Assessment On Decision Analysis, Value Of Information, And Profitability Analysis

Assessing Utility Function and Decision Matrix

• A mathematical function which highlights the risk and return related viewpoint of a given investor is termed as utility function. As highlighted in the definition, utility function tends to differ from one person to other and also tends to alter over time. Finally, the underlying liquidity also tends to act as a factor changing utility function. Standard gamble is a direct method for determining utility which relies on obtaining the preferences when uncertainty is there. This is exhibited in the healthcare setting where the quality of life is ascertained by indicating the respective utility of the different health conditions (Hillier, 2016).

• (i)The stock returns in the given case are dependent on the market conditions which may be categorised as good, fait and bad. The respective payoffs are computed below.The respective decision matrix highlighting the payoffs of the two choices under different states of nature is exhibited below.

(ii) The MAXIMAX strategy would be preferred by an optimist (Medhi, 2015). The maximum possible payoff is $ 1,400 which can arise when investment in stock is made. Hence, an optimist would invest in stock.

(iii) The MAXIMIN strategy would be preferred by an pessimist (Flick, 2015). The worst state returns for the given assets is summarised below.A pessimist would apply in bonds since this maximises the returns in worst case scenario.(iv) The regret matrix based on the given decision matrix is indicated as follows.

For both the investment choices, the minimum regret is zero. Thus, an investor would be indifferent to the two choices and may prefer any of these.

(v) The EMV approach based on the given probabilities associated with differing states of nature is shown below.

Investment in bonds is recommended since EMV is higher than stocks.

(vi) The EPVI can be computed in the manner exhibited as follows.

EMV calculation for the new electric razor 

It is apparent that calculated EMV comes out to be positive and therefore, production of electric razor would be recommended.

• Prior Probability for both good market and poor market  Expected value for perfect information: For favourable study Expected value for perfect information: For unfavourable studyExpected value in case of perfect information would exceed the cost of information i.e. $ 5,000 and hence services of friend may be taken.
• Simulation to find the average profit for the 12 months through Monte Carlo Simulation  

Average profit (for 12 months) = 4302.60

• The new simulation has been made after making two significant changes.

Change 1: Selling price increased from $160 to $200 and $180 to $220

Change 2: Profit margin increased from 20% to 22% and 30% to 32%

(c) Introduction

The results of simulation have been presented so as to understand the impact of price changes on the profitability of the business.

Findings

It is quite evident that under the given proposal, there would an increase in the profits, both in terms of margin and also absolute number. Therefore, the above proposed changes are likely to be approved by the shareholders. However, a key aspect before migrating to the new prices is management of risk and uncertainty. This may arise on account of adverse sales movements owing to which while the margins may increase but owing to falling sales the improvement in profits may not occur.

Recommendations

As a result, it is recommended that the above proposal should be first implemented on a small scale and only after ascertaining the result should it be applied on the large scale.

• The high- low method for computation of total over-head cost is show furnished below.

It can be seen that p value corresponding to slope coefficient (Machine hours) is higher than alpha and thus slope would not be significant for the analysis and would be taken as zero.

The regression model would not be a good fit model because the significance F is higher than alpha.

Regression Model 2:

It can be seen that p value corresponding to slope coefficient (Batches) is lower than alpha and thus, slope would be statistically significant for the analysis.

The regression model would be a good fit model because the significance F is lower than alpha.

Regression Model 3:

For Machine hours	For Batches
p value corresponding to slope coefficient is higher than alpha (0.79>0.05) Slope is insignificant	p value corresponding to slope coefficient is lower than alpha (0.00<0.05) Slope is significant

The regression model would be a good fit model because the significance F is lower than alpha.

• Regression model 2 is considered as the best model because the coefficient of determination is highest (0.8331). It implies that 83.1% variation of overhead will be explained by variation in batches. Also, the regression models clearly indicate that machine hours is not a significant variable and hence the same must not be considered (Flick, 2015).Regression mode 2: Best Regression Model

• Unit contribution margin of products Number of units (B) needs to be found at break-even points.

Fixed cost = Break even units * Unit contribution margin

Break even units (B) = Fixed cost / Unit contribution margin (B) = 5000/5 = 1000 units

• Number of units (A) needs to be found at break-even points.

Fixed cost = Break even units * Unit contribution margin

Break even units (A) = Fixed cost / Unit contribution margin (A) = 5000/4 = 1250 units Product production ratio for product A to product B is 3:1.

References

Flick, U. (2015) Introducing research methodology: A beginner’s guide to doing a research project (4^th ed.). New York: Sage Publications.

Hillier, F. (2016) Introduction to Operations Research. (6^th ed.). New York: McGraw Hill Publications.

Medhi, J. (2015) Statistical Methods: An Introductory Text (4^th ed.). Sydney: New Age International.