Forecasting the Future Trends:

Forecasting means making assumptions about the business operations future. For example with the help of forecasting we can make an assumption about the business projects demand, inventory levels and about the financial trends like revenue, cash flows, sales.

There are two different methods of forecasting one is qualitative and the other one is quantitative method. The Qualitative forecasting is based on the theoretical forecasting and the quantitative forecasting based on the previous year’s available data. There are many techniques to make a business future time period forecasting. Normally the statistical tools like the liner curve graph lines, customer sales trends, time based sales assumption and the incremental based assumptions available to use for this purpose. But the Time series trend analysis are the more accurate and powerful tools to make the business future forecast assumptions.

In statistical tools the Correlation and Regression analysis are also known as the very reliable sources for the future forecasting. Correlation analysis is using in the quantitative method of forecasting. It is known as the liner relationship between the two available variables like the sales and demand of  a product or services in a give time period and expressed mathematically as coefficient of correlation (r) and can be expressed as graphically. Normally the value of r is known for its relationship. Like the perfectly positive relationship =1 and the perfectly negative relationship = -1 the ranges between the -0.75 or 0.75 is known as higher degree relationship the ranges between the range of -0.25 or 0.25 known as moderate relationship and the rages between them or at the level of 0 is known as weak relationship.

The most popular relationship in the correlation and regression analysis is liner correlation in which any change in one variable leads to change in other variable in same ratio. There is an inverse relationship we also have here and that know as Non-liner relationship in which the change in one variable always goes in the different direction to the other variable mean the whole effect of the change for both variable is completely different from each other.

Correlation has different methods to measure that are known as actual mean method and the assumed mean method. Karl pearcen coefficient of correlation is also an other well known method of statistics to forecast the change factors.

Coefficient of determination (r2) or the coefficient of correlations squared is a measure of how good the fit between the two variables there is. When we move forward in the topic of forecasting and statistical tools there is regression analysis also exist which is a very important with coefficient topic to discuss here. That regression also known as the least squares analysis and the process of deriving the liner equation that describes the relationship between two variables with a nonzero coefficient of correlation. Simple regression is a tool of it and when we have an independent variable we use it there. The mathematical equation for the simple regression is as follows:

                                                  Y = a  +  bx

The key rules for the simple correlation are that, Simple correlation always takes place between two variables. The Partial Correlation is take place when we have more than two variables but we study only two variables at a time. The Multiple correlation deals in at least three variables. Regression analysis is must to calculate the fixed variables costs. That is the reason the regression analysis used in budgeting and for cost accounting purposes. That takes place only in the relevant range and if we assume out of it the regression will not take place. The next assumption about regress is always about the past data or past relationship.

Learning Curve analysis is another forecasting technique in this technique we take a process and with the passage of time the results of the continuous processing or production is going in improvement like a labor performance is going to be better with the passage of time. That also can be expressed as graphysical representation and can be expressed as a percentage to reduce the time of a production continuous process. The mostly business organization use the percentage in practice as 80% in their calculations. There are two different methods of Applying the liner curve analysis and that are the cumulative average-time learning method in which the unit produced is multiplied by the cumulative average timer per unit and them the time spent on the activity is reduce form the previous activity like the first activity take 100 minutes if we combine the efforts till the 2 unit produced the result will be 160. Then we subtract it form the first production activity and the result will be 60. The Incremental unit time learning method a method in which we divide the number of units produced with the cumulative average timer per-unit and achieve the results like the time for the first and second activity is 100 +80=180 then the 180/80=90 is the answer.

I am thankful to the Gliem Book Part-1 for the CMA-USA student’s preparation. The topic is taken from this book and the examples of the forecasting also take form it. The Forecasting is not finishing there the Time Series Analysis, Expected Value and Sensitivity Analysis is the other forecasting topic which will be discuss in the upcoming blogs.