Sales Trends and Forecast v.11

The tool to calculate sale trends and make prediction for future sales statistically. Sales Forecast

18.0 17.0 16.0 15.0 14.0 13.0 12.0 11.0

98€

Buy in the Odoo Apps Store

The tool does not require extra dependencies beside standard Odoo apps.

Current version: 11.0.1.0.2

Enterprise

Community

Odoo.sh

Shrink sales by products, teams, countries

Different statistical methods for forecast

If future sales are a black box for you, you might hardly make profitable decisions right now. How many items to purchase? Which products require aggressive advertising? Where are the best markets for us for the next year? Luckily, statistics might help if you have enough historical data. This tool let you generate sales by periods and apply statistical methods to forecast further periods.

Sales trends and forecast are shown in comfortable manner of your choice: as an Odoo chart, as an Odoo report (pivot), as an Excel table. The latter might be also used to import data in certain statistical software

Apply the statistical method which you consider as the most suitable: Autoregression, Moving Average, Autoregressive Integrated Moving average, Seasonal Autoregressive Integrated Moving Average, Simple Exponential Smoothing, Holt Winter’s Exponential Smoothing. Look at the section Statistical methods for forecast

Shrink your analysis for definite products, templates, categories, sales teams, or country to make rational decisions

Apply time frames of historical data which is used as an analytic basis. Forecast periods are ones which follow after the end of defined frame. In such a way you make check statistical reliability through 'predicting' actually passed intervals

Forecast as many intervals as you like, but remember that prediction for the next 10 years would be hardly reliable

Based on your historical data and applied coefficients, sometimes Odoo is not able to reveal trends and make forecasts. In that case only historical data would be shown in reports. But even historical trends might have an analytical use

As sales per periods all sales orders in the states 'Locked' and 'Sale Order' are taken into account

Grant the right for trends analysis for any sales user, but be cautious: all sales of a current company will be under consideration

Statistical methods for forecast

For more technical details have a look at the page: https://www.statsmodels.org/stable/api.html.

Autoregression (AutoReg)

It is the simplest but still widely used statistical method for time series forecast.

Autoregressive Distributed Lag (ARDR)

The moving average method takes into account 'errors' in previous observations.

The autoregressive moving average method is a combination of both AR and MA methods. To apply the ARMA method use the MA method with auto regression coefficient (P coefficient) as 2

Autoregressive Integrated Moving Average (ARIMA)

The method which also combines the methods AR and MA, but also tries to make data stationary. It is appropriate to use for historical data with pure trend but without seasonal changes.

Seasonal Autoregressive Integrated Moving-Average (SARIMA)

The SARIMA method enriches the ARIMA method with considering seasonal changes. It is one of the most complex and wide spread methods utilized for forecasting time series now

Simple Exponential Smoothing (SES)

The SES model usage is similar to the AR method, but instead of relying upon linear function, it exploits exponential one

Holt Winter’s Exponential Smoothing (HWES)

The HWES method enriches the SES method to work with time series trends and seasonal effects.

When this tool should be used

You have enough historical sales data, since it is senseless to make forecast based on last 5 days of sales
Your sales are regular and they are not chaotic, meaning that your decisions do not have 100% impact on your sales and there is at least some correlation between market demand and your sales
You have some seasonal changes and/or trends, which you noticed but can't fully analyse

Python dependencies

To guarantee tool correct work you would need a number of Python libraries: pandas, numpy, statsmodels, scipy, xlsxwriter. To install those packages execute the command:

pip3 install pandas numpy statsmodels scipy xlsxwriter

If you run Odoo on Python prior or equal to version 3.7, please install the following versions of the packages pandas==1.3.5, statsmodels==0.13.1

Default values

In the most cases you apply the same statistical model and forecast the same number of periods. To save time you can assign default values to the report wizard. Go to Sales > Settings and find the section 'Sales Trends and Forecast'

Sales trends and forecast configuration

Bug Report Raise Question Share Features Ideas

Bug reporting

In case you have faced any bugs or inconsistent behavior, do not hesitate to contact us. We guarantee to provide fixes within 60 days after the purchase, while even after this period we are strongly interested to improve our tools.

No phone number or credit card is required to contact us: only a short email sign up which does not take more than 30 seconds.

Please include in your request as many details as possible: screenshots, Odoo server logs, a full description of how to reproduce your problem, and so on. Usually, it takes a few business days to prepare a working plan for an issue (if a bug is confirmed) or provide you with guidelines on what should be done (otherwise).

Public features requests and module ideas (free development)

We are strongly motivated to improve our tools and would be grateful for any sort of feedback. In case your requirements are of public use and might be efficiently implemented, the team would include those in our to-do list.

Such a to-do list is processed on a regular basis and does not assume extra fees. Although we cannot promise deadlines and final design, it might be a good way to get desired features without investments and risks.

No phone number or credit card is required to contact us: only a short email sign up which does not take more than 30 seconds.

You may also like the tools