Inventory Management System

Overview

This Inventory Management System is a Streamlit-based web application designed to help businesses optimize their inventory control processes. It provides powerful tools for demand forecasting, inventory policy calculation, and performance analysis.

Features

• Data Upload: Easy Excel file upload for SKU data
• Demand Forecasting: Multiple forecasting models including Exponential Smoothing, ARIMA, and Normal Distribution
• Inventory Policy Calculation: Various inventory models including Periodic Review, Continuous Review, Base Stock, and Newsvendor
• Interactive Visualizations: Detailed plots for inventory levels, demand, and various performance metrics
• Sensitivity Analysis: Tools to understand how different parameters affect inventory performance
• Performance Metrics: Comprehensive set of KPIs including service level, profit, and inventory turnover
• Supply and Demand Planning: Visualization and analysis of supply plans and demand forecasts
• Customizable Parameters: Adjustable lead times, service levels, costs, and more

Installation

1. Clone this repository:

   git clone https://github.com/?
   cd inventory-management-system
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

Running the Application

To run the Streamlit app, use the following command in your terminal:

streamlit run app.py

The application will open in your default web browser.

How to Use

1. Upload Data: Start by uploading your SKU data in Excel format. The file should contain columns for SKU, Date, and Quantity.

2. Select SKU: Choose the specific SKU you want to analyze from the dropdown menu.

3. Set Parameters: Adjust various parameters such as lead time, service level, and costs according to your business needs.

4. Choose Models: Select your preferred forecasting and inventory models.

5. Run Simulation: Click the "Run Simulation" button to generate forecasts and inventory policies.

6. Analyze Results: Navigate through different tabs to view forecasts, inventory levels, performance metrics, and more.

7. Sensitivity Analysis: Use the sensitivity analysis tool to understand how changes in parameters affect your inventory performance.

8. Download Results: Export your results and updated policies for further analysis or implementation.

File Structure

• app.py: Main Streamlit application file
• app_utils.py: Utility functions for the application
• forecast.py: Forecasting models
• inventory.py: Inventory policy models
• sku.py: SKU class definition and related functions
• requirements.txt: List of Python dependencies
• logo.svg: Company logo file

Dependencies

Main dependencies include:

• Streamlit
• Pandas
• NumPy
• Plotly
• Statsmodels

For a complete list, see requirements.txt.