Assessfy Industry Projects Lab Advanced 6 milestones 100 marks

Optimising Last-Mile Delivery Routes and ETA Prediction for Urban Indian Logistics

Industry: Logistics & Mobility Industry: Logistics & Mobility Function: Operations Type: Industry-vertical applied project Team: up to 4 Assessment: 6 milestones (100 marks)

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About this project

Objective: To design and implement a data-driven system for real-time route optimisation and accurate ETA prediction for last-mile deliveries in Indian cities.

Problem & Context: Indian logistics firms face major inefficiencies in last-mile delivery due to urban congestion, unpredictable traffic, and fragmented address systems. Poor route planning leads to delayed deliveries, higher costs, and unsatisfied customers.

Approach/Methodology: The team will analyse real-world delivery data, integrate open-source traffic APIs (e.g., Google Maps, MapmyIndia), and apply advanced route optimisation algorithms and machine learning for ETA prediction. The system will be tailored for Indian city conditions, considering local constraints and address ambiguities.

Deliverables & Analysis: Students will deliver a working prototype or dashboard showing optimised delivery routes, predicted ETAs, and analysis of cost/time savings, validated on historical or sample logistics data. Documentation and recommendations for operational integration will be included.

Business Impact: The solution will enable logistics companies to reduce delivery times and fuel costs, improve customer satisfaction, and make data-driven decisions on fleet utilisation and resource allocation in the Indian urban context.

Milestones

1. Problem Definition & Business Case

10 marks 18d

Define the last-mile delivery problem for an Indian city, outline key metrics, identify stakeholders, and present a business case. Reviewed by faculty with a 5-minute pitch.

2. Domain Research & Data Gathering

12 marks 22d

Conduct field/domain research, collect and clean sample delivery and traffic datasets, and document data sources. Submit a data report and receive feedback.

3. Solution Design / Methodology

15 marks 21d

Design the route optimisation and ETA prediction methodology, select algorithms, and prepare a technical design document. Review via group viva and technical evaluation.

4. Build / Analysis & Implementation

28 marks 35d

Develop the system prototype, integrate APIs, implement route optimisation and ETA models, and produce dashboards. Submit code, demo, and intermediate results for review.

5. Validation & Results

25 marks 28d

Test the system with real or simulated data, compare with baseline methods, measure accuracy and efficiency improvements, and document findings. Reviewed through validation report and Q&A.

6. Final Report & Presentation

10 marks 16d

Prepare a comprehensive business and technical report, executive summary, and present results to a faculty/industry panel. Evaluation based on clarity, insight, and actionable recommendations.

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Skills you'll learn

Logistics & MobilityOperationsLogistics and supply chain domain understanding (Indian context)Data cleaning and feature engineeringMachine learning for time-series and regressionRoute optimisation algorithms (e.g.Vehicle Routing ProblemDijkstra’s Algorithm)API integration (Google MapsMapmyIndia)Data visualisation and dashboardingBusiness case presentation and communication

Tools used

Python (pandasscikit-learnnetworkxortools)Google Maps or MapmyIndia APIsJupyter NotebookExcelTableau or Power BIOpen Government Data Portal India (for sample logistics/traffic data)GitHub

Prerequisites

Basic Python programmingStatistics and data analysisIntroduction to Operations Research or Supply Chain ManagementDatabase management (SQL basics)

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