Sales Trends and Forecast v.11

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

198€

The tool does not require extra dependencies beside standard Odoo apps.
Current version: 11.0.1.0.2

Enterprise
Community
Odoo.sh
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

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

Default values to generate a report

Frequently Asked Questions about Sales Trends and Forecast Odoo v.11

According to the current Odoo Apps Store policies:

  • every module bought for version 12.0 and prior gives you access to all versions up to 12.0.
  • starting from version 13.0, every module version should be purchased separately.
  • disregarding the version, purchasing a tool grants you a right to all updates and bug fixes within a major version.

Take into account that the faOtools team does not control those policies. For all questions, please contact the Odoo Apps Store representatives directly.

The easiest approach is to use the Odoo store built-in workflow:

1. Open the module's page and click the button Deploy on odoo.sh

2. After that, you will be redirected to the GitHub page. Login to your account and click 'Create a new repo' or use the existing one. Please, make sure, that your repository is private. It is not permitted to publish the apps under the OPL-1 license. If necessary, create a new repo for your Odoo.sh project

3. Then, go to odoo.sh and click on the deploy button, submit the decision in the pop-up window and click 'Continue'. The action will trigger the installation process.

These steps would install the app for your project production branch. If you wanted to deploy the apps for other branches or update the module, you should undertake the following actions:

1. Upload the source code for the app from the Odoo store

2. Commit the module to a required GitHub repository. Make sure that none of the app folders/files are ignored (included in the .gitignore of your repo). Repositories are automatically created by odoo.sh, which might add by default some crucial items there (e.g. /lib). You should upload all module directories, subdirectories, and files without exceptions

3. Deploy a target branch of the odoo.sh project or wait until it is automatically built if your settings assume that.

  1. Unzip the source code of the purchased tools in one of your Odoo add-ons' directories;

  2. Re-start the Odoo server;

  3. Turn on the developer mode (technical settings);

  4. Update the apps' list (the apps' menu);

  5. Find the app and push the button 'Install';

  6. Follow the guidelines on the app's page if those exist.

Yes, sure. Take into account that Odoo automatically adds all dependencies to a cart. You should exclude previously purchased tools.

A 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).

Regretfully, we do not have a technical possibility to provide individual prices.

No, third party apps can not be used on Odoo Online.

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.  

The price for our modules is set up in euros. The Odoo store converts prices in others currencies according to its internal exchange rate. Thus, the price in US Dollars may change, when exchange rate changes.

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.

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