Decision Support Tools Assignment - Business Analysis Solutions

This document presents a comprehensive solution to a Decision Support Tools assignment, addressing various aspects of business decision-making. The solution includes detailed explanations and calculations for different scenarios, such as decision-making under certainty, risk, and uncertainty, utilizing tools like EMV, decision trees, and scenario analysis. It covers topics like expected monetary value, expected value of perfect information (EVPI), and probability revision. The assignment also delves into Monte Carlo simulation to compute expected monthly profit and explores the high-low method and regression models to analyze overhead costs, providing insights into model fitting and variable significance. Finally, it addresses break-even analysis and unit contribution margin calculations for product sales, offering a complete overview of decision support tools and their applications in business analysis.

DECISION SUPPORT TOOLS
Assignment - 3
Student id
[Pick the date]

Question 1
(a) The various differences between the three situations are highlighted below (Taylor &
Cihon, 2004).
 Level of information available is the highest for the decision under certainty and
lowest for decision under complete uncertainty.
 The decision takes under certainty are programmable decisions and easy to make. On
the contrary, the decision making under complete uncertainty is very tough and
hence non-programmable decision. The decision making under risk lies in between
the above two.
 The tools used are different. For decision making under certainty, conventions serve
the purpose. For decision making under risk, there are tools such as decision tree and
scenario analysis, NPV. However, for decision making under complete uncertainty,
reliance is on intuition and creativity based on experience.
(b) Given payoff matrix
1. The alternative which an optimist would select is shown below:
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2. The alternative which a pessimist would select is shown below:
3. The alternative which is the indication by the criteria of regret is shown below:
4. Probability of good economy = 0.3
The optimum action after using expected monetary values =?
It I apparent that a maximum expected monetary value is $23,000 and hence, it is recommend to
make investment in the bonds.
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5. Expected value of perfect information (EVPI) is computed below:
EVPI = (0.3*80000) + (0.7*20000) – 23000
EVPI = $ 15,000
Question 2
(a) Calculation of EMV is highlighted below:
It present case, Jerry has two possible options i.e. to open a large shop or small shop. The profit
computation for both the scenario favourable and unfavourable market is highlighted below
(Medhi, 2001).
The final table is highlighted below:
The EMV is highest for large shop and hence, this is the best option for Jerry.
(b) The probability revision data
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The probability revision for favourable study is highlighted below:
The probability revision for unfavourable study is highlighted below:
(c) The posterior probability for good market is shown below:
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Hence, the desired posterior probability is computed as 0.25.
(d) The expected net gain/loss for respective utility of market researcher is highlighted below
(Mittra, 2006):
For favourable case
For unfavourable case
The expected utility = (40000*0.6) + (0*0.4) = $24,240
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EVSI = 24240 – 20000 =$4,240
Cost incurred in market research by Jerry’s friend = $3000
Gains = EVSI - Cost incurred in market research by Jerry’s friend
= 4240-3000 = $1,240
The conclusion can be made that that gains has been incurred from the market research and
hence, it can be said that Jerry’s friend should be engaged in the market research (Medhi, 2001).
Question 3
“Monte Carlo Simulation”
(a) The requisite model to compute the expected monthly profit is shown below (Taylor
Cihon. 2004).
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(b) As per the above made model, the average monthly profit (for 12 months) is $836.47
(c) The modified model by taking average sale price from $80 to $100 and the profit margin
from 22% to 32%.
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Date: September 21 2017
Addressed to: Manager, Tully Tyres
Dear Sir
Acting on your advice and insight about the average price hike to the extent of $ 20 per unit, the
statistical simulation model has been suitably altered. The profit yielded is on the increase due to
traction of profit margins for the company. This clearly hints at the utility of the suggestion from
the perspective of the shareholders. However, the advice needs to viewed along with the
underlying assumption of sale not being adversely impacted. The same would need to be verified
through implementation only as the response of the competitors would also need to be mapped.
Further, considering the implications of price rise on customer loyalty and market share, it makes
sense to limit this rise only to a limited product line or geography so that before full scale rollout,
an empirical run has been done which should justify the same. Also, during implementation also,
recording of sales on a daily basis would be crucial so as to detect any adverse impact on an
early basis. These precautions are necessary so as not impact the reputation and competitive
advantage of the firm adversely.
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Question 4
(a) Applied method: High low method
VC ¿ ( 48000)−( 46000)
(3800)−(1800) =$ 1
FC = (48000) – (3800*1) = $ 44,200
Hence, the regression equation (by high low method) can be formed as shown below:
Over-head cost = 44200 + (1*Machine Hours)
The over-head cost foe machine hours as 3000.
Over-head cost = 44200 + (1*3000) = $47,200
(b) Regression model (1)
Depended variable: Overhead cost
Independent variable – Machine hours (MH)
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Comments
The value of R square is considerably low which shows that model is not a good fit model for
analysis.
The p value for MH = 0.77
Alpha = 5% (Assuming)
The p value > alpha and hence, the slope can be said as zero and insignificant. For the model the,
it can be seen from the ANOVA table that F statistics is 0.087 and p value is 0.774. Here, also
the p value > alpha and thus, that model is not a good fit model for analysis (Halhn &
Doganaksoy, 2011).
Regression model (II)
Depended variable: Overhead cost
Independent variable – Batches
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