Contents
Introduction: 2
Inventory Stock 2
Management Information System 11
Inventory Stock 12
Material Requirements Planning 16
Project Plan 19
Investment Appraisal Techniques 21
Conclusion: 22
Conference list 24

Introduction:
Gia Hoang Company Ltd is located HCMC which manufactures and sells range of personal computers in Dong Nai industries park. In 2005, the company sells their products locally and also exports its products to foreign markets especially in the South East Asia. The company has provided many services such as design, run project in IT as well as management for public and private companies in Vietnam. On the other hand, Gia Hoang also wanted to create a good reputation and the best company and building effective solution and technology applications related to IT and Services. So, they also want to plan to make further management decisions.
As a result, this report will provide forecasting techniques about sales, management information systems, inventory control, material requirements planning, project plan, and investment appraisal techniques for Gia Hoang Company. These tools would help Gia Hoang Company run and business efficiently and successfully, to complete with a fierce market.
Inventory Stock
Quarterly sales data, in thousands units, from the past 5 years are provided as follow:

Forecast for sales in 2011 through the least squares regression analysis
According to Carl Friedrich Gauss (1974), “the least squares correspond to the maximum likelihood criterion if the experimental errors have a normal distribution and can also be derived as a method of moment’s estimator.”
From data above, we have the table and scatter diagram as follow:
Years
Quarter
No. years
X
2006
Q1

96

Q2
1
92

Q3
2
54

Q4
3
90
2007
Q1
4
104

Q2
5
96

Q3
6
64

Q4
7
100
2008
Q1
8
108

Q2
9
110

Q3
10
84

Q4
11
104
2009
Q1
12
100

Q2
13
108

Q3
14
74

Q4
15
92
2010
Q1
16
110

Q2
17
100

Q3
18
72

Q4
19
100
The form y = a + bx is the equation a straight line, where x and y are related variation, a is the intercept of the line on the vertical axis, and b is the gradient of the line.
After calculating on spreadsheet, the equation of straight line is y = 0.4707x + 88.429 with the first quarter of 2006 where x = 0, the second quarter of 2006 where x = 1…to the five quarter of 2010 where x = 19
Combining with the trend line, forecast sales for 2011 as the following table:
Year
Quarter
No.
Trend
Y = S+T
2010
Quarter 1
16
93,750

Quarter 2
17
94,500

Quarter 3
18
95,156

Quarter 4
19
95,813 2011
Quarter 1
20
96,469
107,781

Quarter 2
21
97,125
105,563

Quarter 3
22
97,781
74,094

Quarter 4
23
98,438
101,563

Forecast sales for 2011 through additive model
Year
Quarter
No
Sales
Moving Average 4
Moving Average 2
S=Y-T
Trend (Regression)
2006
1
0
96,000

88,429 2
1
92,000

88,899 3
2
54,000
83,000
84,000
-30,000
89,370 4
3
90,000
85,000
85,500
4,500
89,841
2007
1
4
104,000
86,000
87,250
16,750
90,311 2
5
96,000
88,500
89,750
6,250
90,782 3
6
64,000
91,000
91,500
-27,500
91,253 4
7
100,000
92,000
93,750
6,250
91,723
2008
1
8
108,000
95,500
98,000
10,000
92,194 2
9
110,000
100,500
101,000
9,000
92,665 3
10
84,000
101,500
100,500
-16,500
93,135 4
11
104,000
99,500
99,250
4,750
93,606
2009
1
12
100,000
99,000
97,750
2,250
94,077 2
13
108,000
96,500
95,000
13,000
94,547 3
14
74,000
93,500
94,750
-20,750
95,018 4
15
92,000
96,000
95,000
-3,000
95,489
2010
1
16
110,000
94,000
93,750
16,250
95,959 2
17
100,000
93,500
94,500
5,500
96,430 3
18
72,000
95,500

96,901 4
19
100,000

97,371

From outcome above, the summary of seasonal variation as the table below:
Year
Quarter 1
Quarter 2
Quarter 3
Quarter 4
Residual
2006

-30,000
4,500

2007
16,750
6,250
-27,500
6,250 2008
10,000
9,000
-16,500
4,750 2009
2,250
13,000
-20,750
-3,000 2010
16,250
5,500

11,313
8,438
-23,688
3,125
-812.50

Final estimate of average quarter variation might be rounded up and down as