Sales Trends and Forecast v.15
The tool to calculate sale trends and make prediction for future sales statistically. Sales Forecast. Sales prediction
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 Odoo tool lets you generate sales by periods and apply statistical methods to forecast further periods.
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 sales for definite products, templates, categories, teams, a country, or customers: trends might be different in different markets or functional areas
Topical data
Apply time frames for historical data to check statistical reliability through 'predicting' actually passed intervals. All orders in the states 'Locked' and 'Sale Order' would be taken into account
Trends interfaces
Work with sales 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 absolute quantities and revenues (in a company default currency). The app might be of especial use for markets experiencing seasonal changes: try to define statistical factors in your industry to make reliable forecasts
Sales trends analysts
Grant the right for forecast tools for any sales user (the group Other > Sales Forecast). To start analysis it is needed to push the menu entry Sales > Reporting > Sales Forecast. Be cautious: all sales of a current company will be under consideration.
Statistical methods to forecast sales
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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 sales data, since it is senseless to make forecast based on last 5 days of sales
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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
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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 Sales 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
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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