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Shipment Tracking and Monitoring
Real-time tracking and monitoring of shipments ensure transparency and timely updates for both the company and customers
Overview
The dashboard provides a comprehensive view of delivery operations for a logistics or e-commerce company in Indonesia, focusing on efficiency, geographic distribution, and courier performance. It highlights trends and areas for improvement, ultimately aiming to enhance service quality and operational effectiveness.
Business Question
How can the company optimize its delivery operations and improve customer satisfaction based on current delivery performance, geographic trends, and courier efficiencies?
Project Structure
- Data Collection: Gathering delivery data from various sources.
- Data Cleaning: Ensuring accuracy and consistency in the dataset.
- Data Analysis: Examining trends and performance metrics.
- Visualization: Creating graphical representations of the data.
- Reporting: Summarizing findings and making recommendations.
Column Descriptions
- Periode: The month of the delivery record.
- Batch_Id: Unique identifier for the batch of records.
- Order_Id: Unique identifier for each order.
- No_Resi_OEX / No_Resi_3PL: Tracking numbers for different couriers.
- Reference_Code: Reference identifier for tracking.
- Kurir: Courier service used.
- Submit_date: Date the order was submitted.
- schedule_pickup: Scheduled pickup date.
- Tgl_pickup: Actual pickup date.
- SLA Pickup: Service Level Agreement for pickup time.
- Lead time delivery: Time taken from pickup to delivery.
- Pengirim: Sender’s name.
- Dest_address: Delivery address.
- shipping_service: Type of shipping service chosen.
- Last_status: Final status of the delivery.
- Tgl_last_status: Date of last status update.
- penerima: Recipient’s name.
- Telp_penerima: Recipient’s phone number.
- shipping_price: Cost of shipping.
- metode_pembayaran: Payment method.
- COD_amount: Cash on delivery amount.
- Insurance_fee: Fee for insurance on the shipment.
- Firstmile Oexpress / Lastmile Oexpress: First and last-mile delivery metrics.
- Oexpress coverage: Coverage area for Oexpress.
Workflow
- Data Import: Load the delivery dataset into the analysis tool.
- Data Preparation: Clean and format the data for analysis.
- Exploratory Data Analysis (EDA): Identify trends and patterns.
- Visualization Creation: Generate graphs and charts to illustrate findings.
- Report Drafting: Compile insights into a coherent report with recommendations.
Exploring the Data
- Delivery Performance: Examine metrics such as average delays, success rates, and types of delivery statuses.
- Geographic Analysis: Map delivery density across Indonesia, identifying regions with high and low activity.
- Courier Analysis: Compare performance metrics of different couriers, focusing on delivery times and success rates.
Analyzing Trends
- Delivery Timing: Analyze variations in delivery times to identify trends over periods.
- Courier Performance: Evaluate which couriers consistently meet delivery expectations and which fall short.
- Customer Behavior: Investigate the reasons behind “failed-requests” and their impact on overall delivery performance.
Visualizations
- Map of Delivery Activity: Illustrates geographic distribution of deliveries.
- Pie Chart of Couriers Used: Shows proportions of deliveries handled by each courier.
- Bar Charts for Delivery Status: Displays counts of different delivery outcomes.
- Vertical Bar Chart for Delay Analysis: Compares average delays across different couriers.
Reporting/Conclusion
The analysis indicates a generally efficient delivery operation with slight over-performance in delivery timing. However, attention is required for high “failed-request” counts, which could reflect issues needing resolution. Recommendations include:
- Investigating the causes of “failed-requests” to enhance order fulfillment.
- Analyzing courier performance for better selection based on delivery metrics.
- Leveraging geographic insights to optimize delivery routes and expand into high-demand areas.
