E-Commerce Shipping Optimization

1. Problem Statement

Imagine you're a data analyst at a major e-commerce company focused on optimizing its delivery operations. Customers are increasingly demanding faster and more reliable shipping, and on-time delivery is a critical factor in maintaining customer satisfaction and loyalty. However, the company is facing challenges in consistently meeting its delivery deadlines due to various factors, such as warehouse inefficiencies, transportation delays, and unexpected disruptions. They are looking to explore these factors to see which combination makes a delivery more likely to be done on time.

Your task is to analyze a dataset containing delivery information for thousands of orders, including warehouse locations, shipment modes, customer service interactions, product details, and delivery performance metrics. By building a predictive model that identifies the factors that most strongly influence on-time delivery, you can provide actionable insights to the logistics team for improving their processes, optimizing resource allocation, and minimizing delivery delays. A successful model will contribute to higher customer satisfaction, reduced shipping costs, and a stronger competitive position for the e-commerce business.

Goal: The objective of this project is to analyze e-commerce shipping data to identify factors that influence on-time delivery performance. By understanding these factors, the e-commerce company can optimize its logistics operations, reduce costs associated with delays, and improve customer satisfaction by ensuring timely deliveries.

2. Data Description

This dataset contains information about e-commerce shipments, including order details, customer information, product details, shipping dates, shipping carriers, shipping costs, and actual delivery times.

• Rows: 11000
• Columns: 12
• Variables: (This will vary based on the specific dataset – adjust as needed. This example uses common variables.)
  • ID: Unique identifier for each shipment.
  • Warehouse_block: Code identifying the warehouse block (e.g., A, B, C, D).
  • Mode_of_Shipment: Mode of shipment (e.g., Flight, Road, Ship).
  • Customer_care_calls: Number of customer care calls made for the shipment.
  • Customer_rating: Customer rating (e.g., 1 to 5).
  • Cost_of_the_Product: Cost of the product.
  • Prior_purchases: Number of prior purchases made by the customer.
  • Product_importance: Importance level of the product (e.g., low, medium, high).
  • Gender: Gender of the customer (e.g., M, F).
  • Discount_offered: Discount offered on the product.
  • Weight_in_gms: Weight of the product in grams.
  • Reached.on.Time_Y.N: Whether the shipment reached on time (1: Yes, 0: No) - This is your target variable.

Data Source: Kaggle - Customer Analytics

3. Your Task

Your task is to analyze the shipping data and build a model to predict delivery times or the risk of late delivery. Here's a suggested workflow:

1. Data Exploration and Preprocessing:
   • Load the dataset using Pandas.
   • Inspect the data for missing values and outliers.
   • Handle missing values appropriately (e.g., imputation).
   • Convert date columns to datetime objects using Pandas to_datetime.
   • Calculate the actual delivery time in days using Delivery Date and Shipping Date.
   • Encode categorical variables using one-hot encoding or label encoding.
2. Feature Engineering (Important):
   • Calculate the difference between Estimated Delivery Time and Actual Delivery Time to determine the delay.
   • Create features representing seasonality (e.g., month of the year, day of the week).
   • Create interaction features (e.g., shipping cost per product, order value * order quantity).
3. Model Building:
   • Split the data into training and testing sets.
   • Choose a suitable regression algorithm for predicting delivery time (e.g., Linear Regression, Random Forest, Gradient Boosting) or a classification algorithm for predicting late delivery risk (e.g., Logistic Regression, Random Forest).
   • Train the model on the training data.
4. Model Evaluation:
   • Evaluate the model's performance on the testing data.
   • Use appropriate evaluation metrics such as RMSE, MAE, and R-squared for regression models, or accuracy, precision, recall, and F1-score for classification models.
   • Visualize the results using scatter plots or confusion matrices.
5. Insights and Recommendations:
   • Identify the most important factors affecting delivery time or late delivery risk.
   • Provide recommendations for improving shipping efficiency, such as optimizing shipping routes, negotiating better rates with carriers, or improving inventory management.

Python Libraries: Pandas, NumPy, scikit-learn, Matplotlib/Seaborn.