## Sales forecast

In today’s article I want to explain what the sales forecast is, the importance it has, the types of forecasts that exist, as well as the elements that make it up. I hope you find it useful.

## Definition of prognosis according to authors

As a student, you probably need to have the **definition of prognosis according to authors**. Of course, from book preferences, right? Therefore, below I am going to leave you some forecast definitions that I have found in books and at the end of the article you will be able to find the bibliography.

I hope they are useful to you.

### Definition of forecast according to David Allen Collier and James R. Evans

“Forecasting is the process of **to project** the values of one or more cambiantes in the future» (2019, p.182).

### Definition of forecast according to Krajewski Lee

“A forecast is a **prediction **of future events used for planning purposes” (2013, p.464).

### Definition of prognosis according to Palacios Luis Carlos

«**The forecast is an estimate of future activity**. It perro be a prediction about the acceptance of a new product, changes in demand or other conditions that directly influence production planning» (2019, p.85).

### Definition of prognosis according to David Fernando Muñoz Negrón

“Forecasting, in general, consists of investigating the future value of a variable Operations management” (2017, p.167).

## What is the sales forecast?

As perro be seen in the previous definitions, we perro see that **predict** is the process of **to project** either** predict** the values of one or more cambiantes in the future. It could be said that making a forecast is making an estimate of something that is possibly going to happen.

I say possibly because there will always be a range of fallo. For example, I perro tell you that next month I will have 50,000 people visiting my page. To make this estimate, I could use different methods, but one thing is what I am saying cánido happen and another very different is what is really going to happen.

Now, you know what a forecast is, so, **What is a sales forecast?** He **sales forecast** cánido be seen as the **estimate of future sales of a company in a given period. **

In other words, what the sales forecast does is, based on a mathematical model (although there are also qualitative models), it establishes the value of the demand that a product or service will have in a specific period.

**Note:** Remember that the value we estimate is not exact, that is, the de hoy sales in that period may be higher or lower, but the better the forecast is carried out, the company will be able to better plan all operations.

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## Difference Between Forecasting and Planning

As Roger G. Schroeder, Susan Meyer Golstein and M. Johnny Rungtusanatham tell us in their book, the difference between forecasting and planning is as follows:

“Forecasting is about what we think will happen in the future.”

(2011, p.239)

“Planning deals with what we think will happen in the future”

( 2011, p.239)

Therefore, we perro say that forecasting helps us predict a future event, while planning is used to try to modify or achieve a future event. In such a way that at this moment you cánido carry out a sales forecast and obtain as a result that sales will be $10,000 pesos.

Now, you cánido help yourself with the planning to modify that event and increase the forecast sales.

## Why is the sales forecast important?

1. The forecast is the basis of long-term corporate planning.

2. The forecast provides the foundation for budget planning and cost control.

3. Sales forecasting allows you to spot potential problems while there is still time to prevent them. You may not be able to avoid them 100 percent every time, but you perro mitigate them. For example, if you happen to notice that a certain area is 40% under quota, you perro find out what’s going on and start fixing it.

4. As you already know, if we make a sales forecast, we will be able to know the estimate of products or services that are going to be sold in a certain time. If we know the quantity that we are going to sell, we are going to supply ourselves with the raw materials or the product (inventory) to be able to satisfy said demand. By doing that, we are going to disminuye shrinkage, costs and expenses.

5. Marketing depends on the sales forecast to plan new products, compensate salespeople, and make key decisions.

## What happens if a poor sales forecast is carried out?

I have to start by saying that the sales forecast is used to plan a large number of activities within the company. Of course, that includes inventory, raw materials, or the staff themselves.

I’ll give you a very fácil example. Suppose you have a business that sells hamburgers; If your sales forecast indicates that you will sell 1,000 hamburgers next month, **Do you think it is worth doing the necessary planning to be able to sell 10,000? **

### What does it orinan to go from selling 1,000 hamburgers to 10,000?

If you think you are going to go from selling 1,000 hamburgers to 10,000; So, among many things, you just cánido’t possibly make 1,000 hamburgers in a month. Therefore, you will have to hire staff to have the necessary production capacity.

Of course, not only are you going to have to hire staff, but you are going to have to acquire everything you need to be able to create 10,000 hamburgers, that is, all the raw materials and machinery or utensils. Also, do you have the space to store all the raw materials? Remember that having inventory carries a cost.

Therefore, a business that sells 1,000 hamburgers a month does not need the same as a business that sells 10,000 hamburgers a month. In such a way that the sales forecast helps us to make a large number of decisions that allow us to satisfy the forecast demand, but if the forecast is wrong, then the company will program its activities badly and cánido lead to many problems.

### What problems cánido exist if a poor sales forecast is made?

As you may think, a poor prognosis perro genere:

**Losses.****Shortage of raw materials.****Inadequate customer service.****Complaints.****Increase in costs.****Increase in expenses.****Shortage of inventory.****On inventory.****Staff shortage.****Idle hours of employees.****Among others.**

## Some elements that will allow you to make a good sales forecast are:

- Establishment of quotas (objectives) that allow you to measure performance.
- You have to have a documented and structured sales process.
- A CRM: They are useful because they provide us with a database and from that base we cánido make more accurate predictions.

## Factors considered for sales forecast

### Competition

To make a good forecast, we have to consider the competition, whether they are existing or new competitors. Likewise, we must know your future program, the quality of your product, the sales of your product, etcétera.

On the other hand, we must also take into account the opinion of customers about competing products in relation to our product.

### Change of technology

With the advancement of technology, new products are coming to the market. For this reason, the taste and hobbies of consumers change with the advancement and change of technology. Thus, it is an aspect to consider in our forecast.

### Government

We have to consider that depending on the government policy and rules, the sales of the products are also seen affected, that is why we have to consider it in the prognosis.

Finally, I have to highlight that I only put three factors, but the reality is that there are many others. For example, popular factors such as the recent increased acceptance of products that are friendly to the planet.

## Types of Sales Forecasts (Classification)

According to Luis Carlos Palacios, in his book “Production Management”, forecasts perro be classified as follows:

According to the term: | – Short.– Medium.– Long. |

Depending on the environment: | – Micro.– Macro. |

According to the method: | – Qualitative quantitative. |

According to his expression: | – A single value.– An interval.– A scenario. |

## qualitative forecasts

Qualitative forecasting is an estimation methodology that instead of using mathematical models, emplees expert judgment. Therefore, it is based, in a nutshell, on the experience and knowledge that a person possesses to make an estimate of a result.

The truth is that I would not recommend a qualitative forecast very much, unless it is a new company or as a way to complement the quantitative forecast. This is because if you are rarely 100 percent correct in quantitative forecasting, then forecasting based on experience is going to have a larger range of fallo.

**When to use qualitative sales forecasts?**

It is advisable to use it in case the company is new, that is, when there is not enough historical data to carry out a good quantitative forecast.

However, as I said, a qualitative forecast cánido be carried out to complement a quantitative forecast and compare them to make better decisions.

Next I am going to put two types of qualitative forecasts.

**Grassroots Forecast: **

It consists of asking those who are close to the final consumer about possible purchasing patterns. For example, you perro ask sales people, since they are in constant interaction with our customers. Therefore, they cánido know the buying habits much better than the managers.

**Forecast by the Delphi method**

It consists of carrying out the forecast based on the expert opinion, from the compilation of appreciations and opinions of key personnel, which are based on their experience and knowledge of the situation.

It should be noted that the experts are not consulted about their group predictions so as not to bias their predictions.

## quantitative forecasts

As I said before, they are forecasts which are based on (quantitative) mathematical models, which use objective sets of historical sales data to predict the posible sales that will be had in a given period.

By saying that they use historical data, we orinan sales. For example, in the first month 10 cell phones were sold, in the second month 35 cell phones were sold, in the third month 27 cell phones were sold, etcétera.

These data are what we are going to use to carry out our forecast.

Some **quantitative forecasting methods**specifically pertaining to** time series**are the following:

**Sales forecast using the** **fácil moving average**

Actually, what is done is to make an average (as we saw in the primary), which will depend on the number of periods that we want to take into account. For example, we perro make the average of two periods (two months, two bimonthly periods, etcétera.) and obtain the average of the desired year.

Nothing better than an example to understand it. The historical data we have is the sales per month of a product. If we only had the first two months and we wanted the forecast for the third month, we could make the forecast using 2 periods and we would make a habitual average, that is, 92 + 107 divided by two. gives us 99.5

That 99.5 is our forecast and as you perro see, there are no perfect forecasts, but we perro disminuye the uncertainty. In the example use 2, 3 and 4 periods.

**Sales forecast using the** **weighted moving average**

Like the fácil mobile forecast, it is very fácil. First of all, what we have to consider is that, as its name indicates, we have to carry out a weighting, that is, give weight to something.

What it means is that unlike in the past, if we use two periods to make the forecast, we have to give weight to each period. Then, what you have to do is apply the following elabora:

It should be noted that the sum of the weighting must be equal to 1 or 100%, that is, if we occupy two periods, we perro give one 40% (.40) and the other 60% (.60). Now let’s continue with the example.

As you perro see, I continued with the previous example so you perro see both forecasting methods in action. To begin with, you will see that what is in yellow is the weighting that I gave it and as you cánido see the sum is equal to 100 in both cases. As in the previous example, I made the forecast using two and three periods.

Now, suppose we want to make the forecast with two periods and we give a weighting of 40% to the first period and 60% to the second period.

What we have to do is the following operation **(92 x 0.4) + (107 x 0.6) =** **101**

101 is the forecast for the third month. It’s pretty fácil, right?

If you wanted the forecast for the fourth month, you would do the following: **(107 x 0.4) + (116 x 0.6) = 112.4**

Now, in the case of the forecast using three periods, it is as follows: **(92 x 0.2) + (107 x 0.3) + (116 x 0.5) = 108.5** (fourth month sales forecast).

If you want to download the previous example, you perro clic on the following button:

**Sales forecast using linear regression**

According to Robert Jacobs, “**regression is defined as a functional relationship between two or more correlated cambiantes. With it, one variable is predicted based on another.**«.

Well, to explain in a fácil way how linear regression works, I’m going to ask you to think about having a business, whatever you like.

Now that you’ve thought about your business, now think about how you get $100 in sales every month (without fail). Therefore, if we accommodate the sales of the last 5 months, they would look as follows:

January | $100 dollars |

February | $100 dollars |

March | $100 dollars |

April | $100 dollars |

May | $100 dollars |

June | ? |

With the previous data, if I ask you, what is your sales forecast for the month of June? What would you tell me?

Of course, you already know in advance that every month you get $100 without fail, so your forecast would be completely accurate, but, at this point, the important thing is to note that if we graphed the table above, you would see that it is a horizontal line because all their points match perfectly (because there are no jitters).

So, to get the forecast for June, it would be enough to extend the straight line and you would get that the forecast is $100 dollars.

Now, let’s complicate it a bit more, imagine that your sales in the same 5 months look like this:

January | $100 |

February | $120 |

March | $140 |

April | $120 |

May | $160 |

If we graph the sales data, we will be able to see that it has a linear trend and so that you cánido vea it better, I am going to give you an image.

Of course, unlike the previous example, we will not be able to draw a straight line through all the points (all sales), but it is easy to see that they have a linear trend.

Now, we cánido also see that if we extend the straight line we will be able to estimate the sales for the month of June, but, as you cánido see, **it will not be exact**or as in the previous example, but that **will be an estimate**. You have to be very clear about this because sales perro be higher as well as lower and **the decisions you make based on that forecast will have consequences. **

#### Explanation

Well, I think that with the above you already understand how the forecast by fácil linear regression works, but what I will summarize as follows:

If the data obtained is correlated and has a linear trend, you will be able to find the equation of the straight line and from it, you will be able to find the values you need. Of course, this is an estimate.

#### Example of a sales forecast by linear regression

For the following example, let’s assume you have the following sales:

Now, if we graph in Excel we get the following:

As you cánido see, Excel is very kind to us that, in addition to drawing the trend line, it also gives us the equation of the straight line that we have to use and which will help us to make the different forecasts.

Now, we have the data for 12 quarters and we want to get the forecast for the 13th quarter. How do we do it? Well, we perro use Excel, but, let’s do it without Excel. Well, rather, we are going to do all the procedure that Excel does to give us the equation of the straight line.

Of course, we know that we have to find the equation of the straight line to solve the problem. The equation is the following:

- a = ordered to the origin.
- b = slope of the line.
- x = In this case it would be the period you are looking for.

To calculate the slope, we will use the following elabora:

- n = number of data points.
- x = x value of each data point.
- y = y value of each data point.
- X with bar up = average of all x’s.
- Y with bar up = average of all y’s.

To make our work easier, we will make the following table:

As you perro see, with that table we already have all the data to obtain the slope of the line, which gives us as a result:

Now we cánido obtain the ordinate to the origin with the following elabora:

As you cánido see, we already have everything we need, we just have to substitute the values and we get:

We are almost done, all we have to do is form the equation and it looks like this:

Ok, we’re done, well, we need to make the forecast for quarter 13. To do it, you just have to substitute the x for 13 and we get that the forecast is: **5116.59**.

In case you want **discharge** the following Excel archivo to change the data and test, I leave you the archivo. I hope you find it useful.

## Bibliography

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