Business Decision Simulation: Optimizing ABC Company Strategy

This report analyzes the business decisions of ABC company using Expected Monetary Value (EMV) and Sensitivity Analysis. The core problem revolves around determining optimal fixed and variable costs to maximize returns, given limited production and sales units across varying market conditions (good, average, and bad). The report evaluates different scenarios, including reducing variable costs, reducing fixed costs, and a combined strategy, comparing their respective EMVs against a base case. Sensitivity analysis is performed to understand the impact of sales volume changes on profitability. The report concludes that a strategy combining reduced variable and fixed costs yields the highest EMV, making it the most effective approach for ABC company. It also briefly touches upon ethical considerations in decision-making and the role of big data, identifying a potential research gap in the integration of these aspects. The report uses quantitative risk analysis to show the effect of probability of different possible market conditions.

Business Decision Simulation
Assessment

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
Part 1: Business Decision Simulation..............................................................................................3
Summary of the problem.............................................................................................................3
Decision making tools.................................................................................................................3
Expected Monetary Value...........................................................................................................3
Sensitivity Analysis.....................................................................................................................4
Introduction..................................................................................................................................5
Scenario 1: Base case...................................................................................................................6
Scenario 2: Reducing Variable Cost............................................................................................6
Scenario 3: Reducing Fixed Costs...............................................................................................7
Scenario 4: Your Strategy............................................................................................................7
Sensitivity Summary....................................................................................................................8
Evaluation: Problem Solving Process..........................................................................................8
Part 2................................................................................................................................................9
Introduction..................................................................................................................................9
Decision making within an ethical context..................................................................................9
Big data within a cultural context..............................................................................................11
Research Gap.............................................................................................................................12
Conclusion.................................................................................................................................12
REFERENCES................................................................................................................................1

INTRODUCTION
Business decision refers to the decision taken by the management of the company for
determining the short and long term activities of the company in order to meet its objectives.
There are various risks that the business might face as a result of the uncertainties existing in the
business environment. The report will analyse the issue that is faced by the ABC company. The
company faces the issue of deciding the fixed and variable cost that will yield maximum returns
for the company under its limitation to produce and sale limited number of units.
MAIN BODY
Part 1: Business Decision Simulation
Summary of the problem
The problem that the ABC company is facing currently is that the sales unit of the
company in all three of the market situations that are good, average and bad are fixed. The
company will be able to sell 1500 units is the market conditions will be good. In case the market
conditions will be average the company expects to sell 1200 number of units. And in the case of
bad market conditions the company will be able to sell 800 units only as per the expected trend
(Huang, Silitonga and Wu, 2022). The issue that the company facing is to decide the variable
cost and the fixed cost that will when incurred be able to make fair returns for the company.
Decision making tools
The decision making tools used in the report for solving the issue that ABC company is
facing are expected monetary value and sensitivity analysis. The reason for selecting the
expected monetary value tool is that it quantifies the risk. Expressing the risk in the quantitative
format and then performing analysis of the risk accordingly helps in undertaking the most
optimum decision. Further sensitivity analysis helps decision makers to make full use of the all
the content available (Gatti, Ulrich and Seele, 2019). The benefits and drawbacks of the situation
along with the limitations attached and scope of the decision can be taken into consideration.
Using the particular method, the predicted outcomes of the decisions can be compared with the
base outcome from the key prediction.
Expected Monetary Value
Expected monetary value is a part of risk management. It is used for performing the
analysis for risk by expressing it in quantitative terms. The technique includes mathematical
computations. The method is basically based on using only one formula. The calculation of EMV

involves probability. The chances of occurring of an event is known as the probability of the
particular event. EMV calculation hence helps in finding out the average of all the possible
future outcomes. The formula for Expected Monetary Value is equals to probability of the
occurrence of a particular event multiplied with the monetary impact of that particular event
(Ahmed and Alsisi, 2022). The expected monetary value is computed for every risk either
positive or negative. After the EMV is calculated the estimated costs of the work are added for
the generation of the cost baseline. This give the value for contingency reserve. The sum total of
EMVs that are calculated helps to reach the contingency reserve, all the values gets add up.
The expected monetary value assists the planners of the projects to know the amount of
contingency reserve for managing the costs and associated risks. As the monetary value of each
alternative is known the it helps the decision makers to decide one alternate. It is the cost
effective way in which with the use of past data risks are evaluated. It gives average outcome of
all risks that are identified.
The method is recommended for large projects only and hence should not be used for
small projects as the accuracy rates are low in such cases. The major determinants, impact and
probability are depended on the opinions of the experts and historic data. So it can be biased
based on the subjectivity of the determinants (Mariotto and et.al., 2020). Both the positive and
negative risks are to be identified for the computation of accurate EMV. The positive risks are
the opportunities for the company and negative risks are the threats to the company.
Sensitivity Analysis
Through sensitivity analysis the effect of change in the independent value over the
dependent variable is determined. It is the study of contribution of different uncertain risk
sources in mathematical model in the overall uncertainty (A Ashley and F Parmeter, 2020). It is
popularly also known as what if analysis among the economists and financial analysts. The
analysis is used by businesses.
It is a financial model for determining the affect in targeted variables that comes with the
change in other variables referred to as input variables. Sensitivity analysis is also known as
What-If analysis. The analysis helps in making prediction about share prices of public
companies. Investors find the sensitivity analysis to determine the effect different variable have
on investment returns (Pang and et.al 2020). This analysis is useful in study of “black box
process” where the output is an opaque function of several inputs. The user who conducts the

analysis can see the change in variable movement as well as how target is affected by the input
variable. As this helps in making informed choices, decisions taken by the analyst can help in
achieving tangible conclusions and making optimal decisions. This also allows decision makers
to identify what improvement can be done in the future. The sensitivity analysis directly
approaches the valuation which financial model processes using Eps and variable price.
There are different methods to approach sensitivity analysis
Modelling and simulation technique
Modelling and simulation involves a process of designing a real life model or anticipated
system which conducts experiments with the model for the purpose of understanding the
performance of system under different operating conditions and hence evaluating their
performance with the benchmark set which can be used for decision-making process.
Scenario management tools through Microsoft excel
Scenario management in excel is part of what if analysis tools which is a built in feature.
This allows the user to change modify or substitute input values for multiple cells (Qian and
Mahdi 2020). This allows the user to view the result of different scenarios in with different
inputs at the same time.
This are two approaches to analyse sensitivity analysis
Local sensitivity analysis: This analysis is a one at a time technique which analyses effect
of one parameter on the cost function at that time keeping all other parameters fixed.
Global sensitivity analysis: This analysis is used as Monte Carlo technique which uses
global set of samples to explore the design space.
So, sensitivity analysis provides user/decision makers with more than one solution to a problem.
Application of the tool – Evaluation
Introduction
Sensitivity analysis is a tool used in financial modelling to analyse how different
independent variable affects a specific dependent variable under specific conditions. The analysis
used by sensitivity consists of wide range of fields, ranging from biology to engineering. The
below report will analyse how to identify variations in input values for a given variable impact
with financial modelling. The analysis therefore used will critically evaluate a projects return
over the period.

Scenario 1: Base case
Sensitivity Analysis
% Decrease Sales
Expected
Monetary
Value (EMV)
% Drop in
EMV
Scenario 1 13,825
5% 12,884 -6.81%
10% 11,943 -13.62%
15% 11,001 -20.42%
The scenario 1 is the base case it is based on the assumption that the variable costs of the
company will be $ 10 per unit. The fixed cost as per this base scenario are $ 5000. The above
table is the summary of the output of the calculation of expected monetary value for the scenario
1. The price of the product per unit is $ 25. The contribution is the price less variable cost that is
$ 15 per unit. EMV is calculated with the formula Impact * probability (Moreno-Sader, Meramo-
Hurtado & González-Delgado,2019). The probability for good, average, bad market conditions
are 0.45, 0.35, 0.2 respectively. The three assumptions are decrease in sales volume by 5, 10 or
15 percent. The EMV for 5% decrease in sales volume is $12884, 10% decrease is $11,943 and
15% decrease gives EMV as $11001.
Scenario 2: Reducing Variable Cost
Sensitivity Analysis
% Decrease Sales
Expected
Monetary
Value (EMV)
% Drop in
EMV
Scenario 2 16,335
5% 15,268 -6.53%
10% 14,202 -13.06%
15% 13,135 -19.59%
The above table shows the computed values of EMV under each possible market
conditions when the variable cost will be reduced to $8 from $10. The three market threats
identified are fall in sales volume by 5%, 10% or 15%. The EMV in each of the following
condition will be $15268, $14202 and $13135. Percentage drop in EMV as calculated will be
6.53%, 13.06% and 19.59%.

Scenario 3: Reducing Fixed Costs
% Decrease Sales Expected Monetary
Value (EMV) % Drop in EMV
Scenario 3 15,325
5% 14,384 -6%
10% 13,443 -12%
15% 12,501 -18%
In scenario 3, to combat with three market conditions namely good, average and bad in
which maximum 1500, 1200 and 800 units can be sold and further decrease in the sales volume
by 5%, 10% and 15%, the fixed cost is assumed to be decreased from $5000 to $3500 in this
scenario. In the given scenario, although EMV decreased from base case in case of 5% decrease
in sale, 10% decrease in sale and 15% decrease in sale but on comparison from base case in
which fixed cost was $5000 the EMV shows improvement in all the sales reduction options.
Scenario 4: Your Strategy
Sensitivity Analysis
% Decrease Sales Expected Monetary
Value (EMV) % Drop in EMV
Scenario 3 17,835
5% 16,768 -6%
10% 15,702 -12%
15% 14,635 -18%
In scenario 4, a strategy is required to be designed to combat the decrease in sales volume
by 5%, 10% and 15% therefore, the most appropriate strategy to be designed and implemented is
to decrease variable costs from $10 to $8 and decrease fixed costs from $5000 to $3500. Such a
strategy. Therefore, assumptions are reduction in variable costs along with simultaneous
reduction in fixed costs. Such assumptions and strategy will lead to increase in EMVs in in all
sales reduction options as compared to the EMVs in the base case where neither variable costs
were reduced not fixed costs were reduced.

Sensitivity Summary
Summary Table:
Sensitivity Analysis For ABC Company
Expected Monetary Value (EMV)
Scenario 1 Scenario 2 Scenario 3 Scenario 4
Base Case 13,825 16,335 15,325 17,835
5% 12,884 15,268 14,384 16,768
10% 11,943 14,202 13,443 15,702
15% 11,001 13,135 12,501 14,635
On the basis of all the strategies, a sensitivity summary analysis table is being formulated
to compare EMVs in scenario 2, 3 and 4 from the base case scenario 1. From the summary table
of sensitivity analysis, it can be observed that EMV when compared to base case scenario 1,
EMVs shows increase which is the ultimate objective behind reduction of such variable and
fixed costs. For example, EMV for 5% decrease in sales volume in scenario 1 is $12884
whereas, it increased to $15268, $14384 and $16768 in scenario 2, 3 and 4 respectively. Same is
the case with 10% and 15% decrease in sales volume which shows effectiveness of EMV
decision making tool for ABC company.
Evaluation: Problem Solving Process
 EMV is calculated as Probability*Impact which shows the probability of various i.e.,
effect of probability of different possible outcomes like market conditions. Therefore,
decreasing EMV shall indicate problems and risks in the respective business which are to
be addressed. It is a quantitative risk analysis which will be quantifying the risk and
comparing such risks and related calculations with the resultant calculations and reduced
risks after employing corrective measures and strategies.
 Since EMV will be quantifying the risk therefore, there will not be any vagueness or
approximations regarding any risk and entity will be able to calculate exact amount of
risk faced by the entity and therefore, substantive and immediate corrective actions can
be taken which will lead to effective decision making.

 There are other tools as well for improving problem solving process of an entity. One of
those tools is Expected Opportunity Loss criterion (EOL) which includes comparison
between actual payoff and optimal payoff of the entity. Another tool being, Expected
Value of Perfect information which is a difference between payoffs expected with no
information (EMV) and payoff expected with perfect information (EPPI).
Part 2
Introduction
The combination of structured and unstructured data along with the semi structured data
is referred to as big data. Decision making is process of making choice between alternative. The
challenges faced during decision making refers to confusion in deciding which alternative will
yield maximum returns.
Decision making within an ethical context
Valentine and Godkin, (2019) explains that ethical decision making motivates
whistle blowing in the entity which can be considered employee sensitive ethical issue and
problem. Such whistle blowing is defined herein as disclosure of immoral, unethical or illegal
practices in the entity by those in the entity to those who are able to influence the actions in the
entity. In case of ethical decision making, decisions and responses are based on judgement of an
individual in the given circumstance and thus may vary from individual to individual. Ethical
decision making involves an ethical scenario purely based on the perception of the individual.
Such a reporting on illegal or illicit issue will also affect the public interest of the entity
adversely. Although ethical, but whistle blowing will have its own disadvantages like they may
be socially isolated, they may be harmed mentally, may receive poor review from other staff
members which will result in professional disadvantage at the work place or even financial
disadvantage or even may leave the entity.
Iqbal et.al. (2018, March) defines big data as data of huge volumes with high velocity,
high variability and high complexity which will require highly advanced techniques and methods
for management and analysis of the data. Decision making based on big data will have certain
challenges like issues of security, issues of storage of such huge volume data, analysis of such
complex data will require effective personnel and talents, to bring out valuable information of
the such complicated and such huge volume data, being of high volume the data will originate

from various sources and such sources will require proper management. Small and medium
enterprises will also require processing of such data and resultantly will be able to achieve
accurate decision making.
Casali and Perano, (2021) pointed out that ethical decision making regarding ethical
issues are components of ethical values have become an important aspect for the success of any
organisation. There are various factors which influence the ethical decision making like –
personal individual aspects like age, education, gender and moral development stage, whole
organisational aspect like organisational climate, size of the organisation, ethical codes relevant
for the organisation, etc. Now to test such factors, questionnaire based research was followed.
The literature tells about the substantive progress that have been made in the field of research of
ethical decision making in the last 40 years. The concerns from such a study are communicated
to members of board, organisations, managers and society in general and that such researchers
shall be supported.
Loe, Ferrell, & Mansfield, (2000) in the given literatures explains two types of models
namely, positive model which encapsulates what is actually occurring in the entity and normative
model which encapsulates what should occur in the entity. Also, it was concluded that more
research shall be conducted even if in depth empirical examination was conducted of ethical
decision making. Studies in the empirical examinations also needs further understanding to
assess how ethical training impacts the ethical climate in the entity. Such empirical study ethical
decision making in the entity will involve comprehensive research of areas namely, gender in
ethical decision making, moral philosophy, ethical climate and culture, work experience and
education, code of ethics, awareness, sanctions and rewards and other.
Elm and Radin, (2012) in the given literature discussed whether ethical decision making
is different from the other decision making processes or is ‘no different’ and basically a part of it.
It is concluded in the literature at a preliminary stage that it not significantly special and
implications from such a research can be used as an argument against other decision making
processes. In general, it is known that ethical decision making is different but the wide and
varied results of empirical studies on the same contradicts the same. So conclusively, on the

basis of randomly collected data, there is no real difference is the main outcome. But it can be
said that such a decision is not the end and further researches will clear the haziness if any in this
matter and findings in this literature will act as a base for further researches.
Big data within a cultural context
Janssen, van der Voort and Wahyudi, (2017) describes how big data has become a
priority for entities in making decisions based on high volume and high velocity data. Such
reliance on big data for decision making is based on various factors considered in the literature.
Such a decision making on the basis of big data will be based on various factors namely –
contractual and agreement governance, governance based on the relation between the
organisations to build the trust and sharing of knowledge to understand and analyse the big data,
capabilities of big data analytics as big data will be containing various variable information and
parameters and judgement is required as to what tools to be used and how to visualize it, data
needs to be transferred and knowledge about how the data will be received, collected, analysed
and interpreted, a well synched big data chain is required in which source of data, big data
analytics and users of such big data to be collaborated, quality of decision making is important as
more knowledgeable and experienced decision maker will be able to make better inferences from
the data, quality of sources of big data needs to be high and accurate to derive a meaningful
conclusion from the data, workforce who can handle big data efficiently is a must, etc.
Lurie and Albin, (2007) states that to enhance moral reasoning theories, there has been
many applications of applied ethics in history and such literature aims at developing aspects for
resolving moral dilemmas which are prevalent in the moral reasoning. This literature includes
moral dilemmas which may lead to decision making regarding whether an event is morally
obligated or not. With a view to edify perspectives related to moral dilemmas in the business
ethics, mainly, 2 models are discussed here namely – particularism and casuistry. Casuistry’s
main idea is that it is not a universal moral theory.
Research Gap
On thorough analysis and understanding of the above mentioned literature works there are
certain literature gaps on the topics of our literature review i.e., big data and decision making
within cultural contexts and decision making challenges within ethical contexts. It has been

observed that although ethical decision making is not different from other decision making but
further bases and researches shall be done for the same.
Conclusion
Based on the above report the concept and importance of decision making have been
discussed. The report has used expected monetary value and sensitivity analysis as the decision
making tools. The report has computed the EMV for the four scenarios. Variable cost was
reduced in scenario 2 and fixed cost were reduced in scenario 3. Problem solving process have
been discussed. The part 2 of the report has included literature review of Decision making within
an ethical context and Big data within a cultural context. Lastly in the report research gap has
been outlined.