1. Why was Unicord successful in Thailand Describe the opportuni.docx

1. Why was Unicord successful in Thailand? Describe the
opportunities, challenges and the strategic choices taken by
Unicord to overcome the institutional voids in becoming
successful
2. What was the rationale for Unicord for acquiring Bumble Bee
in USA? Why did Dumri persist in acquiring Bumble Bee in the
midst of many bidders?
3. What caused the failure of the acquisition and the eventual
collapse of Unicord ?
4. Unicord is a family conglomerate founded by Dumri. Would
Unicord be better positioned if Unicord is managed by
professional managers instead of centrally controlled by Dumri?
PROJECT SCHEDULING WITH PERT/CPM
********************************
*** PROJECT ACTIVITY LIST ***
IMMEDIATE OPTIMISTIC MOST PROBABLE
PESSIMISTIC
ACTIVITY PREDECESSORS TIME TIME
TIME

------------------------------------------------------------------------
A - 1.0 5.0 12.0
B - 1.0 1.5 5.0
C A 2.0 3.0 4.0
D A 3.0 4.0 11.0
E A 2.0 3.0 4.0
F C 1.5 2.0 2.5
G D 1.5 3.0 4.5
H B,E 2.5 3.5 7.5
I H 1.5 2.0 2.5
J F,G,I 1.0 2.0 3.0
------------------------------------------------------------------------
EXPECTED TIMES AND VARIANCES FOR
ACTIVITIES
ACTIVITY EXPECTED TIME VARIANCE
-------------------------------------------

A 5.5 3.36
B 2.0 0.44
C 3.0 0.11
D 5.0 1.78
E 3.0 0.11
F 2.0 0.03
G 3.0 0.25
H 4.0 0.69
I 2.0 0.03
J 2.0 0.11
-------------------------------------------
*** ACTIVITY SCHEDULE ***
EARLIEST LATEST EARLIEST LATEST
CRITICAL
ACTIVITY START START FINISH FINISH
SLACK ACTIVITY

------------------------------------------------------------------------
A 0.0 0.0 5.5 5.5 0.0 YES
B 0.0 6.5 2.0 8.5 6.5
C 5.5 9.5 8.5 12.5 4.0
D 5.5 6.5 10.5 11.5 1.0
E 5.5 5.5 8.5 8.5 0.0 YES
F 8.5 12.5 10.5 14.5 4.0
G 10.5 11.5 13.5 14.5 1.0
H 8.5 8.5 12.5 12.5 0.0 YES
I 12.5 12.5 14.5 14.5 0.0 YES
J 14.5 14.5 16.5 16.5 0.0 YES
------------------------------------------------------------------------
CRITICAL PATH: A-E-H-I-J
EXPECTED PROJECT COMPLETION TIME = 16.5
VARIANCE OF PROJECT COMPLETION TIME = 4.31

A Project Map is NOT required
LINEAR PROGRAMMING PROBLEM
MAX 65X1+90X2+40X3+60X4+20X5
S.T.
1) 1X1<15
2) 1X2<10
3) 1X3<25
4) 1X4<4
5) 1X5<30

6) 1500X1+3000X2+400X3+1000X4+100X5<30000
7) 1X1+1X2>10
8) 1500X1+3000X2<18000
9) 1000X1+2000X2+1500X3+2500X4+3000X5>50000
OPTIMAL SOLUTION
Objective Function Value = 2370.000
Variable Value Reduced Costs
-------------- --------------- ------------------
X1 10.000 0.000
X2 0.000 65.000
X3 25.000 0.000
X4 2.000 0.000
X5 30.000 0.000
Constraint Slack/Surplus Dual Prices

-------------- --------------- ------------------
1 5.000 0.000
2 10.000 0.000
3 0.000 16.000
4 2.000 0.000
5 0.000 14.000
6 0.000 0.060
7 0.000 -25.000
8 3000.000 0.000
9 92500.000 0.000
OBJECTIVE COEFFICIENT RANGES

Variable Lower Limit Current Value Upper Limit
------------ --------------- --------------- ---------------
X1 0.000 65.000 90.000
X2 No Lower Limit 90.000 155.000
X3 24.000 40.000 No Upper Limit
X4 43.333 60.000 100.000
X5 6.000 20.000 No Upper Limit
RIGHT HAND SIDE RANGES
Constraint Lower Limit Current Value Upper Limit
------------ --------------- --------------- ---------------
1 10.000 15.000 No Upper Limit
3 20.000 25.000 30.000
5 10.000 30.000 50.000
6 28000.000 30000.000 32000.000

7 8.667 10.000 11.333
8 15000.000 18000.000 No Upper Limit
9 No Lower Limit 50000.000 142500.000
FORECASTING WITH MOVING AVERAGE
**************************************
TIME PERIOD TIME SERIES VALUE FORECAST
FORECAST ERROR
=========== ================= ========
==============

1 17
2 21 17.00 4.00
3 19 21.00 2.00
4 23 19.00 4.00
5 18 23.00 -5.00
6 16 18.00 -2.00
7 20 16.00 4.00
8 18 20.00 -3.00
9 22 18.00 4.00
10 20 22.00 -2.00
11 15 20.00 -5.00
12 22 15.00 7.00
THE MEAN SQUARE ERROR 16.73
THE FORECAST FOR PERIOD 13 22.00

FORECASTING WITH LINEAR TREND
*****************************
THE LINEAR TREND EQUATION:
T = 20.4 + 1.1 t
where T = trend value of the time series in period t
FORECAST ERROR
=========== ================= ========
==============

1 21.6 21.50 0.10
2 22.9 22.60 0.30
3 25.5 23.70 1.80
4 21.9 24.80 -2.90
5 23.9 25.90 -2.00
6 27.5 27.00 0.50
7 31.5 28.10 3.40
8 29.7 29.20 0.50
9 28.6 30.30 -1.70
10 31.4 31.40 0.00

Regression
Store #
Population
x
Sales
y
Forecast Error Error
2
1 2 58 70 -12 144
2 6 105 90 15 225
3 8 88 100 -12 144
4 8 118 100 18 324
5 12 117 120 -3 9
6 16 137 140 -3 9
7 20 157 160 -3 9

8 20 169 160 9 81
9 22 149 170 -21 441
10 26 202 190 12 144
Slope 5
MSE 153
y-int 60
r 0.950123
Trend Line 60 + 5x
Forecast 16
140(,000)
OR
Store #
Population
(1,000s)
x
Sales
y

1 2 58
2 6 105
3 8 88
4 8 118
5 12 117
6 16 137
7 20 157
8 20 169
9 22 149
10 26 202
b1 = 5
b0 = 60
16 140

SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.950
R Square 0.903
Adjusted R Square 0.891
Standard Error 13.829
Observations 10
ANOVA
df SS MS F Sig F
Regression 1 14200 14200 74.248 0.000
Total 9 15730
Coefficients
Standard
Error t Stat
P-
value
Lower
95%
Upper
95%
Lower

95.0%
Upper
95.0%
Intercept 60 9.226 6.503 0.000 38.725 81.275 38.725 81.275
Population
(1,000s) 5 0.580 8.617 0.000 3.662 6.338 3.662 6.338
Population
(1,000s) Sales
Population
(1,000s) 1
Sales 0.950 1
Integrative Project
Microsoft
Virtual Reality Glasses
BMGT 3371
Dr.Bazzy

Executive Summary
Microsoft have been always the one of the best companies for
all age consumers. Microsoft is always being one of the world’s
top leading names in technology, and it has always provided the
ultimate quality for its products and software. The Virtual
Reality Glasses technology is a continuance of Microsoft’s
greatness. The innovative technology under the glasses is
designed to take consumers to the future by hearing, feeling,
and living the moment, rather than wearing a traditional
headphone, watching a movie with a naked eye, or talking over
a regular phone. The Virtual Reality Glasses is technology that
replicates the environment, and simulates the user’s physical
presence and interactions within the simulated environment. The
Virtual Reality Glasses are designed for the people to
experience the real world and develop the way of living.
The technological market is risen. Researches have shown that
technological industries are risen quickly and the more delay to
process an innovation, there will be a chance that another
company will enter the market with a better idea. Individuals
and businesses are always looking for the best way to enjoy and
solve problems with a minimum work time and a maximum
result. The Virtual Glasses will make the world come closer
together. People are contacting each other’s remotely, but with
the Glasses it will make it remotely and realistic.
The Virtual Reality Glasses will deal with a huge element in
entertaining and solving problems. Overall, the technology will
reduce boredom, and will increase entertainments, along with a
huge impact on the world.

There is need for Microsoft to effectively find a solution by the
use of these forecasting, PERT/CPM, and linear programming
models to create the Virtual Reality Glasses. The choice of
these three models present the best chance to implement the
best possible solution to pave way for the generation of Virtual
Reality Glasses product. The forecasting model in this research
includes both regression analysis and trend analysis. Other
models include the project management models PERT/CPM
which generally helps in finding the necessary steps that can be
followed to have a better strategy to implement a given task
which is creating a new product to the market. Linear
Programming helps to resolve the challenges and to help
maximize the product’s efficiency, along with Microsoft’s
revenue for selling the product.
TABLE OF CONTENTS
Introduction5
Analysis of Forcasting Model
Regression6-7
Time Series 8
Analysis of Pert/cpm Model
Overview9

Steps9-11
Analysis of linear programming Model
Overview12
constraints12
Objectives13
Implementation14
Conclusion15-16
Appendix a17-18
Appendix B19-20
Appendix C21
Introduction
Microsoft, whose headquarters are in Redmond, WA, a
multinational technology Company. It is the leading developer
of computer software and applications. In addition, the company
publishes books, offers email services, sells electronic game
systems and publishes multimedia titles. Its sales offices are
distributed throughout the world and has also opened research
labs in different parts of the world.
The company was founded by Bill Gates together with Paul
Allen in 1975. By mid 1980s, the company had rose to dominate
the personal computer operating system market. This time it had
developed the MS-DOS operating system, which it later
upgraded to Microsoft Windows. Over the years, the company
has been able to upgrade its operating system. In addition, the
company has been able to make various acquisitions. For
instance, it was able to acquire Skype Technologies in May,
2011, which is its largest acquisition so far. Microsoft is also
set to make an acquisition of LinkedIn for $26 billion.
Microsoft become a publicly owned corporation in the year
1986. It has always been one for the most profitable and

powerful companies in the US. The company made an entry in
the gaming and phone market in 2001 by producing its first
gaming application, the Xbox. The game was able to take the
second place in the gaming market. Bill Gates was the
company’s CEO until 2000 where he relinquished his role to
Steve Ballmer. As can be seen, the company has been able to
make huge progress and has been able to remain competitive in
the market. Its move to buy LinkedIn is one of its next major
move.
Analysis of Forecasting Model
Regression analysis models (Appendix A) provide a clearer
picture in trying to uncover a certain action in the future using
current data. Regression models provide a powerful tool,
allowing predictions about past, present, or future events. The
researcher employs these models either because it is less
expensive in terms of time and money to gather the information
to make the predictions than to gather the information about the
event itself, or because the event to be predicted will happen in
some time in the future.
Interpretation
R2= 0.787 which shows that 78.7% of the variations in revenue
are due to variations in independent variables (inventories,
investments, diluted earnings per share and basic earnings per
share). Sincemultiple R is close to one (0.887), we can conclude
that our model is good and there is a strong relationship
between dependent (Revenue) and independent variables
(inventories, investments, diluted earnings per share and basic
earnings per share).
P=0.062>0.05, hence we do not reject null hypothesis that
regression coefficients are equal and conclude that there is a no
statistical significant difference in regression coefficients.
Since p>0.05 then it means that changes in predictor variables
(inventories, investments, diluted earnings per share and basic
earnings per share) are not associated with changes in the

response variable; which is Revenue.
Revenue has a positive relationship with Inventories (billion),
Inventories (billion), Investments (billion) and Basic earnings
per share, while Basic earnings per share has a negative
relationship that is as Basic earnings per share increase the
Revenue decreases.
Since P-values for all coefficients are greater than 0.05 there we
do not reject null hypothesis and conclude that all there is no
statistically significant difference in the coefficients. Also from
confidence intervals we find that zero is inclusive to all
intervals of the coefficients for both 90% and 95% confidence
intervals hence we conclude that there is no statistically
significant difference among the regression coefficients.
The following is an interpatient for the correlation results for
Microsoft:
· There is a strong positive relationship between Revenue and
Inventories=0.76966
· There is a strong positive relationship between Revenue and
investments=0.83014
· There is a moderate positive relationship between Revenue
and Diluted earnings per share =0.51019
· There is a moderate positive relationship between Revenue
and Basic earnings per share =0.50535
· There is a strong positive relationship between Inventories and
investments=0.94130
· There is a weak positive relationship between Inventories and
Diluted earnings per share=0.18704
· There is a weak positive relationship between Inventories and
Basic earnings per share=0.18249
· There is a weak positive relationship between Investments and
Diluted earnings per share=0.28270
· There is a weak positive relationship between Investments and
Basic earnings per share=0.27746
· There is a strong positive relationship between Diluted
earnings per share and Basic earnings per share=0.99995

Time series model (Appendix A)
Negative forecast errors show that forecast value is higher than
time series value, while negative forecast errors show that
forecast values are less than time series value. Zero error means
that they are the same. In our question periods 1, 4,5,6,7 & 8
have predicted values more than time series values, while
periods 2, 3, 9 & 10 have lower predicted values than time
series values. Mean square error for the 10 periods is 6.08.
Period 11, 12, 13, 14, & 15 are predicted as 95.67, 100.72,
105.78, 110.83 and 115.89 respectively. The predictions show
that Microsoft will generate more profit in the future from
selling the glasses. It clearly tells us how successful it would
be.
Analysis of PERT/CPM model
PERT (Program Evaluation and Review Technique) and CPM
(Critical Path Method) are project management models
(Appendix B) that are employed in ensuring the activities are
well planned and executed in the system for effective product
generation. From the project activity that constitutes elements
that can enable the proper environment for the creation of a new
product list table. In order to make the product, it have to go
through certain steps; which are:
A. Brain Storm
B. Research
C. Develop a design
D. Develop a Model
E. Testing the Model
F. Adjustments

G. Production
H. Marketing
I. Price/Cost
J. Final Results
The project starts by brainstorming and ends with the final
report. For the development of the design, brainstorm and
research must have been completed. For the development of the
model, the project needs to develop the design first.
To test the model, we need first need to develop the model,
before adjustments are made, the project needs first to develop
the model and test the model. For production to take place
testing of the model must have been done. Marketing can only
take off if adjustments and production have been completed.
Price cost to be determined production and marketing must have
been completed. To finish the project, the price cost must have
been determined.
Variances of the activities show the degree of variation from the
mean. The larger the variation, the larger the risk in our project
price cost has the largest variance. Hence, there is a greater risk
likely to take when deciding on the price cost.
Slack is the difference between earliest start and latest start or
earliest finish and latest finish. The amount of time a non-
critical activity can be delayed with affecting the completion
time of the project. Inactivity schedule table, we find that
brainstorm and production have the slack time of 1.67 and 0.83
respectively. If 1.67 delays brainstorm, it will not affect the
completion time for the project. Also, we find that critical path
which the longest time path is taken for the project to be
complete is research, development of design, development of
the model, adjustments, marketing, price cost then final.
Expected time for the project to be completed is 36.5 weeks.
The variance time for the project to be completed is 5.33 weeks,
which is the variability of the completion time from the average
time taken for the project to be completed.
For Microsoft to complete the creation of the Virtual Reality
Glasses, the project must start by brainstorming and end by

final result, in order to sell the product. For the development of
the design, brainstorm and research must have been completed;
that means that if for the research on Virtual Reality Glasses to
be done, and brainstorm must have been done first otherwise the
research process would not take place.
For the development of the model, the project needs to be
developing the design. First, elsewhere the company it will not
proceed to the next activity (development of the model). For
Microsoft to the model, it will have to develop the model.
Before adjustments are made, the project needs first to develop
the model and test the model. For production to take place
testing of the model must have been done. Marketing can only
take if adjustments and production have been completed.
Analysis of Linear Programming model
Microsoft Company is one of the biggest tech companies who
strive to maximize the profit from the its products that they
launch in the market. In maximizing the profitability of there
are various factors that need to be considered for effective
outcome. When it comes to create the Virtual Reality Glasses
product, there are several factors need to be considered
including market, labor input, machinery, and marketing to
produce the Virtual Reality Glasses. Linear programing
provides the best possible way that can help Microsoft to
enhance their business operations in relation to maximizing the
profits and minimizing the costs.
The objective function of 5752.874 (Appendix C) in creating the
Virtual Reality Glasses product is to maximize the resulting
profits from the sale of the product. From the calculations done,
the best strategy that Microsoft can adapt to maximize the profit
is to make a profit of $300 from markets coverage, $200 from
laborers, $600 from production machines, $100 from offering
discounts given to customers, and $250 from advertisements.

To achieve maximum profit of the new Virtual Reality Products,
there are some limitations and constraints that are supposed to
be considered.
Microsoft can make a maximum profit of $450 from sales of the
Glasses, the company must get a profit of $20 from markets,
$20 from laborers, $146 from production machines, $ 26 from
discounts to customers, and $10 from advertisements. Another
constraint, for Microsoft to earn a profitof $ 350, the company
have to make a profit of $20 from markets, $10 from labor,
$165 from production machines, $82 from discounts offered to
customers, and $26 from advertisements. Additional constraint
to be considered by Microsoft company in order to make
maximum profit of $ 220 of Virtual Reality Glasses, Microsoft
company must earn a profit of $12 from markets, $5 from
labor,$24 from production machines, $70 from discountsto
customers, and $ 2 advertisements made. Another constraint is
that to for the company to incur minimum cost in production of
virtual reality glasses production of $80, Microsoft must spend
$ 26 on markets, $58 on labor, $2 on production machines, $74
on discounts they offer to customers, and $ 25 on
advertisements. To make maximum profit of $ 350, Microsoft
have to get profit of$20 markets, $5 from laborers, $16 from
production machines, $ 90 from discounts they get on materials
used in making the Virtual Reality Glasses, and $21 on
advertisements which is an additional constraint.
To achieve the objective function which is to maximize profit,
Microsoft identified what profit each component taking part in
achieving its objective function should make: markets should
give $300 profit, labor give $200 production machines $600,
discounts $100 and advertisements $ 250. this would give an
objective function/maximum profit of $ 5752.874.the binding
constraints for this model were markets, labor, machines used in
production, discounts given to customers and advertisements
made.After the linear programming model was run, it was
determined that the optimal solution would be to sell the Virtual
Reality Glasses to 16.494 markets and hire 4 laborers, make no

use of machines, no discounts to be offered and no
advertisements to be made. Linear programming results also
shows that Microsoft Company should add more profit by
$91.149 from machines, $1356.322 from discounts offered and
$109.540 from advertisements. Microsoft also needs to reduce
markets profit by $93.655, machines profit by $1.954 and
discounts offered by $582.184.
Implementation
Virtual Reality Glasses will take our life into new level of
living. Microsoft should identify the role of management agency
the specific responsibilities of the key staff during project
implementation, and monitoring should be outlined. There are
some majors steps and responsibilities should be on place in
order for the project to be completely finished with higher
quality and standards.
· Beneficiary participation. The involvement of the
beneficiaries in planningand what the company is expected of
the team to be done.
· The organizational structure. Microsoft has to give the
structure for the purses of management, priorities from the
highest to lowest. Should also check the qualifications and
skills for each staff member, job descriptions and
specifications, because every step of the project matters and
needed to be perfect.
· Financial management. It will coverthe management’s
funding, financial reports and financial statements. These
statements most to be accuratefor the public, so Microsoft can
rise funding from its investors, and the better the statements
are, the more money it will come.
· Reporting system. This system will concentrate onreporting to
whom and how often.
· Sustainability.Microsoft need to develop more sustainability
on the project. It is important for Microsoft to be sustained for
the project to be perfect and done in time.

Conclusion
The three models provide the most basic chance present that can
generate a solution to ensure that there is the creation of the
Virtual Reality Glasses that Microsoft intends to introduce to
the market. Among the three alternative model groups, the
forecasting models provide the best possible solution that the
company can take to create the Virtual Reality Glasses product.
The reason is that the forecasting models can involve almost all
the variables that can be considered vital in the creation of the
new product.
Microsoft is a business oriented firm, and the need to create a
new product must be economically viable. Thus the regression
analysis and the trend analysis models outlined provide the
necessary solution to the changes that the variables can be able
to undergo until the company establishes an equilibrium point
where the created of the new product will be without any
negative financial implications or incurring extra costs as
witnessed with the creation of new products by different
companies.
Although the PERT/CPM model provide an illustration on how
various variable can interact to produce a final solution to the
company, they do not highlight the changes or the different
positions that can be assumed by the company to provide a final
important solution to the company. Thus, we would advise the
company to adopt the forecasting models because they provide a
clear interaction of the variables to provide a viable optimal
solution for the product creation.
The linear programming results shows that Microsoft Company
can still maximize profits. in creating the Virtual Reality
Glasses. For Microsoft to realize its objective function of
maximizing profits by $ 5752.874 it to should put some
mechanisms to ensure that it raises the profits made on
machines by $ 91.149, profit made on discounts by $1356.322
and profits gained from making advertisements of the product
by $109.540.With the above adjustments made there will be no
doubt of Microsoft will achieve its objective functions and

making more profits in future.
Appendix A: Forecasting
Regression
FORECASTING WITH LINEAR TREND
*****************************
THE LINEAR TREND EQUATION:
T = 40.063 + 5.055 t
where T = trend value of the time series in period t

FORECAST ERROR
=========== ================= ========
==============
1 44.28 45.12 -0.84
2 51.12 50.17 0.95
3 60.42 55.23 5.19
4 58.44 60.28 -1.85
5 62.48 65.34 -2.85
6 69.94 70.39 -0.45
7 73.72 75.45 -1.73
8 77.85 80.50 -2.65
9 86.83 85.56 1.28
10 93.58 90.61 2.97
Appendix B: PERR/CPM
PROJECT SCHEDULING WITH PERT/CPM
********************************
*** PROJECT ACTIVITY LIST ***
IMMEDIATE OPTIMISTIC MOST PROBABLE
PESSIMISTIC
ACTIVITY PREDECESSORS TIME TIMETIME

------------------------------------------------------------------------
A - 1 3 5
B - 2 5 6
C A,B 2 4 9
D C 1 2 6
E D 3 8 10
F D,E 4 5 6
G E 2 4 7
H F,G 2 5 6
I G,H 1 3 10
J I 3 4 4
------------------------------------------------------------------------
EXPECTED TIMES AND VARIANCES FOR
ACTIVITIES
ACTIVITY EXPECTED TIME VARIANCE
-------------------------------------------
A 3.00 0.44
B 4.67 0.44
C 4.50 1.36
D 2.50 0.69
E 7.50 1.36
F 5.00 0.11
G 4.17 0.69
H 4.67 0.44
I 3.83 2.25
J 3.83 0.03
-------------------------------------------

*** ACTIVITY SCHEDULE ***
EARLIEST LATEST EARLIEST LATEST
CRITICAL
ACTIVITY START START FINISH FINISH
SLACK ACTIVITY
------------------------------------------------------------------------
A 0.00 1.67 3.00 4.67 1.67
YES B 0.00 0.00 4.67 4.67 0.00
YES C 4.67 4.67 9.17 9.17 0.00
YES D 9.17 9.17 11.67 11.67 0.00
YES E 11.67 11.67 19.17 19.17 0.00
YES F 19.17 19.17 24.17 24.17 0.00
G 19.17 20.00 23.33 24.17 0.83
YES H 24.17 24.17 28.83 28.83 0.00
YES I 28.83 28.83 32.67 32.67 0.00
YES J 32.67 32.67 36.50 36.50 0.00
------------------------------------------------------------------------
CRITICAL PATH: B-C-D-F-H-I-J
EXPECTED PROJECT COMPLETION TIME = 36.5
VARIANCE OF PROJECT COMPLETION TIME = 5.33

Appendix C: Linear Programming
LINEAR PROGRAMMING PROBLEM
MAX 300X1+200X2+600X3+100X4+250X5
S.T.
1) 20X1+20X2+146X3+26X4+10X5<450
2) 10X1+46X2+165X3+82X4+26X5<350
3) 12X1+5X2+24X3+50X4+2X5<220
4) 26X1+58X2+2X3+70X4+5X5>80
5) 20X1+5X2+16X3+90X4+21X5<350
OPTIMAL SOLUTION
Objective Function Value = 5752.874
Variable Value Reduced Costs
-------------- --------------- ------------------
X1 16.494 0.000
X2 4.023 0.000
X3 0.000 91.149
X4 0.000 1356.322
X5 0.000 109.540
Constraint Slack/Surplus Dual Prices
-------------- --------------- ------------------
1 39.655 0.000
2 0.000 2.874
3 1.954 0.000
4 582.184 0.000
5 0.000 13.563
OBJECTIVE COEFFICIENT RANGES

Variable Lower Limit Current Value Upper Limit
------------ --------------- --------------- ---------------
X1 186.005 300.000 800.000
X2 174.745 200.000 1380.000
X3 No Lower Limit 600.000 691.149
X4 No Lower Limit 100.000 1456.322
X5 No Lower Limit 250.000 359.540
RIGHT HAND SIDE RANGES
Constraint Lower Limit Current Value Upper Limit
------------ --------------- --------------- ---------------
1 410.345 450.000 No Upper Limit
2 175.000 350.000 392.500
3 218.046 220.000 No Upper Limit
4 No Lower Limit 80.000 662.184
5 38.043 350.000 353.386
21
BMGT 3371 Integrative Project
You and your partners have been hired by _____and you will be
responsible for the formulation, solution, and analysis of three
mathematical models.The models should be designed to fix or
accentuate an issue for your organization.
The objective of this assignment is to illustrate the applicability
of models designed to address a business decision. Think about
your company and how you can use the models we’ve learned in
class to address an issue the company may have, create

something new for the company, or even enhance their existing
operations. The specifics of the project are up to you, but the
following models must be used.
Analysis of a Forecasting Model· You will need to use one
time-series model and one simple regression model (including
correlation), including accuracy measures for each
model.Additionally, you should conduct a confidence interval
analysis for the first forecasted value on the model you believe
to be most appropriate for your project (include the reasons why
you consider that model to be most appropriate).· Your model
must have at least 10 data points.
Analysis of a PERT/CPMModel
· Your model needs to be designed for uncertain conditionsand
should be accompanied by analyses for at least two probability
circumstances, including costs and a budget for the project that
reflect your completion time(s).
· Your model must have at least 9 activities.
Analysis of a Linear ProgrammingModel
· Based on the project you decide to create, you will need to
formulate a Linear Program. Your linear program will have at
least 4 variables and at least 5 constraints. Standardize all
constraints where applicable. Media Selection LPs will not be
accepted.
· You should also solve and interpret the sensitivity analysis,
discussing the results most relevant to your problem.
The order of models used in the system is for you to decide.You
should sequence the models in such a manner that they help
solve your perceived company issue. The models should all be
designed to address the company issue you have identified. In
addition to the final report, you will also rate the participation
and contribution of your partners.

IntegrativeProjectOutline
Cover Page
I) Executive Summary (1 Page)
II) Table of Contents (1 Page)
III) Introduction (1-2 Pages)
The introduction should include a brief summary of your
company. What do they do? What products, services, processes,
and/or inputs do we need to know in order to understand the
company? Moreover, what is the organization’s issue that needs
to be addressed?Describeyoursystemand how it willaddress the
issue.
IV) Analysis of YourModel One(2 Pages)
V) Analysis of YourModel Two (2 Pages)
VI) Analysis of YourModel Three(2 Pages)
Within each model, you should discuss the purpose of the
model. You should describe the details of each model. Exact
formulas do not need to be included in the body of the paper,
but should be clearly identified in the appendices. Additionally,
provide the solution for each model. Be sure to address the
various elements required of each model. Finally, each model
should include an interpretation. What is the meaning of the
results and what do they mean for the company and its issue?
VII) Implementation(1-2 Pages)
Describe how your system will be implemented. Additionally,

based on the results of your analysis, what needs to change in
the organization? What timeframe will you establish for making
these changes? Who should be involved in the changes and who
needs to know about them (e.g., internal and/or external
people)?
VIII) Conclusion(1 Page)
Finally, describe the overall results of the system and explain
how it has addressed the issue within your company.
Appendix A – Model One
Solution
Appendix B – Model Two

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