Sales Trends and Forecast v.17
The tool to calculate sales trends and make predictions for future sales statistically
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 scientifically forecast further periods.
The scientific approach to forecasting
The app assumes applying statistical methods to calculate historical sales trends and make a forecast. Experiment with statistical models and parameters to achieve the most reliable prediction. Leverage the proven statsmodels methods
Innovative data analysis interface
The tool introduces a special interface to instantly calculate sales series. Flexibly switch between different reports and update settings in a few clicks. Control data in pivot, graph, or chart views. Export series as an Excel tabled
Focused analysis
Shrink considered order/quotation lines flexibly or analyze global company sales. Group data into various series
Topical data
Apply time frames for historical data and work with intervals that are of interest to your industry. Work with past and future periods.
Flexible report configuration
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Introduce as many analytic reports as you need. Each report is based on Odoo's sales lines' data
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Keep reports to re-use them, instantly switch between reports, or experiment with real-time changes without saving those
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Show historical and forecast series as a table, line graph, or bar chart, or export data as an Excel table
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Apply as many and as different statistical models as you want to check. Each stats model will peer its own data series and prediction if the latter is possible. See also the section "Statistical models"
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Define the data periods under analysis by entering a start and end. Only sales within this interval will be included in the series
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Split data series into years, quarters, months, weeks, and days depending on seasons assumed in your industry or business area
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Select an unlimited number of periods to predict future periods or check hypotheses
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Grant the right to forecast tools for any salesperson (the group Sales > Sales Forecast)
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Filter sales data by product categories, product templates, product variants, sales teams, salespersons, countries, industries, specific customers, and companies (for multi-company regimes). Introduce your own criteria based on Odoo's dynamic domain constructor, so shrink analysis by any sale line storable fields
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Analyze a sales total, an untaxed total, an untaxed total to invoice, gross weight, or volume
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Introduce a few data series simultaneously with grouping by a product category, a product template, a product variant, an order state, a team, a salesperson, a country, an industry, a customer, or a company
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Hide/show general statistical measurement of series: count, mean, standard deviation, minimum, maximum, 25%, 50%, and 75% factors
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Optionally compare predicted values to real historical data if any. See also the section "Hypothesis Testing"
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Make figures human readable (e.g. 192,217 as 200k) or show them precisely.
Statistical models
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The tool allows calculating sales trends based on predefined statistical models introduced by the Python package statsmodels
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Methods have different complexity, and their own unique parameters, and might be suitable for a definite market or a specific business. The final choice of the model and related statistical parameters is only up to you. Nobody except you can better understand tendencies in your company sales, and hence which methods and factors should be applied
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Available statistical models are Autoregression (AutoReg), Autoregressive Distributed Lag (ARDR), Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving-Average (SARIMAX), Holt Winter's Exponential Smoothing (HWES), Simple Exponential Smoothing (SES)
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For deep details about what each stats model means and which parameters might be applied, look through the tab "Help" on the stats model form
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Keep and use an unlimited number of stats models with various parameters. Check, compare, and update those flexibly through the forecast manager interface
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Often models with specific parameters will not result in any prediction or the prediction will be unrealistic. This is normal, only a deep understating of trends and much experimenting might give a viable forecast for your specific case.
Hypothesis Testing
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To get a proper prediction, it is usually needed to experiment a lot with statistical models and introduced parameters. To check the results, the app allows compare forecast data with real values
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To check a certain assumption with historical values, you may make an analysis and prediction for some past periods, and turn on the setting "Hypothesis testing"
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For example, you have sales orders from 2017 to 2022. Then, you can get report historical data for 2017-2021, and make the forecast for 12 months of 2022. In this way, you will get the prediction for the interval for which you already have actual values. By comparing forecast and actual values, you can decide whether a chosen model and factors are good enough
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The app allows checking the hypotheses for a few stats models simultaneously. In this way, it is possible to reveal what is actually the most appropriate, or whether experimenting should be continued.
Usage requirements
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To reveal trends, there should be enough historical sales data. It is senseless to make a forecast based on the last 5 days of sales since no trend cannot be retrieved. Take into account that data should be enough for the chosen filters and for each grouped series
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There should be not only enough sales lines but also there should be some actual trends. Chaotic data will not result in reliable predictions. To use the app, you should understand which assumptions (e.g. seasonal changes) you check
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To use the app, you either should have a clear understanding of how statistics works or a will to understand that. Regretfully, there is no way to make a "general" analysis, each specific case is unique and requires its own elaboration.
Tipy k konfiguraci a instalaci pro Sales Trends and Forecast Odoo v.17
Python dependencies
To guarantee the tool works correctly, a number of Python libraries are required: 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 to or equal to version 3.7, please install the following versions of the packages pandas==1.3.5, statsmodels==0.13.1
Server memory limits
Running the statistical analysis and making the forecast is quite a resource-demanding operation. If users face the hanging interface, make sure that server memory limits and timeouts are enough. Otherwise, increase those settings.
Často kladené otázky o Sales Trends and Forecast Odoo v.17
Jaka jsou pravidla aktualizaci vasich nastroju?
Podle aktualnich pravidel Odoo Apps Store:
- Modul koupeny pro verzi 12.0 nebo starsi dava pristup ke vsem verzim az do 12.0.
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Jak nainstaluji aplikaci na Odoo.sh?
Nejprimejsi postup je vestaveny tok obchodu Odoo:
1. Otevrete stranku modulu a kliknete na Deploy on odoo.sh
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Tyto kroky nainstaluji aplikaci na production vetev. Pro jine vetve nebo aktualizaci:
1. Stahnete zdrojovy kod z obchodu Odoo
2. Commitnete modul do GitHub repozitare. Zadna slozka/soubor aplikace nesmi byt v .gitignore. Repa vytvorena odoo.sh mohou ignorovat dulezite cesty (napr. /lib). Nahrajte vsechny adresare a soubory modulu
3. Deployujte cilovou vetev projektu odoo.sh, nebo pockejte na automaticky build, pokud to nastaveni predpoklada.
Jak nainstaluji aplikaci na vlastni server?
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Aplikace ma dalsi rozsireni. Mohu je koupit pozdeji?
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Chtel bych slevu
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Mohu aplikaci nainstalovat na databazi Odoo Online (SaaS)?
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Nastroj zavisi na jinych aplikacich. Mam je koupit?
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Demonstrační databáze Odoo (živá náhledy)
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Chyba Výkazy
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Veřejný Vlastnosti Požadavky Modul Nápady ZDARMA Vývoj
Jsme silně motivováni zlepšovat nástroje a vděční za každou zpětnou vazbu. Pokud jsou požadavky veřejně užitečné a dají se efektivně implementovat, tým je zařadí na seznam úkolů.
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