(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.
Average profit (for 12 months) = 4302.60
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.
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.
Fixed cost = Break even units * Unit contribution margin
Break even units (B) = Fixed cost / Unit contribution margin (B) = 5000/5 = 1000 units
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 (4th ed.). New York: Sage Publications.
Hillier, F. (2016) Introduction to Operations Research. (6th ed.). New York: McGraw Hill Publications.
Medhi, J. (2015) Statistical Methods: An Introductory Text (4th ed.). Sydney: New Age International.
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