Name
Institution
Date
Evaluating personal decision making
The purchase of stocks is a complex process that requires adequate knowledge and understanding of the trends in the stock market. During the past year, I made a significant loss due to bad stock trading decisions. I purchased a stock that declined in value exponentially for the next six months, and after one year, it was still struggling to regain stability. I sold the stocks at a loss due to fears of continuous losses. After adequate forecasting studies, I understand that it is relevant to study the post trends and determine the future prices of the stock. For instance, the company’s stocks’ future forecast was relatively low due to the downward trend in the past years. Using straight-line and moving average techniques can help in establishing whether the stock will rise in the future.
The saving plan is a crucial step that can help a person achieve their goals and objectives. However, I failed to establish the amount I will receive at the end of the savings period. Considering the benefits of interest rates and accumulation, I desired to save a certain amount of money from attaining financial goals within a specified period. However, I failed to acquire the target due to the lack of forecasting and final gain calculation. With a moving average and regression technique, it is easier to determine the highest output of a particular savings goal.
Regression
Regression is a metric for determining the relationship in a set data to extract useful information. The metric tends to define the data and bring higher values down towards the average (Schroeder, Sjoquist, and Stephan, 2016). It is useful in the instance of a comparison of two or more dependent and independent variables. For example, when examining the population’s data in a certain area, the age, height, and weight are regressed against the gender. The method is vital in cases whereby the data is random and has no definable characteristics. Further, it is a good metric for predictions and forecasting instances, such as predicting the amount of rainfall and snowfall.
Calculating regression is vital to evaluate and analyze the available data. Regression acts as a pathway to understanding data instead of making guesses. It provides reliable values that the user can utilize to make progress in their analysis. It helps determine the strength of a particular variable concerning another variable. In this case, it can avail vital information regarding the interrelationship and how various aspects interact normally. Without regression output, the data is usually scattered and lacks adequate meaning. Moreover, a regression can verify the data sets that have a significant impact on the real outcome.
Regression analysis consists of an equation that allows the user to input values and makes predictions. The equation comprises of y values, x values, and slope of the graph. The slope is multiplied by their value. The example indicates a regression analysis for predicting the units of cars to sell in the next years. The historical data in the previous years is available hence forming the independent variables in the equation.
Forecasting methods
There are four major types of forecasting methods: straight-line methods, moving average, simple linear regression, and multiple linear regression. The straight-line method utilizes a constant growth rate to determine future historical trends (Antoniuk and Zholnerchyk, 2018). Historical figures were vital in this type of method to establish the outcome of the problem at hand. The moving average method establishes the future values of the data set. In this case, it is a crucial technique for evaluating an underlying pattern of a data set. The simple linear regression is a method that utilizes a comparison of one independent variable to one dependent variable. It is vital in evaluating a set of relevant observations. The multiple linear regression is a technique to compare one dependent variable to multiple independent variables.
The first step of developing a forecast begins with the indemnification of the problem—understanding who and where the forecast is directed to establish a pathway to proper judgments on the data. The second step is collecting information, which entails gathering all the data and information relevant to solve the problem. The third step is performing a preliminary analysis, which entails evaluating whether the data is usable. The step consists of choosing the model that is the best fit for particular data. Moreover, the data can reveal useful patterns and trends to utilize in the analysis. The fourth step is choosing the right model that works in all situations.
Microsoft excel is an imperative software that provides a platform for computations and model construction. The various tools such as analysis toll pack and regression analysis ease the efforts of constructing a new model hence making it easier for analysis of data. The first step is to categorize the data into various titles according to their characteristics. The next step is inputting the data information into the cells in excel under each topic. The third step is selecting the right analysis tool and inputting the variables as guided in the platform.
Material requirements planning system (MRP)
NetSuite is a vital material requirements planning system that features real-time planning. It is built specifically to handle demand planning functionalities and integrate with accounting software. These features make it is a vital tool to use in the manufacturing industry to alleviate inventory problems (Ivanov, Tsipoulanidis, and Schönberger, 2017). The system ensures that the manager keeps track of all the inventories and sales made in the industry. In this case, it consists of inputs that verify the company’s type of inventory handling criteria. Further, it is cloud-based hence enabling easier storage and retrieval of data by the user.
The system is easy to maintain and doesn’t require a huge filling of information and storage. In this case, it reduces the accompany inventory’s high costs in terms of storage and retrieval. Since it is easy to use and efficient, it facilitates a low inventory turnover rate and the amount of obsolete inventory. The management can handle and track all the inventories with the system hence reducing the wastage in the manufacturing company. Further, the system keeps vital information, such as customer metrics and demand levels. It helps in providing the right products that satisfy the consumers.
The NetSuite is an efficient system that helps sin executing just in time management strategy. The system has the following protocol that ensures the manager keeps track of the inventory purchases. In this case, they can increase efficiency by selecting the right raw materials to satisfy their operational demand. More so, just in times, the strategy seeks to cut inventory management costs, which is a crucial output of the NetSuite system.
The NetSuite system increases the control while the just in time system increases the value of the process. The inputs in an MRP system are the production schedule, production structure records, and the produced units. The calculation is viable using forecasts technique to schedule the inputs. The just in time calculation involves dividing the average inventory by two.
Minimization and maximization problems using Linear programming
Minimization and maximization are techniques to define a linear programming problem. Each of the problems is in quantitative terms and requires a particular approach to identifying the solution. Both the objective function and constraint function are vital in determining the type of computational approach to applying. A person ought to understand the problem before the selection of particular criteria. The first step of the formulation is finding the question to solve, such as the implied user’s goal and objective. For example, a firm may seek to evaluate the number of units for inputs to maximize profits.
Both minimization and maximization approaches influence the objective functions to deliver the solution. They involve establishing a set of functions that will solve the problem. In both cases, there exist certain restrictions on the values that limit the output of the function. However, problems without restrictions provide direct access to the solution. The bound solution occurs only to the corner points of the linear graph. In this case, it consists of both maximum and minimum functionalities.
When there are restrictions and the constraints are unbound, the solution is always minimum; hence minimization approach is applicable. In such an instance, the function has positive coefficients. The maximum value does not exist in an unbounded feasible region. In this case, the solution comprises of choosing between three minimum corner points consisting of solving three equations. On itself, the problem should establish the grounds regarding its constraints. In this instance, the criteria are used to either maximize or minimize to attain the best solution. However, a minimization problem can act as a maximization solution after the conversion process.
When constructing a graph to solve the problem, the inside region after drawing the lines forms the feasible region. However, the region indicates all the maximum possible values to the problem. The outer region away from the lines indicates all the possible minimum values after constructing the lines. The actual solution resides on the interception point between the different variables lines and constraints.
References
Antoniuk, V., & Zholnerchyk, H. (2018). The difference between the Straight-line method and the Diminishing Balance method.
Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2017). Production and material requirements planning. In Global supply chain and operations management (pp. 317-343). Springer, Cham.
Schroeder, L. D., Sjoquist, D. L., & Stephan, P. E. (2016). Understanding regression analysis: An introductory guide (Vol. 57). Sage Publications.