アプリ

Odoo を表示しています 12.0 のページ Stock Demand Trends and Forecast. 最新のサポート対象バージョンは Stock Demand Trends and Forecast v.19.0 . このビルドを次から直接入手 Odoo Apps Store .

Stock Demand Trends and Forecast v.12

Name: Stock Demand Trends and Forecast
Price: 198.00 EUR
Availability: InStock

The tool to calculate stock demand trends and make prediction for future demand statistically. Stock Forecast

19.0 18.0 17.0 16.0 15.0 14.0 13.0 12.0 11.0

198€

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このツールは標準の Odoo アプリ以外の追加依存を必要としません。

現在のバージョン: 12.0.1.0.2

Enterprise

Community

Odoo.sh

Analyse stock demand per location or company

Different statistical methods for forecast

Stock demand trends and forecast security

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.

When this tool should be used

You have enough historical stock demand data (per location or 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 how statistics works.

Stock demand forecasting interface

Demand trends and forecast are shown in comfortable manner of your choice: as an Odoo chart, as an Odoo report (pivot), as an Excel table. The latter might be also used to import data in certain statistical software.
The right for trends analysis might be granted to any WMS user, but be cautious: all stock moves of a current company will be under consideration.

スクリーンショット

Stock demand trends and forecast chart

Stock demand trends and forecast as an xlsx table

Odoo pivot view of stock trends and forecast

Stock demand forecast in a few clicks

Grant the right for the forecast report

Scientific approach for forecasting

Apply the statistical method which you consider as the most suitable: Autoregression, Moving Average, Autoregressive Integrated Moving average, Seasonal Autoregressive Integrated Moving Average, Simple Exponential Smoothing, Holt Winter's Exponential Smoothing.
Forecast as many intervals as you like, but remember that prediction for the next 10 years would be hardly reliable.
Based on your historical data and applied coefficients, sometimes Odoo is not able to reveal trends and make forecasts. In that case only historical data would be shown in reports. But even historical trends might have analytical use.

Statistical methods for forecast

For more technical details have a look at the page: https://www.statsmodels.org/stable/api.html.

Autoregression (AutoReg)

It is the simplest but still widely used statistical method for time series forecast.

Autoregressive Distributed Lag (ARDR)

The moving average method takes into account 'errors' in previous observations.

The autoregressive moving average method 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)

The method which also combines the methods AR and MA, but also 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)

The SARIMA method 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)

The SES model usage is similar to the AR method, but instead of relying upon linear function, it exploits exponential one

Holt Winter's Exponential Smoothing (HWES)

The HWES method enriches the SES method to work with time series trends and seasonal effects.

スクリーンショット

Experiment with various statistical models

Topical data under consideration

Demand trends and forecast are constructed for product templates in general (e.g. all iPads) or specific product variants (iPad 32Gb). Use the button 'Stock Trends' on a product form for that purpose. The product under analysis should be storable.
Make analysis per the whole company or per a definite location. In the latter case optionally include or exclude child locations.
Apply time frames of historical data which is used as an analytic basis. Forecast periods are ones which follow after the end of defined frame. In such a way you make check statistical reliability 'predicting' actually passed intervals.
Stock demand is calculated as all done stock moves for this period which source location is one of internal location under consideration and which destination location is not of this range.

スクリーンショット

Target your analysis by locations

設定とインストールのヒント： Stock Demand Trends and Forecast Odoo v.12

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

Default values

In the most cases you apply the same statistical model and forecast the same number of periods. To save time you can assign default values to the report wizard. Go to Inventory > Settings and find the section 'Sales Trends and Forecast'

Demand trends and forecast configuration

に関するよくある質問 Stock Demand Trends and Forecast Odoo v.12

ツールの更新ポリシーは何ですか？

現行の Odoo Apps Store ポリシーによると:

バージョン 12.0 以前向けに購入したモジュールは、12.0 までの全バージョンにアクセスできます。
バージョン 13.0 以降、各モジュールバージョンは個別に購入します。
バージョンに関係なく、購入するとメジャーバージョン内のすべての更新と不具合修正の権利が得られます。

faOtools チームはこれらのポリシーを管理していません。ご質問は Odoo Apps Store の担当者へ直接お問い合わせください。

アプリを Odoo.sh にインストールする方法は？

最も単純な方法は、Odooストア組み込みのワークフローを使うことです:

1. モジュールのページを開き、Deploy on odoo.sh ボタンをクリックします。

2. その後 GitHub ページにリダイレクトされます。アカウントにログインし、'Create a new repo' をクリックするか既存のものを使います。リポジトリはプライベートにしてください。OPL-1 ライセンスのアプリを公開することは許可されていません。必要なら Odoo.sh プロジェクト用に新しい repo を作成します。

3. 次に odoo.sh に行き、Deploy ボタンをクリックします。ポップアップで決定を送信し、'Continue.' をクリックします。これでインストールが始まります。

これらの手順でプロジェクトの本番ブランチにアプリがインストールされます。他のブランチにデプロイする、またはモジュールを更新する場合は、次の操作を行います:

1. Odooストアからアプリのソースコードをアップロードします。

2. モジュールを必要な GitHub リポジトリにコミットします。アプリのフォルダ/ファイルが無視されていないこと（repo の .gitignore に含まれていないこと）を確認してください。リポジトリは odoo.sh が自動作成し、既定で重要な項目（例: /lib）が含まれることがあります。モジュールのディレクトリ、サブディレクトリ、ファイルを例外なくすべてアップロードしてください。

3. odoo.sh プロジェクトの対象ブランチを Deploy するか、設定がそうなっている場合は自動ビルドされるまで待ちます。

自分のサーバーにアプリをインストールするには？

1. 購入したツールのソースコードをいずれかの Odoo アドオンディレクトリに解凍します;
2. Odoo サーバーを再起動します;
3. 開発者モードをオンにします（技術設定）;
4. アプリ一覧を更新します（アプリメニュー）;
5. アプリを見つけて「Activate」/「Install」を押します;
6. アプリページにガイドラインがあれば従ってください。