Stock Demand Trends and Forecast v.15
The tool to calculate stock demand trends and make prediction for future demand statistically. Stock prediction. Inventory forecast. Stock forecast
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 an Odoo tool for that goal. The app lets you construct stock demand per periods and forecast further demand.
Scientific approach for forecasting
The app assumes applying statistical methods to calculate historical trends and make a forecast. Make a few experiments with suggested solutions and coefficients to achieve the most accurate prediction
Focused analysis
Shrink considered stock operations for a definite warehouse location (with or without child locations) or analyze global company stocks demand
Topical data
Apply time frames for historical data to check statistical reliability through 'predicting' actually passed intervals. Stock demand is calculated as all done stock moves for this period which source location is one of internal locations under consideration and which destination location is not of this range
Trends interfaces
Work with stock demands forecasts in a way you like: as an Odoo chart, as an Odoo report (pivot), as an Excel table
Widely applicable solutions
Analyze trends for both a product template in general (e.g. all iPads) or a specific product variant (iPad 32Gb). The app might be of especial use for markets experiencing seasonal changes: try to define statistical factors in your industry to make reliable forecasts
Inventory trends analysts
Grant the right for forecast tools for any WMS user (the group Other > Stock Demand Forecast). To start analysis it is needed to push the button 'Stock Trends' on a product template, variant, or stock location form. Be cautious: all stock operations of a current company would be under consideration.
Statistical methods to forecast stock demand
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The tool allows to calculate inventory demand trends based on a number of statistical methods. They have different complexity and might be suitable for a definite market or a specific business. The final choice of the method and related statistical parameters is up to end users
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Autoregression (AutoReg) is the simplest but still widely used statistical method for time series forecast
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Autoregressive Distributed Lag (ARDR) takes into account 'errors' in previous observations
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Autoregressive Moving Average (ARMA) 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
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Autoregressive Integrated Moving Average (ARIMA) 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
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Seasonal Autoregressive Integrated Moving-Average (SARIMA) 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
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Simple Exponential Smoothing (SES) is similar to the AR method, but instead of relying upon linear function, it exploits exponential one
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Holt Winter's Exponential Smoothing (HWES) enriches the SES method to work with time series trends and seasonal effects
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For more technical details have a look at the page: https://www.statsmodels.org/stable/api.html
Usage requirements
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You have enough historical stock moves data (per location or a company), since it is senseless to make forecast based on last 5 days of operations
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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.
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You consider seasonal changes and/or trends, which you noticed but can't fully analyze. You understand which assumptions you try to check.
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You have clear understanding of how statistics works.
Configuration and Installation Tips for Stock Demand Trends and Forecast Odoo v.15
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
Frequently Asked Questions about Stock Demand Trends and Forecast Odoo v.15
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 will install the app for your project production branch. If you want 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.
Unzip the source code of the purchased tools in one of your Odoo add-ons' directories;
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, 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).
No, we distribute the tools only through the official Odoo apps store
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.
Odoo demonstration databases (live previews)
For this app, we might provide a free personalized demo database.
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.
By your request, we will prepare an individual live preview database, where you would be able to apply any tests and check assumptions for 14 days.
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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