Research Proposal
AI Framework for Route
Optimization and Logistics
Author: Rishabh Kumar Mandal
Akanksha Chauhan
30 November, 2024
Hello!
Warm greetings to all present. As we
gather here today, I am excited to
introduce our research proposal
Company, which aims to address key
challenges and capitalize on emerging
opportunities in the market.
01 Introduction
02 Background to Study
03 Problem Statement
Agenda
04 Framework
Overview
05 Methodology
06 Qualitative Data
07 Quantitative Data
Background of
the Study
Supply chain management is a critical function for businesses,
involving planning, logistics, and the movement of goods.
Traditional supply chains face inefficiencies, including forecasting
errors, high operational costs, and slow adaptation to real-time
changes. AI and ML have emerged as powerful tools to address
these challenges, enabling predictive analytics, IoT-enhanced
monitoring, and automation. Technologies such as AR/VR and GPS
tracking further enhance supply chain resilience and efficiency.
Scope of the Study
Supply chain systems often struggle with demand
volatility, inefficient routing, and inventory
mismanagement.
Relevance of the Study
These issues lead to high costs and lost
opportunities. Traditional methods fail to adapt to
Problem dynamic market conditions or process large-scale
data efficiently.
Statement Research Question
This research identifies the critical need for a real-
time, data-driven, adaptive approach to overcome
Our study focuses on evaluating the market
these challenges and enhance decision-making
landscape, consumer trends, and competition
across the supply chain.
pertinent to the new product.
Framework
Overview
High-level diagram of AI and ML integration in supply chain components.
Modules: Demand Forecasting, Route Optimization, Fleet Management, etc.
Visuals: Framework chart or architecture diagram.
Methodology
Qualitative Methods Quantitative Methods
Qualitative research methods involve Quantitative research methods involve
collecting and analyzing non-numerical collecting and analyzing numerical data to
data to explore the underlying quantify relationships, trends, and
motivations, perceptions, and behaviors of patterns. In our research proposal,
individuals. In our research proposal, quantitative methods will be employed to
qualitative methods will be utilized to gain measure the prevalence, frequency, and
in-depth insights into consumer magnitude of certain phenomena related
preferences, attitudes, and experiences to the new product.
related to the new product.
Qualitative
Data
Case studies reveal significant improvements after
implementing AI and ML in supply chains.
Companies have reported more accurate
forecasting, with predictive analytics reducing errors
and optimizing inventory. Real-time adaptive routing
enhances delivery efficiency, while predictive
maintenance extends vehicle lifespans. These
qualitative insights highlight the transformative
potential of these technologies.
Quantitative
Data Demand Forecasting
31.6%
Quantitative analysis demonstrates tangible benefits of AI and Routing Errors
ML integration in supply chains. Demand forecasting accuracy 42.1%
improves by 20-30%, while fleet management costs decrease
by up to 25%. Routing errors are reduced by 40%, and overall
operational efficiency increases substantially. These metrics
showcase the direct impact of technology adoption on
performance and profitability.
Fleet Management
26.3%
Analysis
Road Miles Geodesic Miles
20
15
10
0
Net Links Node j flow Constraints Node j input constraints
Conclusion
In conclusion, AI and ML are critical to modernizing supply chains,
enabling real-time, adaptive solutions to complex challenges. This
research demonstrates their potential to enhance logistics, optimize
routes, and improve demand forecasting. Thank you for your attention.
I am happy to answer any questions and discuss the findings further.
Thank You
30 November, 2024
