Forecasts in companies
In this article I have the objective that you learn what the forecast is, the elements and the characteristics that you need to prepare one.
Forecast Definition
Definition of forecast according to Krajewski Lee
“A forecast is a prediction of future events used for planning purposes»
Definition of forecast according to Koontz
“Is a future expectation and is usually expressed in financial terms, of plans already developed”
What is the forecast?
We cánido see forecasting as the process of using analysis, data, perspectives and experience so that we cánido make predictions to determine future economic conditions.
Another way of understanding what a forecast is is as follows:
A forecast consists of using a method, based on certain rules in order to estimate (with the best degree of fallo) a necessary variable to help improve the organization’s decision-making.
Therefore, it could be said that It consists of estimating a future value. For example, the best known forecast is the sales or revenue forecast.
Importance of forecast
The importance of the forecast is not as such knowing the estimate of what could happen, but the importance lies in the decisions that you are going to make based on said estimate.
For example, if you obtain in a sales forecast that you will not be able to satisfy the demand in two months, the importance lies in all that equipo of decisions that you are going to make in order to meet the demand.
Following the example above, you could increase production capacity, hire more staff, carry out an analysis to find out if there are bottlenecks, etcétera.
Therefore, the information obtained by the forecast allows us to automate and optimize the processes of the organization.
Classification of forecasts
Forecasts are classified based on the following two characteristics:
Methodology
As its name implies, it is related to the type of information that is used and the method that is used to make the forecast.
They perro be quantitative or qualitative.
Quantitative methods
They involve using historical data to help us predict the future values of a variable.
For example, sales, income, costs, inventories, market demand, etcétera.
Historical data perro be obtained:
- Of the records that the company has, for example, the record of monthly sales that have been had in 4 years.
- From official records external to the company; for example, from the government, associations, international organizations, etcétera.
Some of the methods used to obtain the forecast are:
- Time series.
- exponential smoothing.
- regression models linear.
Qualitative methods
A qualitative forecast involves the experience, common sense and criterion of the person or group of people who are in charge of preparing the forecast.
By not using historical data or statistical methods to develop the forecast, they usually have a higher degree of fallo, but they are very useful in new companies, because a new company does not have previous data on which to work.
Some methods used to make qualitative forecasts are:
- Delphi method.
- Usuario expectations method.
time horizon
It is classified according to the time in which the forecast is formulated, that is, if they are at short, medium or long term.
short term forecast
It cánido include a period of one to three months and forecasts are usually made about the raw material Necessary for schedule productionthe distributionthe inventoriesetcétera.
Medium-term forecast
It covers a period of three months to less than two years and forecasts are usually made about the price fixingthe budgetsthe consumption projectionetcétera.
Long-term forecast
It covers a period of two years and up and forecasts are usually made about the salesthe plant capacityabout the investmentsetcétera.
It should be noted that not an absolute rule, cánido change depending on the company or the situation, for example, the demand forecast perro be made for one year or six months.
Therefore, you perro take it only as a base.
In fact, I found those periods of time in a book, but it may be that in another book you will find a different arrangement.
Method for preparing the forecast by linear regression
Linear regression analysis is a model that allows us to know the relationship between a dependent variable and one or more independent cambiantes.
In short, we are going to predict as accurately as possible the value of the dependent variable based on the independent cambiantes.
I will start by giving an example.
Suppose you have a small business and you want to know if your website is working for you to increase the sales of your company.
Simply put, you want to know if there is a relationship between those two cambiantes.
To start with, we need to collect the data, standardize the data, and we perro put it in a table like the following:
Well, of course, to make it a little more understandable, use small numbers.
Next, what you have to do is make a scatterplot with the aim of seeing if both cambiantes have a linear trend.
Since we’ve verified that it does indeed have a linear trend, it’s time to do some math.
Have you ever wondered… What is mathematics for me? Well, I think most of us have asked ourselves that question at least once.
Being more specific, do you know what the equation of the straight line is for? Well, today I will espectáculo you one of its emplees (to make a forecast).
Do you remember what the equation of the straight line is? Well, if it isn’t, don’t worry, it’s this:
- y: dependent variable.
- x: independent variable.
- a: ordered to the origin.
- b: is the slope of the line.
Well, in order to calculate the equation of the straight line, we need to find the ordinate to the origin and the slope.
So, let’s do it.
To make the article shorter, I only put the result of the example, but if you want me to explain it or you want the excel archivo, you perro tell me in the comments.
Now, the elabora to calculate the ordinate to the origin is the following:
Finally, since we have the slope and the ordinate to the origin, we only have to substitute the data in the equation of the straight line and we are left y = 0.893x + 1.75
Ready, now, suppose you want to forecast the number of users needed to sell 30 products.
In that case, you just have to substitute the x for 30 and you would get that you should have 28.54 users to be able to sell that amount of products.
We hope you liked our article Forecasts in companies
and everything related to earning money, getting a job, and the economy of our house.
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