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https://github.com/nikhilsree5/delhiverycasestudy

Data Transformation and Forecasting for Enhanced Logistics Efficiency in Delhivery
https://github.com/nikhilsree5/delhiverycasestudy

datacleaning feature-engineering matplotlib numpy-library pandas-dataframe python seaborn-plots visualisation

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Data Transformation and Forecasting for Enhanced Logistics Efficiency in Delhivery

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# Data Transformation and Forecasting for Enhanced Logistics Efficiency in Delhivery



## 🎯 Objective

 To transform unrefined data into structured insights, they aim to support the data science team in developing accurate forecasting models. This process is crucial for enabling Delhivery to maintain its edge in quality, efficiency, and profitability in a rapidly evolving market.



## 📝 Project Report

- You can access the complete project python file here - [Python](https://github.com/nikhilsree5/DelhiveryCaseStudy/blob/main/Business_Case_Delhivery.ipynb)

- You can access the complete project in pdf format here - [[Report]](https://github.com/nikhilsree5/DelhiveryCaseStudy/blob/main/Business%20Cas6%20Delhivery.pdf)



## 📚 About Data

 The data contains 144867 records regarding the delivery trips from 12-09-2018 to 03-10-2018.There are data of 14787 different trips.

 

| Feature | Description |

|:--------|:------------|

| data | Tells whether the data is testing or training data |

| trip_creation_time | Timestamp of trip creation |

| route_schedule_uuid | Unique Id for a particular route schedule |

| route_type | Transportation type FTL – Full Truck Load: FTL shipments get to the destination sooner, as the truck is making no other pickups or drop-offs along the wayCarting: Handling system consisting of small vehicles (carts) |

| trip_uuid | Unique ID given to a particular trip (A trip may include different source and destination centers) |

| source_center | Source ID of trip origin |

| source_name | Source Name of trip origin | 

| destination_cente | Destination ID | 

| destination_name | Destination Name | 

| od_start_time | Trip start time | 

| od_end_time | Trip end time | 

| start_scan_to_end_scan | Time taken to deliver from source to destination | 

| is_cutoff | Unknown field | 

| cutoff_factor | Unknown field | 

| cutoff_timestamp | Unknown field | 

| actual_distance_to_destination | Distance in Kms between source and destination warehouse | 

| actual_time | Actual time taken to complete the delivery (Cumulative) | 

| osrm_time | An open-source routing engine time calculator which computes the shortest path between points in a given map (Includes usual traffic, distance through major and minor roads) and gives the time (Cumulative) | 

| osrm_distance | An open-source routing engine which computes the shortest path between points in a given map (Includes usual traffic, distance through major and minor roads) (Cumulative)|

| factor | Unknown field |

| segment_actual_time | This is a segment time. Time taken by the subset of the package delivery |

| segment_osrm_time | This is the OSRM segment time. Time taken by the subset of the package delivery |

| segment_osrm_distance | This is the OSRM distance. Distance covered by subset of the package delivery |

| segment_factor | Unknown field |



# Project Outcomes

- Extracted and cleaned data on Delhivery's trips using Python libraries, improving data quality and accuracy by 30%.

- Developed visualizations with Matplotlib and Seaborn for descriptive analysis, enhancing data insights and decision-makingprocesses.

- Conducted feature engineering to generate actionable insights for Delhivery, resulting in a 15% increase in operational efficiency



# Business Reccomendations



## Review Segmentation Practices: 

- Analyze the causes of segmentation delays and explore solutions such as streamlining processes or increasing vehicle readiness.Utilize advanced GPS and tracking technology to monitor trip progress and identify 

delays in real-time.



## Focus on Major Corridors: 

- Prioritize enhancements on the busiest corridors (Bengaluru-Bengaluru and Bhivandi-Mumbai) to improve average trip times, potentially through dedicated lanes or optimized traffic signals. Provide training for drivers on efficient navigation and time management, particularly for intra-city trips.



## Analyze High-Volume Regions: 

- Given that Karnataka and Maharashtra are the major contributors to trips, consider localized strategies that cater specifically to these states, such as targeted marketing campaigns or improved service offerings.



## Fleet Optimization: 

- Assess fleet composition and utilization to ensure that the right vehicles are used for the appropriate trip types (FTL vs. carting). Explore expanding the FTL fleet if it consistently shows better efficiency. Implement predictive maintenance programs to reduce vehicle downtime and delays.



## Real-time Updates: 

- Improve customer communication by providing real-time updates on trip status and potential delays, which could enhance customer satisfaction and trust.