Can you complete the outline and site credible sources in APA format

There are two organizational patterns for solving a problem. The first uses an
inductiveapproach where you state your proposed solution upfront and then support it in the paper’s body. The other option uses a
deductiveapproach, where you explain the problem, share possible solutions, before stating the final recommendation for how to solve the problem.

Regardless of which style you use, you will want to do the following:

Identify a problem and demonstrates it exists
Appeal to your audience
Propose one or more solutions to the problem
Clearly state your claim of which solution is the best to solve the problem
Persuade your audience that your solution or solutions are feasible and worthwhile

Inductive Approach

I. Introduction
a. Provide a snapshot of what the problem is and that it exists
b. Your thesis should be your claim about the problem. Your claim is how the problem should be solved.
II. Explanation of problem.
a. You briefly explained the problem in the Introduction, but this where you full expand on the problem.
b. This may be several paragraphs.
III. Proposed Solution
a. State your proposed solution to address the problem.
b. Remember, your claim and thesis stated how the problem should be solved in the introduction, but there is where explain and expand upon the solution.
c. This may be several paragraphs.
IV. Support for proposed solution
a. Provide specific evidence from sources to show how this solution will solve the problem.
b. This may be several paragraphs.
Additionally, you can incorporate the support for the proposed solution as you explain the solution.

V. Conclusion
a. Summarize the problem and solution.
b. End with a call to action for your readers.

Deductive Approach

You would only use this approach if you want to present several solutions to the problem before you make your final recommendation of which solution is best.

I. Introduction
a. Provide a snapshot of what the problem is and that it exists
b. Your thesis for this paper would instead briefly touch on the many proposed solutions. (Ex. To reduce vandalism in the apartment buildings, several solutions could be explored, such as installing security cameras, building a gate with passcodes, and hiring a security guard.
II. Solution One
a. Evaluate the pros and cons for Solution 1
b. Use source material to show the pros and cons for Solution 1
III. Solution Two
a. Evaluate the pros and cons for Solution 2
b. Use source material to show the pros and cons for Solution 2
IV. Solution Three (
if needed)

a. Evaluate the pros and cons for Solution 3
b. Use source material to show the pros and cons for Solution 3
V. Conclusion
a. Provide your final recommendation of which solution should be used and why
b. End with a call to action for your reader

SHOW MORE…

MUST HAVE SAS PROGRAMING

ASSIGNMENT QUESTIONS
1. What are the relevant factors to be considered for modelling a demand function for Maa mustard oil? How is each factor related to elasticities of demand? How does the estimation of demand function incorporate the impact of each factor using the multiple regression technique?
2. Analyze the estimated demand function and calculate the elasticities of demand for Hind Oil Industries’ product. What do these calculations suggest about the effects of changes in each of these variables?
3. What would be the impact of a price change by HOI on the total revenue of Maa mustard oil, keeping other variables constant?
4. What is the optimum price at which total revenue can be maximized for Maa mustard oil if the competitors’ prices do not increase in October 2015 (scenario l)? If competitors increase their prices by around 6 per cent, as suggested in the case (scenario 2), what would be the optimum price? Does the company benefit if competitors increase their prices? Perform all calculations under the assumption of no increase in promotional expenditure in the next month and a I-per-cent increase in the per capita income of consumers.
5. Plot a demand curve and a total revenue curve using the estimated values of quantity demanded from question 4. Analyze the findings of question 4 by using the concept of the total revenue test and the plotted graphs.

ASSIGNMENT QUESTIONS

1. What are the relevant factors to be considered for modelling a demand function for Maa mustard oil? How is each factor related to elasticities of demand? How does the estimation of demand function incorporate the impact of each factor using the multiple regression technique?
2. Analyze the estimated demand function and calculate the elasticities of demand for Hind Oil Industries’ product. What do these calculations suggest about the effects of changes in each of these variables?
3. What would be the impact of a price change by HOI on the total revenue of Maa mustard oil, keeping other variables constant?

4. What is the optimum price at which total revenue can be maximized for Maa mustard oil if the competitors’ prices do not increase in October 2015 (scenario l)? If competitors increase their prices by around 6 per cent, as suggested in the case (scenario 2), what would be the optimum price? Does the company benefit if competitors increase their prices? Perform all calculations under the assumption of no increase in promotional expenditure in the next month and a I-per-cent increase in the per capita income of consumers.
5. Plot a demand curve and a total revenue curve using the estimated values of quantity demanded from question 4. Analyze the findings of question 4 by using the concept of the total revenue test and the plotted graphs.

image1.jpg data sheet

Date
demand_Maa
own_price
compe_price
inc_per_capita
pro_exp
dummy 1
dummy 2
dummy 3
dummy 4
dummy 5
dummy 6
dummy 7
dummy 8
dummy 9
dummy 10
dummy 11

Apr-11
12,890.00
65.00
69.22
4,343.17
1170.00
1
0
0
0
0
0
0
0
0
0
0

May-11
12,850.00
65.90
70.67
4,347.59
1162.26
0
1
0
0
0
0
0
0
0
0
0

Jun-11
13,180.00
65.40
74.77
4,356.44
1164.06
0
0
1
0
0
0
0
0
0
0
0

Jul-11
13,785.00
65.34
75.00
4,369.71
1193.13
0
0
0
1
0
0
0
0
0
0
0

Aug-11
13,880.00
66.00
75.00
4,387.40
1241.55
0
0
0
0
1
0
0
0
0
0
0

Sep-11
12,680.00
69.00
77.29
4,409.51
1254.15
0
0
0
0
0
1
0
0
0
0
0

Oct-11
13,290.00
69.00
79.06
4,436.05
1149.57
0
0
0
0
0
0
1
0
0
0
0

Nov-11
13,498.00
69.25
81.37
4,467.01
1200.06
0
0
0
0
0
0
0
1
0
0
0

Dec-11
11,980.00
74.56
84.00
4,502.40
1220.40
0
0
0
0
0
0
0
0
1
0
0

Jan-12
12,085.00
78.00
91.61
4,542.20
1082.70
0
0
0
0
0
0
0
0
0
1
0

Feb-12
10,980.00
84.00
91.11
4,586.43
1092.06
0
0
0
0
0
0
0
0
0
0
1

Mar-12
10,820.00
87.00
95.79
4,635.08
989.37
0
0
0
0
0
0
0
0
0
0
0

Apr-12
10,768.00
90.00
99.13
4,688.16
978.03
1
0
0
0
0
0
0
0
0
0
0

May-12
11,125.00
90.00
99.70
4,743.18
972.09
0
1
0
0
0
0
0
0
0
0
0

Jun-12
11,985.00
88.00
98.44
4,800.15
1004.94
0
0
1
0
0
0
0
0
0
0
0

Jul-12
12,248.00
89.00
104.62
4,859.07
1081.71
0
0
0
1
0
0
0
0
0
0
0

Aug-12
13,985.00
89.00
105.42
4,919.94
1110.15
0
0
0
0
1
0
0
0
0
0
0

Sep-12
13,580.00
92.00
104.45
4,982.76
1262.25
0
0
0
0
0
1
0
0
0
0
0

Oct-12
13,895.00
91.00
104.94
5,047.52
1229.22
0
0
0
0
0
0
1
0
0
0
0

Nov-12
11,270.00
94.00
105.95
5,114.23
1257.75
0
0
0
0
0
0
0
1
0
0
0

Dec-12
10,485.00
95.00
106.00
5,182.89
1021.50
0
0
0
0
0
0
0
0
1
0
0

Jan-13
11,545.00
91.00
106.00
5,253.50
943.92
0
0
0
0
0
0
0
0
0
1
0

Feb-13
12,465.00
86.00
105.58
5,326.05
1043.46
0
0
0
0
0
0
0
0
0
0
1

Mar-13
12,025.00
86.00
102.84
5,400.55
1125.90
0
0
0
0
0
0
0
0
0
0
0

Apr-13
11,985.00
85.00
96.84
5,477.00
1090.08
1
0
0
0
0
0
0
0
0
0
0

May-13
12,245.00
80.00
94.26
5,550.97
1087.20
0
1
0
0
0
0
0
0
0
0
0

Jun-13
11,976.00
80.01
94.00
5,622.45
1105.92
0
0
1
0
0
0
0
0
0
0
0

Jul-13
12,095.00
79.64
95.00
5,691.44
1084.50
0
0
0
1
0
0
0
0
0
0
0

Aug-13
11,958.00
78.00
92.72
5,757.95
1096.92
0
0
0
0
1
0
0
0
0
0
0

Sep-13
11,874.00
78.09
95.00
5,821.98
1080.63
0
0
0
0
0
1
0
0
0
0
0

Oct-13
11,850.00
78.14
95.25
5,883.52
1069.38
0
0
0
0
0
0
1
0
0
0
0

Nov-13
11,535.00
81.54
96.53
5,942.57
1069.47
0
0
0
0
0
0
0
1
0
0
0

Dec-13
10,258.00
81.27
98.00
5,999.14
1039.14
0
0
0
0
0
0
0
0
1
0
0

Jan-14
11,680.00
80.01
97.89
6,053.22
932.04
0
0
0
0
0
0
0
0
0
1
0

Feb-14
10,768.00
80.00
93.47
6,104.82
1052.37
0
0
0
0
0
0
0
0
0
0
1

Mar-14
10,457.00
80.81
95.30
6,153.93
970.11
0
0
0
0
0
0
0
0
0
0
0

Apr-14
11,527.00
78.98
93.76
6,200.56
943.47
1
0
0
0
0
0
0
0
0
0
0

May-14
11,615.00
78.76
92.06
6,252.15
1043.37
0
1
0
0
0
0
0
0
0
0
0

Jun-14
11,675.00
78.23
92.00
6,308.70
1050.03
0
0
1
0
0
0
0
0
0
0
0

Jul-14
11,600.00
78.25
92.00
6,370.21
1056.51
0
0
0
1
0
0
0
0
0
0
0

Aug-14
10,980.00
77.88
92.00
6,436.68
1048.14
0
0
0
0
1
0
0
0
0
0
0

Sep-14
11,099.00
77.34
94.29
6,508.11
993.69
0
0
0
0
0
1
0
0
0
0
0

Oct-14
10,657.00
78.59
95.29
6,584.51
1004.22
0
0
0
0
0
0
1
0
0
0
0

Nov-14
9,645.00
83.53
98.11
6,665.86
961.38
0
0
0
0
0
0
0
1
0
0
0

Dec-14
9,026.00
87.29
100.00
6,752.18
869.13
0
0
0
0
0
0
0
0
1
0
0

Jan-15
8,875.00
90.72
101.06
6,843.45
814.68
0
0
0
0
0
0
0
0
0
1
0

Feb-15
9,585.00
88.03
101.10
6,939.69
801.99
0
0
0
0
0
0
0
0
0
0
1

Mar-15
10,256.00
85.63
99.40
7,040.89
868.68
0
0
0
0
0
0
0
0
0
0
0

Apr-15
10,985.00
84.25
99.22
7,147.05
925.11
1
0
0
0
0
0
0
0
0
0
0

May-15
10,542.00
87.90
101.58
7,244.36
991.26
0
1
0
0
0
0
0
0
0
0
0

Jun-15
12,465.00
90.01
107.95
7,332.83
956.79
0
0
1
0
0
0
0
0
0
0
0

Jul-15
13,275.00
90.00
108.52
7,412.45
1123.20
0
0
0
1
0
0
0
0
0
0
0

Aug-15
13,786.00
88.89
107.76
7,483.22
1202.13
0
0
0
0
1
0
0
0
0
0
0

Sep-15
13,256.00
91.38
109.14
7,545.15
1247.31
0
0
0
0
0
1
0
0
0
0
0

Regression results

SUMMARY OUTPUT

Regression Statistics

Multiple R
0.91

R Square
0.83

Adjusted R Square
0.76

Standard Error
614.03

Observations
54.00

ANOVA

df
SS
MS
F
Significance F

Regression
15
69873730.3540417
4658248.69026944
12.36
0.00

Residual
38
14327173.5718842
377030.883470636

Total
53
84200903.9259258

Coefficients
Standard Error
t-Statistic
p-Value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%

Intercept
3934.25
1893.36
2.08
0.04
101.34
7767.16
101.34
7767.16

own_price
-121.15
41.65
-2.91
0.01
-205.47
-36.84
-205.47
-36.84

compe_price
113.49
36.81
3.08
0.00
38.97
188.01
38.97
188.01

inc_per_capita
-0.31
0.14
-2.26
0.03
-0.59
-0.03
-0.59
-0.03

pro_exp
7.99
1.16
6.90
0.00
5.64
10.33
5.64
10.33

dummy 1
655.01
419.70
1.56
0.13
-194.63
1504.66
-194.63
1504.66

dummy 2
459.81
423.62
1.09
0.28
-397.76
1317.38
-397.76
1317.38

dummy 3
793.62
420.14
1.89
0.07
-56.90
1644.15
-56.90
1644.15

dummy 4
577.63
436.75
1.32
0.19
-306.53
1461.78
-306.53
1461.78

dummy 5
648.43
450.55
1.44
0.16
-263.66
1560.51
-263.66
1560.51

dummy 6
53.66
462.74
0.12
0.91
-883.12
990.43
-883.12
990.43

dummy 7
283.72
458.39
0.62
0.54
-644.25
1211.69
-644.25
1211.69

dummy 8
-565.55
455.83
-1.24
0.22
-1488.33
357.24
-1488.33
357.24

dummy 9
-794.68
437.51
-1.82
0.08
-1680.38
91.01
-1680.38
91.01

dummy 10
392.61
440.69
0.89
0.38
-499.52
1284.75
-499.52
1284.75

dummy 11
-16.89
434.26
-0.04
0.97
-896.01
862.23
-896.01
862.23

Elasticities

Type of elasticity
Value

PED
-0.84

CPED
0.93

IED
-0.18

Log data sheet

Date
log_demand_Maa
log_own_price
log_compe_price
log_inc_per_capita
log_pro_exp
dummy 1
dummy 2
dummy 3
dummy 4
dummy 5
dummy 6
dummy 7
dummy 8
dummy 9
dummy 10
dummy 11

Apr-11
9.46
4.17
4.24
8.38
7.06
1
0
0
0
0
0
0
0
0
0
0

May-11
9.46
4.19
4.26
8.38
7.06
0
1
0
0
0
0
0
0
0
0
0

Jun-11
9.49
4.18
4.31
8.38
7.06
0
0
1
0
0
0
0
0
0
0
0

Jul-11
9.53
4.18
4.32
8.38
7.08
0
0
0
1
0
0
0
0
0
0
0

Aug-11
9.54
4.19
4.32
8.39
7.12
0
0
0
0
1
0
0
0
0
0
0

Sep-11
9.45
4.23
4.35
8.39
7.13
0
0
0
0
0
1
0
0
0
0
0

Oct-11
9.49
4.23
4.37
8.40
7.05
0
0
0
0
0
0
1
0
0
0
0

Nov-11
9.51
4.24
4.40
8.40
7.09
0
0
0
0
0
0
0
1
0
0
0

Dec-11
9.39
4.31
4.43
8.41
7.11
0
0
0
0
0
0
0
0
1
0
0

Jan-12
9.40
4.36
4.52
8.42
6.99
0
0
0
0
0
0
0
0
0
1
0

Feb-12
9.30
4.43
4.51
8.43
7.00
0
0
0
0
0
0
0
0
0
0
1

Mar-12
9.29
4.47
4.56
8.44
6.90
0
0
0
0
0
0
0
0
0
0
0

Apr-12
9.28
4.50
4.60
8.45
6.89
1
0
0
0
0
0
0
0
0
0
0

May-12
9.32
4.50
4.60
8.46
6.88
0
1
0
0
0
0
0
0
0
0
0

Jun-12
9.39
4.48
4.59
8.48
6.91
0
0
1
0
0
0
0
0
0
0
0

Jul-12
9.41
4.49
4.65
8.49
6.99
0
0
0
1
0
0
0
0
0
0
0

Aug-12
9.55
4.49
4.66
8.50
7.01
0
0
0
0
1
0
0
0
0
0
0

Sep-12
9.52
4.52
4.65
8.51
7.14
0
0
0
0
0
1
0
0
0
0
0

Oct-12
9.54
4.51
4.65
8.53
7.11
0
0
0
0
0
0
1
0
0
0
0

Nov-12
9.33
4.54
4.66
8.54
7.14
0
0
0
0
0
0
0
1
0
0
0

Dec-12
9.26
4.55
4.66
8.55
6.93
0
0
0
0
0
0
0
0
1
0
0

Jan-13
9.35
4.51
4.66
8.57
6.85
0
0
0
0
0
0
0
0
0
1
0

Feb-13
9.43
4.45
4.66
8.58
6.95
0
0
0
0
0
0
0
0
0
0
1

Mar-13
9.39
4.45
4.63
8.59
7.03
0
0
0
0
0
0
0
0
0
0
0

Apr-13
9.39
4.44
4.57
8.61
6.99
1
0
0
0
0
0
0
0
0
0
0

May-13
9.41
4.38
4.55
8.62
6.99
0
1
0
0
0
0
0
0
0
0
0

Jun-13
9.39
4.38
4.54
8.63
7.01
0
0
1
0
0
0
0
0
0
0
0

Jul-13
9.40
4.38
4.55
8.65
6.99
0
0
0
1
0
0
0
0
0
0
0

Aug-13
9.39
4.36
4.53
8.66
7.00
0
0
0
0
1
0
0
0
0
0
0

Sep-13
9.38
4.36
4.55
8.67
6.99
0
0
0
0
0
1
0
0
0
0
0

Oct-13
9.38
4.36
4.56
8.68
6.97
0
0
0
0
0
0
1
0
0
0
0

Nov-13
9.35
4.40
4.57
8.69
6.97
0
0
0
0
0
0
0
1
0
0
0

Dec-13
9.24
4.40
4.58
8.70
6.95
0
0
0
0
0
0
0
0
1
0
0

Jan-14
9.37
4.38
4.58
8.71
6.84
0
0
0
0
0
0
0
0
0
1
0

Feb-14
9.28
4.38
4.54
8.72
6.96
0
0
0
0
0
0
0
0
0
0
1

Mar-14
9.26
4.39
4.56
8.72
6.88
0
0
0
0
0
0
0
0
0
0
0

Apr-14
9.35
4.37
4.54
8.73
6.85
1
0
0
0
0
0
0
0
0
0
0

May-14
9.36
4.37
4.52
8.74
6.95
0
1
0
0
0
0
0
0
0
0
0

Jun-14
9.37
4.36
4.52
8.75
6.96
0
0
1
0
0
0
0
0
0
0
0

Jul-14
9.36
4.36
4.52
8.76
6.96
0
0
0
1
0
0
0
0
0
0
0

Aug-14
9.30
4.36
4.52
8.77
6.95
0
0
0
0
1
0
0
0
0
0
0

Sep-14
9.31
4.35
4.55
8.78
6.90
0
0
0
0
0
1
0
0
0
0
0

Oct-14
9.27
4.36
4.56
8.79
6.91
0
0
0
0
0
0
1
0
0
0
0

Nov-14
9.17
4.43
4.59
8.80
6.87
0
0
0
0
0
0
0
1
0
0
0

Dec-14
9.11
4.47
4.61
8.82
6.77
0
0
0
0
0
0
0
0
1
0
0

Jan-15
9.09
4.51
4.62
8.83
6.70
0
0
0
0
0
0
0
0
0
1
0

Feb-15
9.17
4.48
4.62
8.85
6.69
0
0
0
0
0
0
0
0
0
0
1

Mar-15
9.24
4.45
4.60
8.86
6.77
0
0
0
0
0
0
0
0
0
0
0

Apr-15
9.30
4.43
4.60
8.87
6.83
1
0
0
0
0
0
0
0
0
0
0

May-15
9.26
4.48
4.62
8.89
6.90
0
1
0
0
0
0
0
0
0
0
0

Jun-15
9.43
4.50
4.68
8.90
6.86
0
0
1
0
0
0
0
0
0
0
0

Jul-15
9.49
4.50
4.69
8.91
7.02
0
0
0
1
0
0
0
0
0
0
0

Aug-15
9.53
4.49
4.68
8.92
7.09
0
0
0
0
1
0
0
0
0
0
0

Sep-15
9.49
4.52
4.69
8.93
7.13
0
0
0
0
0
1
0
0
0
0
0

Log reg results

SUMMARY OUTPUT

Regression Statistics

Multiple R
0.9144974044

R Square
0.8363055026

Adjusted R Square
0.7716892537

Standard Error
0.0521207367

Observations
54

ANOVA

df
SS
MS
F
Significance F

Regression
15
0.5273944581
0.0351596305
12.94
0.00

Residual
38
0.1032297055
0.0027165712

Total
53
0.6306241636

Coefficients
Standard Error
t-Stat
p-Value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%

Intercept
5.4634039989
1.1525587585
4.7402390191
0.0000297852
3.1301707944
7.7966372033
3.1301707944
7.7966372033

log_own_price
-0.8545215124
0.2971376387
-2.8758440576
0.006570811
-1.4560452089
-0.252997816
-1.4560452089
-0.252997816

log_compe_price
0.9064861808
0.2985615077
3.0361790033
0.0043118676
0.3020800122
1.5108923494
0.3020800122
1.5108923494

log_inc_per_capita
-0.1656979458
0.0699851021
-2.3676174049
0.023096459
-0.3073753769
-0.0240205148
-0.3073753769
-0.0240205148

log_pro_exp
0.7109869098
0.1048669301
6.7798962824
0.0000000489
0.4986949102
0.9232789094
0.4986949102
0.9232789094

Dummy1
0.0578655428
0.0357390939
1.6191105144
0.1136946265
-0.0144844698
0.1302155554
-0.0144844698
0.1302155554

Dummy2
0.0389032482
0.0361116567
1.0773044444
0.2881360674
-0.0342009782
0.1120074746
-0.0342009782
0.1120074746

Dummy3
0.0667610894
0.035734265
1.8682653587
0.0694498547
-0.0055791475
0.1391013263
-0.0055791475
0.1391013263

Dummy4
0.0466145004
0.0371631313
1.2543211174
0.2173862323
-0.0286183251
0.1218473259
-0.0286183251
0.1218473259

Dummy5
0.0504285179
0.0382458745
1.3185348365
0.1952167674
-0.0269962066
0.1278532424
-0.0269962066
0.1278532424

Dummy6
0.0077841197
0.0390792594
0.1991880051
0.8431776817
-0.0713277043
0.0868959438
-0.0713277043
0.0868959438

Dummy7
0.0194214407
0.0390048766
0.4979233981
0.6214075299
-0.0595398032
0.0983826845
-0.0595398032
0.0983826845

Dummy8
-0.0514600068
0.0386857651
-1.3302052231
0.1913793976
-0.1297752433
0.0268552296
-0.1297752433
0.0268552296

Dummy9
-0.0747312235
0.0371175031
-2.0133688216
0.05119843
-0.1498716795
0.0004092325
-0.1498716795
0.0004092325

Dummy10
0.0313237821
0.0375434979
0.8343330751
0.4093078461
-0.0446790554
0.1073266196
-0.0446790554
0.1073266196

Dummy11
-0.0009847948
0.0368602658
-0.026716975
0.978825291
-0.0756045012
0.0736349116
-0.0756045012
0.0736349116

Scenario 1

Explanatory variables
Intercept
own_price
compe_price
inc_per_capita
pro_exp
dummy 7

Quantity demanded (in kg.)
Total revenue (in INR)
PED

3934.25
91.38
109.14
7,620.60
1,247.31
283.72

13109.17
1197915.55
-0.84

3934.25
92
109.14
7,620.60
1,247.31
283.72

13034.05
1199132.61
-0.86

3934.25
93
109.14
7,620.60
1,247.31
283.72

12912.90
1200899.33
-0.87

3934.25
94
109.14
7,620.60
1,247.31
283.72

12791.74
1202423.75
-0.89

3934.25
95
109.14
7,620.60
1,247.31
283.72

12670.59
1203705.86
-0.91

3934.25
96
109.14
7,620.60
1,247.31
283.72

12549.43
1204745.66
-0.93

3934.25
97
109.14
7,620.60
1,247.31
283.72

12428.28
1205543.15
-0.95

3934.25
98
109.14
7,620.60
1,247.31
283.72

12307.13
1206098.33
-0.96

3934.25
99
109.14
7,620.60
1,247.31
283.72

12185.97
1206411.20
-0.98

3934.25
100
109.14
7,620.60
1,247.31
283.72

12064.82
1206481.77
-1.00

3934.25
101
109.14
7,620.60
1,247.31
283.72

11943.66
1206310.03
-1.02

3934.25
102
109.14
7,620.60
1,247.31
283.72

11822.51
1205895.98
-1.05

3934.25
103
109.14
7,620.60
1,247.31
283.72

11701.36
1205239.62
-1.07

3934.25
104
109.14
7,620.60
1,247.31
283.72

11580.20
1204340.96
-1.09

3934.25
105
109.14
7,620.60
1,247.31
283.72

11459.05
1203199.98
-1.11

Scenario 2

Explanatory variables
Intercept
own_price
compe_price
inc_per_capita
pro_exp
dummy 7

Quantity demanded (in kg.)
Total revenue (in INR)
PED

3934.25
91.38
115.69
7,620.60
1,247.31
283.72

13852.33
1265825.91
-0.80

3934.25
92
115.69
7,620.60
1,247.31
283.72

13777.21
1267503.72
-0.81

3934.25
93
115.69
7,620.60
1,247.31
283.72

13656.06
1270013.61
-0.83

3934.25
94
115.69
7,620.60
1,247.31
283.72

13534.91
1272281.19
-0.84

3934.25
95
115.69
7,620.60
1,247.31
283.72

13413.75
1274306.46
-0.86

3934.25
96
115.69
7,620.60
1,247.31
283.72

13292.60
1276089.43
-0.87

3934.25
97
115.69
7,620.60
1,247.31
283.72

13171.44
1277630.08
-0.89

3934.25
98
115.69
7,620.60
1,247.31
283.72

13050.29
1278928.43
-0.91

3934.25
99
115.69
7,620.60
1,247.31
283.72

12929.14
1279984.47
-0.93

3934.25
100
115.69
7,620.60
1,247.31
283.72

12807.98
1280798.20
-0.95

3934.25
101
115.69
7,620.60
1,247.31
283.72

12686.83
1281369.62
-0.96

3934.25
102
115.69
7,620.60
1,247.31
283.72

12565.67
1281698.74
-0.98

3934.25
103
115.69
7,620.60
1,247.31
283.72

12444.52
1281785.54
-1.00

3934.25
104
115.69
7,620.60
1,247.31
283.72

12323.37
1281630.04
-1.02

3934.25
105
115.69
7,620.60
1,247.31
283.72

12202.21
1281232.23
-1.04

3934.25
106
115.69
7,620.60
1,247.31
283.72

12081.06
1280592.12
-1.06

3934.25
107
115.69
7,620.60
1,247.31
283.72

11959.90
1279709.69
-1.08

3934.25
108
115.69
7,620.60
1,247.31
283.72

11838.75
1278584.96
-1.11

Sheet1 W17229

HIND OIL INDUSTRIES: DEMAND ANALYSIS

Abhishek Rohit, Debdatta Pal, and Pradyumna Dash wrote this case solely to provide material for class discussion. The authors do
not intend to illustrate either effective or ineffective handling of a managerial situation. The authors may have disguised certain names
and other identifying information to protect confidentiality.

This publication may not be transmitted, photocopied, digitized, or otherwise reproduced in any form or by any means without the
permission of the copyright holder. Reproduction of this material is not covered under authorization by any reproduction rights
organization. To order copies or request permission to reproduce materials, contact Ivey Publishing, Ivey Business School, Western
University, London, Ontario, Canada, N6G 0N1; (t) 519.661.3208; (e) [emailprotected]; www.iveycases.com.

Copyright 2017, Richard Ivey School of Business Foundation Version: 2017-04-20

The digital clock blinked 2:40 a.m. as Abhishek Khemka, the manager of Hind Oil Industries (HOI), tossed
and turned in his bed on a rainy September night in 2015. The next day at breakfast, he approached his
father, Ashok Khemka, the veteran businessman who had founded HOI, an edible mustard oil
manufacturing and selling unit based in the Indian city of Asansol.1

Abhishek had joined HOI as an assistant in 2011 after earning a master of business administration degree
with a focus on finance. After gaining an in-depth understanding of the business affairs, he took over from
his father in March, 2014. Nevertheless, he continued to consult his father on every critical issue.

At this rate we will be running losses in a few weeks time. Our price is too low, Abhishek blurted out.
The price of mustard seeds has skyrocketed. Our price cannot absorb the high production costs any
longer.2 Ashok looked at his son thoughtfully and remarked, This is the third time in a week that you
have brought this up. We have always sold our product at a price considerably lower than that of the market.
That has been our strategy from the beginning. Who will buy our product if we price it so close to that of
major national brands?

We are priced too low, Abhishek repeated. Our brand has earned the reputation of being a quality
product for all these years and its time we leveraged that reputation in the present situation. I disagree that
an increase in price will affect our revenue adversely. Abhishek paused for a moment and then concluded,
I shall get back to you after some calculations.

Ever since Abhishek had joined the business, he had meticulously maintained monthly records of all the
business transactions in a spreadsheet. He regularly used this along with the tools and techniques he had
learned in college to take business decisions. However, the current dilemma was new to him, and he
wondered if he could combine some of the theories of microeconomics with demand-modelling techniques
to resolve the issue.

1 Asansol was the second-largest city in West Bengal after Kolkata and was ranked the 42nd-fastest-growing city in the world
by city mayors in 2010.
2 The cost of mustard seeds accounted for around 75 per cent of the total production costs of Maa mustard oil. Do

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Page 2 9B17M054

HIND OIL INDUSTRIES

HOI was incorporated in July 1992 with a single oil-processing machine. It sold its product, Maa mustard
oil, to local consumers in Asansol. HOI focused solely on edible mustard oil, which was extensively used
as a cooking medium in eastern India. Owing to its focus on providing high-quality mustard oil, HOI
established a reputation for purity with time. This allowed the company to steadily grow and expand the
scale of its operations. In 2001, it became the first mill in Asansol to produce small sachets of edible oil,
and this strengthened its foothold in the retail segment. Thereafter, the company operated with three
processing machines and gradually established its presence in the entire Asansol market and nearby
suburban areas.

Maa mustard oil was primarily sold through retail outlets to household consumers. Bulk consumers,
including restaurants, food service companies, and caterers, either contacted the company directly for
discounted deals or negotiated through brokers. Abhishek explained:

Around 60 per cent of our sales are through retail stores. Households do not switch brands
frequently in this product category as the taste and signature pungent smell of mustard oil varies
across brands. The same does not hold true for bulk consumers, who are always looking for
discounted deals.

The sales were seasonal in nature as well. There was a significant hike in the sales in certain months owing
to festivals and marriage ceremonies.3

EDIBLE OIL MARKET IN INDIA

The edible oil market in India was very diverse and fragmented. The consumption pattern of edible oils
across the country varied depending on the oil seeds that were cultivated in each particular agro-climatic
zone. The indigenous edible oil industry in India was primarily dependent on the production of soybeans,
groundnuts, and rapeseeds, which accounted for 88 per cent of the total oil seeds produced. However, the
cultivation of oil seeds and the manufacturing of indigenous edible oils lagged far behind the demand for
edible oil. This demand gap was met by imports, which rose from 38.95 per cent of edible oil consumption
in 20042005 to 67.33 per cent in 20142015. More importantly, cheap palm oil and palm oil blends
accounted for about 54 per cent of the crude edible oil imported in India. India was the largest importer of
palm oil and soybean oil in the world in 2015.4

In terms of market share, palm oil, soybean oil, and mustard oil represented 42 per cent, 17 per cent, and
13 per cent of the edible oil market in India, respectively. Imported palm oil dominated the edible oil market
because of its cheap price, which was supported by a falling cost, insurance, and freight price and a
reduction in import duties (see Exhibit 1). This had stiffened the competition in the price-sensitive market
and had made small localized oil manufacturers unprofitable.

New varieties of oil, including cottonseed oil, sunflower oil, rice bran oil, and olive oil, had also entered
the market in recent years. While the market for the edible oil industry in India was heavily fragmented,
with competitors struggling to grab marginal shares, it was always exposed to threats from new entrants as

3 In India, Hindu marriages were held on auspicious marriage dates, which were based on the Hindu calendar, Panchang.
4 Credit Analysis & Research Limited (CARE Ratings), Outlook of Indian Edible Oil Industry, Care Ratings, accessed October
25, 2016, www.careratings.com/upload/NewsFiles/SplAnalysis/Outlook%20of%20Indian%20Edible%20Oil%20Industry.pdf. Do

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Page 3 9B17M054

it was underpenetrated. The level of per capita edible oil consumption in India was 14.4 kilograms (kg) per
yearfar lower than the global average of 24 kg per year.5 Population expansion and a rise in per capita
income were fuelling an increasing demand for edible oil, making it a lucrative industry. Consumers rising
income levels led to a gradual shift in preference toward healthier refined oils and healthier imported oils
like canola, olive, and castor oils, and this affected the market shares of various edible oils.

The edible oil industry in India had two main categories: an organized branded segment, which sold
packaged oil, and an unorganized segment, which sold loose oil. The unorganized segment was dominant
and represented around 75 per cent of the total market.6 However, a rise in income levels and fears of
adulterationblending of edible oils in undesirable proportionsmeant that consumers were gradually
shifting their preferences toward packaged branded oil.7 This had drawn several large new companies into
the organized branded market in the recent years.

The consumers of edible oil included a household retail segmentthose whose monthly consumption was
under 5 kgand bulk consumers, who purchased and consumed edible oil in bulk. Consumers in the
household retail segment had diverse tastes and preferences, which were influenced by geographical
location and income level. Location was important, as consumers became accustomed to the taste of the
edible oils that were primarily produced in their region. For example, coconut was extensively cultivated
and easily available in the southern part of India, and this was responsible for the use of coconut oil as the
primary edible oil in the region. Similarly, mustard seeds were cultivated in the north, and mustard oil was
primarily consumed in the eastern and northern parts of the country.

However, income levels also had an important role to play. Consumers in higher income groups preferred
healthier oils with a high smoke point,8 like refined oils. Even if they consumed indigenously produced
edible oils, consumers preferred premium brands. Consumers with lower income levels preferred loose
edible oils or non-premium packaged brands that were manufactured locally because of the price benefits.
In the last few years, easy availability of cheaper imported oils such as palm oil had hurt the market share
of local non-premium packaged brands such as HOI, as lower income consumers were shifting away from
non-premium packaged indigenous oils to packaged palm oils, which were quite cheap.

MUSTARD OIL MARKET IN INDIA

Mustard seeds were grown only in the rabi (winter) season and were harvested between February and April
every year. Thus, the market received its fresh stock in those months. Mustard oil was extensively consumed in
northern, eastern, and north-eastern India as an edible oil for daily consumption and making pickles, and as a
skin and hair oil. Mustard oil consumption in India was growing at a rate of around 20 per cent every year.9
Mustard oil processing in India was mainly an unorganized business; there were around 7,0009,000
manufacturing units, and only 20 per cent of these units were registered.10 However, the number of

5 Ibid.
6 Prasenjit Chakraborty, Strategising Growth Through Brand Acquisition, July, 2013, accessed November 27, 2016,
www.mrssindia.com/media/data/strategy-mncs-in-edible-oil-industry-july-2013.pdf.
7 Umesh Isalkarl, FDA Seizes Stock of Adulterated Soybean, Sunflower Oil, May 28, 2015, accessed November 27, 2016,
http://timesofindia.indiatimes.com/city/pune/FDA-seizes-stock-of-adulterated-soybean-sunflower-
oil/articleshow/47451097.cms.
8 The smoke point was the temperature at which cooking oil began to break down when heated. Edible oils with higher smoke
points were healthier as cooking oil.
9 Hema Yadav, Sayed Kokab Habeeb, and Manjushree Deshpande, The Hindu Business Line, 3 Ways to Promote Mustard
Oil, January 19, 2015, accessed November 27, 2016, www.thehindubusinessline.com/markets/commodities/3-ways-to-
promote-mustard-oil/article6802273.ece.
10 Ibid. Do

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Page 4 9B17M054

processing units had gone down over the years. Many small manufacturers had been pushed out of the
market due to unprofitability caused by the emergence of large companies, increasing competition from
cheaper imported palm oil, and changing tastes and preferences among consumers. Small mustard oil
manufacturers used mechanical crushing processors to extract oil from oil seeds. This was an inefficient
process that led to a loss of around 10 per cent of the edible oil during extraction. The by-products of
mustard oil production were used as cattle feed. Larger companies, which spent heavily on technological
innovation and modernization of their extraction process, were at a considerable advantage.

The mustard oil industry as a whole was not significantly threatened by the factors that affected other edible
oil varieties. This was because of the strong affinity of consumers toward the pungency of mustard oil,
which was greatly appreciated as a taste enhancer and an appetite stimulant and could not be substituted by
other edible oils. Its use for multiple other purposes also made it difficult to substitute. However, the
expansion of mustard oil consumption remained limited to certain areas of the country as its pungency made
it unacceptable in various other areas. Mustard oil was not suitable as an export because its high content of
erucic acid was found to have an adverse impact on health.11 (The U.S. Food and Drug Administration
required all mustard oil to be labelled for external use only.) Indias National Institute of Agricultural
Marketing created a strategy to reduce the erucic acid content and viscosity of the oil in order to make it
more acceptable to consumers around the country and worldwide. However, the mustard oil industry
remained a largely unorganized business serving consumers with low income levels in rural and semi-urban
areas. Mustard oil thus remained positioned as a poor persons oil.

THE MUSTARD OIL MARKET IN ASANSOL

In Asansol, the number of active mustard oil-processing mills had been on a steep decline since 2005. HOI
was one of the 2025 mustard oil mills operating in Asansol that sold packaged mustard oil and were able
to survive the ever-increasing competition. The West Bengal state of India, where Asansol was located,
accounted for a third of the entire 3 billion12 mustard oil market in the country,13 because mustard oil was
indispensable for Bengali cuisine. Therefore, the threats from other edible oils were not too pronounced in
Asansol.

However, the mustard oil industry had strong internal competition. The significant rise in per capita income
in West Bengal (see Exhibit 2) was reshaping consumers preferences; there was a shift in demand away
from loose oils and toward packaged oils and away from non-premium local varieties toward premium
varieties. Another important factor in this shift was the rampant adulteration that was prevalent in the loose
mustard oil segment. Such a shift was less conspicuous for bulk consumers, who were lured by the cheaper
price of loose mustard oil. HOI had closed its operations in the loose oil segment and had shifted completely
to selling packaged Maa mustard oil since 2009. This helped the company protect its reputation in Asansol
as a pure quality mustard oil brand, but it had hurt its bulk sales significantly, as loose oil was priced around
810 per cent lower than the packaged variety. However, household retail consumers welcomed this move,
and overall sales did not drop much.

11 Amy Zavatto, Essential Oil? The Controversy over Mustard Oil, FoxNews, January 23, 2012, accessed November 17,
2016, www.foxnews.com/food-drink/2012/01/23/essential-oil-controversy-over-mustard-oil.html.
12 = INR = Indian rupee; all currency amounts are in unless otherwise specified; US$1.00 = 65.57 on September 30,
2015.
13 Abhishek Law and Shobha Roy, Ruchi Soyas New Recipe to Market Mustard Oil Brand, The Hindu Business Line,
February 4, 2013, accessed October 28, 2016, www.thehindubusinessline.com/c