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Inventory Optimization Tool

This project provides an inventory optimization tool that uses synthetic data and applies demand forecasting, inventory classification, safety stock calculation, and other inventory management techniques. It is containerized using Docker to ensure easy deployment and portability.

Blog

a detailed write up to understand the tool in-depth.

Requirements

Before you start, ensure you have the following installed:

• Docker: Get Docker

How to Run

Step 1: Clone the Repository

Clone the project to your local machine.

git clone https://github.com/saru2020/InventoryOptimizer.git
cd InventoryOptimizer

Step 2: Add Data Directory

Make sure to have a /data folder at the root of the project to store input and output CSV files.

mkdir data

Step 3: Build and Run the Docker Container

Use the provided run.sh script to build and run the Docker container. This will automatically build the image, start the container, and map the /data folder for storing input and output files.

chmod +x run.sh
./run.sh

Alternatively, you can manually run the commands:

# Build Docker image
docker build -t inventory_optimizer .

# Run Docker container
docker run -v $(pwd)/data:/data --name inventory_optimizer inventory_optimizer

The container will generate synthetic data and save the results to the /data/output.csv and /data/store_turnover.csv files.

Step 4: View Results

Once the container has finished processing, the results will be stored in the /data folder. The main outputs are:

• output.csv: Contains the daily inventory orders, forecast data, and other information for each SKU and store.
• store_turnover.csv: Contains inventory turnover ratios for each store.

Example Data

Input

The script generates synthetic data that simulates store-level sales and inventory information. Below is a sample of what the synthetic data (source.csv) might look like:

Date	Store	SKU	SalesQuantity	InventoryLevel
2023-01-01	Store001	SKU001	10	55
2023-01-01	Store001	SKU002	14	42
2023-01-01	Store001	SKU003	9	75
2023-01-02	Store001	SKU001	7	68
2023-01-02	Store001	SKU002	15	34

Output

After processing, the tool will output optimized inventory data. Here is a sample from the output.csv file:

Date	Store	SKU	OrderQuantity	InventoryTurnover
2023-01-01	Store001	SKU001	12	2.5
2023-01-01	Store001	SKU002	5	1.8
2023-01-01	Store001	SKU003	0	0.9

And a sample from the store_turnover.csv file:

Store	InventoryTurnover
Store001	2.0
Store002	1.7
Store003	1.5

Configuration

• source.csv: The input file is generated during execution with synthetic data based on Tamil Nadu holidays.
• output.csv: Contains optimized daily order quantities and other calculated metrics like demand forecast and reorder points.
• store_turnover.csv: Contains the inventory turnover ratios at the store level.

Additional Configuration

• The Python script generates synthetic data by default, but you can replace the generate_synthetic_data function to load real data from a CSV file.
• Modify the Dockerfile to install additional dependencies or adjust project settings.

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Feel free to adapt this to include more examples or additional details based on your specific project requirements!