Business Decision Making Assignment Sample

• Type Assignment
• Downloads936
• Pages13

Introduction

Get free samples written by our Top-Notch subject experts for taking online Assignment Help services.

The ability to make good business decisions is essential to its long-term viability. Small businesses are forced to make decisions based on the information they have at their disposal. A typical manager is expected to make choices on a regular basis in almost every aspect of his or her job. The nature and extent of his job, authority, and abilities all factor into these judgments. It is a judgement on the best course of action to take in order to attain certain objectives that is the basis of every decision (Syed and Lawryshyn, 2020). A person’s ability to complete any work relies heavily on their ability to make sound judgments.

Probabilistic and deterministic decision-making challenges can be separated into two categories. Uncontrollable factors and the ongoing process of optimising system performance constitute the basis of deterministic issue solutions. Under the assumption that the current business situation is correct, a model is constructed. Developing a model that accurately depicts actual business situations is impossible if assumptions are false (Wallström and Segerstedt, 2010). The business decision may be derived from mathematical equations through the use of mathematical optimization.

Various Segments of Business Decision Making to Use Quantitative Methods

Forecasting and Forecasting Error Techniques

Using historical data as an input, forecasters can make educated guesses about the future direction of trends. Businesses use forecasting to figure out how to distribute their budgets and make plans for impending spending (Khair et al., 2017). This is typically based on the anticipated demand for the goods and services offered. For example, investors use forecasting to analyse whether or not a company’s sales estimates would impact the price of its stock. Firms that require a long-term view of operations can use forecasting to set important benchmarks.

When attempting to extrapolate future trends, such as GDP or unemployment, stock analysts turn to forecasting. The more time that has passed since the prognosis was made, the more likely it is that the estimate will be incorrect. Finally, statisticians can make use of forecasting to assess the effects of a shift in business practises (Taylor and Letham, 2018). Customers’ happiness and employee productivity can be measured, for example, by changing company hours or by altering work circumstances, for example.

In the context of forecasting, an issue or data set is being tackled. Economists make assumptions about the issue under study before determining the forecasting factors. In order to manipulate data, a suitable data collection must be selected based on the items specified. The data is analysed, and a forecast is made based on that analysis. A period of verification follows, during which the forecast is compared to the actual findings in order to develop a more precise model for future forecasting (Makridakis, Spiliotis and Assimakopoulos, 2018).

Many different strategies are used by analysts to predict how a stock’s price will change in the future. As an example, one might look at revenue and compare it to the economy. Financial or statistical data changes are monitored to discover the correlation between different factors. Depending on how much time has passed, or what specific events have taken place, these connections can be forged. There are a variety of factors that can influence a sales forecast, such as the passage of time or the occurrence of an event.

Forecasts with a narrow scope might benefit from qualitative models. Some of these models have a short-term advantage because of their dependence on expert opinion. In qualitative forecasting, the Delphi method, surveys, and market research are common examples. Statistical data based on quantitative information is used in quantitative methods of forecasting, which exclude expert opinions (Hofmann and Rutschmann, 2018). Analysis of leading and lagging indicators, econometric modelling and time series methods are all part and parcel of quantitative forecasting techniques.

Differences in actual and predicted values over a given time period are referred to as “forecast error.” Accuracy can be gauged by looking at forecast error. Forecast errors can be summarised in a variety of ways so that the manager can make sense of them. For example, standard forecast errors can be broken down into relative and standard errors. The error is usually expressed in the same units as the data, which is known as a standard error measurement. For managers, relative error measures make it easier to gauge the forecast’s accuracy by looking at percentages. It’s common to use MAPE, which is the average of all percentage errors for a given data set, regardless of sign, as a measure of relative error (Kim and Kim, 2016). Tools for measuring and monitoring forecast errors include bias, MAD, and tracking signal.

An example has been shown below illustrate how company X should make forecasts and derive forecast error: 

Attitudes Towards Risks and Decision Criterion

Depending on the context, the term “risk” can have a variety of connotations. An investment’s risk can be defined as the variation in its return. Revenue and cost are examples of performance measures that are used to measure risk in the business setting (Sadgrove, 2016). When it comes to public safety, risk is defined as the frequency and severity of incidents that might result in death. The impact of uncertainty on goals is how risk is defined by the international standard for risk management. It’s true that risk analysis as a field has had difficulty defining what it means to analyse risk. According to Stan Kaplan, one of the world’s leading experts in risk analysis, the language of risk analysis has and continues to be a concern (Baecher, 2018).

Risk management is a planned collection of operations inside an organisation with the goal of reducing or eliminating the risk of a certain event. Identifying, analysing, and evaluating potential risks is part of a continual risk management process. The execution of risk treatment is also part of risk management decision-making (Wallström and Segerstedt, 2010). In accordance with ISO 31000, the following are the essential aspects of risk management:

 

Identifying the undesirable occurrences that might have a negative influence on a portion or the entire system is the first step in doing a risk assessment. Risk or threat identification is a common term for this process. Following the identification of undesirable occurrences, the next stage in risk assessment is to determine the likelihood and consequences of these events. The goal of a probability analysis is to find out how often (on average, say once a year) a bad thing will happen. The goal of a risk analysis is to determine how much money a company stands to lose if something bad happens. Finally, risk tolerance criteria are evaluated against the risk measure (Gonzalez, 2018). A risk matrix is a standard tool for assessing risk in a logical and unbiased manner. An evaluation of a single scenario’s risk may be done using a risk matrix, which is formed by specifying likelihood and consequences along a row (or column) (or row). Each row-column pair (i.e., a likelihood-consequence pair) in the matrix has an associated tolerability, which directs a decision-maker by specifying the urgency of risk treatment in each cell (Wallström and Segerstedt, 2010). As may be seen in the example below, there are several different types of risk:

• Intolerable; prompt treatment is required.
• ALARP; stands for As Low as Reasonably Practicable.
• Broadly tolerable; although it is not necessary, risk treatment may be used

In order to make decisions about risk treatment, risk assessment data are used. Treatment alternatives are not always mutually exclusive and there are numerous ways to go about it. Treatment might involve minimising the possibility of the undesirable event (Prevention), minimizing the risk(s) (Mitigation) or spreading the risk via insurance (Transfer) (Biais, Heider and Hoerova, 2016). Treatments at the extreme end of the spectrum may involve eradicating the danger entirely.

An exemplary risk matrix of the company X has been shown below:

Decision Optimisation

As a part of mathematics, decision optimisation works to maximise output from many input factors that have different influences on the final result. Mechanics and engineering can benefit from it as well as fields like economics, game theory, and operations research. A decision optimization technique uses analytics to determine the optimal option from a variety of possibilities (Anderson et al., 2019). Decision optimization issues, on the other hand, take into account a wide range of factors and deliver the best potential answer from a sea of many.

Decision optimization, in general, is based on basic analytical modelling, and it may be used to handle a wide range of business challenges. With some mechanical and technical applications, decision optimization is widely employed in the fields of economics, game theory, and operations research, among other disciplines. Decision optimization may be applied in a variety of applications, including the travelling salesman issue, dynamic pricing, truck routing, sales pricing tactics, optimum marketing model mixtures, manufacturing strategies, and more (Calvet et al., 2020). Decision optimization, in its simplest form, is the process of using a large amount of data to determine the optimal answer to a certain problem.

As part of Gartner’s Analytic Ascendancy Model, the analytical ladder begins with descriptive analytics (what happened?), progresses to diagnostic analytics (why did it happen?), and ends with prescriptive analytics (what should happen?) (Jain, Shao and Shin, 2017). Decision optimization should be at the top.

There are numerous factors to take into account while optimising, and the mathematics may be mind-bogglingly complicated. This makes it difficult for a company to succeed in this area. The highest payoff and greatest return on investment (ROI) come from using analytics here. Nowadays, advanced analytics software can handle problems involving millions of variables, many limitations and multiple trade-offs, and deliver benefits that justify all the work invested (Frangopol, Dong and Sabatino, 2017).

The following are the measures to take:

• The overall notion of the system should be grasped:

In order to keep costs down, producers must guarantee the shortest route is chosen to ensure the quickest time possible. The first step for every business should be to set out the concept, identify the tasks to be completed, and map out the many routes to get there (Rostami, Neri and Epitropakis, 2017).

• Define the objectives:

As far as I can tell, this is the simplest and most important part of the procedure. Businesses should have clearly defined objectives, as well as sub-goals, to keep them on track.

• Identify the factors that can be controlled and the restrictions that cannot be controlled:

Analysts will choose the variables that will be independent and dependent, and they will hypothesise about the optimal model to use to solve the problem.

• Identify the inputs and outputs that can be controlled:

There will be an emphasis on data accuracy and completeness here, as well as data cleaning, by the analysts. At this point, the conventional modeler’s admonition of “trash in, garbage out” is doubly crucial. A decision optimization model can easily provide erroneous conclusions if the input data is incorrect (Damij et al., 2016).

• All amounts must be stated numerically:

Here, theory is put into practise. Each variable is transformed into a numerical value, ensuring that all of the problem’s variables are taken into consideration.

• Execute the model:

This is when things become serious. You should be able to achieve your original aims using a model that truly depicts the real-world situation you’re trying to solve.

• Ensure that the model is accurate and complete:

It is possible for analysts to review results to make sure there are no errors and that all relevant modelling processes have been performed.

• Prove, explore, fine-tune and get feedback to make improvements:

This entire process should be viewed as repetitive and continuous effort since models are living creatures that change and adapt to new inputs, new restrictions, new model findings, and so on. This fine-tuning helps protect a model from becoming stale (Rostami, Neri and Epitropakis, 2017).

Ethical Decision Making

By definition, ethical business decisions flow from a well-designed ethics and compliance programme. In order to include ethics into the equation, someone wouldn’t need to be an expert in philosophy, history, or ethics (Ferrell and Fraedrich, 2021). Making ethical decisions requires practise. It is a strategy to identifying and addressing difficulties in the business setting. Of course, there are several additional instances of this. Furthermore, ethical decision-making in business can’t be reduced to just “doing what’s right.” That’s not all there is to it.

Applying ethics in the business setting demands an awareness or sensitivity to ethical difficulties that may occur and a technique to address relevant issues as part of an overall decision. To guarantee sufficient assessment of ethical concerns, there are a number of fundamental questions that may be evaluated (Zeni et al., 2016). There are many ways to use this strategy, and it may be tailored to match the needs of a particular business. Ethical business choices are frequently the result of lengthy deliberations involving a wide range of stakeholders. The discussion is vital to conduct and analyse. If routinely exercised, a corporation and its management can employ this discipline in making crucial decisions based on ethical standards (De Los Reyes Jr, Kim and Weaver, 2017).

Ethical decision-making is an essential part of ethical behaviour, but it also provides clarity in challenging situations. In order to arrive at a practical answer, decision-makers need to follow the procedure. Avoiding unethical options and undesirable outcomes is made easier by following this paradigm. Decisions made in accordance with ethical principles instil trust, responsibility, and concern for others (Mandal, 2019). Ethical decision making acknowledges these situations and demands a thorough assessment of all possibilities, the elimination of unethical perspectives, and the selection of a better one.

Effective and ethical judgments go hand in hand. Good judgments establish respect and trust in professional relationships and are typically in line with good citizenship. When a choice accomplishes its stated purpose, it is considered successful. A decision that has unforeseen consequences is a bad one. The key to making sound judgments is to consider all of the options available to a business in order to reach its goals. As a result, it’s critical to distinguish between short-term and medium- to long-term goals. Ethical decision-making demands attention to ethical concerns and a process for evaluating all of the factors involved in making a choice (Garrigan, Adlam and Langdon, 2018). Because of this, it is critical to have a system in place for making ethical judgments. After a few tries, the procedure becomes second nature and is much simpler to follow.

Following is a breakdown of many approaches to making ethical decisions:

What is the foundation of ethics if not religion, feelings, legislation, social customs, or science? This crucial topic has been addressed by countless philosophers and ethicists. Ethical norms and standards have been presented by at least five distinct people. Here are the most significant points.

• The Utilitarian Approach

An ethical activity is one that accomplishes as much as possible while doing as little harm as possible. To put it another way, the choice that strikes the best balance between good and evil. In a corporate setting, the best option is the one that maximises profits while minimising harm to stakeholders, including consumers, workers, shareholders, and the environment (De Moura et al., 2020).

• The Right Approach

As long as everyone’s moral rights are protected and upheld, the ethically sound choice will be made. Human nature or the freedom to freely choose how one wants to conduct one’s life is cited as the basis for this theory’s claim that all humans are endowed with dignity (Schwartz, 2016). They have the entitlement to be treated equally by others because of their dignity.

• The Fairness or Justice Approach

Equal treatment should be given to all people, regardless of their status. Some of the first proponents of this theory include the ancient Greek philosopher Aristotle. This concept has come to mean that ethical judgments should be applied equally to all people (Arar et al., 2016). A standard must be established if this is not equal. People get rewarded more for their efforts if they make a significant contribution to the company.

• The Common Good Approach

People have been saying for centuries that it’s better to live in a community from the time of the Greek philosophers Individuals’ thoughts, words, and deeds must all play a role in this. This theory claims that ethical thinking and action are based on social ties (Bastain et al., 2016). Maintaining a moral lifestyle requires a deep respect and care for others, especially those who are less fortunate.

• The Virtue Approach

Ethical conduct must be based on a set of universal values that promote the growth of mankind as a whole. People who exhibit virtues are able to perform at their best level of human character (Latta and Dugan, 2019).

Conclusion

This report has discussed four aspects of business decision-making, Forecasting, Risk Management through decision criteria, Decision Optimisation and Ethical Decision Making. To rank projects based on risk tolerance, cost-benefit analysis, and uncertainty reduction metrics, the technique employs quantitative risk assessments. In the literature on risk and asset management, a unified framework is required for evaluating quantitative risk against tolerance criteria and making trade-off judgments amongst risk treatment methods. This framework is needed. Quantitative risk metrics for casualties, loss of output, and loss of property are used in the technique. Also, the methodologies for Decision Optimisation and Ethical Decision Making can help the business to improve the performance in making decisions.