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.
配置与安装提示： Sales Trends and Forecast Odoo v.18
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.
相关常见问题 Sales Trends and Forecast Odoo v.18
您的工具的更新政策是什么？
根据当前 Odoo Apps Store 政策：
- 为 12.0 及更早版本购买的每个模块，均可使用直至 12.0 的所有版本。
- 从 13.0 起，每个模块版本需单独购买。
- 无论版本如何，购买工具即获得该主版本内的 全部更新和缺陷修复权利。
请注意，faOtools 团队不控制这些政策。如有疑问，请直接联系 Odoo Apps Store 代表。
如何在 Odoo.sh 上安装您的应用？
最直接的做法是使用 Odoo 商店的内置流程：
1. 打开模块页面并点击 Deploy on odoo.sh 按钮
2. 之后会跳转到 GitHub 页面。登录帐户，然后点击 'Create a new repo' 或使用现有仓库。请确保仓库为私有。不得以 OPL-1 许可公开发布应用。如有需要，为 Odoo.sh 项目新建 repo
3. 然后转到 odoo.sh 并点击 Deploy 按钮。在弹出窗口中提交决定并点击 'Continue.' 这将触发安装过程。
这些步骤会将应用安装到项目的生产分支。若要部署到其他分支或更新模块，请执行以下操作：
1. 从 Odoo 商店上传应用的源代码
2. 将模块提交到所需的 GitHub 仓库。确保没有任何应用文件夹/文件被忽略（即未列入 repo 的 .gitignore）。仓库由 odoo.sh 自动创建，默认可能包含关键项（例如 /lib）。应上传模块的全部目录、子目录和文件，不得遗漏
3. 部署 odoo.sh 项目的目标分支，或在设置如此假定时 等待其自动构建。
应如何在自己的服务器上安装您的应用？
2. 重新启动 Odoo 服务器;
3. 打开开发者模式（技术设置）;
4. 更新应用列表（应用菜单）;
5. 找到应用并点击“Activate”/“Install”;
6. 如应用页面有指南，请按指南操作。
我注意到您的应用有额外附加模块。可以之后再购买吗？
可以。同时 Odoo 会自动把所有依赖加入购物车，因此应排除已购买的工具以免重复付款。
我在 Odoo.sh 上部署了您的应用，测试中出现警告/错误
红/橙色警告不影响应用功能。遗憾的是，标准自动测试有时假定与本应用目标冲突的行为，因此我们的模块会未通过。例如我们改了价格计算，而标准 Odoo 模块测试把最终价格与标准算法比较。
请先检查已部署数据库的功能。是否都正常？
若仍认为该警告影响真实功能，请联系我们并转发完整安装日志以及已部署模块的完整清单（含核心与第三方）。
我可以直接向贵公司购买应用吗？
不，我们只通过 官方 Odoo 应用商店.
希望获得折扣
很遗憾，我们没有技术手段 提供单独报价。
我可以在 Odoo Online (SaaS) 数据库上安装该应用吗？
不能，第三方应用无法在 Odoo Online 上使用。遗憾的是 Odoo SaaS 会阻止这种可能。
您的工具依赖其他应用。我需要购买那些应用吗？
是的，工具需要依赖中列出的全部模块才能正常工作。应用页上的价格已包含所有必要依赖。
该模块的美元价格是多少？为什么变了？
我们的模块价格以欧元标价。Odoo 商店按内部汇率换成其他货币。因此美元价格可能随汇率更新而变化。
非英语的应用和网站措辞由 AI 辅助 并与官方 Odoo 翻译对齐。若发现错误， 发送翻译正确性工单 我们会修好。
Odoo 演示数据库（在线预览）
对于此应用，我们可能提供免费个性化演示数据库。
联系我们无需电话号码或信用卡。只需完成不超过 30 秒的简短电子邮件注册。
按您的申请，我们将准备独立的在线预览数据库，约两周内可进行任意测试并核验假设。
缺陷反馈
如遇缺陷或不一致行为，请随时联系我们。我们保证在购买后 60 天内提供修复，并在此期限后仍积极改进工具。
联系我们无需电话号码或信用卡。只需完成不超过 30 秒的简短电子邮件注册。
请在请求中尽可能提供详细信息：截图、Odoo 服务器日志、完整的问题复现说明等。通常需要几个工作日来准备问题的工作计划（若确认是缺陷）或提供应采取的指引（否则）。
公开功能请求与模块构想（免费开发）
我们非常希望改进工具，并感谢任何反馈。若您的需求具有普遍价值且能高效实现，团队会将其列入待办清单。
此类待办清单会定期处理，不产生额外费用。虽然我们无法承诺截止日期和最终设计，但这可能是在无投入、无风险的情况下获得所需功能的好办法。
联系我们无需电话号码或信用卡。只需完成不超过 30 秒的简短电子邮件注册。
18.0.1.0.9
- The bug with pandas compatibility has been fixed.
18.0.1.0.8
- 已修复几乎不可见的筛选器问题。
18.0.1.0.7
- 已修复未翻译警告的问题。
18.0.1.0.6
- 已为模型和已保存报表的表单视图添加非活动记录的 Archived 功能条。
18.0.1.0.5
- 该应用已发布到版本 18。
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