Research and Projects

There are 4 verticals in FedEx SMART Centre. Each one has it’s core research group having faculty members from IIT Madras.

  • Supply Chain Sustainability
  • Logistics Worker Wellness
  • Algorithms and ML
  • Logistics Infrastructure

P1

Digital and sustainable Supply chain Modelling and Analytics

Vertical : Supply Chain Sustainability

Faculty members

Focus area

Sustainability- Environmental and Net Zero

Objective

  • To explore how the logistics sector incorporates environmental and green practices toward achieving environmental,  sustainable and efficient logistics. 

 

Social Sustainability

Objective

  • To develop frameworks of socially responsible supply chains considering ESG factors, mental health and wellbeing 

  • To understand how digital tools like blockchain and mobile applications can empower workers in supply chains, the research aims to assess their impact on job satisfaction, worker rights, and overall social well-being 

 

Digital Transformation and Automation, Prediction and Optimization & Digital interventions

Objectives

  • To develop predictive models for lead time estimation and customer demand forecasting and capacity planning

  • To integrate planning, production, inventory, distribution and routing with end-to-end optimization solutions using AI and ML

  • To explore and develop prototypical systems using digital twins and digital platforms that can enable the seamless tracking, monitoring, and optimization of shipments, warehousing and packaging containers in global supply chains 

 

Multi-modal and Global Logistics 

Objectives

  • To assess the current state of global supply chains across different transportation modes, identifying inefficiencies and sustainability gaps.

  • To enhance collaboration among stakeholders including governments, industries, and international organizations to facilitate the implementation of efficient and sustainable global supply chain practices.

  • To enhance supply chain resilience by diversifying transportation routes and developing risk management frameworks



P2

Holistic Human machine collaboration with biomedical-cognitive measures in logistics and supply chain services

Vertical : Logistics Worker Wellness

Faculty members

Focus area

Objectives

Worker Health Management:

  • Monitor signs of physical fatigue related effects using bio-signal measurements 

  • Estimate the cognitive workload, Vigilance level & mental fatigue using eye gaze parameters.

 

Worker Training:  

  • Synchronize data from the process, automation system and Human activities

  • Notify with visual/audio alarms and assistance to rectify the shortcomings.

  • Create new policies/ training methods to prepare the workers better for the task 

 

Human-Machine Teaming:

  • Develop algorithms for COBOTS that can resolve human-human and human-machine conflicts of actions.

  • Switch task/work between humans as well as between humans and machines based on situation awareness of humans and the significance of task/work.



P3

Accelerating Learning and Algorithms for Logistics Problems

Vertical : Algorithms and ML

Faculty members

Focus area

The objective of this project is to develop algorithms, machine learning techniques and software modules that can be used by stakeholders in the logistics domain in order to improve overall efficiency of logistics operations.


Software system, Algorithms and API access to software implementations. 

[Prof. N.S. Narayanaswamy]


Data and Analysis

  • Data generation 

  • Simulation

  • Impact analysis

  • Tracking using Fastag data

  • Tracking using crowdsourced data

  • Consignment flow analysis

  • Customer feedback mining

Optimizations

  • Route planning and optimizations

  • Vehicle container space sharing

  • Container space packing

  • On-demand Asset sharing

  • Custom clearance optimization

  • Optimal Service points location finding

  • Rail network efficiency improvement

  • Cargo movement scheduling

  • Multimodal logistics operation & Transshipment

Platform

  • Integrated platform

  • Open Network and Framework for Logistics

  • Methods to implement KYC collection

  • Easy onboarding and verification

Prediction based

  • Fare validation

  • Demand forecasting

  • Employee requirement forecasting

Specific functionality

  • AI based logistics specific customer service

  • AI based eSRG

  • Skill training / upskilling through App

  • Seamless Communication System


Scalable solutions to Logistics problems using parallelization

[Prof. Rupesh Nasre]


CVRP (Capacitated Vehicle Routing Problem) is a combinatorial optimization problem, which is NP-hard and is of practical importance in the logistics industry. Plan is to utilize parallelism to improve the scalability of the CVRP computation


RL (Reinforcement Learning) for logistics – for example Railway Dispatching

[Prof. Chandrashekar Lakshminarayanan]


Because of the inefficiencies like low speed and preference given to passenger trains as compared to freight trains, the potential of railway logistics is not fully utilized in India. The goal here is to develop an RL based system that can provide recommendations for better efficiency of the rail network.


Quantum Computing based implementations to help us find better solutions to some of the NP-hard problems. 

[Prof. Anil Prabhakhar]


Quantum Machine Learning will augment the software system through the use of hybrid quantum-classical algorithms that will enhance and hasten the search for feasible solutions to the NP-hard problems in route planning and optimization, and bin-packing.



P4

Development of Modules capable of integrating with current infrastructure to enable Autonomous Delivery Agents to adopt Advanced Delivery Vehicles

Vertical : Logistics Infrastructure

Faculty members

Focus area

Adopting autonomous delivery vehicles like drones and rovers can greatly improve logistics management. However, current systems are fragmented among operators, logistics companies, and regulators, creating integration and coordination challenges. As the use of these vehicles grows, we need a unified system to ensure they comply with regulations and work well within existing frameworks.

 

Objectives

Building Autonomous Delivery Modules

Developing specialized modules for autonomous delivery agents within logistics operations.

 

Addressing Infrastructure Gaps

Focusing on bridging the gap in infrastructure between operators, logistics companies, and regulators to streamline operations.

 

Ensuring Regulatory Compliance

Prioritizing regulatory compliance and operational safety standards to meet industry requirements.

 

Enhancing Efficiency and Sustainability

Aiming to improve operational efficiency and reduce carbon footprint through innovative solutions.