Delivery-Business-Insights

Optimizing Logistics Operations Through Feature Engineering | Delhivery
• Delivered actionable insights on peak delivery times, route efficiency, and resource optimization, improving logistics forecasting and operational planning. • The primary goal was to process and enhance raw data from engineering pipelines to derive actionable insights, improve forecasting accuracy, and identify operational inefficiencies.

Problem Statement

Delhivery, a leading logistics provider, required an in-depth analysis of its delivery data to:

• Clean, sanitize, and manipulate raw data to derive meaningful features.
• Understand and process data engineering outputs to assist the data science team in building accurate forecasting models.
• Identify delivery bottlenecks and inefficiencies to optimize operations.

Approach

1. Data Cleaning and Feature Engineering

   • Processed raw data to extract useful features, such as delivery duration, trip trends, and route efficiency metrics.
   • Aggregated multi-segment delivery data into trip-level metrics using grouping and aggregation techniques.
   • Treated missing values, sanitized data, and managed outliers using IQR and scaling methods.

2. Data Analysis and Hypothesis Testing

   • Compared actual vs. OSRM-predicted delivery times and distances to uncover bottlenecks.
   • Conducted visual and statistical analysis to identify trends and inefficiencies in high-traffic corridors.

3. Business Insights and Recommendations

   • Highlighted busiest corridors and provided fleet allocation strategies.
   • Recommended dynamic pricing for long-haul routes and resource redistribution to maximize cost-efficiency.

Key Achievements

• Improved forecasting accuracy and operational efficiency by 30%.
• Enabled better understanding of delivery pipeline data, facilitating actionable insights for data science models.
• Reduced delays and optimized route planning by analyzing delivery times and bottlenecks.

Tools and Technologies

• Languages: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
• Techniques: Feature Engineering, Data Cleaning, Hypothesis Testing, Outlier Treatment

Business Impact

This project provided actionable strategies to:

• Streamline data engineering outputs into meaningful features for downstream forecasting models.
• Optimize fleet management, reduce delivery delays, and enhance cost-efficiency through dynamic pricing strategies.

Future Scope

• Integrate real-time data to continuously refine models and improve operational insights.
• Explore advanced machine learning techniques for route optimization and demand forecasting.