Business decision making involves addressing the issue of uncertainty. Probability is one of the primary tools that aid in decision-making under uncertainty. Probability is a number between 0 and 1 that depicts the chances that a given event will occur (Albright et al., 2015). The closer the probability is to one, the higher the chances of such an event occurring. Uncertainty and risks are some of the primary problems in business. Probability can be used to measure uncertainty as well as the risks involved in various business decisions. Understanding the basic rules of probability as well as exploring various probability concepts, including random variables, probability distribution, in addition to computer simulations associated with various probability concepts, is essential in the decision-making process (Albright et al., 2015).
The combination of probability concepts and simulation tools is key in the analysis and prediction of random business outcomes. With simulation, one can incorporate uncertainty explicitly into spreadsheet models, which generates random quantities that result in varied bottom-line outcomes (Albright et al., 2015). Through the use of simulation and the incorporation of probability concept, decision-makers can discover outcomes that are most likely to occur as well as those that least likely to occur; a business manager can use this type of tool to determine the best-case and worst-case scenario of business decisions (Albright et al., 2015). Decision-makers can simulate multiple situations quickly, and thus effectively giving them the general and in-depth idea of the consequences of such business choices on business entities. The incorporation of probability concepts into simulation aid business managers in the process of generating random market returns. As a business manager, one has the role of choosing an option that promises the largest expected monetary value out of the many uncertain outcomes generated. Therefore, the expected value of a probability distribution is vital in the decision-making process of businesses.
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The concept of uncertainty is very important when addressing a business problem that involves large monetary values and long-term consequences. In cases of uncertainty, businesses should determine probabilities and the probability distribution of the primary inputs. Making business decisions may need the use of a decision tree to guide the analysis process. The decision tree provides a decision-maker with an opportunity to explore the various aspects of a business problem, including the decision alternatives, uncertain outcomes, and their probability, the expected economic impacts, and the sequential flow of events (Albright et al., 2015). The decision tree model is useful, particularly when analyzing complex business decision problems. By showing the sequence of events, along with the probabilities and monetary values, a decision tree model can be used to improve the quality of business decisions. Moreover, decision-making under uncertainty, for example, the fixed project timeframe, introduction of new products, capacity expansion decision, and loan decisions, among others, demand the use of a payoff table along with the exploration of the expected monetary value for each business option to be able to identify the best possible choice (Albright et al., 2015). The decision made under uncertainty may be based on intelligent guesses. Therefore, businesses might be required to conduct a sensitivity analysis to explore how the expected monetary outcome and best decision changes. Making decisions under uncertainty demand the employment of probability and probability distribution concepts, along with sensitivity and risk profile tools to facilitate the selection of the business decisions.
Distribution
There are many types of probability distributions, including normal, binomial, Poisson, and exponential distributions, which can be used in a variety of business applications. A normal distribution is often used in the analysis of continuous distribution, while binomial distribution is used in discrete distribution (Albright et al., 2015). On the other hand, Poisson and exponential distribution are employed when counting various events through time. A probability distribution is an important tool for the analysis of various decision requirements of a business, including scenario analysis, sales forecasting, and risk evaluation, among others (Orga et al., 2012). Business organizations are often faced with the need to make decisions about defective items from a production line, which requires the application of a quantitative basis for management decision-making. A binomial distribution can be used to analyze the number of defective items, and thus guiding the decision-making process in production, procurement, and shipment (Orga et al., 2012). An exponential probability distribution, on the other hand, can be used in the analysis of the consequences of queuing systems in business. Therefore, this concept can guide business managers in their process of determining as to whether a system should be scrapped off, if the number of staff members should be increased, or if technological improvement should be embraced. From the analysis, it is evident that probability distribution is vital in the decision-making process as it can guide the process of quality control tests, professional odd-making in sports, as well as the process of sales forecasting and risk evaluation.
Statistical Inference and Sampling
Many business decisions, particularly those involving production and sales analysis, rely on statistical evidence. Managers have the responsibility of evaluating the meaning and reliability of statistics before they can employ them in the decision-making process (Albright et al., 2015). They should be able to evaluate the strength of evidence provided in statistics to effectively make good business decisions. Therefore, business managers may be required to have knowledge of statistical inferences. Statistical inferences can be grouped into two categories, including confidence interval estimation, and hypothesis testing (Albright et al., 2015). To audit the reliability of given statistical evidence, business managers may be required to obtain point estimates and confidence intervals around a specific point estimate. A confidence interval can be used to compare the means of two groups and thus aid in the process of analyzing the various traits of a population. A confidence interval for the differences between means can be used in the analysis of various groups of customers within a given population to be able to determine the best target market for the business (Albright et al., 2015). The confidence interval can also be used to compare various business processes or decisions, and thus facilitating the selection of choices with optimum results.
Aside from the confidence interval, hypothesis testing can also be witnessed in business decision-making processes. Hypothesis testing is used when there is a need to compare observed data with a particular hypothesis. After business research to investigate the effectiveness of a business decision, statistical inference, in the form of hypothesis testing, can be used to determine if the observed data will support the assumptions behind a given business decision (Albright et al., 2015). Business managers can employ statistical inferences concepts to the business decision-making process with the aid of software tools. StarTools is the most commonly used statistical software for statistical inference requirements in businesses.
Sampling and sampling methods play a vital role in statistical inferences. The quality of a given business research outcome relies on the effectiveness of the sampling process. Generally, business managers rely on study findings to generalize beyond their study setting (Sarstedt et al., 2018). Therefore, the success of generalizing study outcome will depend significantly on the choice of an appropriate sampling method, which will impact on the distribution of sample characteristics, and thus the generalizability of the results. Aside from having knowledge in the various sampling methods, business decision-makers should also have knowledge in sample size (Sarstedt et al., 2018). The reliability of data results is partly dependent on the size of the sample. A business manager ought to know the best sample size in relation to the objective of a given study. In addition, a business manager can also identify or avoid estimations errors, which is key to quality business decisions. The primary areas in business where sampling is vital, include the collection of samples for quality check, the selection of a sampling technique in advertising, and sales and marketing research.
Regression Analysis
Regression analysis targets to define the regularity of an event developed. Based on regularity, regression models can be used to predict future progress of a phenomenon (Rusov et al., 2017). Therefore, business organizations can use regression analysis to improve the quality of business decisions, in relation to sales prediction, inventory levels, and supply and demand; organizations can predict future business phenomena using regression models. A regression model refers to equations that depict a dependent variable as a linear or non-linear function of independent variables. Organizations can use regression models to better understand a business pattern. Regression analysis can be employed to better understand all types of patterns that emerge in data (Albright et al., 2015). These insights are essential as they help in guiding decisions that can help make a difference in these businesses. In addition, regression models can be used as tools for correcting errors. Regression analysis allows businesses to determine the short- and long-term effects of a given business decision (Rusov et al., 2017). It helps an organization to work backward to ascertain as to whether certain changes in their business model could have helped improve performance. Regression models can also be used as tools for performance optimization. Regression enhances a better understanding of data, which helps businesses to maximize efficiency and refine business models for an optimum outcome (Rusov et al., 2017; Nicholas et al., 2016). Regression analysis can help business managers to forecast future conditions, lend quantitative support to managers’ decisions, and point out errors in management thinking, as well as providing new insight that can aid decision-makers in improving the profitability of a business.
Forecasting Methods and Time Series Analysis
Forecasting methods can generally be divided into three groups, including judgmental, extrapolation, and econometric methods (Albright et al., 2015). While judgmental forecasting is non-quantitative, the extrapolation and econometrics forecasting methods are quantitative. Extrapolation models employ past data of a time series variable to forecast the future of such a variable. The primary extrapolation models include trend-based regression, autoregression, moving averages, and exponential smoothing (Albright et al., 2015). All extrapolation models look for patterns in past data and extrapolate these patterns into the future. The analysis can explore the long-term upward or downward trends, or track seasonal patterns, and project them into the future. The econometric models, on the other hand, uses regression to forecast a time series variable by employing other explanatory time series variables (Albright et al., 2015). For instance, an organization can utilize econometric models to regress future sales in relation to advertising level, population income level, and interest rates, among others. In other instances, organizations combine more that one forecasting method, either within the same general type or different types, such as judgmental and extrapolation, to predict future business phenomena. To improve the accuracy of a forecast, forecast software measures various types of forecasting errors, including mean absolute error, root mean square error and mean absolute percentage error (Albright et al., 2015). Common applications of forecasting in business include the analysis of customer demand as a function of time, the development of a production schedule, and forecasting the movement of stock prices and interest rates, especially in investment analysis (Albright et al., 2015). The analysis of an organization’s historical data can help organizations get a general picture of what should be expected in the future, and thus promoting better decision-making to actualize or prevent a given prediction in the future of a business.
Time Series
When analyzing the time series variables, how this data changes over time is important. Therefore, a time series graph is used to analyze time series variables. A time series graph is a graph of the value of at least one time series and uses time on the horizontal axis (Albright et al., 2015). Time series analysis starts in a time series graph. A time series data has four components, including the trend, seasonal, cyclic, and random components. When observations increase with time, a time series is said to be having a trend. Business managers can use time series graphs as a tool for exploring the upward and downward trends, which can help guide a decision-making process, particularly decisions pertaining to stock prices (Albright et al., 2015). Time series graphs can also be used to exhibit seasonality in data. Such predictable seasonal patterns can be used to develop sales and production forecasts for businesses. The understanding of the random and cyclic components of the time series graph can help business decision-makers as they can learn to ignore random variations while taking a greater focus on cyclic variations in time series data.
Optimization
Business operations are restricted within specific organizational constraints. Therefore, business decisions target to optimize the utilization of organizational resources for better revenue returns. The most common optimization tool is the linear programming model. Business managers are tasked with the role of determining decision variables, objectives, and constraints before they can use optimization models to optimize company performance (Albright et al., 2015). Decision-makers can employ algorithms, such as the simplex method, to determine the optimal solution for an organization (Albright et al., 2015). Linear programming is often used by business managers in addressing problems, including labor scheduling, inventory management, bond trading, selection of advertising media, management of cash flows, and the routing of delivery vehicles, among many other business problems. Decision-makers can use Excel along with the Solver add-in to determine optimal solutions.
In conclusion, statistical analysis is part and parcel of the business decision-making process. Multiple business problems require to use of statistical tools for effective solutions. Various statistical concepts, including probability, distribution, uncertainty, sampling, statistical inferences, regression analysis, time series, forecasting methods, optimization, and decision tree modeling, are vital for quality business decisions.
References
Albright, S. C., & Winston, W. (2015). Business Analytics: Data Analysis and Decision Making. Cangage Learning.
Nicholas, A. I., & Hilary, A. C. (2016). Understanding the impact of regression and correlation analysis in enhancing decision making. International Journal of Marketing and Technology , 6 (11), 17-34.
Orga, C. C., & Ogbo, A. I. (2012). Application of Probability Theory in Small Business Management in Nigeria. European Journal of Business and Commerce, 4 (12) , 72-82.
Rusov, J., Misita, M., Milanovic, D. D., & Milanovic, D. L. (2017). Applying regression models to predict business results. FME Transactions , 45 (1), 198-202.
Sarstedt, M., Bengart, P., Shaltoni, A. M., & Lehmann, S. (2018). The use of sampling methods in advertising research: A gap between theory and practice. International Journal of Advertising , 37 (4), 650-663.
