# What is sales forecasting

Sales forecasting is the process of estimating future sales based on analysis of historical sales. It’s a computational process in which historical data is analyzed by each of our algorithms individually.

Each algorithm uses different logic and technique to produce an accurate forecast. Theses results are then evaluated on their accuracy, and the one with the best answer is then used to create results for your eyes in the Inventoro’s software. This process is created on each item in your inventory and further cross-checked of interdependencies of your products on sale. In other words, we check if one item’s sales also influence the sales of another product. All of these calculations happen overnight as you sleep to create results in the morning for each day.

In this article, we will give you a sneak peek into ways how we calculate forecasts and show you the complexity and determination we have to do to get it right. Just read on to find out more.

## Sales Forecasting

Sales forecasting is the core functionality of Inventoro. It is the one with most advanced calculations and it took us 15 year to put into perfection. As a matter of fact, it is so advanced, that it can easily compete with the most advanced forecasting software on the planet.

Inventoro Sales Forecasting is designed to compute the most accurate forecast of future sales, with precision up to days. There are two subsections: the specific preparation of a data series (so that the data can be used for sales forecasting) and the actual mathematical core of the forecasting.

As a rule, we forecast sales at the lowest possible level: in most cases, we forecast sales for a product at a particular warehouse or a store. Under certain circumstances, we may be able to refine the forecast even more and thus predict unique sales to a customer.

## Preparing a data series

Before sales can be forecast, it is extremely important to adjust an individual data series (sales of individual items) for algorithmic forecasting.

First and foremost, this means cleaning the data by removing extreme sales and adjusting the sales for possible stockouts (the product would have been sold if there had been enough inventory).

In this section, we deal with various analyses of the data series: they will – if the right mathematical algorithm is applied – help us to choose the correct method of sales forecasting.

### Identification of seasonality

One of the most important parts of this section is the analysis of seasonal sales.

The algorithm can identify extreme seasons, such as Christmas or extremely seasonal items like Easter eggs. The Corona crisis also
produced a set of seasonal changes to sales. For us though, nothing changes. Our software threats these peaks, like any other peaks.

### Analysis of extreme sales

Inventoro detects extremes in sales on one hand and orders received from individual customers on the other. The received orders also need to be cleaned, as they enter into sales forecasts as well.

The basic principle is to identify sales that are so extreme that they are not likely to be repeated, and even if they do repeat, we do not want them to be our base for sale forecasts and stock level computations. The reason behind this is that if we were to keep safety stock at such a level to satisfy extreme sales, the stock would have to be enormously high (expensive).

The module is able to take into account promotions, and it also works with seasonality of sales (including movable holidays).

### Analysis of stockouts

Inventoro identifies and subsequently analyzes stockouts, periods with no stock on hand. This data is then used to compute lost sales at stockout of a specific item in your warehouse.

#### Thanks to this we can

Evaluate losses in revenue and profit that the company experiences due to the stockout and to compute the desired service level.

Use stockout history as an input to the module to manage customer service levels.

Straighten historical sales and thus provide an appropriate input to sales forecasting. The sale forecast must reckon with the fact that sales would have continued if there had been no stockout.

### Trimming irrelevant history

Forecast algorithms give the best results if the inputs are as meaningful as possible; in this case, a reasonable sales history is a must. One of the recommended modifications is to trim such a sales history that no longer makes sense: either a trend in sales has changed dramatically or average monthly sales have significantly increased or decreased.

But in case you wonder. The ideal sales sales history for our software is two year minimum. But this doesn’t mean that our software will not work with less data. It will. Just not as accurately as its full potential promises.

The module is also useful for trimming sales around the introduction of a new product, where the product sells at a considerably lower level (before the market accommodates the product) or on the contrary, sells considerably more at first than later (especially if the sale of the product started with a massive campaign).

## Computing sales forecasts

When the forecast uses an adjusted sales history (see above), we use three different sales forecasting strategies:

• bottom-up
• top-down
• middle-out

Our mathematical engine has more than 100 different forecasting methods, from very primitive ones like moving averages to very complex machine learning using deep learning approaches.

Inventoro calculates sales forecasts on a monthly, weekly, and daily basis.

Our architecture is built on internal competition and a day-to-day mentality of improvement, like professional sportsmen. They don’t become champions overnight. Instead, they work on the little things every day to reach world class results. The way it works is that we use our algorithms one by one to analyze data sets and produce forecasts.

Then we put them up against each other and let them compete with one another to see which performs best. The next day we do it again, and the day after that again, and again, and again. This helps us to see progress every day, constantly improving our results.

Around half of the methods we use are built upon the work of the great mathematicians of our generation. We use forecasting equations from great minds like Holt, Winters, and Croston. We admire their work, and us being us we constantly look for ways to improve their equations even more. It has taken years, but we have found our own paths to perfection.

The second half of our methods use principles of AI and mainly deep learning. We use complex four-layer models which learn datasets and often come up with surprising forecasting results. The issue with these methods is that they become effective with only vast amounts of data, and not every product in the customer’s portfolio can produce such quantities.

So for us it is not about using buzzwords and insisting that only AI is the future. It probably is, but remember, we are data driven. We measure everything constantly. If AI outperforms standard math, fine! If it doesn’t, that’s okay, too. It’s the results that matter, not the marketing headlines or trendy blog posts.

## Fast adaptation of a sales forecast

The module rapidly captures all changes in sales (both increases and decreases). It then analyzes the last few weeks of sales, and if they significantly differ from the sales forecast based on longer-term history (going back several years), then the forecast for the upcoming period is adjusted. We call this feature “Fast Adapt”.

These changes can occur for various reasons (competition, weather conditions, etc.), and the algorithm is designed to respond to these influences. The module Fast adaptation is particularly useful for fast-moving consumer goods and goods with a high elasticity of sales.

The purpose of the module Fast adaptation is to detect a sudden non-standard market development and adapt your offer to step changes in the demand for your products. The Fast adaptation module adjusts and smooths the current forecast in such a way as to increase the level of customer service, which could drop under the
onslaught of demand growth.

As you can imagine sales forecasting, at the end of the day, is an ability to look at past data and make calculations based on these numbers. No matter how complex these calculation are, no matter how advanced AI features they use, they are always based on the past. That applies to Inventoro, that applies to Amazon IT and to any other forecasting tool there is. With no data, no forecast is accurate.

So you also agree with us that getting this data clean is essential for the calculations. Let’s assume that you count something simple as “Average”. You would grow 10 sunflowers to maximum length to calculate the average length of a sunflower. Two of your sunflowers will never grow, as the seeds died early on in your gardening project. The numbers would thus look like this in centimeters: 112, 110, 108, 120, 111, 108, 104, 122, 0, 0.

By clicking on the average function in excel you would get an average height of a sunflower to be 89,5 cm. That doesn’t sound right, does it? The two dead seeds completely destroy your calculation leading to bad results. Should you clean your data and not add the dead seeds to your calculation, than the number would be 111,9 cm, which is much closer to reality.

So the process of cleaning data sets is essential for creating accurate forecasting and the “Fast adapt” feature is a key element for us in this process. To see stars, you need to clear clouds first.

## Sales forecast for promotions

Inventoro can work with promotions and the number of products sold in a promotion. The analysis is intended for short promotions (up to 60 days), and it is ideal for one- or two-week promotions with leaflets or any other type of promotion, whether in your e-shop or some other type of discount.

#### There are three main parts to the analysis of promotions:

First, we must estimate what would have been sold in a given period in the past without any promotions.

Second, we have to forecast how much of the product will be sold in promotion with the given parameters (length of the promotion, discount, leaflets, secondary placement, gifts, etc.)

Finally, we have to split the forecast quantity of product sold into individual days, based on a typical promotion of the given type.

#### How do we calculate real promotions?

Our engine is able to automatically detect four parts of promotions:

Pre promotion effect – is the situation before the promotion, when your customers are waiting for the promotion and your sales is little bit decreasing

Promo affect – forecast of sales due to promotion

Post promotion effect – after promotion the market is saturated and your sales will decrease for next for
example 14 days

Cannibalization effect – when you decide to put some article to the promotion there will be a list of articles which will decrease in sales due to this promotion

So as you see. Sales forecasting is a complex field of study, if done properly and accurately. Inventoro’s prepared to create highly accurate forecasts come what may. Peaks, weather, price promotion, you name it. Inventoro is created for all case scenarios. Proven to work.

### Amazing sales forecasting

If sales forecasting is so complicated, then why are we giving it this cheap, you might ask. We strongly believe that those retail, wholesale, and e-commerce business who forecast well have an unprecedented advantage to those don’t forecast at all.

By rule of thumb, the bigger the organisation the stronger IT department they have and the better they are in forecasting. Forecasting is then used for business optimisation and eventually leading to free cash in hand, lower costs, higher sales and profits.

There is a wall between those who forecast and those who don’t. The bigger are getting massive and the smaller are closing down, unable to compete no matter how many after hours and weekends they work.

That’s not good. That destroys the market. That weakens customer choice and that makes the world a bad place to live in for us all. For us, it was a dilemma. What do we do about it and how do we go about making things right.

After days of discussions and hours of late night bar talks, we finally concluded to offer our service for a low enough price if we want to make a change. So world, if you’re listening. Answer this call. Start using Inventoro and help us to make a difference.

## Stay in touch with Inventoro

Supply chain management software can be complicated. We have spent thousands of hours to make it as simple for you as possible. Still, we believe that we can still improve and your feedback on our software is always appreciated. Please contact us in our chatbot or by email. You may also find it useful to read through our knowledge base where we go deeper in the subjects mentioned here.