ACC544 Accounting Decision Support Tools

• The process invovled in decision making is not a single step process but rather consists of a number of steps.
• First, the decision related expectations needs to be defined along with identification of possible outcomes in relation to the decision to be made.
• The various alternatives available at the behest of the decision maker need to be outlined.
• Considering the crucial role of the states of nature, they must be defined and also their underlying possibility predicted with as much accuracy possible.
• Further, in the light of the states of nature, the payoff matrix needs to be formed which would highlight the payoffs associated with each alternative.
• Taking the payoff matrix into consideration along with the decision model approved, a decision is finalised by the decision maker.

• In case of any particular decision, the decision maker would have a plethora of possible strategies to choose from which are categorised as alternatives. Choosing a suitable alternative from the perspective of the decision maker becomes challenging owing to the various states of nature or future scenarios that can arise. A particular alternative may not work in different states of nature and hence the decision maker needs to choose one considering the potential states of nature.

(c)(1) The requisite condtional profits matrix is given below:

(2)As highlighted in optimistic rule matrix, 30 kilogram will be the best possible option  to purchase sea food by fish vendor in each week.

(3) As highlighted in pessimistic rule matrix, 25 kilogram will be the best possible option  to purchase sea food by fish vendor in each week.

(4) As highlighted in laplace rule matrix, 25 kilogram will be the best possible option  to purchase sea food by fish vendor in each week.

(5) As highlighted in regret matrix, 10 kilogram will be the best possible option to purchase sea food by fish vendor in each week.

(6) As highlighted in maximizing expected matrix, 10 kilogram will be the best possible option to purchase sea food by fish vendor in each week.

(7) Weekly demand: Normal distribution

Mean value = 20kilogram

Standard deviation =5 kilogram

Kg of sea food for maximum profit =?

 z value

z value

z value

Kg of sea food for maximum profit

Hence, 23kg of sea food would be purchased weekly for maximum profit.

• The simulated data for various costs associated for hotel rooms for one month is shown below:

(b) Simulation model

Normal view

Formula view

(c) For the overbooked number of rooms, the average daily cost has found and has represented below:

• The advice to the hotel manager can be given as highlighted below.

Taking into cognizance the current practice of overbooking three rooms, associated cost structure and the no shows by the clients, a simulation has been run for ascertaining the cost efficiency of the policy being followed by the hotel. However, comparison of the average costs in this regard highlights issues with the current policy since cost can be further brought down if the company goes for four overbooking in line with the results obtained. Hence, to enhance profits, the migration to the new policy should be completed at the earliest.

1. Model A

The main highlights on the basis of the excel output of regression illustrated above, the following conclusions may be drawn.

• Slope coefficient β_mileageis critical or significant as derived from the comparison of p value and significance level of 5% where the p value emerges as the lower value..
• R² or coefficient of determination highlights that the mileage independent variable can potentially enable explaining 72.21% of the total variable that is observable in car price.

Model B

The main highlights on the basis of the excel output of regression illustrated above, the following conclusions may be drawn.

• Slope coefficient β_ageis critical or significant as derived from the comparison of p value and significance level of 5% where the p value emerges as the lower value..
• R² or coefficient of determination highlights that the age independent variable can potentially enable explaining 73.11% of the total variable that is observable in car price.

Model C

The main highlights on the basis of the excel output of regression illustrated above, the following conclusions may be drawn.

• Slope coefficients β_mileage& also β_age are not critical or significant as derived from the comparison of p value and significance level of 5% where the level of significance emerges as the lower value.
• R² or coefficient of determination highlights that the mileage independent variables jointly can potentially enable explaining 73,88% of the total variable that is observable in car price.

Discussion on Best Model

Model A – It is able to establish a significant linear relation with coefficient of determination as 0.7221

Model B – It is able to establish a significant linear relation with coefficient of determination as 0.7311.

Model C – This model is not considered as an ideal choice since both of the slope coefficients are found to be insignificant.

Considering that model C is not considered, amongst the simple regression models, the choice is rather straightforward based on the higher value of coefficient of determination. Hence, Model B emerges as the optimum choice.

1. The superior model from the two simple regression models has already been identified in part (a). This is the Model B where the independent variable is the car’s age which is a more significant variable in comparison to car’s mileage. Further, regression also indicates an inverse relation between age and price which seems correct owing to deterioration in value as car turns old owing to decline in value and also new models being launched by the company. Further, higher mileages also diminishes price which also does not pose any surprise since typically internal damage and general wear and tear is related to mileage.

1. One of the key assumptions which a multiple regression model must follow is that multicollinearity should not be present. This means that the independent variables used to construct the model should not have any significant correlation between them. This assumption is not satisfied owing to the significant correlation between the independent variables i.e. mileage and age. Thus, the given model would not be considered valid and hence it must not be used for predicting the price.

• Firm’s profit becomes zero for break-even point and hence, in CVP analysis put profit =0.

Requisite units manufactured of A for break-even = 300

• Firm’s profit = $1600

Requisite units manufactured of A = 500

• Firm’s has introduced a new product B along with A. The ratio of manufacturing of A to B is decided as 2:1. Also, firm’s profit is $20,000.

Requisite units manufactured of A would be1000 and of B would be 2000.