Delhivery-Analysis

Delhivery is the largest and fastest-growing fully integrated player in India by revenue in Fiscal 2021. They aim to build the operating system for commerce, through a combination of world-class infrastructure, logistics operations of the highest quality, and cutting-edge engineering and technology capabilities.

Key Findings:

• Bengaluru, Mumbai, Delhi, and Bhiwandi are major delivery hubs.
• Most deliveries are intra-city and short-distance (under 150km).
• Inter-state deliveries are infrequent.
• Trip times are around 4 hours on average.
• Top 10 corridors have slightly lower average speeds due to intra-city traffic.
• Most deliveries are on time for top corridors.
• A bimodal distribution is observed in trip metrics (time and distance).
• Ontime and OnDist metrics are left-skewed.

• Revolutionized: Delivery time analysis revealed a 99% on-time delivery failure rate, with over 56% of missed deliveries exceeding the expected time by 100%, highlighting a critical operational inefficiency.

• Optimized: Identified top 10 delivery corridors with an average speed of 23 km/h (maximum 46 km/h), demonstrating the impact of intra-city traffic on delivery performance. Successfully pinpointed areas for route optimization.

• Accelerated: Discovered that while most deliveries achieve optimal distance, significant time delays persist, indicating a need to address speed discrepancies and route optimization strategies, especially for shorter trips (under 150km).

• Streamlined: Analyzed segment performance, revealing that despite overall delivery time issues, a significant percentage of segments were on time, suggesting potential improvements in individual segment scheduling and resource allocation.

• Elevated: Developed a comprehensive analysis showcasing that although trips achieve optimal distances, timeliness is significantly compromised. This highlights the need for data-driven optimizations for a more efficient delivery system.

Business Recommendations:

Based on the analysis of the trip data, here are some key findings and recommendations for the business:

1. Focus on Intra-city Deliveries:

   • The majority of trips are short, intra-city deliveries. Optimize operations for this segment.
   • Explore strategies to improve efficiency and reduce costs for these shorter trips.

2. Optimize Top 10 Corridors:

   • While most deliveries are on time for the top 10 corridors, the average speed is lower than overall average.
   • Analyze these corridors for bottlenecks and inefficiencies. Route optimization and better traffic management can improve speed.

3. Understand the Trade-off between On-time Delivery and Distance:

   • There is a correlation between on-time delivery and distance, potentially indicating longer routes may avoid traffic.
   • Evaluate whether slight route deviations can be beneficial for on-time delivery without incurring significant additional costs or time.

4. Analyze Speed Discrepancies:

   • Average speed is lower for the top 10 corridors compared to overall trips.
   • Investigate reasons for the lower speeds in high-volume areas. Consider factors like traffic, road conditions, and delivery schedules.

5. Monitor Segment Performance:

   • The data indicates a left skew in ontime and ondist metrics, with a notable number of missed segments.
   • Implement monitoring systems to identify and address potential problems.

6. Continuously Monitor Performance:

   • Regularly review key performance indicators (KPIs), such as on-time delivery rates, average trip times, and speed.
   • Use data-driven insights to make adjustments to operations and improve overall efficiency.

In-depth Analysis :

• Optimize delivery routes for intra-city trips to improve speed and efficiency within top corridors. Consider real-time traffic data integration.
• Investigate the reasons for longer trip times, even for short distances, and explore strategies for route optimization and traffic avoidance. Examine the "bimodal distribution" to identify longer trips and see if they are due to specific factors that can be mitigated.
• Explore demand patterns and adjust resources (drivers, vehicles) dynamically based on the city and time of day, especially during peak hours in major cities.
• Analyze the correlation between on-time performance and distance, and adapt delivery strategies accordingly. Focus on improving on-time delivery for longer routes or trips with unique constraints.
• Further investigate the "segment_ontime_miss" and "segment_ondist_successed" metrics' linear trend to understand its implications for resource allocation and operational efficiency.
• Monitor and analyze delivery performance for top corridors to maintain their high on-time delivery rate. Early warning systems for potential delays or route disruptions can minimize negative impacts.
• Consider developing a predictive model for trip times, leveraging historical data, traffic patterns, and other relevant factors. This model can be used for proactive scheduling and improved customer communication.
• Segment customers based on their delivery needs and preferences (e.g. speed vs cost). Implement targeted logistics strategies for different segments.

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