Stock Demand Trends and Forecast
The tool to calculate stock demand trends and make prediction for future demand statistically. Stock Forecast
The tool does not require extra dependencies beside standard Odoo apps
If you knew stock demand trends per 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.
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 consider seasonal changes and/or trends, which you noticed but can't fully analyze. You understand which assumptions you try to check.
- You have clear understanding how statistics works.
Stock demand forecasting interface
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.
The right for trends analysis might be granted to any WMS user, but be cautious: all stock moves of a current company will be under consideration.
Stock demand trends and forecast chart
Stock demand trends and forecast as an xlsx table
Odoo pivot view of stock trends and forecast
Stock demand forecast in a few clicks
Grant the right for the forecast report
Scientific approach for forecasting
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.
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.
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.
Experiment with various statistical models
Topical data under consideration
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.
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.
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.
Target your analysis by locations
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
According to the current Odoo Apps Store policies:
every module bought for the version 12.0 and prior gives you an access to the all versions up to 12.0.
starting from the version 13.0, every version of the module should be purchased separately.
disregarding the version, purchasing a tool grants you a right for all updates and bug fixes within a major version.
Take into account that Odoo Tools team does not control those policies. By all questions please contact the Odoo Apps Store representatives directly.
Sorry, but no. We distribute the tools only through the official Odoo apps store
Unzip source code of purchased tools in one of your Odoo add-ons directory
Re-start the Odoo server
Turn on the developer mode (technical settings)
Update the apps' list (the apps' menu)
Find the app and push the button 'Install'
Follow the guidelines on the app's page if those exist.
Yes, all modules marked in dependencies are absolutely required for a correct work of our tool. Take into account that price marked on the app page already includes all necessary dependencies.
Yes, sure. Take into account that Odoo automatically adds all dependencies to a cart. You should exclude previously purchased tools.
Regretfully, we do not have a technical possibility to provide individual prices.
Red / orange warning itself does not influence features of the app. Regretfully, sometimes our modules do not pass standard automatic tests, since the latter assumes behavior which is in conflict with our apps goals. For example, we change price calculation, while standard Odoo module tests compare final price to standard algorithm.
So, first of all, please check deployed database features. Does everything work correctly?
If you still assume that warning influences real features, please contact us and forward full installation logs and the full lists of deployed modules (including core and third party ones).
As soon as you purchased the app, the button 'Deploy on Odoo.sh' will appear on the app's page in the Odoo store. Push this button and follow the instructions.
Take into account that for paid tools you need to have a private GIT repository linked to your Odoo.sh projects
No, third party apps can not be used on Odoo Online.
The module features and support depend on your Odoo version. Please select required one on the top right of this page.
We guarantee to provide a working plan by your issue within 5 days. The most of issues are solved within 2 business days.
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