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Program Name Source 1.3 Pritchett Clock Repair Shop Excel QM 1.4 Pritchett Clock Repair Shop Excel QM 2.1 Expected Value and Variance Excel 2.2 Binomial Probabilities Excel 3.1 Thompson Lumber Excel QM 3.5 Bayes Theorem for Thompson Lumber Example Excel 4.1 Triple A Construction Company Sales Excel QM 4.2 Jenny Wilson Realty Excel QM 4.3 Jenny Wilson Realty Excel QM 4.4 MPG Data Excel QM 4.5 MPG Data Excel QM 4.6 Solved Problem 4-2 Excel 5.1 Wallace Garden Supply Shed Sales Excel QM 5.2 Port of Baltimore Excel QM 5.3 Midwestern Manufacturing's Demand Excel 5.4 Midwestern Manufacturing's Demand Excel QM 5.6 Turner Industries Excel 6.1 Sumco Pump Company Excel QM 6.2 Brown Manufacturing Excel QM 6.3 Brass Department Store Excel QM 7.2 Flair Furniture Excel 7.4 Holiday Meal Turkey Ranch Excel 7.6 High note sound company Excel 8.1 Win Big Gambling Club Excel 8.3 Fifth Avenue Industries Excel 8.5 Top Speed Bicycle Company Excel 8.6 Goodman Shipping Excel 9.1 High note sound company Excel 9.2 Manufacturing Example Excel 10.1 Executive Furniture Company Excel QM 10.2 Birmingham Plant Excel QM 10.3 Fix-It Shop Assignment Excel QM 11.2 Harrison Electric IP Analysis Excel 11.4 Bagwell Chemical Company Excel 11.5 Simkin, Simkin and Steinberg Excel 11.7 Great Western Appliance Excel 11.8 Hospicare Corp Excel 11.9 Thermlock Gaskets Excel 11.10 Solved Problem 11-1 Excel 13.1 Crashing General Foundry Problem Excel 14.1 Arnold's Muffler Shop Excel QM 14.2 Arnold's Muffler Shop Excel QM 14.3 Golding Recycling, Inc. Excel QM 14.4 Department of Commerce Excel QM 15.2 Harry's Tire Shop Excel 15.3 Generating Normal Random Numbers Excel 15.4 Port of New Orleans Barge Unloadings Excel 15.5 Three Hills Power Company Excel 16.4 Three Grocery Example Excel 16.5 Accounts Receivable Example Excel

Excel and Excel QM Examples

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Page 1: Excel and Excel QM Examples

Program Name Source Content1.3 Pritchett Clock Repair Shop Excel QM Breakeven Analysis1.4 Pritchett Clock Repair Shop Excel QM Goal Seek2.1 Expected Value and Variance Excel Expected Value and Variance2.2 Binomial Probabilities Excel Binomial Probabilities3.1 Thompson Lumber Excel QM Decision Table3.5 Bayes Theorem for Thompson Lumber Example Excel Bayes Theorem4.1 Triple A Construction Company Sales Excel QM Regression4.2 Jenny Wilson Realty Excel QM Multiple Regression4.3 Jenny Wilson Realty Excel QM Dummy Variables - Regression4.4 MPG Data Excel QM Linear Regression4.5 MPG Data Excel QM Nonlinear Regression4.6 Solved Problem 4-2 Excel Regression5.1 Wallace Garden Supply Shed Sales Excel QM Weighted Moving Average5.2 Port of Baltimore Excel QM Exponential Smoothing5.3 Midwestern Manufacturing's Demand Excel Trend Analysis5.4 Midwestern Manufacturing's Demand Excel QM Trend Analysis5.6 Turner Industries Excel Regression6.1 Sumco Pump Company Excel QM EOQ Model6.2 Brown Manufacturing Excel QM Production Run Model6.3 Brass Department Store Excel QM Quantity Discount Model7.2 Flair Furniture Excel Linear Programming7.4 Holiday Meal Turkey Ranch Excel Linear Programming7.6 High note sound company Excel Linear Programming8.1 Win Big Gambling Club Excel Linear Programming8.3 Fifth Avenue Industries Excel Linear Programming8.5 Top Speed Bicycle Company Excel Linear Programming8.6 Goodman Shipping Excel Linear Programming9.1 High note sound company Excel Linear Programming9.2 Manufacturing Example Excel Linear Programming

10.1 Executive Furniture Company Excel QM Transportation10.2 Birmingham Plant Excel QM Transportation10.3 Fix-It Shop Assignment Excel QM Assignment11.2 Harrison Electric IP Analysis Excel Integer programming11.4 Bagwell Chemical Company Excel Integer programming11.5 Simkin, Simkin and Steinberg Excel Integer programming11.7 Great Western Appliance Excel Nonlinear programming11.8 Hospicare Corp Excel Nonlinear programming11.9 Thermlock Gaskets Excel Nonlinear programming

11.10 Solved Problem 11-1 Excel 0-1 programming13.1 Crashing General Foundry Problem Excel Crashing14.1 Arnold's Muffler Shop Excel QM Single Server (M/M/1) system14.2 Arnold's Muffler Shop Excel QM Multi-Server (M/M/m) system14.3 Golding Recycling, Inc. Excel QM Constant Service Rate (M/D/1)14.4 Department of Commerce Excel QM Finite population queue15.2 Harry's Tire Shop Excel Simulation (inventory)15.3 Generating Normal Random Numbers Excel Random #s and Frequency15.4 Port of New Orleans Barge Unloadings Excel Simulation (waiting line)15.5 Three Hills Power Company Excel Maintenance Simulation16.4 Three Grocery Example Excel Markov Analysis16.5 Accounts Receivable Example Excel Fundamental Matrix & Absorbing States17.1 ARCO Excel p-Chart Analysis

ModuleM1.1 AHP Excel

Page 2: Excel and Excel QM Examples

M5.1 Matrix Multiplication Excel

Page 3: Excel and Excel QM Examples

Dummy Variables - Regression

Constant Service Rate (M/D/1)

Fundamental Matrix & Absorbing States

Page 4: Excel and Excel QM Examples

Pritchett Clock Repair Shop

Breakeven Analysis

DataRebuilt Springs

Fixed cost 1000Variable cost 5Revenue 10

ResultsBreakeven points

Units 200Dollars $ 2,000.00

GraphUnits Costs Revenue

0 1000 0400 3000 4000

0 2 4 6 8 10 120

2

4

6

8

10

12Cost-volume analysis

Units

$

Enter the fixed and variable costs and the selling price in the data area.Enter the fixed and variable costs and the selling price in the data area.

Page 5: Excel and Excel QM Examples

Pritchett Clock Repair Shop

Breakeven Analysis

DataRebuilt Springs

Fixed cost 1000Variable cost 5Revenue 10.71Volume (optional) 250

ResultsBreakeven points

Units 175Dollars $ 1,875.00

Volume Analysis@ 250 Costs $ 2,250.00 Revenue $ 2,678.57 Profit $ 428.57

GraphUnits Costs Revenue

0 1000 0350 2750 3750

Enter the fixed and variable costs and the selling price in the data area.Enter the fixed and variable costs and the selling price in the data area.

Page 6: Excel and Excel QM Examples

x P(x) xP(x) (x-mean)squared*P(x)10 0.2 2 54.4520 0.25 5 10.562530 0.25 7.5 3.062540 0.3 12 54.675

26.5 122.75Mean Variance

Page 7: Excel and Excel QM Examples

The Binomial Distributionn= 5p= 0.5r= 4

Cumulative probability 0.9688P(r) 0.1563

P(r<_)

Page 8: Excel and Excel QM Examples

Thompson Lumber

Decision Tables

Data Results

Profit EMV Minimum Maximum HurwiczProbability 0.5 0.5 coefficient 0.8Large Plant 200000 -180000 10000 -180000 200000 124000Small plant 100000 -20000 40000 -20000 100000 76000Do nothing 0 0 0 0 0

Maximum 40000 0 200000 124000

Expected Value of Perfect InformationColumn best 200000 0 100000 <-Expected value under certainty

40000 <-Best expected value60000 <-Expected value of perfect information

RegretFavorable MUnfavorable Market Expected Maximum

Probability 0.5 0.5Large Plant 0 180000 90000 180000Small plant 100000 20000 60000 100000Do nothing 200000 0 100000 200000

Minimum 60000 100000

Favorable Market

Unfavorable Market

Enter the profits or costs in the main body of the data table. Enter probabilities in the first row if you want to compute the expected value.Enter the profits or costs in the main body of the data table. Enter probabilities in the first row if you want to compute the expected value.

Page 9: Excel and Excel QM Examples

Bayes Theorem for Thompson Lumber Example

Fill in cells B7, B8, and C7

Probability Revisions Given a Positive Survey

State of Nature P(Sur.Pos.|state of nature) Prior Prob. Joint Prob.FM 0.7 0.5 0.35 0.78UM 0.2 0.5 0.1 0.22

P(Sur.pos.)= 0.45

Probability Revisions Given a Negative Survey

State of Nature P(Sur.Pos.|state of nature) Prior Prob. Joint Prob.FM 0.3 0.5 0.15 0.27UM 0.8 0.5 0.4 0.73

P(Sur.neg.)= 0.55

Posterior Probability

Posterior Probability

Page 10: Excel and Excel QM Examples

Triple A Construction Co SUMMARY OUTPUT

Sales (Y)Payroll (X) Regression Statistics

6 3 Multiple R 0.833333

8 4 R Square 0.694444

9 6 Adjusted R 0.618056

5 4 Standard E 1.311011

4.5 2 Observatio 6

9.5 5ANOVA

df SS MS F Significance F

Regression 1 15.625 15.625 9.090909 0.039352

Residual 4 6.875 1.71875

Total 5 22.5

CoefficientsStandard Error t Stat P-value Lower 95%

Intercept 2 1.742544 1.147747 0.31505 -2.838077

Payroll (X) 1.25 0.414578 3.015113 0.039352 0.098947

Page 11: Excel and Excel QM Examples

Significance F

Upper 95%Lower 95.0%Upper 95.0%

6.838077 -2.838077 6.838077

2.401053 0.098947 2.401053

Page 12: Excel and Excel QM Examples

SELL PRICE SF AGE35000 1926 3047000 2069 4049900 1720 3055000 1396 1558900 1706 3260000 1847 3867000 1950 2770000 2323 3078500 2285 2679000 3752 3587500 2300 1893000 2525 1795000 3800 4097000 1740 12

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.81968R Square 0.67188Adjusted R Sq 0.61222Standard Erro 12156.3Observations 14

ANOVAdf SS MS F Significance F

Regression 2 3328484242 1.66E+09 11.26195 0.002179Residual 11 1625532901 1.48E+08Total 13 4954017143

CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept 60815.4 12741.04143 4.773193 0.000578 32772.6 88858.29 32772.6 88858.29SF 21.9097 5.140482535 4.262184 0.001338 10.59556 33.22381 10.59556 33.22381AGE -1449.34 398.282471 -3.638983 0.003895 -2325.957 -572.7293 -2325.957 -572.7293

Page 13: Excel and Excel QM Examples

Upper 95.0%

Page 14: Excel and Excel QM Examples

SELL PRI SF AGE X3(Exc) X4(Mint) Condition35000 1926 30 0 0 Good47000 2069 40 1 0 Excellent49900 1720 30 1 0 Excellent55000 1396 15 0 0 Good58900 1706 32 0 1 Mint60000 1847 38 0 1 Mint67000 1950 27 0 1 Mint70000 2323 30 1 0 Excellent78500 2285 26 0 1 Mint79000 3752 35 0 0 Good87500 2300 18 0 0 Good93000 2525 17 0 0 Good95000 3800 40 1 0 Excellent97000 1740 12 0 1 Mint

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.947618R Square 0.89798Adjusted R 0.852637Standard E 7493.777Observatio 14

ANOVAdf SS MS F Significance F

Regression 4 4.45E+09 1.11E+09 19.80444 0.000174Residual 9 5.05E+08 56156698Total 13 4.95E+09

CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept 48329.23 8713.307 5.5466 0.000358 28618.36 68040.1 28618.36 68040.1SF 28.2138 3.473758 8.121981 1.96E-05 20.35561 36.07199 20.35561 36.07199AGE -1981.41 298.0139 -6.648716 9.39E-05 -2655.564 -1307.256 -2655.564 -1307.256X3(Exc) 16581.32 6089.81 2.722798 0.0235 2805.216 30357.43 2805.216 30357.43X4(Mint) 23684.62 5324.635 4.448122 0.001605 11639.46 35729.78 11639.46 35729.78

Page 15: Excel and Excel QM Examples

Upper 95.0%

Page 16: Excel and Excel QM Examples

Automobile Weight vs. MPG SUMMARY OUTPUT

MPG (Y) Weight (X1) Regression Statistics12 4.58 Multiple R 0.8628813 4.66 R Square 0.7445615 4.02 Adjusted R 0.7190218 2.53 Standard E 5.0075719 3.09 Observatio 1219 3.1120 3.18 ANOVA23 2.68 df SS MS F Significance F24 2.65 Regression 1 730.909 730.909 29.14802 0.00030233 1.70 Residual 10 250.7577 25.0757736 1.95 Total 11 981.666742 1.92

CoefficientsStandard Error t Stat P-value Lower 95%Intercept 47.6193 4.813151 9.89359 1.75E-06 36.89498Weight (X1 -8.24597 1.527345 -5.398891 0.000302 -11.64911

Page 17: Excel and Excel QM Examples

Significance F

Upper 95%Lower 95.0%Upper 95.0%58.34371 36.89498 58.34371

-4.842833 -11.64911 -4.842833

Page 18: Excel and Excel QM Examples

Automobile Weight vs. MPG SUMMARY OUTPUT

MPG (Y) Weight (X1) WeightSq.(X2) Regression Statistics12 4.58 20.98 Multiple R 0.920813 4.66 21.72 R Square 0.847815 4.02 16.16 Adjusted R 0.814018 2.53 6.40 Standard E 4.074519 3.09 9.55 Observatio 1219 3.11 9.6720 3.18 10.11 ANOVA23 2.68 7.18 df SS MS F Significance F24 2.65 7.02 Regression 2 832.2557 416.1278 25.0661 0.00020933 1.70 2.89 Residual 9 149.411 16.6012236 1.95 3.80 Total 11 981.666742 1.92 3.69

CoefficientsStandard Error t Stat P-value Lower 95%Intercept 79.7888 13.5962 5.8685 0.0002 49.0321Weight (X1 -30.2224 8.9809 -3.3652 0.0083 -50.5386WeightSq.( 3.4124 1.3811 2.4708 0.0355 0.2881

Page 19: Excel and Excel QM Examples

Significance F

Upper 95%Lower 95.0%Upper 95.0%110.5454 49.0321 110.5454

-9.9062 -50.5386 -9.90626.5367 0.2881 6.5367

Page 20: Excel and Excel QM Examples

Solved Problem 4-2

Advertising ($100) Y Sales X11 5

6 310 7

6 212 8

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.9014R Square 0.8125Adjusted R Square 0.7500Standard Error 1.4142Observations 5

ANOVAdf SS MS F Significance F

Regression 1 26 26 13 0.036618Residual 3 6 2Total 4 32

CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Intercept 4 1.5242 2.6244 0.0787 -0.8506 8.8506 -0.8506Sales X 1 0.2774 3.6056 0.0366 0.1173 1.8827 0.1173

Page 21: Excel and Excel QM Examples

Upper 95.0%8.85061.8827

Page 22: Excel and Excel QM Examples

Wallace Garden Supply Shed Sales

Forecasting Weighted moving averages 3 period moving average

Data Error analysisPeriod Demand Weights Forecast Error Absolute SquaredJanuary 10 1February 12 2March 13 3April 16 12.16667 3.833333 3.833333 14.69444May 19 14.33333 4.666667 4.666667 21.77778June 23 17 6 6 36July 26 20.5 5.5 5.5 30.25August 30 23.83333 6.166667 6.166667 38.02778September 28 27.5 0.5 0.5 0.25October 18 28.33333 -10.33333 10.33333 106.7778November 16 23.33333 -7.333333 7.333333 53.77778December 14 18.66667 -4.666667 4.666667 21.77778

Total 4.333333 49 323.3333Average 0.481481 5.444444 35.92593

Bias MAD MSESE 6.796358

Next period 15.3333333

Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.

Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.

Page 23: Excel and Excel QM Examples

Port of Baltimore

Forecasting Exponential smoothing

Alpha 0.1Data Error AnalysisPeriod Demand Forecast Error Absolute SquaredQuarter 1 180 175 5 5 25Quarter 2 168 175.5 -7.5 7.5 56.25Quarter 3 159 174.75 -15.75 15.75 248.0625Quarter 4 175 173.175 1.825 1.825 3.330625Quarter 5 190 173.3575 16.6425 16.6425 276.97281Quarter 6 205 175.0218 29.97825 29.97825 898.69547Quarter 7 180 178.0196 1.980425 1.980425 3.9220832Quarter 8 182 178.2176 3.782382 3.782382 14.306417

Total 35.95856 82.45856 1526.5399Average 4.49482 10.30732 190.81749

Bias MAD MSESE 15.950653

Next period 178.595856

Enter alpha (between 0 and 1) then enter the past demands in the shaded area.Enter alpha (between 0 and 1) then enter the past demands in the shaded area.

Page 24: Excel and Excel QM Examples

Midwestern Manufacturing

Time (X) Demand (Y)1 742 793 804 905 1056 1427 122

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.8949096R Square 0.8008632Adjusted R 0.7610359Standard E 12.432389Observatio 7

ANOVAdf SS MS F Significance F

Regression 1 3108.0357 3108.036 20.10837 0.0064933Residual 5 772.82143 154.5643Total 6 3880.8571

CoefficientsStandard Error t Stat P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0%Intercept 56.71429 10.50729 5.39762 0.00295 29.70445 83.72412 29.70445 83.72412Time (X) 10.53571 2.34950 4.48424 0.00649 4.49613 16.57530 4.49613 16.57530

Page 25: Excel and Excel QM Examples

Upper 95.0%

Page 26: Excel and Excel QM Examples

Midwestern Manufacturing's Demand

Forecasting Regression/Trend analysis

Data Error analysisPeriod Demand (y) Period(x) Forecast Error Absolute Squared1993 74 1 67.25 6.75 6.75 45.56251994 79 2 77.78571 1.214286 1.2142857 1.474491995 80 3 88.32143 -8.321429 8.3214286 69.246171996 90 4 98.85714 -8.857143 8.8571429 78.448981997 105 5 109.3929 -4.392857 4.3928571 19.297191998 142 6 119.9286 22.07143 22.071429 487.1481999 122 7 130.4643 -8.464286 8.4642857 71.64413

Total 0.00 60.071429 772.8214Intercept 56.7142857 Average 0.00 8.5816327 110.4031Slope 10.5357143 Bias MAD MSE

SE 12.43239Next period 141 8

Correlation 0.89491

If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y.

If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y.

Page 27: Excel and Excel QM Examples

Year Quarter SalesX1 Time PeriodX2 Qtr 2 X3 Qtr 3 X4 Qtr 41 1 108 1 0 0 0

2 125 2 1 0 03 150 3 0 1 04 141 4 0 0 1

2 1 116 5 0 0 02 134 6 1 0 03 159 7 0 1 04 152 8 0 0 1

3 1 123 9 0 0 02 142 10 1 0 03 168 11 0 1 04 165 12 0 0 1

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.99718R Square 0.99436Adjusted R 0.99114Standard E 1.83225Observatio 12

ANOVAdf SS MS F Significance F

Regression 4 4144.75 1036.188 308.6516 6.03E-08Residual 7 23.5 3.357143Total 11 4168.25

CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept 104.104 1.332194 78.14493 1.48E-11 100.954 107.2543 100.954 107.2543X1 Time Pe 2.3125 0.16195 14.27913 1.96E-06 1.92955 2.69545 1.92955 2.69545X2 Qtr 2 15.6875 1.504767 10.4252 1.62E-05 12.12929 19.24571 12.12929 19.24571X3 Qtr 3 38.7083 1.530688 25.28819 3.86E-08 35.08883 42.32784 35.08883 42.32784X4 Qtr 4 30.0625 1.572941 19.11228 2.67E-07 26.34308 33.78192 26.34308 33.78192

Page 28: Excel and Excel QM Examples

Sumco Pump Company

Inventory Economic Order Quantity Model

DataDemand rate, D 1000Setup cost, S 10Holding cost, H 0.5 (fixed amount)Unit Price, P 0

ResultsOptimal Order Quantity, Q* 200Maximum Inventory 200Average Inventory 100Number of Setups 5

Holding cost $50.00 Setup cost $50.00

Unit costs $0.00

$100.00

COST TABLE Start at 25 Increment 15

Q Setup cost Holding cosTotal cost25 400 6.25 406.2540 250 10 26055 181.8182 13.75 195.568270 142.8571 17.5 160.357185 117.6471 21.25 138.8971

100 100 25 125115 86.95652 28.75 115.7065130 76.92308 32.5 109.4231145 68.96552 36.25 105.2155160 62.5 40 102.5175 57.14286 43.75 100.8929190 52.63158 47.5 100.1316205 48.78049 51.25 100.0305220 45.45455 55 100.4545235 42.55319 58.75 101.3032250 40 62.5 102.5265 37.73585 66.25 103.9858280 35.71429 70 105.7143295 33.89831 73.75 107.6483310 32.25806 77.5 109.7581325 30.76923 81.25 112.0192340 29.41176 85 114.4118355 28.16901 88.75 116.919370 27.02703 92.5 119.527

Total cost, Tc 0

2

4

6

8

10

12

Inventory: Cost vs Quantity

Order Quantity (Q)

Co

st (

$)

Enter the data in the shaded areaEnter the data in the shaded area

Page 29: Excel and Excel QM Examples

Brown Manufacturing

Inventory Production Order Quantity Model

DataDemand rate, D 10000Setup cost, S 100Holding cost, H 0.5 (fixed amount)Daily production rate, p 80Daily demand rate, d 60Unit price, P 0

ResultsOptimal production quantity, Q* 4000Maximum Inventory 1000Average Inventory 500Number of Setups 2.5

Holding cost 250Setup cost 250

Unit costs 0

500

COST TABLE Start at 1000 Increment 333.3333

Q Setup cost Holding cosTotal cost1000 1000 62.5 1062.5

1333.333 750 83.33333 833.33331666.667 600 104.1667 704.1667

2000 500 125 6252333.333 428.5714 145.8333 574.40482666.667 375 166.6667 541.6667

3000 333.3333 187.5 520.83333333.333 300 208.3333 508.33333666.667 272.7273 229.1667 501.8939

4000 250 250 5004333.333 230.7692 270.8333 501.60264666.667 214.2857 291.6667 505.9524

5000 200 312.5 512.55333.333 187.5 333.3333 520.83335666.667 176.4706 354.1667 530.6373

6000 166.6667 375 541.66676333.333 157.8947 395.8333 553.72816666.667 150 416.6667 566.6667

7000 142.8571 437.5 580.35717333.333 136.3636 458.3333 594.6977666.667 130.4348 479.1667 609.6014

8000 125 500 6258333.333 120 520.8333 640.83338666.667 115.3846 541.6667 657.0513

Total cost, Tc

0

2

4

6

8

10

12

Inventory: Cost vs Quantity

Order Quantity (Q)C

ost

($)

Enter the data in the shaded area. You may have to do some work to enter the daily production rate.Enter the data in the shaded area. You may have to do some work to enter the daily production rate.

Page 30: Excel and Excel QM Examples

0

2

4

6

8

10

12

Inventory: Cost vs Quantity

Order Quantity (Q)

Co

st (

$)

Enter the data in the shaded area. You may have to do some work to enter the daily production rate.Enter the data in the shaded area. You may have to do some work to enter the daily production rate.

Page 31: Excel and Excel QM Examples

Brass Department Store

Inventory Quantity Discount Model

DataDemand rate, D 5000Setup cost, S 49Holding cost %, I 20%

Range 1 Range 2 Range 3Minimum quantity 0 1000 2000Unit Price, P 5 4.8 4.75

ResultsRange 1 Range 2 Range 3

Q* (Square root formula) 700 714.4345083118 718.18484646Order Quantity 700 1000 2000

Holding cost $350.00 $480.00 $950.00 Setup cost $350.00 $245.00 $122.50

Unit costs $25,000.00 $24,000.00 $23,750.00

$25,700.00 $24,725.00 $24,822.50 minimumOptimal Order Quantity 1000Total cost, Tc

Page 32: Excel and Excel QM Examples

=

$24,725.00

Page 33: Excel and Excel QM Examples

Flair Furniture

Tables Chairs SlackObjective function 70 50 4100Carpentry 4 3 240 <= 240 0Painting 2 1 100 <= 100 0

Solution Values 30 40

Left Hand Side

Right Hand Side

Page 34: Excel and Excel QM Examples

Holiday Meal Turkey Ranch

Brand 1 Brand 2 SurplusObjective function 2 3 31.2Ingredient A 5 10 90 >= 90Ingredient B 4 3 48 >= 48 0Ingredient C 0.5 0 4.2 >= 1.5 2.7

Solution Values 8.4 4.8

Left Hand Side

Right Hand Side

Page 35: Excel and Excel QM Examples

High note sound company

CD PlayersReceiversValue 0 20

TotalProfit 50 120 2400

Used Sign AvailableElectrician hours 2 4 80 <= 80Audio technician hours 3 1 20 <= 60

Page 36: Excel and Excel QM Examples

Win Big Gambling Club

Solution 1.96875 5 6.20689655 0Variables X1 X2 X3 X4Audience reached per ad 5000 8500 2400 2800Maximum TV 1Maximum Newspaper 1Maximum 30-second radio 1Maximum 1 min. radio 1Cost per ad 800 925 290 380Radio dollars 290 380Radio spots 1 1

1 minute TV spots

newspaper ads

30 second radio spots

1 minute radio spots

Page 37: Excel and Excel QM Examples

RHS67240.302

1.96875 <= 125 <= 5

6.2068966 <= 250 <= 20

8000 <= 80001800 <= 1800

6.2068966 >= 5

Page 38: Excel and Excel QM Examples

Fifth Avenue Industries

Variety silk polyester cottonAll silk 6400 6.7 6000 7000 0.125 100%

All polyester 14000 3.55 10000 14000 0.08 100%

16000 4.31 13000 16000 0.1 50% 50%

8500 4.81 6000 8500 0.1 30% 70%

Total revenue 202425 800 2175 1395

Material Cost Available UsedSilk 21 800 800Polyester 6 3000 2175Cotton 9 1600 1395

Total Cost 42405

Total Profit 160020

Number (X)

Selling price

Monthly minimum

Monthly demand

Material (yards)

Poly-cotton blend 1

Poly-cotton blend 2

Page 39: Excel and Excel QM Examples

Top Speed Bicycle Company

Transportation

DataCOSTS New York Chicago Los Angel SupplyNew Orleans 2 3 5 20000 Omaha 3 1 4 15000 Demand 10000 8000 15000 33000 \ 35000

ShipmentsShipments New York Chicago Los Angel Row Total New Orleans 10000 0 8000 18000Omaha 0 8000 7000 15000Column Total 10000 8000 15000 33000 \ 33000 Total Cost 96000

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Page 40: Excel and Excel QM Examples

Goodman Shipping

Item Value ($) weight (lbs)1 0.333333 1 22500 75002 1 1 24000 75003 0 1 8000 30004 0 1 9500 35005 0 1 11500 40006 0 1 9750 3500

Total $ 31,500 10000

Weight Capacity 10000

Percent loaded

Max percent loaded

Page 41: Excel and Excel QM Examples

High note sound company

CD PlayersReceiversValue 0 20

TotalProfit 50 120 2400

Used Sign AvailableElectrician hours 2 4 80 <= 80Audio technician hours 3 1 20 <= 60

Page 42: Excel and Excel QM Examples

Manufacturing Example

mower blowervariable-> 100 200

Total profitprofit 30 80 19000

used availablelabor hours 2 4 1000 < 1000steel (lbs) 6 2 1000 < 1200snowblower engines 1 200 < 200

Page 43: Excel and Excel QM Examples

Executive Furniture Company

Transportation

DataCOSTS Albuquerq Boston Cleveland SupplyDes Moines 5 4 3 100 Evansville 8 4 3 300 Fort Lauderdale 9 7 5 300 Demand 300 200 200 700 \ 700

ShipmentsShipments Albuquerq Boston Cleveland Row Total Des Moines 100 0 0 100Evansville 0 200 100 300Fort Lauderdale 200 0 100 300Column Total 300 200 200 700 \ 700 Total Cost 3900

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Page 44: Excel and Excel QM Examples

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Page 45: Excel and Excel QM Examples

Birmingham Plant

Transportation

DataCOSTS Detroit Dallas New York Los Angel SupplyCincinnati 73 103 88 108 15000 Salt Lake 85 80 100 90 6000 Pittsburgh 88 97 78 118 14000 Birmingha 84 79 90 99 11000 Demand 10000 12000 15000 9000 46000 \ 46000

ShipmentsShipments Detroit Dallas New York Los Angel Column Tot Cincinnati 10000 0 1000 4000 15000Salt Lake 0 1000 0 5000 6000Pittsburgh 0 0 14000 0 14000Birmingha 0 11000 0 0 11000Column Tot 10000 12000 15000 9000 46000 \ 46000 Total Cost 3741000

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Page 46: Excel and Excel QM Examples

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Enter the transportation costs, supplies and demands in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS.

Page 47: Excel and Excel QM Examples

Fix-It Shop Assignment

Fix-It Shop Assignment

Assignment

DataCOSTS Project 1 Project 2 Project 3Adams 11 14 6 Brown 8 10 11 Cooper 9 12 7

AssignmentsShipments Project 1 Project 2 Project 3 Row Total Adams 0 0 1 1 Brown 0 1 0 1 Cooper 1 0 0 1 Column Total 1 1 1 3 Total Cost 25

Enter the assignment costs in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS. If SOLVER is not an addin option then reinstall Excel.

Enter the assignment costs in the shaded area. Then go to TOOLS, SOLVER, SOLVE on the menu bar at the top.If SOLVER is not a menu option in the Tools menu then go to TOOLS, ADD-INS. If SOLVER is not an addin option then reinstall Excel.

Page 48: Excel and Excel QM Examples

Harrison Electric IP Analysis

Chandeliers FansSolution 5 0

TotalProfit 7 6 35

Used Sign Limitwiring hours 2 3 10 < 12assembly hours 6 5 30 < 30

Page 49: Excel and Excel QM Examples

Bagwell Chemical Company

xyline (bags) hexall (lbs)value 44 20

profit 85 1.5 3770used sign available

ingredient a 30 0.5 1330 <= 2000ingredient b 18 0.4 800 <= 800ingredient c 2 0.1 90 <= 200

Page 50: Excel and Excel QM Examples

Simkin, Simkin and Steinberg

Stock Company Name Invest Return Cost1 Trans-Texas Oil 0 50 4802 British Petroleum 0 80 5403 Dutch Shell 1 90 6804 Houston Drilling 1 120 10005 Texas Petroleum 1 110 7006 San Diego Oil 1 40 5107 California Petro 0 75 900

Total 360 2890Limit 3000

BoundTexas Constraint 2 >= 2Foreign oil constraint 1 <= 1California Constraint 1 = 1

Page 51: Excel and Excel QM Examples

Great Western Appliance

Microtoast Self-clean TotalNumber 0 1000 1000 < 1000Profit 0 271000 $ 271,000.00

used Sign capacityHours 0.5 0.4 400 < 500

Page 52: Excel and Excel QM Examples

Hospicare Corpx1 x2

value 6.066259 4.100253

terms x1 x1^2 x1*x2 x2 x2^3 1/x2values 6.066259 36.79949 24.87319 4.100253 68.93374 0.243887

totalrevenue 13 6 5 1 248.846

constraint 1 2 4 90 < 90constraint 2 1 1 75 < 75constraint 3 8 -2 40.3296 < 61

Page 53: Excel and Excel QM Examples

Thermlock Gaskets

x1 x2value 3.325326 14.67227

totalcost 5 7 119.3325

constraintsx1 x1^2 x1^3 x2 x2^2

value 3.325326 11.05779 36.77076 14.67227 215.2756 TotalConstraint 1 3 0.25 4 0.3 136.0122 > 125Constraint 2 13 1 80 > 80Constraint 3 0.7 1 17 > 17

Page 54: Excel and Excel QM Examples

0-1 integer Program

x1 x2 x3values 1 1 0

totalmaximize 50 45 48 95

Limitconstraint 19 27 34 46 < 80

22 13 12 35 < 401 1 1 2 < 2

Page 55: Excel and Excel QM Examples

Crashing General Foundry ProblemYA YB YC YD YE YF YG YH XST XA XB XC XD XE XF XG XH XFIN

Values 0 0 1 0 0 0 2 0 0 2 3 3 7 7 6 10 12 12Minimize cost 1000 2000 1000 1000 1000 500 2000 3000A crash max. 1B crash max. 1C crash max. 1D crash max. 1E crash max. 1F crash max. 1G crash max. 1H crash max. 1Due date 1Start 1A constraint 1 -1 1B constraint 1 -1 1C constraint 1 -1 1D constraint 1 -1 1E constraint 1 -1 1F constraint 1 -1 1G constraint 1 1 -1 1G constraint 2 1 -1 1H constraint 1 1 -1 1H constraint 2 1 -1 1Finish constraint -1 1

Page 56: Excel and Excel QM Examples

Totals5000

0 < 10 < 21 < 10 < 10 < 20 < 12 < 30 < 1

12 < 120 = 02 > 23 > 32 > 24 > 44 > 43 > 35 > 55 > 56 > 22 > 20 > 0

Page 57: Excel and Excel QM Examples

Arnold's Muffler Shop

Waiting Lines M/M/1 (Single Server Model)

Data Results2 0.666667

3 1.333333Average number of customers in the system(L) 2

0.666667Average time in the system(W) 1

0.333333

ProbabilitiesNumber Probability Cumulative Probability

0 0.333333 0.3333331 0.222222 0.5555562 0.148148 0.7037043 0.098765 0.8024694 0.065844 0.8683135 0.043896 0.9122096 0.029264 0.9414727 0.019509 0.9609828 0.013006 0.9739889 0.008671 0.982658

10 0.005781 0.98843911 0.003854 0.99229312 0.002569 0.99486213 0.001713 0.99657514 0.001142 0.99771615 0.000761 0.99847816 0.000507 0.99898517 0.000338 0.99932318 0.000226 0.99954919 0.000150 0.99969920 0.000100 0.999800

Arrival rate (l) Average server utilization(r)

Service rate (m) Average number of customers in the queue(Lq)

Average waiting time in the queue(Wq)

Probability (% of time) system is empty (P0)

The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.

Page 58: Excel and Excel QM Examples

The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.

Page 59: Excel and Excel QM Examples

Arnold's Muffler Shop

Waiting Lines M/M/s

Data Results2 0.33333

3 0.08333Number of servers(s) 2 Average number of customers in the system(L) 0.75

0.04167Average time in the system(W) 0.375

0.5ProbabilitiesNumber Probability Cumulative Probability

0 0.500000 0.5000001 0.333333 0.8333332 0.111111 0.9444443 0.037037 0.9814814 0.012346 0.9938275 0.004115 0.9979426 0.001372 0.9993147 0.000457 0.9997718 0.000152 0.9999249 0.000051 0.999975

10 0.000017 0.99999211 0.000006 0.99999712 0.000002 0.99999913 0.000001 1.00000014 0.000000 1.00000015 0.000000 1.00000016 0.000000 1.00000017 0.000000 1.00000018 0.000000 1.00000019 0.000000 1.00000020 0.000000 1.000000

Computationsn or s (lam/mu)^nCumsum(n-term2 P0(s)

0 11 0.666667 1 2 0.333332 0.222222 1.666667 0.333333333333333 0.53 0.049383 1.888889 0.0634920634920635 0.51224 0.00823 1.938272 0.00987654320987654 0.513315 0.001097 1.946502 0.00126622348844571 0.513416 0.000122 1.947599 0.000137174211248285 0.513427 1.16E-05 1.947721 1.28350139179682E-05 0.513428 9.68E-07 1.947733 1.05569378546059E-06 0.513429 7.17E-08 1.947734 7.74175442671098E-08 0.51342

10 4.78E-09 1.947734 5.12020795417393E-09 0.5134211 2.9E-10 1.947734 3.08313597240581E-10 0.5134212 1.61E-11 1.947734 1.70369367459144E-11 0.5134213 8.25E-13 1.947734 8.69753527569206E-13 0.5134214 3.93E-14 1.947734 4.12575391282828E-14 0.51342

Arrival rate (l) Average server utilization(r)

Service rate (m) Average number of customers in the queue(Lq)

Average waiting time in the queue(Wq)

Probability (% of time) system is empty (P0)

The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.

Page 60: Excel and Excel QM Examples

15 1.75E-15 1.947734 1.82757648408783E-15 0.5134216 7.28E-17 1.947734 7.59282983727312E-17 0.5134217 2.85E-18 1.947734 2.96998446015785E-18 0.5134218 1.06E-19 1.947734 1.09750556974159E-19 0.5134219 3.71E-21 1.947734 3.84311714656988E-21 0.5134220 1.24E-22 1.947734 1.27871411410371E-22 0.5134221 3.92E-24 1.947734 4.05275511573854E-24 0.5134222 1.19E-25 1.947734 1.22628006974231E-25 0.513422324252627282930

Page 61: Excel and Excel QM Examples

Rho(s) Lq(s) L(s) Wq(s) W(S)

0.666667 1.333333 2 0.666667 10.333333 0.083333 0.75 0.041667 0.3750.222222 0.009292 0.675958 0.004646 0.3379790.166667 0.001014 0.667681 0.000507 0.333840.133333 0.0001 0.666767 5E-05 0.3333830.111111 8.8E-06 0.666675 4.4E-06 0.3333380.095238 6.94E-07 0.666667 3.47E-07 0.3333340.083333 4.93E-08 0.666667 2.46E-08 0.3333330.074074 3.18E-09 0.666667 1.59E-09 0.3333330.066667 1.88E-10 0.666667 9.39E-11 0.3333330.060606 1.02E-11 0.666667 5.11E-12 0.3333330.055556 5.15E-13 0.666667 2.57E-13 0.3333330.051282 2.41E-14 0.666667 1.21E-14 0.3333330.047619 1.06E-15 0.666667 5.3E-16 0.333333

Page 62: Excel and Excel QM Examples

0.044444 4.36E-17 0.666667 2.18E-17 0.3333330.041667 1.69E-18 0.666667 8.47E-19 0.3333330.039216 6.22E-20 0.666667 3.11E-20 0.3333330.037037 2.17E-21 0.666667 1.08E-21 0.3333330.035088 7.17E-23 0.666667 3.59E-23 0.3333330.033333 2.26E-24 0.666667 1.13E-24 0.3333330.031746 6.82E-26 0.666667 3.41E-26 0.3333330.030303 1.97E-27 0.666667 9.84E-28 0.333333

Page 63: Excel and Excel QM Examples

Garcia-Golding Recycling

Waiting Lines M/D/1 (Constant Service Times)

Data Results8 0.666667

12 0.666667Average number of customers in the system(L) 1.333333

0.083333Average time in the system(W) 0.166667

0.333333

Waiting cost/hour $ 60.00 Waiting cost/trip $ 5.00

Arrival rate (l) Average server utilization(r)

Service rate (m) Average number of customers in the queue(Lq)

Average waiting time in the queue(Wq)

Probability (% of time) system is empty (P0)

The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.The arrival RATE and service RATE both must be rates and use the same time unit. Given a time such as 10 minutes, convert it to a rate such as 6 per hour.

Page 64: Excel and Excel QM Examples

Department of Commerce

Waiting Lines M/M/s with a finite population

Data Results

0.05 0.436048

0.5 0.203474Number of servers 1 Average number of customers in the system(L) 0.639522

Population size (N) 5 0.933264Average time in the system(W) 2.933264

0.563952Effective arrival rate 0.218024

Probabilities

Number, n Number waiting0 0.5639522 0.56395218 0 0.251 0.2819761 0.84592827 0 0.22 0.1127904 0.9587187 1 0.153 0.0338371 0.99255583 2 0.14 0.0067674 0.99932326 3 0.055 0.0006767 1 4 06789

10111213141516171819202122232425262728293031

Arrival rate (l) per customer Average server utilization(r)

Service rate (m) Average number of customers in the queue(Lq)

Average waiting time in the queue(Wq)

Probability (% of time) system is empty (P0)

Probability, P(n)

Cumulative Probability

Arrival rate(n)

The arrival rate is for each member of the population. If they go for service every 20 minutes then enter 3 (per hour).The arrival rate is for each member of the population. If they go for service every 20 minutes then enter 3 (per hour).

Page 65: Excel and Excel QM Examples

1.7732

Term 1 Term 2 P0(s)1 1 1 1 0.7732

0.5 1.5 0.5 1.5 0.2732 0.5639520.2 1.7 0.0732

0.06 1.76 0.01320.012 1.772 0.0012

0.0012 1.7732 0

Sum term 1

Sum term 2

Decum term 2

Page 66: Excel and Excel QM Examples

Harry's Tire Shop NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.

Probability Day0.05 0 0.05 0 1 0.10838 1

0.1 0.05 0.15 1 2 0.772863 40.2 0.15 0.35 2 3 0.789351 40.3 0.35 0.65 3 4 0.511857 30.2 0.65 0.85 4 5 0.801233 4

0.15 0.85 1 5 6 0.440355 37 0.550327 38 0.482025 39 0.234368 2

10 0.553767 3Average 3

Results (Frequency table)

Frequency Percentage Cum %0 0 0% 0%1 1 10% 10%2 1 10% 20%3 5 50% 70%4 3 30% 100%5 0 0% 100%

10

Probability Range (Lower)

Cumulative Probability

Tires Demand

Random Number

Simulated Demand

Tires Demanded

Page 67: Excel and Excel QM Examples

NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.

Page 68: Excel and Excel QM Examples

Generating Normal Random Numbers NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.

Random number Value Frenquency Percentage42.004161470523 26 1 0.5%

43.6166685678545 28 1 0.5%38.7555889874489 30 2 1.0%38.3596562306756 32 9 4.5%43.8513434558299 34 8 4.0%40.0546082617959 36 17 8.5%43.9940540943639 38 30 15.0%44.1155535603764 40 23 11.5%

47.990037026023 42 33 16.5%41.8364019067895 44 33 16.5%42.8410869545674 46 20 10.0%

34.006271096758 48 14 7.0%34.3852830371233 50 6 3.0%41.6224675703723 52 1 0.5%31.8622824985997 54 1 0.5%40.3382927536189 56 1 0.5%30.2193672153786 20026.465334415001641.808734578983530.265442570838240.804846701123137.999055593424341.358267286592437.116091534605435.434134714665148.283060269080440.203903445265538.664155025632844.397685216767635.308728879807229.070631798822936.286111602969737.243544593034637.562927958252934.9165441655781

41.18619304269928.310910771256738.910010916005545.940723682098741.110397716888736.176588592564745.934401427165943.717576760895535.432230367246544.974181635920543.199697609328845.5275067052154

Page 69: Excel and Excel QM Examples

41.408491477600244.3662743531233

36.02322624758747.212550462624342.464352182405446.264620415068130.044147818988545.369973575021940.669920342684841.383884279480340.972574522119936.837370074111544.410871602102943.979717681767641.990833842569941.5215961681065

42.55615727086746.8431116901892

41.06275717981748.471575117394749.598847364713440.054042335559944.992147701226238.646925255444943.6729957222102

44.58074494521239.633870192587941.855104681661547.311234640207747.112170187109349.835897462610244.244732582880334.642690943546941.230213549822455.246210248142843.960754055194833.732463491333847.556683111060146.936067446602137.281123799481736.994771248093442.428498786547540.954137965934536.765403785476438.497741303655537.6730719930521

42.40418182633337.494997189711

45.013359586506933.845314662985934.0487957542751

Page 70: Excel and Excel QM Examples

33.213823528338543.565413493533242.326370309882145.849340708986939.966900791565740.571589441231444.868864986866439.368050890671239.635947194542341.700086946960835.070755208686140.732971157215836.991944533487538.203800723510946.423799996618140.406013605907336.438189544677342.868368488436240.349122796370441.469027540275736.793416338441833.253636913723743.759752975002331.881570918679539.4090447709118

41.14328684694230.605980546009737.139591393213133.111851030910652.337172630013138.606912994573943.314396685398444.182038857990342.308578213857334.610313958986238.0769363791143

38.269446815439.008940083526731.2720895571357

38.26177426039950.393974070303241.956735775536535.468603073700434.876958429944242.710879995732436.728963147377142.721128849429940.386098085442330.5984117407848

34.72716638625343.1345921665582

Page 71: Excel and Excel QM Examples

44.14343304305640.836376608100434.610027661771848.546791822209636.6970326447897

36.64528462453237.507396669988641.213141963621736.608608480028247.047557312707337.604385393228133.493180445356139.066632787827546.733284587672532.820197857299536.833511063167348.297832092896739.169004552686625.991569557983844.591393690508742.669414788753241.029582943202442.487242089524143.334836598563942.025427156394246.118354844953438.564453759901742.450473868508643.095077207643243.8227898943934

34.92564362795238.3392035237526

43.89607786061137.7865260886732

32.53242669826536.859892562065244.756362717974436.899199000133430.623374703545943.157334301782737.272764237847939.218548061574635.369171445027934.944602565573547.421284561057637.565292498364938.716992207726845.9013806306768

46.15168878607143.257696396960437.8650315760613

Page 72: Excel and Excel QM Examples

NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.

Page 73: Excel and Excel QM Examples

Port of New Orleans Barge Unloadings NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.

Day Arrivals Unloaded1 0 0.06407 0 0 0.196947 2 02 0 0.33732 2 2 0.120067 2 23 0 0.797373 4 4 0.390929 3 34 1 0.469619 3 4 0.231749 3 35 1 0.553819 3 4 0.525827 3 36 1 0.025666 0 1 0.575644 3 17 0 0.048624 0 0 0.144905 2 08 0 0.542187 3 3 0.976314 5 39 0 0.989611 5 5 0.838528 4 4

10 1 0.302016 2 3 0.609019 3 3

Barge Arrivals Unloading ratesDemand Probability Lower CumulativeDemand Number Probability Lower

0 0.13 0 0.13 0 1 0.05 01 0.17 0.13 0.3 1 2 0.15 0.052 0.15 0.3 0.45 2 3 0.5 0.23 0.25 0.45 0.7 3 4 0.2 0.74 0.2 0.7 0.9 4 5 0.1 0.95 0.1 0.9 1 5

Previously delayed

Random number

Total to be unoaded

Random Number

Possibly unloaded

Page 74: Excel and Excel QM Examples

NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.

CumulativeUnloading0.05 1

0.2 20.7 30.9 4

1 5

Page 75: Excel and Excel QM Examples

Three Hills Power NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.

Repair time1 0.4974439 2 2 2 0.04409677 1 32 0.03654599 0.5 2.5 3 0.85222897 3 63 0.70350733 2.5 5 6 0.50631825 2 84 0.35945809 2 7 8 0.48779373 2 105 0.4712546 2 9 10 0.09273529 1 116 0.93263795 3 12 12 0.88321115 3 157 0.76994122 2.5 14.5 15 0.031501 1 168 0.34418468 2 16.5 16.5 0.88179192 3 19.59 0.07834147 1 17.5 19.5 0.19933693 1 20.5

10 0.55444484 2 19.5 20.5 0.98681188 3 23.5

Demand Table Repair timesTime betweeProbability Lower Cumulative Demand Time Probability

0.5 0.05 0 0.05 0.5 1 0.281 0.06 0.05 0.11 1 2 0.52

1.5 0.16 0.11 0.27 1.5 3 0.22 0.33 0.27 0.6 2

2.5 0.21 0.6 0.81 2.53 0.19 0.81 1 3

Breakdown number

Random number

Time between breakdowns

Time of breakdowns

Time repairperson is free

Random Number

Repair ends

Page 76: Excel and Excel QM Examples

NOTE: The random numbers appearing here may not be the same as the ones in the book, but the formulas are the same.

Lower CumulativeLead time0 0.28 1

0.28 0.8 20.8 1 3

Page 77: Excel and Excel QM Examples

Three Grocery Example

State ProbabilitiesAmerican Food SFood Mart Atlas Foods

Time #1 #2 #3 Matrix of Transition Probabilities0 0.4 0.3 0.3 0.8 0.1 0.11 0.41 0.31 0.28 0.1 0.7 0.22 0.415 0.314 0.271 0.2 0.2 0.63 0.4176 0.3155 0.26694 0.41901 0.31599 0.2655 0.419807 0.316094 0.2640996 0.4202748 0.3160663 0.2636589

Page 78: Excel and Excel QM Examples

Accounts Receivable Example

1 0 0 0P= I : 0 = 0 1 0 0

A : B 0.6 0 0.2 0.20.4 0.1 0.3 0.2

I - B = 0.8 -0.2-0.3 0.8

F = (I - B) inverse 1.37931 0.3448280.517241 1.37931

FA = 0.965517 0.0344830.862069 0.137931

Page 79: Excel and Excel QM Examples

ARCO Quality Control

Number of samples 20Sample size 100

Data Results# Defects % Defects Total Sample Size 2000

Sample 1 6 0.06 Total Defects 80Sample 2 5 0.05 Percentage defects 0.04Sample 3 0 0 Std dev of p-bar 0.019596Sample 4 1 0.01Sample 5 4 0.04 Upper Control Limit 0.098788Sample 6 2 0.02 Center Line 0.04Sample 7 5 0.05 Lower Control Limit 0Sample 8 3 0.03Sample 9 3 0.03Sample 10 2 0.02Sample 11 6 0.06Sample 12 1 0.01Sample 13 8 0.08Sample 14 7 0.07Sample 15 5 0.05Sample 16 4 0.04Sample 17 11 0.11 Above UCLSample 18 3 0.03Sample 19 0 0Sample 20 4 0.04

Graph informationSample 1 0.06 0 0Sample 2 0.05 0 0Sample 3 0 0 0Sample 4 0.01 0 0Sample 5 0.04 0 0Sample 6 0.02 0 0Sample 7 0.05 0 0Sample 8 0.03 0 0Sample 9 0.03 0 0Sample 10 0.02 0 0Sample 11 0.06 0 0Sample 12 0.01 0 0Sample 13 0.08 0 0Sample 14 0.07 0 0Sample 15 0.05 0 0Sample 16 0.04 0 0Sample 17 0.11 0 0Sample 18 0.03 0 0Sample 19 0 0 0Sample 20 0.04 0 0

Enter the sample size then enter the number of defects in each sample.Enter the sample size then enter the number of defects in each sample.

Page 80: Excel and Excel QM Examples

AHP n= 3

Hardware Sys.1 Sys.2 Sys.3 Sys.1 Sys.2 Sys.3 Priority Wt. sum vector Consistency vector

Sys.1 1 3 9 Sys.1 0.6923 0.7200 0.5625 0.6583 2.0423 3.1025

Sys.2 0.3333 1 6 Sys.2 0.2308 0.2400 0.3750 0.2819 0.8602 3.0512

Sys.3 0.1111 0.1667 1 Sys.3 0.0769 0.0400 0.0625 0.0598 0.1799 3.0086

Column Total 1.4444 4.1667 16

Software Sys.1 Sys.2 Sys.3 Sys.1 Sys.2 Sys.3 Priority Wt. sum vector

Sys.1 1 0.5 0.125 Sys.1 0.0909 0.0769 0.0943 0.0874 0.2623 3.0014

Sys.2 2 1 0.2 Sys.2 0.1818 0.1538 0.1509 0.1622 0.4871 3.0028

Sys.3 8 5 1 Sys.3 0.7273 0.7692 0.7547 0.7504 2.2605 3.0124

Column Total 11 6.5 1.325

Vendor Sys.1 Sys.2 Sys.3 Sys.1 Sys.2 Sys.3 Priority Wt. sum vector

Sys.1 1 1 6 Sys.1 0.4615 0.4286 0.6000 0.4967 1.5330 3.0863

Sys.2 1 1 3 Sys.2 0.4615 0.4286 0.3000 0.3967 1.2132 3.0582

Sys.3 0.1667 0.3333 1 Sys.3 0.0769 0.1429 0.1000 0.1066 0.3216 3.0172

Column Total 2.1667 2.3333 10

Factor Hard. Soft. Vendor Hardware Software Vendor Priority Wt. sum vector

Hardware 1 0.125 0.3333 Hardware 0.0833 0.0857 0.0769 0.0820 0.2460 3.0004

Software 8 1 3 Software 0.6667 0.6857 0.6923 0.6816 2.0468 3.0031

Vendor 3 0.3333 1 Vendor 0.2500 0.2286 0.2308 0.2364 0.7096 3.0011

Column Total 12 1.4583 4.3333

n RI Hardware Software Vendor Priority

2 0.00 Sys.1 0.658 0.087 0.497 0.231

3 0.58 Sys.2 0.282 0.162 0.397 0.227

4 0.90 Sys.3 0.060 0.750 0.107 0.542

5 1.12

6 1.24

7 1.32

8 1.41

Page 81: Excel and Excel QM Examples

Consistency vector

Lambd 3.0541

CI 0.0270

CR 0.0466

Lambd 3.0055430750418

CI 0.0028

CR 0.0048

Lambd 3.0539

CI 0.0269

CR 0.0464

Lambd 3.0015

CI 0.0008

CR 0.0013

Page 82: Excel and Excel QM Examples

Matrix Multiplication

A= 1 2 3 B= 2 11 2 0 1 1

3 2

AxB = 13 94 3

Matrix Inverse

A= 2 1 A-inverse= 1.5 -0.54 3 -2 1

Matrix Determinant

A= 3 4 det(A)= -104 2