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.
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 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.
Sales forecasting interface
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.
The right for trends analysis might be granted to any sales user, but be cautious: all sales of a current company will be under consideration.
Sales trends and forecast chart
Sales trends and forecast as an xlsx table
Odoo pivot view of sales trends and forecast
Sales 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: Auto regression, 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 an analytical use.
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.
Experiment with various statistical models
Topical data under consideration
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.
As sales per periods all sales orders in the states 'Locked' and 'Sale Order' are taken into account.
Target your analytics
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
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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.
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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?
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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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