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Brisneyland Oil Refinery Company Add in library

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Question:

Describe about the Brisneyland Oil Refinery Company?
 
 

Answer:

Brisneyland Oil Refinery Company is producing or manufacturing petrol oil by the help of crude oil. The Brisneyland Oil Refinery Company is collecting or need approx 300,000 barrels of oil each month. The Brisneyland Oil Refinery Company is provided last three months dataset, which is including with the last three months cost price of oil and also shipping charge or cost for prt barrel of oil. The Brisneyland Oil Refinery Company dataset is giving below.

Kind of oil

Cost

Feb

2015

Cost

Mar

2015

Cost

Apr

2015

Shipping

Per

Barrel

Sweet light West Texas

$87.60

$87.00

$86.50

$6.78

Brent light sweet 5

$89.60

$89.00

$88.50

$8.05

Petropavlovsk medium

$82.50

$83.30

$84.00

$5.46

Aceh medium

$83.50

$84.00

$83.00

$3.46

Maracaibo heavy

$79.40

$79.90

$80.10

$5.98

Veracruz medium

$81.40

$82.00

$81.70

$6.64

Dharhan light sweet

$85.00

$84.80

$84.50

$4.77

Basrah light sweet

$84.00

$84.20

$84.10

$4.87

Alexandretta medium

$80.90

$81.40

$81.60

$6.18

Rostov-on-Don medium

$81.10

$78.00

$80.40

$6.27

Lagos medium

$80.00

$80.00

$80.20

$7.98

Long Beach medium

$86.60

$87.20

$86.20

$4.94


By the help of the above company dataset, we can easily find out or calculating the total profit of the company for last three months (like cost price of February 2015, cost price of March 2015 and cost price of April 2015). The total profit of Brisneyland Oil Refinery Company is calculating and measuring by the help of the Microsoft Excel program.

By utilizing the Microsoft Excel program, the ideal arrangement values for this model are demonstrated in datasets or attached Microsoft Excel program sheet. In place of datasets, firstly, we will give the model and presentation the outcomes, understudy use solver to give answer reports introducing the outcomes in a more detail, in addition the affectability reports will give an assortment of useful data for administration understanding of the arrangements (Borgelt, 2013). It would permit the administration of the organization appreciate the ideal of target worth and ideal choice variable that are influenced by changed in the target capacity coefficients, the effect of changes in the limitations assets limits.

According to the Microsoft Excel programmed the outcomes altogether income of $2,197.80, aggregate generation expense of $768.65, returning a benefit commitment of $1429.16. The outcomes or results are shown in datasets or attached Microsoft Excel program worksheet, which is shown in below.

Kind of oil

Cost price

Selling price per Barrel

Profit Per Barrel

Sweet light West Texas

$66.97

$183.15

$116.18

Brent light sweet 5

$68.79

$183.15

$114.36

Petropavlovsk medium

$63.82

$183.15

$119.34

Aceh medium

$63.49

$183.15

$119.66

Maracaibo heavy

$61.35

$183.15

$121.81

Veracruz medium

$62.94

$183.15

$120.22

Dharhan light sweet

$64.77

$183.15

$118.38

Basrah light sweet

$64.29

$183.15

$118.86

Alexandretta medium

$62.52

$183.15

$120.63

Rostov-on-Don medium

$61.44

$183.15

$121.71

Lagos medium

$62.05

$183.15

$121.11

Long Beach medium

$66.24

$183.15

$116.92

Total

$768.65

$2,197.80

$1,429.16

 

 

References

Borgelt, C. (2013). Towards advanced data analysis by combining soft computing and statistics. Berlin: Springer.

Cohen, P., Adams, N., & Berthold, M. (2010). Advances in intelligent data analysis IX. Berlin: Springer.

HALLS, J. (2003). River run: an interactive GIS and dynamic graphing website for decision support and exploratory data analysis of water quality parameters of the lower Cape Fear river.Environmental Modelling & Software18(6), 513-520. doi:10.1016/s1364-8152(03)00026-4

Louviere, J., Hensher, D., & Swait, J. (2000). Stated choice methods. Cambridge: Cambridge University Press.

Ma, C., Buontempo, F., & Wang, X. (2011). Inductive data mining: automatic generation of decision trees from data for QSAR modelling and process historical data analysis. IJMIC12(1/2), 101. doi:10.1504/ijmic.2011.037837

Maglaveras, N. (2006). Biological and medical data analysis. Berlin: Springer.

Rong, Y. (2011). Practical environmental statistics and data analysis. Glendale, AZ: ILM Publications.

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