ronaldosc / autopvm

AutoPVM project aims at conducting the Price Variance Mix analysis automatically. The main purpose of PVM analysis is to provide a high-level overview view into the past, and to break down the change in revenue or margins into some key components or categories.

autoPVM v0.3

Automatically conduct Price-Volume-Mix analysis on datasets.
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Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

This project aims at conducting the Price Variance Mix analysis automatically. The main purpose of PVM analysis is to provide a high-level overview view into the past, and to break down the change in revenue or margins into some key components or categories. The categories are used to highlight and help explain how much of the overall change in revenue or margins was caused by, e.g. the implemented Price changes, versus changes in total costs, versus the impact from change in Volumes, versus changes other effects, comparing two different time periods.

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Installation

The autoPVM package can be installed using pip.

1. autoPVM uses Numpy, Pandas & Plotly as dependencies.

2. Install package

   pip install autoPVM
   

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Usage

Import the PVM class using

from autoPVM import PVMAnalysis

Read a Pandas dataframe

data = pd.read_csv('Sample Dataset/Supermarket Sales.csv')

Create an analysis object and pass the dataframe

pvm = PVMAnalysis(data)

Set column name markers of required quantities and margins

pvm.setMarkers(\
                 quantity_pr='QTY_PM'
               , quantity_ac='QTY_AM'
               , margin_pr='MARGIN_PM'
               , margin_ac='MARGIN_AM'
               , hierarchy=['Customer type', 'Gender', 'Branch', 'Product line'])

quantity_pr marks previous time period quantity.
quantity_ac marks current/next time period quantity.
margin_pr marks previous time period margin.
margin_ac marks current/next time period margin.
hierarchy marks dimensional heirarchy: [Highest Level, .. , Lowest Level].

Calculate the margin bridge using pvm.calculateMarginBridge()

Plot the bridge using

pvm.plotPVMBridge()

Final dimension aggregate can be exported using

pvm.exportMarginBridgeFile()

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the Apache-2.0 License. See LICENSE.txt for more information.

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Contact

Akash Sonthalia - @LinkedIn - [email protected] Project Link: https://github.com/asonthalia/autoPVM

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Acknowledgments

• Supermarket Sales Dataset
• PVM Analysis Explanation

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