Question

Manufacturing Organization: Caterpillar

Question: Differentiate at least two or three key elements for each of the four primary forecasting techniques and how they apply to your chosen organization.

Answer 1

here I am going to differentiate at least two or three key elements for each of the four primary forecasting techniques along with I will describe how the apply to my organisation that is caterpillar.

There for primary forecasting techniques such as :-

1) STRAIGHT LINE :- straight line forecasting technique is the most used and the easiest technique as the analyst mainly used this technique to use the past figures and with the help of those past figure they protect the future.

2) MOVING AVERAGE :- 4D moving average data revenue is used in the vertical column of the data table. It has a set of data which is established on the base of the the future values.

3) SIMPLE LINEAR REGRESSION :- this is the most popular and most used method. with the help of simple linear regression the relationship between the variables can be determined for the prediction purpose.

4) MULTIPLE LINEAR REGRESSION :- this is a bit complex method as it requires two or more independent variable for the projection of the future. It is helpful in the forecasting of of revenues.

The two three key elements that differentiate above four primary forecasting techniques are :- straight line method and average moving method contains the minimum maths while as simple linear regression and multiple linear regression requires the more statistical knowledge.

similarly straight line method and moving average method requires the historical cost or the historical figures while the other two requires the sample relevant observations.

In my organisation caterpillar they can be e applied according to the accordance on the need of the work. if there is need of historical figures I would definitely apply the the straight line method for the moving average method but if there is requirement of simple relevant observation I would go for simple linear regression on multiple linear regression.