BUS 301: Decision Support Tools: Assignment Analysis and Solutions

This document presents a comprehensive solution to a Decision Support Tools assignment, addressing various aspects of business decision-making. The assignment covers decision analysis, including the selection of investments based on different criteria and the value of information. It also includes a Monte Carlo simulation to analyze profit margins and a regression analysis to determine the impact of machine hours and batches on overhead costs. Furthermore, the document explores CVP analysis, determining break-even points and profit levels under different scenarios. The solution incorporates calculations, tables, and conclusions to provide a complete understanding of each concept, referencing relevant business research methods and statistical techniques.

DECISION SUPPORT TOOLS
Student id or name
[Pick the date]

Question 1
“Decision Analysis”
(a) The respective table is shown below:
(b) The computation and result are summarized below in the form of tables.
S.
No.
Computation Result
(i) Optimist would choose investment Share market
(ii) Pessimist would choose investment Bonds
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(iii) Considering criteria of regret Real estate
(iv) Expected monetary value and selection of investment Bonds
(v) Expected value of perfect information (EVPI) $ 15,000
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QUESTION 2
“Value of information”
(a) Profit computation for each of the option i.e. large shop and small shop is shown below:
Conclusion
The conclusion can be made that large shop would result maximum profit (EMV) i.e. $20,000
and hence, it is suggested that Jerry should invest in large shop for bicycle in order to derive high
profit.
(b) “The respective prior probability revision table”
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Prior probability revision for favourable study
Prior probability revision for unfavourable study
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(c) Posterior probability
(d) Expected net gain or expected net loss from engaging by Jerry’s friend to run the market
researcher is shown below:
For favourable study
For unfavourable study
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Computation:
Expected utility = (40000*0.6) + (0 *0.4)
Expected utility = $24,240
Now,
Expected Value of Sample Information (EVSI) = $24240 -$20000
EVSI = $4,240
And,
Associated cost in market research = $3000
Expected net gain = 4240 – 3000
Expected net gain = $1,240
It is apparent that market research result expected net gains and hence, it is suggested that Jerry
should engaged his friend in market research.
QUESTION 3
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“Monte Carlo Simulation”
(a) The data and model is shown below (Holsapple, & Whinston, 2013).
Normal view
Formula view
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(b) Monthly average profit ( for 12 months) = $1000.74
(c) Modified result (When average selling is ranging from $80 to $100 and profit margin is
ranging from 22% to 32%) (Holsapple, & Whinston, 2013).
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Date: 22 September, 2017
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Dear Sir
Acting on the revised pricing estimates provided by you, I have reworked the simulation model
and derived the revised estimates in this regards. On account of better realisations, there has been
a sharp increase in the profits which is reflected through various measures. However, it is
imperative to observe caution especially in wake of the assumption that higher price would not
dampen sale. Evidence needs to be collected for that based on market trends, competitor analysis
and also pilot studies if required. Further, if there is consensus on the price increase and it is
implemented, then sales monitoring on a weekly basis to start with and fortnightly basis later
must be carried out to prevent any contingency and ensure that in case of adverse reaction, the
situation should be under control. This would act as a suitable check against any adverse impact
of price rise.
Yours faithfully
STUDENT NAME
QUESTION 4
“Regression Analysis”
(a) Method: High- low method
Y = A +BX
Where,
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Y =48,000
B=Variable cost per unit= ($48,000-$46,000)/(3800-1800) = $ 1
X =3800 hours
Now,
Y = A +BX
Or
A=Y −BX=48000− ( 1 )∗ ( 3800 ) =$ 44,200
Regression equation through high-low method
Overhead cost (OH) = 44200+ Machine hours
At MH = 3000 hours
Overhead cost (OH) = 44200+ 3000
Overhead cost (OH) = $47,200
(b) Regression
Regression mode 1 – MH is as independent variable
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