The application of probability theory in contemporary business can help in making informed decisions. It is impossible to make individual decisions in business since the unpredictability of variables in business management is inevitable. The decision-making process involves choosing among various competing variables; therefore, when probability technique is employed, potential risks can be foreseen and an enhanced decision made. In management, the analysis of the cost of all alternative data is mandatory so that the selected information is beneficial and cost-effective. The best option of data can simply be selected by calculating the probability of success or failure of the data and an intelligent decision made (Chukwudi & Ogbo, 2012). Probability provides an insight into the best outcome out of the existing competing variables, which is significant in risk assessment.
The probability theory can be applied in the analysis of the financial loss of a business policy by looking at the probability of loss occurrence. If the likelihood is low, the system can take on regardless of the damage. The probability model is employed in production planning and control and sales prediction. A company can foresee the best or worst outcomes when a particular portion of products are produced, and a relevant decision concluded. The exponential probability distribution can aid management in making decisions about hiring more staff or adopting improved technology when dealing with the queue system in a business. The probability theory has employed used in assessing the impact of women’s iniquity in Johnson District Court in the USA. Other areas where the probability method is used in making decisions include inventory control, quality control, close-up choices of companies, new product production, and resources forecast (Chukwudi & Ogbo, 2012).
The problems associated with supply chains in inventories can be avoided by the implementation of distribution as a problem-solving technique. The distribution element is the most fundamental method of making decisions in supply chain management since it directly affects supply chain costs. The increased competitive market and customers’ dramatic growth in demand with reduced costs and maximum profitability necessitates the use of distribution networks in decision making. The application of distribution, however, possesses difficulties since data in many management situations are diversified and heterogeneous (Filho et al., 2016). The distribution of data is utilized by supply chain managers to deduce meaningful information for decision making.
Uncertainty in business refers to a lack of comprehension of what the future holds. The difficulties in decision making are inevitable, and this forces a decision-maker to adopt techniques that cope or reduce uncertainty. There are two types of uncertainty in management; measurable uncertainty (outcomes can be calculated using probability and can be insured) and unmeasurable uncertainty (possible results cannot be derived nor guaranteed). Uncertainty in management occurs due to inadequate information on decision variables (Magnani & Zucchella, 2018). Investment opportunities exist in uncertain conditions, and this calls for decision making for maximum utilization of resources. The analysis of uncertainties is crucial for tactical management and entrepreneurship. Companies employ decision-making tools to overcome uncertainties and operate efficiently with maximum profits (Albright & Winston, 2016). The different forms of uncertainties that may occur in business include; behavioral uncertainty, communication uncertainty, cultural uncertainty, organizational uncertainty, quality uncertainty, and transition uncertainty.
Sampling in business decision eases the burden of analyzing data from a massive set of the population by taking a representative of the data and drawing conclusions. The sampling method is time-saving, cost-effective, easy to implement and understand when used in the analysis of big data. The sampling in decision making involves the following stages; identifying the target population, setting a sample frame of the population, choosing a sampling technique to analyze the data, determining the actual sample size relative to population, collection of data, and finally, the assessment of data. The sampling method effectiveness increases when the population is significant; this is an advantage to business decision making since it enables organizations to decisively analyze big data from a small manageable portion of the information. Sampling techniques employed in management research include simple random sampling, systematic sampling, cluster sampling, stratified sampling, quota sampling, snowball sampling, judgmental sampling, and convenience sampling (Taherdoost, 2016).
Sampling is applied to the performance of market research. Since it is impossible to collect data from the entire population of the market, a market niche can be identified by taking data from a sample of the population, and a decision of a potential market can be achieved (Taherdoost, 2016). Sampling has also been employed in new product production or modification in companies. Companies conduct surveys from a sample of the population, listen to their needs, and then design a product that satisfies their needs. Since some essentials of a particular group of people reflect the needs of a larger population, a company can safely decide to supply a new product that meets the market expectations. Business organizations also rely on sampling techniques to enhance customer satisfaction. A random or selected sample of customers can accurately represent the requirements of the broader population, which businesses use to their advantage and therefore boosting profits.
Statistical inference involves attaching knowledgeable significance to random data with minimum error. The application of statistical inference requires a high level of understanding of data to arrive at an intelligent decision. Using statistical inference enables a decision-maker to extend the knowledge acquired from a random sample and to apply it to the whole population. Statistical inference forms the basis of inductive reasoning in management. Statistical inference guides choosing a suitable mathematical model, which in turn translates to a critical conclusion. The probability and ideologies of the sample are the main components that make statistical inference valid. For instance, if two different assumptions are made from a standard population, a probability test needs to be performed to find out whether the difference between the two samples is real or whether it just by chance due to random sampling error (Arsham, n.d.). Managers should be critical in decision making when applying statistical inference by ensuring the conclusions obtained from a sample represent the whole population.
Business decisions should be based on facts but not intuitions, personal beliefs, or opinions. Statistical inference allows the transition of data to information, information to facts, and eventually facts to the knowledge that can be used in decision making (Albright & Winston, 2016). Statistical inference is applied in insurance companies to calculate the appropriate premiums for individuals to secure an insurance cover. The insurance companies publish actuarial tables and employ statistical inference to analyze the average life expectancy of the assured at any age. In the in-plant operation, analytical inference techniques are used to ensure quality control without necessarily performing inspection or testing of the products (Arsham, n.d.). Statistical inference in business decision making provides insight that makes it possible to analyze data with a low chance of risk occurrence.
Regression analysis is a dominant technique that allows analysis of the relationship between dependant variables and independent variables. The independent variables in business include factors and inputs, while the dependant variables include outputs and performance measures. The most straightforward regression equation is the straight-line usually denoted by variables x and y in the horizontal and vertical axis, respectively. An absolute value of the variable x relationship with a different variable y can be identified for analysis through the regression equation. Other regression techniques, apart from simple regression, include multiple linear regression, autoregression, and logistic regression (Arsham, n.d.). When an appropriate regression is selected, we can identify the significant factors and examine their effects on the output of any changes that may occur.
Regression analysis assists managers in the prediction of future demands for the products they produce by using the demand curve analysis. Companies are also able to implement influential advertisement by predicting the number of potential customers that may come across billboards. Insurance companies also employ the technique to determine the number of policyholders that can involve in accidents or risks. Managers also use regression to optimize production by analyzing data from two or more variables. For instance, in the processing factory, a manager can establish a regression model to understand the relationship between power consumption and products processed per power unit. The government also utilizes regression to predict the economic state of the country through the Consumer Price Index. The Consumer Price Index compares the relationship of variables to depict inflation rates in a country (Arsham, n.d.).
Forecasting methods enable decision making in management by predicting the future based on data. Forecasts decisions are fundamental in organizations since they determine the fate of operation, and therefore forecasts usually are done by experts. In many cases, decisions in management become useful in the future. Therefore, forecasting methods are significant in business performance and should be done with high precision so that forecasts align with actual performance in the future. Forecasting methods include regression analysis, Box Jenkins process, time series, smoothing techniques, exponential and its extensions, and causal models. Forecasts are made based on a review of factors that influence the future or inferences from past data (Arsham, n.d.). Either way, estimates need to be accurate since they affect the whole operation of a business. For example, wrong sale forecasts could cause a company to incur excess inventory carrying costs or experience a shortage of goods.
Forecasting methods are applied in supply chain management to determine the future demands of the company’s products. The supply chain manager then uses the estimate of the good forecast made to establish the required resources to produce the predicted amount. The supply chain manager is the determiner of profitability, and therefore the higher the accuracy of forecasts, the greater the profit. Forecast models provide insights into inventory control by predicting demands appropriately with minimum costs and high profit. The state managers use forecasts to estimate budgets from taxes the government is yet to receive. Managers in the government sector have to use forecast methods to avoid misuse of resources. Forecast methods are also applied in prior investment plans; managers have to forecast opportunities from available data before making an investment decision.
Time series models take ordered sets of observed data at given equal times to make decisions that forecast the value of the data in the future. Time series models study the past trend of variables at specified times, and then inferences of the future are made. Time series use models such as linear extrapolation or complex stochastic models to forecast happenings. Time series models, apart from display valuable future information for decision making they also examine the forces that shape the appearance of the observed data. The models used in time series include Box-Jenkins ARIMA models, Box-Jenkins Multivariate models, and exponential smoothing techniques (Arsham, n.d.).
Time series analysis is employed in the prediction of population growth rates by using simple linear extrapolation of past trends in population. The time series model is also applied in the prediction of changes of short-term interest, forecasting demand for airline capacities, and regular telephone demand. Time series are also employed to predict traffic jams, online users at different times, staff turnover, customer satisfaction, and spending patterns of the market. Other areas that apply time series include budget analysis, quality control, census analysis, stock market analysis, and sales predictions.
Optimization is a technique that purposes to use available data during decision making in the best way that maximizes outcomes by cutting costs and increases the competing power of an organization. Optimization methods include fuzzy logic, evolutionary algorithms, the theory of chaos, and neural networks. Soft computing optimization techniques include linear programming, priority weighted goal programming, and Chebyshev goal programming (Dostál, 2013). These optimization methods are used to evaluate problems such as risks that may occur and optimizing the objectives of a business. Optimization is employed portfolio management to determine the necessary stocks to maximize profits while staying within the boundaries of the funds available. In stock management, optimization is required to assess the order of capital the business should place under minimum costs but still attain the business objective. In the traffic light system, optimization helps to display a consistent color at the right time to minimize traffic jams at any time. Optimization helps decision-makers sort shorter routes for transportation of goods and seek optimal production process alternatives, among others.
Decision tree modeling
Decision tree modeling presents all possible decisions and outcomes in a graphical format that symbolizes a tree. The decision tree is categorized into two, the traditional decision tree and the modified decision tree. The conventional tree model delivers decision variables as decision nodes and random variables as chance nodes. When the arrows point at the chance nodes, they show a conditional probability while the ones indicating the decision node display the information available for decision making. An optimal decision is then obtained by averaging out and folding back procedures. The decision nodes are deleted by folding back systems, and the random variables are eliminated by averaging out to get a managerial decision. The modern decision tree analysis involves less calculation compared to the traditional one. It is classified as symmetrical or non-symmetrical. The symmetrical decision tree has the same random variables and decision variables from the root node to the leaf node. Both the traditional and modern decision analysis can be used for single-level and multi-level signals (Zebda, 2011). Decision trees are widely used in management to predict outcomes in the future. For instance, quality control managers apply the decision tree analysis with multi-level signals to test the safety of a product. Money lending institutions use decision tree analysis before lending money; the model is used to make decisions after considering creditworthiness factors.
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