Stock Demand Trends and Forecast v.14
The tool to calculate stock demand trends and make prediction for future demand statistically. 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 let 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
-
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
-
Autoregression (AutoReg) is the simplest but still widely used statistical method for time series forecast
-
Autoregressive Distributed Lag (ARDR) takes into account 'errors' in previous observations
-
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
-
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
-
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
-
Simple Exponential Smoothing (SES) is similar to the AR method, but instead of relying upon linear function, it exploits exponential one
-
Holt Winter's Exponential Smoothing (HWES) enriches the SES method to work with time series trends and seasonal effects
-
For more technical details have a look at the page: https://www.statsmodels.org/stable/api.html
Usage requirements
-
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
-
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 of how statistics works.
Configuration and Installation Tips for Stock Demand Trends and Forecast Odoo v.14
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
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.
The technical core to synchronize your cloud storage solution with Odoo
245€
The tool to build deep and structured knowledge base for internal and external use. Knowledge System. KMS
298€
The tool for time-based service management from booking appointment to sale and reviews
The tool to automatically synchronize Odoo attachments with OneDrive files in both ways
394€
The tool to configure variant prices based on attributes coefficients and surpluses
The tool to administrate vendor data about products, prices and available stocks
