Stock Demand Trends and Forecast

The tool to calculate stock demand trends and make prediction for future demand statistically

12.0 11.0

€ 115


The tool is compatible with both Odoo Enterprise and Odoo Community

The tool price includes all necessary dependencies

If you knew stock demand trends per warehouses warehouses, you would have a clue to decrease keeping costs and to have a flawless supply chain. Regretfully, you can't know the future. However, you can predict it with a certain reliability. This is the tool for that goal. The app let you construct stock demand per periods and forecast further demand.

Demand 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
Demand trends and forecast are constructed for product templates in general (e.g. all iPads) or specific product variants (iPad 32Gb). Use the button 'Stock Trends' on a product form for that purpose. The product under analysis should be storable
Make analysis per the whole company or per a definite location. In the latter case optionally include or exclude child locations
Stock demand is calculated as all done stock moves for this period which source location is one of internal location under consideration and which destination location is not of this range
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 '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 analytical use
Grant the right for trends analysis for any WMS user, but be cautious: all stock moves of a current company will be under consideration

Feedback allows improving Odoo tools for you. If you tested the app, please leave a review on the product page

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 stock demand 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 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 stock demand data (per location or company), since it is senseless to make forecast based on last 5 days of operations
  • Stock demand is regular and is not chaotic, meaning that your decisions do not have 100% impact and there is at least some correlation between market demand and your WMS operations
  • You have some seasonal and from period to period trends, which you noticed but can't fully analyse
Target your analysis by locations
Analyse stock demand per location or company
Experiment with various statistical models
Different statistical methods for forecast
Stock demand trends and forecast chart
Chart of forecasts and historical moves
Stock demand trends and forecast as an xlsx table
Excel table of stock moves per periods
Odoo pivot view of stock trends and forecast
Odoo report for stocks prediction
Stock demand forecast in a few clicks
Demand trends and forecast button
Grant the right for the forecast report
Stock demand 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 Inventory > Settings and find the section 'Sales Trends and Forecast'
Demand trends and forecast configuration
Default values to generate a report
To contact us please register in our support system. Registration form is available by any link below. Registration doesn't take more than 30 seconds. No phone number or credit card are required