Pricing with Dependent Demands Covington Motors is a car dealership that specializes in the sales of sport utility vehicles and station wagons. Due to its reputation for quality and service, Covington has a strong position in the regional market, but demand is somewhat sensitive to price. After examining the new models, Covington’s marketing consultant has come up with the following demand curves.
The dealership’s unit costs are $17,000 for SUVs and $14,000 for wagons. Each SUV requires 2 hours of prep labor, and each wagon requires 3 hours of prep labor. The current staff can supply 320 hours of labor.
- Determine the profit-maximizing prices for SUVs and Wagons. (Round off any fractional demands.)
- What demand levels will result from the prices in (a)?
- What is the marginal value of dealer prep labor?
Locating a Warehouse
Decisions X
Location Results Y
0 Sum 0
764.6948 Data
Site X
1
2
3
4
5
6
7
8
9
10 Summary Y
9
5
26
39
41
38
63
52
81
95 V
29
50
68
79
54
59
6
58
76
93 X-dist
12
15
20
12
8
16
18
20
12
24 Y-dist
9
5
26
39
41
38
63
52
81
95 x y Weighted
Distance Distance
29 30.36445 364.3734
50 50.24938 753.7407
68 72.8011 1456.022
79 88.10221 1057.227
54 67.80118 542.4094
59 70.17834 1122.854
6 63.28507 1139.131
58 77.89737 1557.947
76 111.072 1332.865
93 132.9436 3190.646
764.6948 12517.22 Price-Dependent Demand
Decisions SUV
Price Parameters Wagon
0 0 400
0.014 425
0.018 Objective
Cost
Sales
contribution Constraints
Prep Time 0
0
0 0
0 Total
0 0 <= 320 Locating a Warehouse
Decisions X
Location Results Y
59.16 41.16
Sum Data
Site X
1
2
3
4
5
6
7
8
9
10 Summary Y
9
5
26
39
41
38
63
52
81
95 V
29
50
68
79
54
59
6
58
76
93 Weighted
X-dist
Y-dist
Distance Distance
12
-32.16
-30.16 44.08958 529.075
15
-36.16
-9.16 37.30216 559.5324
20
-15.16
8.84 17.54911 350.9822
12
-2.16
19.84 19.95723 239.4868
8
-0.16
-5.16 5.16248 41.29984
16
-3.16
-0.16 3.164048 50.62477
18
21.84
-53.16 57.47148 1034.487
20
10.84
-1.16 10.90189 218.0378
12
39.84
16.84 43.25288 519.0345
24
53.84
33.84 63.5916 1526.198
x
y
0
0
Decisions X
Location Results Y
0 Sum 0
764.6948 Data
Site X
1
2
3
4
5
6
7
8
9
10 Summary Y
9
5
26
39
41
38
63
52
81
95 V
29
50
68
79
54
59
6
58
76
93 X-dist
12
15
20
12
8
16
18
20
12
24 Y-dist
9
5
26
39
41
38
63
52
81
95 x y Weighted
Distance Distance
29 30.36445 364.3734
50 50.24938 753.7407
68 72.8011 1456.022
79 88.10221 1057.227
54 67.80118 542.4094
59 70.17834 1122.854
6 63.28507 1139.131
58 77.89737 1557.947
76 111.072 1332.865
93 132.9436 3190.646
764.6948 12517.22 Price-Dependent Demand
Decisions SUV
Price Parameters Wagon
0 0 400
0.014 425
0.018 Objective
Cost
Sales
contribution Constraints
Prep Time 0
0
0 0
0 Total
0 0 <= 320 Locating a Warehouse
Decisions X
Location Results Y
59.16 41.16
Sum Data
Site X
1
2
3
4
5
6
7
8
9
10 Summary Y
9
5
26
39
41
38
63
52
81
95 V
29
50
68
79
54
59
6
58
76
93 Weighted
X-dist
Y-dist
Distance Distance
12
-32.16
-30.16 44.08958 529.075
15
-36.16
-9.16 37.30216 559.5324
20
-15.16
8.84 17.54911 350.9822
12
-2.16
19.84 19.95723 239.4868
8
-0.16
-5.16 5.16248 41.29984
16
-3.16
-0.16 3.164048 50.62477
18
21.84
-53.16 57.47148 1034.487
20
10.84
-1.16 10.90189 218.0378
12
39.84
16.84 43.25288 519.0345
24
53.84
33.84 63.5916 1526.198
x
y
0
0
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