Decision Support Tools Assessment - Analysis and Solutions Report

This document presents a detailed analysis and solutions for a Decision Support Tools assessment. The assignment covers various aspects of business decision-making, including investment strategies using optimist, pessimist, and regret criteria, as well as the computation of expected monetary values. It also includes profit calculations for a business, prior probability revision, and the application of Monte Carlo simulation for determining average monthly profit. Furthermore, the assessment involves the application of the high-low method for overhead cost computation, regression analysis, and break-even point analysis. The document provides step-by-step solutions, calculations, and interpretations to guide students through the complexities of decision support tools in a business context. References are also provided for further reading and understanding of the concepts discussed.

DECISION SUPPORT TOOLS
Assessment - 3
[Pick the date]
Student id and name

Question 1
(a)
(b)
(1) Optimist will select share market which is apparent from the below highlighted table:
(2) Pessimist will select bonds which are apparent from the below highlighted table:

(3) By taking the effect of criteria of regret the selected investment would be Real Estate which
are apparent from the below highlighted table (Mittra, 2006).
(4) Computation of expected monetary values for investment options is highlighted below:
Highest expected monetary value is for “Bonds” hence, this is the best option for investment.
(5) Expected value of perfect information
Question 2

(a) Profit computation for each shop
It is apparent that large shop would result prominent profit and therefore, Jerry should open a
large bicycle shop.
(b) Table to represent prior probability revision
 The prior probability revision for favourable case

 The prior probability revision for unfavourable case
(c) Table for required posterior probability
(d) Table for expected net loss or net gains for the utility market research conducted by
Jerry’s friend

 For favourable case
 For unfavourable case
 Table for net gains/loss

Question 3
(a) Monte Carlo simulation model to determine average monthly ( for 12months) profit
(Holsapple & Whinston, 2013)

(a) Average monthly (for 12 months) profit = $970.90
(b) The model with incremental average selling price ($20) and with profit margin (from
22% to 32%).

Date: September 21, 2017

To: Manager, Tully Tyres
Dear Sir
The simulation model has been suitable amended to price in the average rise of $ 20 which has
led to surge in the profit margins and higher profit generation for the company. However, despite
the positives of this move, I would like to take your attention towards a major assumption that
forms the lynchpin of the above analysis. It is estimated that the price increase would not have
any impact on sales and product demand which seems counterintuitive. Hence, the following
measures are advisable.
 Carry a pilot study with increased price to witness the impact on sales
 Vigilant monitoring of the daily sales to measure any dip on sales.
 Consideration to the likely response from the competitors to this move by the company
The above measures would ensure that any potential adverse impact of the move would be
minimised.
Yours Sincerely
STUDENT NAME
Question 4
(a) Computation of over-head cost through high- low method
Variable cost (VC) per unit manufactured = (48000-46000)/ (3800-1800)=$1.00
Fixed cost (FC) = =48000-(3800*1)=$44,200
Regression equation for high- low method
MH= 3000 hours

¿−head cost=44200+ { ( 1∗3000 ) }
¿−head cost=$ 47,20 0
(b) The regression analysis
First model by taking overhead cost (dependent) and machine hours (independent variable)
Analyse and Comment:
R square = 0.0109
The low R square coupled with lack of significance of the regression model (From ANOVA
output) and lack of significance of slope (MH p value > 0.05) hints at the model not being
suitable for estimating overheads cost.

 Second model by taking overhead cost (dependent) and batches (independent variable)
Analyse and Comment:
R square = 0.8313
The high R square coupled with significance of the regression model (From ANOVA output) and
significance of slope (MH p value < 0.05) hints at the model being suitable for estimating
overheads cost (Halhn & Doganaksoy,2011).
 Third model by taking overhead cost (dependent) and machine hours and batches
(independent variables)

Analyse and Comment:
R square = 0.8331
The high R square coupled with significance of the regression model (From ANOVA output) and
significance of slope (Batches p value < 0.05) hints at the model being suitable for estimating
overheads cost. However, the MH variable slope here can be assumed as zero (Halhn &
Doganaksoy,2011).
(c) Best Model – Simple Regression with predictor variable only batches
Reason – The R square corresponding to this model is the highest which implies that this
model is able to offer best explanation in relation to the overhead costs incurred.
(d) The chosen regression mode is model II and the regression equation is shown below:

Question 5
(a) Contribution Margin
(b) Break – even point (units for product B)
(c) Break – even point (units for product A)

(d) (i) Number of units when the ratio of unit sales for product A and B is 2:1
(ii) Number of units when the ratio of unit sales for product A and B

Reference
Halhn, J. G. & Doganaksoy, N. (2011) The Role of Statistics in Business and Industry (7th ed.).
London: London: John Wiley.
Holsapple, C. & Whinston, B.A. (2013) Decision Support Systems: Theory and Application (6th
ed.). Sydney: Springer Science & Business Media.
Mittra, S.S. (2006) Decision support System: Tools and techniques (5th ed.). London: John
Wiley.