Data Analysis and Decision Modelling

This report provides a recommendation on the financial impacts of the alternatives that the management can put in place to optimise the income generated from the casino. It includes decision modelling and comparison of models.

Running head: Data Analysis and Decision Modelling
Data Analysis and Decision Modelling
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Data Analysis and Decision Modelling
Introduction
In a bid to increase its investment a and expand on the company revenue generation
Holiday Entertainment Corporation (HEC) have of late acquired a hotel which possess a
ground-floor casino, stage shows and a buffet dining. The senior managers of the firm 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 management can put in place.
In reviewing 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 (Allen-Zhu & Orecchia, 2015).
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 managers 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 (Evans, 2018). 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.
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Data Analysis and Decision Modelling
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 (Koufogiannakis & Young, 2013). 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
Also, the total number of guests should not exceed the floor capacity which is 1850
people.
Using the information above we used the excel solver add in to design a model that will
maximise the total net revenue generated by the HEC from the casino (Sierksma & Zwols,
2015).
By feeding all the information given into the solver add in the optimum revenue to be
generated from the model will be $ 739, 115. The table below gives a summary of the
revenue generation
Customers Pokies Gamers Show High
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Data Analysis and Decision Modelling
Guests Rollers
No. 600 655 535 60
Base revenue
$120,000.0
0
$196,500.0
0
$53,500.0
0
$300,000.0
0
Food and drink
revenue $75,000.00 $65,500.00
$66,875.0
0 $0.00
Side effect $0.00
-
$58,950.00
-
$25,680.0
0 $8,520.00
Expenses $600.00 $26,200.00 $5,350.00 $30,000.00
Net revenue
$194,400.0
0
$176,850.0
0
$89,345.0
0
$278,520.0
0
Total
$739,115.0
0
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
Under 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
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Data Analysis and Decision Modelling
In addition, the total number of guests allowed in the casino in a given day should not
exceed the capacity which is 1850.
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 below.
Net revenue Generation table
Customers P G S H
No. 600 400 300 195
Base revenue
$120,000.
00
$120,000.
00
$30,000.
00 $975,000.00
Food and drink
revenue
$75,000.0
0
$40,000.0
0
$37,500.
00 $0.00
Side effect $0.00 -$9,000.00
-
$14,400.
00 $7,800.00
Expenses $600.00
$16,000.0
0
$3,000.0
0 $97,500.00
Net revenue
$194,400.
00
$135,000.
00
$50,100.
00 $885,300.00
Total
$1,264,800.
00
This model gives a total net revenue generation of $ 1, 264,800. 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.
Alternative 2
In the alternative 2 model the management is interested in optimising the floor space
so as to raise a net revenue which is above $ 1,500,000. As away of doing this there is need to
reduce the floor space covered by the buffet as well the show dining and dance events (Todd,
2002). This though have an indirect impact on the number of guests visiting the casino and
hence need to be moderated to a reasonable mix.
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Data Analysis and Decision Modelling
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.
This yield the revenue table given below
Customers Pokies Gamers
Show
Guests High Rollers
No. 500 0 0 425
Base revenue $0.00 $0.00 $60,000.00
$2,200,000.0
1
Food and drink
revenue $0.00 $0.00 $75,000.00 $0.00
Side effect $0.00 $0.00 $0.00 $0.00
Expenses $0.00 $0.00 $6,000.00 $220,000.00
Net revenue $0.00 $0.00
$129,000.0
0
$1,980,000.0
1
Total
$2,109,000.0
1
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.
To obtain this revenue the firm will have to attract a minimum of 425 high Rollers as
well as at least 500 Pokies.
Comparison of the models
A 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 (Roos, Terlaky, & Vial, 2006). 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
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Data Analysis and Decision Modelling
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 HEC rise
to $ 1,264, 800. Considering that it has no impact on the consumers attitude this model is
favourable
Furthermore, 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 $ 2, 109,000. 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 $1, 500,000. 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.
Recommendation
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
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Data Analysis and Decision Modelling
dependant in these two categories of consumers hence more resources will need to be
directed towards attracting the them to the firm.
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Data Analysis and Decision Modelling
References
Allen-Zhu, Z., & Orecchia, L. (2015). Using Optimization to Break the Epsilon Barrier: A
Faster and Simpler Width-Independent Algorithm for Solving Positive Linear
Programs in Parallel. ACM-SIAM Symposium.
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
Koufogiannakis, C., & Young, N. E. (2013). A Nearly Linear-Time PTAS for Explicit
Fractional Packing and Covering Linear Programs. Algorithmica, 648–674.
Roos, C., Terlaky, T., & Vial, J. (2006). Interior Point Methods for Linear Optimization.
Springer-Verlag.
Sierksma, G., & Zwols, Y. (2015). Linear and Integer Optimization: Theory and Practice.
CRC Press.
Todd, M. J. (2002). The many facets of linear programming. Mathematical Programming,
417-436.
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