Break-Even Analysis: Unlocking Business Profitability - Essay Sample

Introduction

As a financial tool, break-even analysis helps determine a new product or service at a stage of the company that will be lucrative (Cafferky, 2010). Breakeven Analysis allows businesses to know how to cover costs, particularly the fixed costs, by determining the number sold for the services or products of a company. It is a situation where all costs are covered, and the business is neither creating money nor mislaying money. Generally, break-even analysis in a business tends to be a small break-even point of sale when the fixed costs are low. The use of break-even analysis is when creating a new product for a business, starting a new business, and changing the business model. An example of break-even analysis is in the securities trading business, where it is the point at which losses equal gains. Break-even analysis helps make decision tree analysis by identifying the business needs and the total amount of sales before earning profits.

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Decision Tree Analysis

Decision Tree Analysis is a graphic illustration characterized by a tree-like structure to solve a problem by the availability of various alternative resolutions (Duncan, 1979). When making a choice, the illustration manner often verifies to be decisive, and the analysis after each negative or affirmative response answers several questions until there is a final choice. In business or organization, a decision tree analysis is used to make decisions by management to envisage alternate solutions and ideas. An example of decision tree analysis is when a commercial business in the next year wants to increment its sales and the profits associated. When using a decision tree, diverse alternatives can be on a map for both increases in sales and benefits with two choices. The choices that create two branches are the expansion of sales activities and the development of advertising expenditure. Decision tree analysis helps in forecasting inventory control by exhaustively simulating classification on a sample of items for a predefined system.

Inventory Control

Inventory control involves warehouse management and comprises of comprehensive portfolio lists and counts, reports for reordering and adjustments, variants and stock synchronizing on hand with purchase and sales orders (Axsäter, 2015). Without influencing the levels of customer satisfaction, inventory control goal procedure is with minimum inventory investment to maximize profits. Managing inventory control for business helps to keep stock with the least amount of inventory holding in warehouses, making it more straightforward for more space, better cash flow, and lower holding costs. An example of inventory control is the Smith Widgets business that includes the perpetual inventory system whereby it updates accounts and inventory records continually for subtraction and additions when receiving inventory items, movement from one place to another, and selling from stock. Multiple regression as a tool helps the purpose of inventory control by examining the relationship between the inventory levels and variables individually.

Multiple Regressions

Multiple regression bases the value of two or more variables by predicting when there is a requirement for the benefit of the variable (Keith, 2014). It is an addition of simple linear regression, and the variable in need to have prediction is the dependent variable. For instance, a business J.R. Classic Cars can utilize multiple regression based on gender and test anxiety to comprehend whether the car's performance can be forecast. On the other hand, it can also be used to predict a cigarette business by understanding daily consumption through prediction of gender and age, type of smoker, and smoking duration. Multiple regression allows a model overall fit determination to the total variance explained with the variation and each contribution of the predictors. Multiple Regressions provide the input of forecasting by modeling past demand to forecast future requirements through plugging in seasonality value and the appropriate time.

Forecasting

In business, a prediction is a standard statistical task that guides long-term strategic planning by assisting in informing personnel and transportation, and scheduling production (Hyndman & Athanasopoulos, 2018). At some future point in time, forecasting is an estimation of a variable that offers aid in decision tree analysis by planning decision-making. The application includes forecasting economic growth, for example, a corporate online business by predicting information with the inflation rate and growth in the economy to have an industrial policy. It forecasts financial investment policy by having information such as exchange rate and interest rates. Forecasting product demand in business enables production planning and inventory control by controlling finished goods and raw materials stocks. For a company to predict uncertain events, there is a requirement to develop involving several approaches to a forecasting system. The system needs expertise development for selecting appropriate methods, applying a range of forecast methods, identify forecasting issues, and over time evaluating and refining of the forecasting methods.

Linear Programming

Linear programming is a technique and path to perform optimization for linear constraints. Quantity optimization is the objective function for linear programming, and the goal is to discover the variable's value that minimizes or maximizes the actual purpose (Vanderbei, 2015). It is convenient for businesses that have huge issues and need resource optimization. Automotive companies, for example, Ford, can apply the linear programming to minimize the cost of operations calculation of how to assign machinery and labor. Activities in high-level business can use the programming to select the amount in quantities and which products to sell for maximization of return. In the logistics business, it is useful to use the programming to choose how to get a job done by applying resources in a minimum period. The output of linear programming can input project management by efficiently reducing the duration and cost of a project. By creating a few simplifying assumptions, the programming assists in solving some complex optimization problems.

Project Management

Project management involves planning, it can be a continuing activity or be a one-time development with defined scope and resources; therefore, it is temporary in that there is begin and end in time for the project (Burke, 2013). Project management includes working together of people and is unique in that it has to accomplish a goal with the design of specific set operations that sometimes embrace across multiple geographies and from distinct organizations. The processes of project management include planning from start to end with an outline on how to get off the ground by initiation, execution, and monitoring. For instance, project management of architecture entails the project manager putting together the architect's puzzle with the notion and blueprint drafting drawings with each step coming together with thousands of little pieces. Generally, project management managers can make sound capacity decisions by modeling a system supporting multiple regression while using break-even analysis and decision tree analysis.

References

Axsäter, S. (2015). Inventory control (Vol. 225). Springer. Retrieved from https://www.springer.com/gp/book/9783319157283

Burke, R. (2013). Project management: planning and control techniques. New Jersey, USA, 26.

Cafferky, M. (2010). Breakeven Analysis: The definitive guide to cost-volume-profit analysis. Business Expert Press. Retrieved from https://play.google.com/store/books/details/Breakeven_Analysis_The_Definitive_Guide_to_Cost_Vo?id=6zwuXAYPIXIC

Duncan, R. (1979). What is the right organization structure? Decision tree analysis provides the answer. Organizational Dynamics, 7(3), 59-80. Retrieved from http://connection.ebscohost.com/c/articles/5140929/what-right-organization-structure-decision-tree-analysis-provides-answer

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.

Keith, T. Z. (2014). Multiple regression and beyond: An introduction to multiple regression and structural equation modelling. Routledge.

Vanderbei, R. J. (2015). Linear programming. Heidelberg: Springer.