Supply Chain Optimization for Small Businesses

Project Overview

This project presents a comprehensive supply chain optimization system designed to help small businesses make data-driven inventory decisions. While large enterprises often have sophisticated inventory management systems, small businesses frequently rely on intuition and basic heuristics, leading to inefficiencies and unnecessary costs. This solution bridges that gap by providing advanced analytics capabilities without requiring specialized knowledge.

Technologies Used

• Programming Languages: Python
• Libraries & Frameworks: Pandas, NumPy, Scikit-learn, Matplotlib, statsmodels
• Tools: Jupyter Notebook, Git, GitHub
• Visualization: Dash, plotly

Problem Statement

Small businesses face three critical inventory challenges:

1. Excess inventory tying up valuable working capital
2. Stockouts resulting in lost sales and customer dissatisfaction
3. Inefficient ordering patterns increasing operational costs

These challenges stem from difficulties in accurately forecasting demand and determining optimal inventory policies. This project tackles these issues through a data-driven approach that balances holding costs, stockout costs, and ordering costs.

Technical Implementation

The system consists of four integrated components:

1. Data Processing Pipeline
• Implemented ETL processes for sales and inventory data
• Created synthetic datasets that mimic real-world patterns including seasonality, trends, and random fluctuations
• Built robust data validation and cleaning mechanisms
2. Time Series Forecasting Engine
• Developed and compared four forecasting models:
• Moving Average (baseline)
• Holt-Winters Exponential Smoothing
• SARIMA (Seasonal ARIMA)
• Random Forest with feature engineering
• Implemented cross-validation strategies specific to time series data
• Generated 30-day demand forecasts with confidence intervals
3. Inventory Optimization Models
• Implemented classical inventory models with modern adaptations:
  Economic Order Quantity (EOQ) for optimal order sizing
  Reorder Point calculation based on lead time and demand variability
  Safety stock determination for various service levels
  Cost trade-off analysis between service levels
• Created a reorder scheduling system with specific date recommendations
4. Interactive Dashboard
• Built a multi-tab Dash application showcasing:
• Business overview with key performance indicators
• Demand forecasting with seasonal patterns
• Inventory optimization parameters and recommendations
• Reorder planning with timeline visualization
• Implemented responsive visualizations using Plotly
• Designed for intuitive use by non-technical business users

Results and Insights

The system provides actionable business insights including:

• Potential cost savings of 15-30% through optimized inventory levels
• Reduction in stockout events by over 50% at the recommended service levels
• Improved cash flow through more efficient inventory turnover
• Clear reorder recommendations with specific quantities and dates

Challenges

Key challenges overcome during development:

• Balancing model complexity with interpretability for business users
• Implementing efficient feature engineering for time series data
• Optimizing dashboard performance for large datasets
• Creating a system flexible enough to adapt to different business contexts

Project Gallery

Conclusion

This project demonstrates proficiency in the complete data science lifecycle—from data exploration and model development to creating user-friendly visualizations and deploying production-ready applications.