Data Analysis and Decision Modelling Assignment

Data Analysis and Decision Modelling Assignment

Data Analysis and Decision Modelling Assignment

Introduction

Holiday Entertainment Corporation have of late acquired a hotel which possess a ground-floor casino, stage shows and a buffet dining, in a bid to increase its investment and expand on the company revenue generation, The top officials and the managers of the HEC are interested in remoulding of the casino so as to optimise the income generated from it. The objective of this report is therefore to give a recommendation on the financial impacts of the alternatives that the developing management can put in place.

By the reviewal of the models, a number of issues will be taken into consideration. One is the target market. This will entail the variety of consumers that the hotel expects to attract, they are grouped into four categories: Pokies, gamers, high rollers and show guests. Each type of customer has unique features which leads to a difference in revenue generation, expenses incurred as well as side effects [ CITATION All15 \l 2057 ].

Together with the consumer effects will also analyse the floor space utilisation in verifying the available options.

Decision modelling

In a normal business operation, the managers face the pressure of having to act quickly and make big decisions. This type of decisions is affected by several variables that may induce mistakes. For instance, working with huge amounts of data, this means the organization profits, the firm share value or even the jobs of several people are dependent on the outcome of the manager’s choice. Due to this stressful part of decision making that can cloud judgement of the decision makers, computer-based decision modelling have been introduced. This way the managers are able to make precise decisions which considers all the aspects of a big data.

As the business world becomes competitive managers are capturing big data that is increasingly becoming hard if not impossible for human brains to break down. The decision modelling software makes use of the big data and algorithms to accurately make predictions and assist optimise the choices which otherwise would be confusing. Decision making by human beings is occasionally affected by biasness and physical issues such as tiredness, hunger and stress [ CITATION Eva18 \l 2057 ]. Machines are not affected by these issues, this way the big data and business modelling makes it easy for firms to make decisions using data without being affected by the biasness that humans are prone to.

Furthermore, machines allow the parse of data which is otherwise too technical for the human brains. The use of decision modelling tools is thus an encouraged way of making more accurate decisions.

Discussion

Base Model

The first available option is the base model. In this case the floor space will be utilised as follows; 5000 square feet will be devoted to buffet dining, another 5000 will be left for show dining and dance area. This will leave the casino with an available space of 40000 square feet.

The objective of the case is to maximise the total revenue which comes from this space. To obtain the total revenue will add the net revenue from each customer type. This is the total income from a client less the expenses incurred [ CITATION Kou13 \l 2057 ]. In addition will add/subtract any amount lost due to the side effects.

When designing the model optimum revenue, a number of constraints will have to be evaluated. The table given below gives a summary of the constraints.

Guest type

Minimum

Maximum

Pokies

600

800

Gamers

400

800

Show Guests

300

800

High Rollers

20

60

Moreover, the total number of guests should not be more than the capacity of the floor which is 1850 people.

By using the above in the excel solver add, we then design a model that will maximise the total net revenue generated by the Holiday Entertainment Corporation from the casino [ CITATION Sie15 \l 2057 ]. By feeding all the information given into the solver add in the optimum revenue to be generated from the model will be equivalent to $739115. The table 3 below gives a summary of the revenue generation.

Customers

Pokies ($)

Gamers ($)

Show Guests ($)

High Rollers ($)

No.

600

655

535

60

Base revenue

120000

196500

53500

300000

Food and Drink Revenue

75000

65500

66875

0

Side effect

0

-58950

-25680

8520

Expenses

600

26200

5350

30000

Net revenue

194400

176850

89345

278520

Total

     

739115

The row named “No.” gives the maximum number of each categories of consumers that the firm will need to attract to achieve the total net revenue indicated.

Alternative 1

Based on the alternative 1 model, the income, expense and side effect pattern of each category of customer is assumed to remain the same regardless of the changes made. Here will modify the buffet dining area to 1500 square feet from the 5000 square feet suggested under the base model. The dining and dance area is retained at 5000 square feet. This will leave a total of 43500 square feet for the use by the casino. In this case apart from the floor space the table below summarises the additional constraints to be considered.

Guest type

Minimum

Maximum

Pokies

600

800

Gamers

400

No maximum

Show Guests

300

800

High Rollers

20

No maximum

Moreover, the total number of guests allowed in the casino in a given day should not exceed the capacity which is 1850. By using the solver add in in Microsoft Excel, the information can be fitted in to the model to give a revenue generation summary illustrated in the table 5 below.

Table 5: Net Revenue Generation Table

Alternative 2 

The model thus generates a total net revenue of $ 1264800. The number of consumers that the firm need to attract to meet this target will be 600 pokies, 4oo gamers, 300 show guests as well as 195 high rollers.

Based on the alternative 2 model the management accounting s interested in optimising the floor space so as to raise a net revenue which is above $ 1500000. As a way of doing this, there is need to reduce the floor space covered by the buffet as well the show dining and dance events [ CITATION Tod02 \l 2057 ]. This though have an indirect impact on the number of guests visiting the casino and hence need to be moderated to a reasonable mix. Any mix obtained will need to contain at least two categories of guest for it be acceptable. To obtain an appropriate mix, the above information is fitted in the excel solver add in. The revenue table yielded from the excel solver is as shown in table 4 below.

Table 4 Revenue table from the Excel Solver

Comparison of the Models
To obtain this revenue the firm will have to attract a minimum of 425 high Rollers as well as at least 500 Pokies.Optimum net revenue will be generated when the buffet area and show dining and dance area are eliminated. This though will mean the number of gamers and Show Guests will drop to zero per day for each. Despite the drop in the number the firm will be able to generate a revenue of $ 2, 109, 000 which is high above the expectation of the management.

 

The comparison of the three models gives a variation in the floor space used as well as the revenue generated under each model. The base model allows adequate floor space for the buffet dining and the dance activities [ CITATION Roo06 \l 2057 ]. This model though is disadvantageous as the amount of net revenue generated from it is quite low.

On the other hand, the alternative 2 model allows for enough room for the dance area though it cuts the buffet area. This though have no negative impact on any group of clients as their expenditure and access to the hotel remains steady. When compared to the base model the alternative 1 is a better model as the revenue generated rises. Unlike the less than a million initially generated, adopting the alternative 1 model will see the income of Holiday Entertainment Corporation rise to $ 1264800. Considering that it has no impact on the consumers’ attitude this model is favourable

Consequently, the alternative 2 model will mean the firm has to do away with the buffet and the dance area altogether. This leaves a wider space for the casino and in return leads to generation of a net revenue of $ 2109000. Out of the three models the alternative 2 is the best as it means the firm is able to generate enough revenue to surpass the management target of $1500000. This model though will disadvantage the consumers of category of Gamers and show guests as they will stop coming to the casino altogether.

Conclusions

Quality decision making forms the basis for appropriate running of an organisation which in turn mean optimisation of profitability. For the managers to make decisions that are relevant and able to increase the shareholders capital there is need to have relevant information regarding the industry. From the case study the management have experience in running a hotel and are able to predict the behaviour of the clients.

Using this behaviour, the revenue income and expenses from each client can be computed from which predictions are made.

The analysis of the revenue has been done using the solver add-in in Microsoft Excel. The results if the analysis is a proof that the firm need to eradicate the buffet dining as well as the dance area from the casino. Making the ground floor an exclusively casino area will allow HEC to specialise and serve only two categories of consumers that is the pokies and the High Rollers.

Recommendations

Based on the results, it is therefore recommended that the hotel adopt the alternative 2 model. That is specialise in casino services only. This will yield a net revenue of $ 2, 109, 000 for the firm. This is a way above the $ 739, 115 that can be raised by the current base model.

To assist achieve this target there will be need for the firm to design its advertisements so as to target the pokies and the high rollers. The success of the firm is dependant in these two categories of consumers hence more resources will need to be directed towards attracting them to the firm.

References

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2. Evans, E. (2018, April 19). What Is Decision Modeling, And Why Do You Need It? Retrieved from https://www.omniresources.com/blog/what-is-decision-modeling

3. Koufogiannakis, C., & Young, N. E. (2013). A Nearly Linear-Time PTAS for Explicit Fractional Packing and Covering Linear Programs. Algorithmica, 648–674.

4. Roos, C., Terlaky, T., & Vial, J. (2006). Interior Point Methods for Linear Optimization. Springer-Verlag.
5. Sierksma, G., & Zwols, Y. (2015). Linear and Integer Optimization: Theory and Practice. CRC Press.
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