Sales Trends and Forecast

by
Odoo          
v 12.0 Third Party 3

115.00 €

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Technical name sales_forecast_by_periods
LicenseSee License tab
Websitehttps://odootools.com/apps/12.0/sales-trends-and-forecast-312
Read description for v 11.0
Required Apps Sales (sale)
Invoicing (account)
Technical name sales_forecast_by_periods
LicenseSee License tab
Websitehttps://odootools.com/apps/12.0/sales-trends-and-forecast-312
Read description for v 11.0
Required Apps Sales (sale)
Invoicing (account)

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.

The tool is compatible with both Odoo Enterprise and Odoo Community.

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
The module features depend on your Odoo version. Please select required one on the top right of this page
Shrink sales by products, teams, countries
Chart of forecasts and historical sales
Excel table of sales

Statistical methods for forecast

Some statistical methods require deeper knowledge in statistics. To start with read this, this, and this articles
Autoregression (AR)

It is the simplest but still widely used statistical method for time series forecast. Using the method you consider sales trends being linear without seasonal effects, without a purely defined trend, and without smoothing abnormal observation.

Moving Average (MA) and Autoregressive Moving Average (ARMA)

The moving average method takes into account 'errors' in previous observations, and in comparison to the AR method smooths abnormal data.

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 beside that it 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
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Target your analytics
Shrink sales by products, teams, countries
Experiment with various statistical models
Different statistical methods for forecast
Sales trends and forecast chart
Chart of forecasts and historical sales
Sales trends and forecast as an xlsx table
Excel table of sales
Odoo pivot view of sales trends and forecast
Odoo report for sales prediction
Sales forecast in a few clicks
Sales trends and forecast menu
Grant the right for the forecast report
Sales trends and forecast security

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:

pip install pandas numpy statsmodels scipy xlsxwriter

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
Default values to generate a report

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