Dynamic Inventory Dashboard

Skills, Tech Stack, and Libraries

1. Skills: Data Visualization, Predictive Modeling, Data Wrangling, Real-Time Analytics

2. Tech Stack: Power BI, Tableau, SQL, Python

3. Libraries: Pandas, NumPy, Matplotlib, Scikit-learn

Description and Approach

Objective:

I developed a dynamic inventory dashboard to optimize inventory management by providing real-time insights and predictive restocking alerts. The goal was to help businesses reduce stockouts and overstock situations while improving operational efficiency.

Approach:

1. Data Collection and Cleaning: Gathered inventory data from transactional databases and flat files, using Python to clean and preprocess the data. This included handling missing values, standardizing date formats, and normalizing product categories.

2. Exploratory Data Analysis (EDA): Conducted an in-depth analysis using Matplotlib and NumPy to identify trends in stock levels, reorder frequency, and demand patterns. This step calculated key metrics like inventory turnover rates and restocking thresholds.

3. Predictive Modeling: Built a predictive model using Scikit-learn to forecast future inventory requirements based on historical sales data, seasonal trends, and demand variability.

Dashboard Design:

Designed an interactive dashboard in Power BI to display:

1. Real-time inventory levels.

2. Predicted restocking dates.

3. Alerts for low-stock and overstock situations.

4. Key performance indicators (KPIs) like stock turnover ratio and holding costs.

Automation:

Automated the data pipeline using Python and SQL, ensuring the dashboard refreshed in real-time as new data became available.

Code Flow:

1. Data ingestion using Python from flat files and SQL databases.

2. Data cleaning and transformation using Pandas and NumPy.

3. Predictive modeling with Scikit-learn to generate forecasts for future inventory needs.

4. Data export to Power BI for dashboard creation and deployment.

Results

The project successfully delivered a robust and user-friendly dashboard that:

• Provided real-time visibility into inventory levels, improving decision-making for stock management.

• Reduced stockouts by 20% and decreased holding costs by identifying overstock scenarios.

• Enabled proactive restocking strategies with predictive alerts, minimizing delays in inventory replenishment.

• Streamlined inventory operations, resulting in increased efficiency and cost savings for the organization.

• The dynamic nature of the dashboard empowered stakeholders to monitor inventory health and make data-driven decisions effectively.

Git Link

For more information and code, visit the Git link.