Chicago Denver Houston16 Career Advance. =B4*C4 =B4*D4 =B4*E417 Location =B5*C5 =B5*D5 =B5*E518 Management =B6*C6 =B6*D6 =B6*E619 Salary =B7*C7 =B7*D7 =B7*E720 Prestige =B8*C8 =B8*D8 =B8*E821 Job Security =B9*C9 =B9*D9 =B9*E922 Enjoyable Work =B10*C10 =B10*D10 =B10*E1023 Score =sum(C16:C22) =sum(D16:D22) =sum(E16:E22)

Page 17: A Scoring Model for Job Selection

17 17© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

A Scoring Model for Job SelectionA Scoring Model for Job Selection

Partial Spreadsheet Showing Results of Step 4Partial Spreadsheet Showing Results of Step 4

A B C D E12 SCORING CALCULATIONS1314 Analyst Accountant Auditor15 Criteria Chicago Denver Houston16 Career Advance. 40 30 2017 Location 9 24 2118 Management 20 24 3619 Salary 18 21 1520 Prestige 14 10 821 Job Security 16 28 2422 Enjoyable Work 40 30 2523 Score 157 167 149

Page 18: A Scoring Model for Job Selection

18 18© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Analytic Hierarchy ProcessAnalytic Hierarchy Process

The The Analytic Hierarchy Process (AHP)Analytic Hierarchy Process (AHP), is a , is a procedure designed to quantify managerial procedure designed to quantify managerial judgments of the relative importance of each of judgments of the relative importance of each of several conflicting criteria used in the decision several conflicting criteria used in the decision making process.making process.

Page 19: A Scoring Model for Job Selection

19 19© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Analytic Hierarchy ProcessAnalytic Hierarchy Process

Step 1: List the Overall Goal, Criteria, and Step 1: List the Overall Goal, Criteria, and Decision Decision Alternatives Alternatives

Step 2: Develop a Pairwise Comparison MatrixStep 2: Develop a Pairwise Comparison Matrix

Rate the relative importance between each Rate the relative importance between each pair of decision alternatives. The matrix lists the pair of decision alternatives. The matrix lists the alternatives horizontally and vertically and has the alternatives horizontally and vertically and has the numerical ratings comparing the horizontal (first) numerical ratings comparing the horizontal (first) alternative with the vertical (second) alternative.alternative with the vertical (second) alternative.

Ratings are given as follows:Ratings are given as follows:

. . . continued. . . continued

------- For each criterion, perform steps 2 through 5 -------------- For each criterion, perform steps 2 through 5 -------------- For each criterion, perform steps 2 through 5 -------------- For each criterion, perform steps 2 through 5 -------

Page 20: A Scoring Model for Job Selection

20 20© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Analytic Hierarchy ProcessAnalytic Hierarchy Process

Step 2: Pairwise Comparison Matrix (continued)Step 2: Pairwise Comparison Matrix (continued)

Compared to the secondCompared to the secondalternative, the first alternative isalternative, the first alternative is: : Numerical Numerical ratingrating

extremely preferred extremely preferred 99

very strongly preferred very strongly preferred 77

strongly preferred strongly preferred 55

moderately preferred moderately preferred 33

equally preferred equally preferred 11

Page 21: A Scoring Model for Job Selection

21 21© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Analytic Hierarchy ProcessAnalytic Hierarchy Process

Step 2: Pairwise Comparison Matrix (continued)Step 2: Pairwise Comparison Matrix (continued)

Intermediate numeric ratings of 8, 6, 4, 2 Intermediate numeric ratings of 8, 6, 4, 2 can be assigned. A reciprocal rating (i.e. 1/9, can be assigned. A reciprocal rating (i.e. 1/9, 1/8, etc.) is assigned when the second 1/8, etc.) is assigned when the second alternative is preferred to the first. The value of alternative is preferred to the first. The value of 1 is always assigned when comparing an 1 is always assigned when comparing an alternative with itself. alternative with itself.

Page 22: A Scoring Model for Job Selection

22 22© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Analytic Hierarchy ProcessAnalytic Hierarchy Process

Step 3: Develop a Normalized MatrixStep 3: Develop a Normalized Matrix

Divide each number in a column of the Divide each number in a column of the pairwise comparison matrix by its column sum.pairwise comparison matrix by its column sum.

Step 4: Develop the Priority VectorStep 4: Develop the Priority Vector

Average each row of the normalized Average each row of the normalized matrix. These row averages form the priority matrix. These row averages form the priority vector of alternative preferences with respect vector of alternative preferences with respect to the particular criterion. The values in this to the particular criterion. The values in this vector sum to 1.vector sum to 1.

Page 23: A Scoring Model for Job Selection

23 23© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Analytic Hierarchy ProcessAnalytic Hierarchy Process

Step 5: Calculate a Consistency RatioStep 5: Calculate a Consistency Ratio

The consistency of the subjective input in The consistency of the subjective input in the pairwise comparison matrix can be the pairwise comparison matrix can be measured by calculating a consistency ratio. A measured by calculating a consistency ratio. A consistency ratio of less than .1 is good. For consistency ratio of less than .1 is good. For ratios which are greater than .1, the subjective ratios which are greater than .1, the subjective input should be re-evaluated.input should be re-evaluated.

Step 6: Develop a Priority MatrixStep 6: Develop a Priority Matrix

After steps 2 through 5 has been After steps 2 through 5 has been performed for all criteria, the results of step 4 performed for all criteria, the results of step 4 are summarized in a priority matrix by listing the are summarized in a priority matrix by listing the decision alternatives horizontally and the criteria decision alternatives horizontally and the criteria vertically. The column entries are the priority vertically. The column entries are the priority vectors for each criterion. vectors for each criterion.

Page 24: A Scoring Model for Job Selection

24 24© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Analytic Hierarchy ProcessAnalytic Hierarchy Process

Step 7: Develop a Criteria Pairwise Development Step 7: Develop a Criteria Pairwise Development Matrix Matrix

This is done in the same manner as that This is done in the same manner as that used to construct alternative pairwise used to construct alternative pairwise comparison matrices by using subjective ratings comparison matrices by using subjective ratings (step 2). Similarly, normalize the matrix (step 3) (step 2). Similarly, normalize the matrix (step 3) and develop a criteria priority vector (step 4). and develop a criteria priority vector (step 4).

Step 8: Develop an Overall Priority VectorStep 8: Develop an Overall Priority Vector

Multiply the criteria priority vector (from Multiply the criteria priority vector (from step 7) by the priority matrix (from step 6).step 7) by the priority matrix (from step 6).

Page 25: A Scoring Model for Job Selection

25 25© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Determining the Consistency RatioDetermining the Consistency Ratio

Step 1:Step 1: For each row of the pairwise comparison For each row of the pairwise comparison

matrix, determine a weighted sum by summing matrix, determine a weighted sum by summing the multiples of the entries by the priority of its the multiples of the entries by the priority of its corresponding (column) alternative.corresponding (column) alternative.

Step 2:Step 2: For each row, divide its weighted sum by For each row, divide its weighted sum by

the priority of its corresponding (row) alternative.the priority of its corresponding (row) alternative. Step 3:Step 3:

Determine the average, Determine the average, maxmax, of the results , of the results of step 2.of step 2.

Page 26: A Scoring Model for Job Selection

26 26© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Determining the Consistency RatioDetermining the Consistency Ratio

Step 4:Step 4:

Compute the consistency index, CI, of the Compute the consistency index, CI, of the nn alternatives by: CI = (alternatives by: CI = (maxmax - - nn)/()/(nn - 1). - 1).

Step 5:Step 5:

Determine the random index, RI, as follows:Determine the random index, RI, as follows:

Number of RandomNumber of Random Number of RandomNumber of Random Alternative (Alternative (nn)) Index (RI)Index (RI) Alternative (Alternative (nn)) Index (RI)Index (RI)

3 0.583 0.58 6 6 1.24 1.24 4 0.904 0.90 7 7 1.32 1.32 5 1.125 1.12 8 8 1.41 1.41

Step 6:Step 6:

Compute the consistency ratio: CR = CR/RI.Compute the consistency ratio: CR = CR/RI.

Page 27: A Scoring Model for Job Selection

27 27© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Designer Gill Glass must decide which of Designer Gill Glass must decide which of three manufacturers will develop his three manufacturers will develop his "signature" toothbrushes. Three factors seem "signature" toothbrushes. Three factors seem important to Gill: (1) his costs; (2) reliability of important to Gill: (1) his costs; (2) reliability of the product; and, (3) delivery time of the the product; and, (3) delivery time of the orders.orders.

The three manufacturers are Cornell The three manufacturers are Cornell Industries, Brush Pik, and Picobuy. Cornell Industries, Brush Pik, and Picobuy. Cornell Industries will sell toothbrushes to Gill Glass for Industries will sell toothbrushes to Gill Glass for $100 per gross, Brush Pik for $80 per gross, and $100 per gross, Brush Pik for $80 per gross, and Picobuy for $144 per gross. Gill has decided Picobuy for $144 per gross. Gill has decided that in terms of price, Brush Pik is moderately that in terms of price, Brush Pik is moderately preferred to Cornell and very strongly preferred preferred to Cornell and very strongly preferred to Picobuy. In turn Cornell is strongly to very to Picobuy. In turn Cornell is strongly to very strongly preferred to Picobuy.strongly preferred to Picobuy.

Page 28: A Scoring Model for Job Selection

28 28© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Hierarchy for the Manufacturer Selection Hierarchy for the Manufacturer Selection ProblemProblem

Select the Best Toothbrush ManufacturerSelect the Best Toothbrush ManufacturerSelect the Best Toothbrush ManufacturerSelect the Best Toothbrush Manufacturer

CostCost CostCost ReliabilityReliabilityReliabilityReliability Delivery TimeDelivery TimeDelivery TimeDelivery Time

CornellCornellBrush PikBrush PikPicobuyPicobuy

CornellCornellBrush PikBrush PikPicobuyPicobuy

CornellCornellBrush PikBrush PikPicobuyPicobuy

CornellCornellBrush PikBrush PikPicobuyPicobuy

CornellCornellBrush PikBrush PikPicobuyPicobuy

CornellCornellBrush PikBrush PikPicobuyPicobuy

Overall GoalOverall Goal

CriteriaCriteria

DecisionDecisionAlternativesAlternatives

Page 29: A Scoring Model for Job Selection

29 29© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Forming the Pairwise Comparison Matrix For CostForming the Pairwise Comparison Matrix For Cost

•Since Brush Pik is moderately preferred to Since Brush Pik is moderately preferred to Cornell, Cornell's entry in the Brush Pik row is Cornell, Cornell's entry in the Brush Pik row is 3 and Brush Pik's entry in the Cornell row is 3 and Brush Pik's entry in the Cornell row is 1/3.1/3.

•Since Brush Pik is very strongly preferred to Since Brush Pik is very strongly preferred to Picobuy, Picobuy's entry in the Brush Pik row Picobuy, Picobuy's entry in the Brush Pik row is 7 and Brush Pik's entry in the Picobuy row is is 7 and Brush Pik's entry in the Picobuy row is 1/7.1/7.

•Since Cornell is strongly to very strongly Since Cornell is strongly to very strongly preferred to Picobuy, Picobuy's entry in the preferred to Picobuy, Picobuy's entry in the Cornell row is 6 and Cornell's entry in the Cornell row is 6 and Cornell's entry in the Picobuy row is 1/6.Picobuy row is 1/6.

Page 30: A Scoring Model for Job Selection

30 30© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Pairwise Comparison Matrix for CostPairwise Comparison Matrix for Cost

Cornell Brush Pik PicobuyCornell Brush Pik Picobuy

CornellCornell 1 1/3 1 1/3 6 6

Brush PikBrush Pik 3 3 1 1 7 7

PicobuyPicobuy 1/6 1/6 1/7 1/7 1 1

Page 31: A Scoring Model for Job Selection

31 31© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Normalized Matrix for CostNormalized Matrix for Cost

Divide each entry in the pairwise comparison Divide each entry in the pairwise comparison matrix by its corresponding column sum. For matrix by its corresponding column sum. For example, for Cornell the column sum = 1 + 3 + 1/6 example, for Cornell the column sum = 1 + 3 + 1/6 = 25/6. This gives:= 25/6. This gives:

Cornell Brush Pik PicobuyCornell Brush Pik Picobuy

CornellCornell 6/25 7/31 6/25 7/31 6/14 6/14

Brush PikBrush Pik 18/25 21/31 18/25 21/31 7/14 7/14

PicobuyPicobuy 1/25 3/31 1/25 3/31 1/14 1/14

Page 32: A Scoring Model for Job Selection

32 32© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Priority Vector For CostPriority Vector For Cost

The priority vector is determined by The priority vector is determined by averaging the row entries in the normalized averaging the row entries in the normalized matrix. Converting to decimals we get:matrix. Converting to decimals we get:

Cornell: ( 6/25 + 7/31 + 6/14)/3 Cornell: ( 6/25 + 7/31 + 6/14)/3 = .298 = .298

Brush Pik: (18/25 + 21/31 + 7/14)/3 Brush Pik: (18/25 + 21/31 + 7/14)/3 = .632 = .632

Picobuy: ( 1/25 + 3/31 + 1/14)/3 Picobuy: ( 1/25 + 3/31 + 1/14)/3 = .069 = .069

Example: Gill GlassExample: Gill Glass

Page 33: A Scoring Model for Job Selection

33 33© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Checking ConsistencyChecking Consistency

•Multiply each column of the pairwise Multiply each column of the pairwise comparison matrix by its priority:comparison matrix by its priority:

1 1/3 1 1/3 6 .923 6 .923

.298 3 + .632 1 + .069 7 = .298 3 + .632 1 + .069 7 = 2.009 2.009

1/6 1/7 1/6 1/7 1 .209 1 .209

•Divide these number by their priorities to get:Divide these number by their priorities to get:

.923/.298 = 3.097.923/.298 = 3.097

2.009/.632 = 3.1792.009/.632 = 3.179

.209/.069 = 3.029.209/.069 = 3.029

Example: Gill GlassExample: Gill Glass

Page 34: A Scoring Model for Job Selection

34 34© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Checking ConsistencyChecking Consistency

•Average the above results to get Average the above results to get maxmax..

maxmax = (3.097 + 3.179 + 3.029)/3 = 3.102 = (3.097 + 3.179 + 3.029)/3 = 3.102

•Compute the consistence index, CI, for two terms.Compute the consistence index, CI, for two terms.

CI = (CI = (maxmax - - nn)/()/(nn - 1) = (3.102 - 3)/2 = .051 - 1) = (3.102 - 3)/2 = .051

•Compute the consistency ratio, CR, by CI/RI, Compute the consistency ratio, CR, by CI/RI, where RI = .58 for 3 factors:where RI = .58 for 3 factors:

CR = CI/RI = .051/.58 = .088CR = CI/RI = .051/.58 = .088

Since the consistency ratio, CR, is less than .10, this Since the consistency ratio, CR, is less than .10, this is well within the acceptable range for consistency. is well within the acceptable range for consistency.

Page 35: A Scoring Model for Job Selection

35 35© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Gill Glass has determined that for Gill Glass has determined that for reliabilityreliability, Cornell is very strongly preferable to , Cornell is very strongly preferable to Brush Pik and equally preferable to Picobuy. Brush Pik and equally preferable to Picobuy. Also, Picobuy is strongly preferable to Brush Pik.Also, Picobuy is strongly preferable to Brush Pik.

Page 36: A Scoring Model for Job Selection

36 36© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Pairwise Comparison Matrix for ReliabilityPairwise Comparison Matrix for Reliability

Cornell Brush Pik PicobuyCornell Brush Pik Picobuy

CornellCornell 1 7 1 7 2 2

Brush PikBrush Pik 1/7 1/7 1 1 5 5

PicobuyPicobuy 1/2 1/2 1/5 1/5 1 1

Page 37: A Scoring Model for Job Selection

37 37© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Normalized Matrix for ReliabilityNormalized Matrix for Reliability

Divide each entry in the pairwise Divide each entry in the pairwise comparison matrix by its corresponding column comparison matrix by its corresponding column sum. For example, for Cornell the column sum = sum. For example, for Cornell the column sum = 1 + 1/7 + 1/2 = 23/14. This gives:1 + 1/7 + 1/2 = 23/14. This gives:

Cornell Brush Pik PicobuyCornell Brush Pik Picobuy

CornellCornell 14/23 35/41 14/23 35/41 2/8 2/8

Brush PikBrush Pik 2/23 5/41 2/23 5/41 5/8 5/8

PicobuyPicobuy 7/23 1/41 7/23 1/41 1/8 1/8

Page 38: A Scoring Model for Job Selection

38 38© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Priority Vector For ReliabilityPriority Vector For Reliability

The priority vector is determined by The priority vector is determined by averaging the row entries in the normalized matrix. averaging the row entries in the normalized matrix. Converting to decimals we get: Converting to decimals we get:

Cornell: (14/23 + 35/41 + 2/8)/3 = .571 Cornell: (14/23 + 35/41 + 2/8)/3 = .571

Brush Pik: ( 2/23 + 5/41 + 5/8)/3 = .278 Brush Pik: ( 2/23 + 5/41 + 5/8)/3 = .278

Picobuy: ( 7/23 + 1/41 + 1/8)/3 = .151 Picobuy: ( 7/23 + 1/41 + 1/8)/3 = .151

Checking ConsistencyChecking Consistency

Gill Glass’ responses to reliability could be Gill Glass’ responses to reliability could be checked for consistency in the same manner as checked for consistency in the same manner as was cost.was cost.

Page 39: A Scoring Model for Job Selection

39 39© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Gill Glass has determined that for Gill Glass has determined that for delivery delivery timetime, Cornell is equally preferable to Picobuy. , Cornell is equally preferable to Picobuy. Both Cornell and Picobuy are very strongly to Both Cornell and Picobuy are very strongly to extremely preferable to Brush Pik.extremely preferable to Brush Pik.

Page 40: A Scoring Model for Job Selection

40 40© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Pairwise Comparison Matrix for Delivery TimePairwise Comparison Matrix for Delivery Time

Cornell Brush Pik PicobuyCornell Brush Pik Picobuy

CornellCornell 1 8 1 8 1 1

Brush PikBrush Pik 1/8 1/8 1 1 1/8 1/8

PicobuyPicobuy 1 1 8 8 1 1

Page 41: A Scoring Model for Job Selection

41 41© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Normalized Matrix for Delivery TimeNormalized Matrix for Delivery Time

Divide each entry in the pairwise Divide each entry in the pairwise comparison matrix by its corresponding column comparison matrix by its corresponding column sum. sum.

Cornell Brush Pik PicobuyCornell Brush Pik Picobuy

CornellCornell 8/17 8/17 8/17 8/17 8/17 8/17

Brush PikBrush Pik 1/17 1/17 1/17 1/17 1/17 1/17

PicobuyPicobuy 8/17 8/17 8/17 8/17 8/17 8/17

Page 42: A Scoring Model for Job Selection

42 42© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Priority Vector For Delivery TimePriority Vector For Delivery Time

The priority vector is determined by The priority vector is determined by averaging the row entries in the normalized averaging the row entries in the normalized matrix. Converting to decimals we get:matrix. Converting to decimals we get:

Cornell: (8/17 + 8/17 + 8/17)/3 = .471 Cornell: (8/17 + 8/17 + 8/17)/3 = .471

Brush Pik: (1/17 + 1/17 + 1/17)/3 = .059 Brush Pik: (1/17 + 1/17 + 1/17)/3 = .059

Picobuy: (8/17 + 8/17 + 8/17)/3 = .471 Picobuy: (8/17 + 8/17 + 8/17)/3 = .471

Checking ConsistencyChecking Consistency

Gill Glass’ responses to delivery time could Gill Glass’ responses to delivery time could be checked for consistency in the same manner be checked for consistency in the same manner as was cost.as was cost.

Page 43: A Scoring Model for Job Selection

43 43© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

The accounting department has The accounting department has determined that in terms of determined that in terms of criteriacriteria, cost is , cost is extremely preferable to delivery time and very extremely preferable to delivery time and very strongly preferable to reliability, and that strongly preferable to reliability, and that reliability is very strongly preferable to delivery reliability is very strongly preferable to delivery time.time.

Page 44: A Scoring Model for Job Selection

44 44© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Pairwise Comparison Matrix for CriteriaPairwise Comparison Matrix for Criteria

Cost Reliability DeliveryCost Reliability Delivery

CostCost 1 7 1 7 9 9

ReliabilityReliability 1/7 1/7 1 1 7 7

DeliveryDelivery 1/9 1/9 1/7 1/7 1 1

Page 45: A Scoring Model for Job Selection

45 45© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Normalized Matrix for CriteriaNormalized Matrix for Criteria

Divide each entry in the pairwise Divide each entry in the pairwise comparison matrix by its corresponding column comparison matrix by its corresponding column sum.sum.

Cost Reliability Cost Reliability DeliveryDelivery

CostCost 63/79 49/57 63/79 49/57 9/179/17

ReliabilityReliability 9/79 7/57 9/79 7/57 7/177/17

DeliveryDelivery 7/79 1/57 7/79 1/57 1/17 1/17

Page 46: A Scoring Model for Job Selection

46 46© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Priority Vector For CriteriaPriority Vector For Criteria

The priority vector is determined by The priority vector is determined by averaging the row entries in the normalized averaging the row entries in the normalized matrix. Converting to decimals we get:matrix. Converting to decimals we get:

Cost: Cost: (63/79 + 49/57 + 9/17)/3 (63/79 + 49/57 + 9/17)/3 = .729 = .729

Reliability: Reliability: ( 9/79 + 7/57 + 7/17)/3 ( 9/79 + 7/57 + 7/17)/3 = .216 = .216

Delivery: Delivery: ( 7/79 + 1/57 + 1/17)/3 ( 7/79 + 1/57 + 1/17)/3 = .055= .055

Page 47: A Scoring Model for Job Selection

47 47© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Overall Priority VectorOverall Priority Vector

The overall priorities are determined by The overall priorities are determined by multiplying the priority vector of the criteria by multiplying the priority vector of the criteria by the priorities for each decision alternative for the priorities for each decision alternative for each objective.each objective.

Priority VectorPriority Vector

for Criteriafor Criteria [ .729 .216 [ .729 .216 .055 ] .055 ]

Cost Reliability DeliveryCost Reliability Delivery

Cornell Cornell .298 .571 .298 .571 .471 .471

Brush PikBrush Pik .632 .278 .632 .278 .059 .059

PicobuyPicobuy .069 .151 .069 .151 .471 .471

Example: Gill GlassExample: Gill Glass

Page 48: A Scoring Model for Job Selection

48 48© 2003 Thomson© 2003 Thomson/South-Western/South-Western Slide Slide

Example: Gill GlassExample: Gill Glass

Overall Priority Vector (continued)Overall Priority Vector (continued)

Thus, the overall priority vector is:Thus, the overall priority vector is:

Cornell:Cornell: (.729)(.298) + (.216)(.571) + (.055)(.729)(.298) + (.216)(.571) + (.055)(.471) = .366(.471) = .366

Brush Pik: (.729)(.632) + (.216)(.278) + (.055)Brush Pik: (.729)(.632) + (.216)(.278) + (.055)(.059) = .524(.059) = .524

Picobuy: (.729)(.069) + (.216)(.151) + (.055)Picobuy: (.729)(.069) + (.216)(.151) + (.055)(.471) = .109(.471) = .109

Brush Pik appears to be the overall Brush Pik appears to be the overall recommendation.recommendation.