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Tully Tyres sells cheap imported tyres. The manager believes its profits are in

ID: 3069570 • Letter: T

Question

Tully Tyres sells cheap imported tyres. The manager believes its profits are in decline. You have just been hired as an analyst by the manager of Tully Tyres to investigate the expected profit over the next 12 months based on current data.

•Monthly demand varies from 100 to 200 tyres – probabilities shown in the partial section of the spreadsheet below, but you have to insert formulas to ge the cumulative probability distribution which can be used in Excel with the VLOOKUP command.
•The average selling price per tyre follows a discrete uniform distribution ranging from $160 to $180 each. This means that it can take on equally likely integer values between $160 and $180 – more on this below.
•The average profit margin per tyre after covering variable costs follows a continuous uniform distribution between 20% and 30% of the selling price.
•Fixed costs per month are $2000.

(a)Using Excel set up a model to simulate the next 12 months to determine the expected average monthly profit for the year. You need to have loaded the Analysis Toolpak Add-In to your version of Excel. You must keep the data separate from the model. The model should show only formulas, no numbers whatsoever except for the month number.

The first random number (RN 1) is to simulate monthly demands for tyres.
•The average selling price follows a discrete uniform distribution and can be determined by the function =RANDBETWEEN(160,180) in this case. But of course you will not enter (160,180) but the data cell references where they are recorded.
•The second random number (RN 2) is used to help simulate the profit margin.
•The average profit margin follows a continuous uniform distribution ranging between 20% and 30% and can be determined by the formula =0.2+(0.3-0.2)*the second random number (RN 2). Again you do not enter 0.2 and 0.3 but the data cell references where they are located. Note that if the random number is high, say 1, then 0.3-0.2 becomes 1 and when added to 0.2 it becomes 0.3. If the random number is low, say 0, then 0.3-0.2 becomes zero and the profit margin becomes 0.2.
•Add the 12 monthly profit figures and then find the average monthly profit.

Show the data and the model in two printouts: (1) the results, and (2) the formulas. Both printouts must show the grid (ie., row and column numbers) and be copied from Excel and pasted into Word. See Spreadsheet Advice in Interact Resources for guidance.

(b)Provide the average monthly profit to Ajax Tyres over the 12-month period.

(c)You present your findings to the manager of Ajax Tyres. He thinks that with market forces he can increase the average selling price by $40 (ie from $200 to $220) without losing sales. However he does suggest that the profit margin would then increase from 22% to 32%.

He has suggested that you examine the effect of these changes and report the results to him. Change the data accordingly in your model to make the changes and paste the output in your Word answer then write a report to the manager explaining your conclusions with respect to his suggestions. Also mention any reservations you might have about the change in selling prices.

The report must be dated, addressed to the Manager and signed off by you.
(Word limit: No more than 150 words)

Tully Tyres Data Probability Cumulative Prob Demand Selling price $160 $180 0.05 100 Monthly fixed cost $2000 0.10 120 Profit margin 20% 30% 0.20 140 0.30 160 0.25 180 0.10 200 1 Model Month RN1 Demand Selling price RN2 Profit margin Fixed cost Profit 1 0.23297 #N/A $180 0.227625 0.2

Explanation / Answer

a)Model sheet and Formula Sheet

Model

Month

RN1

Demand

Selling price

RN2

Profit margin

Fixed cost

Profit

Cost price

profit per piece

1

0.94557728

200

171

0.45306216

0.24530622

2000

6736.87521

137.3156

33.68438

2

0.86504377

180

163

0.86540213

0.28654021

2000

6534.64988

126.6964

36.30361

3

0.75401736

180

167

0.31171716

0.23117172

2000

5644.23443

135.6431

31.35686

4

0.84764454

180

161

0.04897888

0.20489789

2000

4928.1693

133.6213

27.37872

5

0.01798387

100

179

0.74707744

0.27470774

2000

3857.56551

140.4243

38.57566

6

0.11064554

120

160

0.55468882

0.25546888

2000

3906.90889

127.4424

32.55757

7

0.65084945

180

176

0.09396328

0.20939633

2000

5485.11312

145.5271

30.47285

8

0.82800777

180

162

0.24019748

0.22401975

2000

5336.85494

132.3508

29.64919

9

0.51640536

160

167

0.38647226

0.23864723

2000

5148.07908

134.8245

32.17549

10

0.08969567

120

175

0.27855721

0.22785572

2000

3897.01335

142.5249

32.47511

11

0.9398299

200

165

0.94256129

0.29425613

2000

7502.72843

127.4864

37.51364

12

0.13051547

120

169

0.50122161

0.25012216

2000

4057.5854

135.1868

33.81321

    Average profit =

5252.98146

Formula

Month

RN1

Demand

Selling price

RN2

Profit margin

Fixed cost

Profit

Cost price

profit per piece

1

=RAND()

=VLOOKUP(B14,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E14

2000

=J14*C14

=D14*100/(100+(F14*100))

=D14-I14

2

=RAND()

=VLOOKUP(B15,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E15

2000

=J15*C15

=D15*100/(100+(F15*100))

=D15-I15

3

=RAND()

=VLOOKUP(B16,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E16

2000

=J16*C16

=D16*100/(100+(F16*100))

=D16-I16

4

=RAND()

=VLOOKUP(B17,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E17

2000

=J17*C17

=D17*100/(100+(F17*100))

=D17-I17

5

=RAND()

=VLOOKUP(B18,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E18

2000

=J18*C18

=D18*100/(100+(F18*100))

=D18-I18

6

=RAND()

=VLOOKUP(B19,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E19

2000

=J19*C19

=D19*100/(100+(F19*100))

=D19-I19

7

=RAND()

=VLOOKUP(B20,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E20

2000

=J20*C20

=D20*100/(100+(F20*100))

=D20-I20

8

=RAND()

=VLOOKUP(B21,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E21

2000

=J21*C21

=D21*100/(100+(F21*100))

=D21-I21

9

=RAND()

=VLOOKUP(B22,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E22

2000

=J22*C22

=D22*100/(100+(F22*100))

=D22-I22

10

=RAND()

=VLOOKUP(B23,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E23

2000

=J23*C23

=D23*100/(100+(F23*100))

=D23-I23

11

=RAND()

=VLOOKUP(B24,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E24

2000

=J24*C24

=D24*100/(100+(F24*100))

=D24-I24

12

=RAND()

=VLOOKUP(B25,$L$4:$M$9,2,TRUE)

=RANDBETWEEN(160,180)

=RAND()

=0.2+(0.3-0.2)*E25

2000

=J25*C25

=D25*100/(100+(F25*100))

=D25-I25

    Average profit =

=AVERAGE(H14:H25)

b)Average monthly profit over 12-month period is 5252.9816

Model

Month

RN1

Demand

Selling price

RN2

Profit margin

Fixed cost

Profit

Cost price

profit per piece

1

0.94557728

200

171

0.45306216

0.24530622

2000

6736.87521

137.3156

33.68438

2

0.86504377

180

163

0.86540213

0.28654021

2000

6534.64988

126.6964

36.30361

3

0.75401736

180

167

0.31171716

0.23117172

2000

5644.23443

135.6431

31.35686

4

0.84764454

180

161

0.04897888

0.20489789

2000

4928.1693

133.6213

27.37872

5

0.01798387

100

179

0.74707744

0.27470774

2000

3857.56551

140.4243

38.57566

6

0.11064554

120

160

0.55468882

0.25546888

2000

3906.90889

127.4424

32.55757

7

0.65084945

180

176

0.09396328

0.20939633

2000

5485.11312

145.5271

30.47285

8

0.82800777

180

162

0.24019748

0.22401975

2000

5336.85494

132.3508

29.64919

9

0.51640536

160

167

0.38647226

0.23864723

2000

5148.07908

134.8245

32.17549

10

0.08969567

120

175

0.27855721

0.22785572

2000

3897.01335

142.5249

32.47511

11

0.9398299

200

165

0.94256129

0.29425613

2000

7502.72843

127.4864

37.51364

12

0.13051547

120

169

0.50122161

0.25012216

2000

4057.5854

135.1868

33.81321

    Average profit =

5252.98146

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