The Missing Link: Data Fragmentation as the Barrier to AI/ML in Indian Logistics
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming global supply chains and logistics networks.
According to a recent McKinsey report, AI could generate $1.3–$2 trillion per year in logistics over the next 20 years. Furthermore, statistics show that technology and ML are no longer optional: 61% of logistics leaders view them as a competitive advantage, and 75% of commercial supply chain vendors are predicted to integrate AI and data science by 2026.
Globally, organizations are using ML to improve delivery accuracy and manage risk with greater agility. India is also moving in this direction, with logistics and e-commerce players experimenting with ML-led pilots.
However, adoption still lags behind global peers due to structural and operational barriers that limit integration into daily workflows.
Indian logistics struggles with legacy systems, fragmented operations, and uneven digital adoption. Among these, data fragmentation is the most fundamental barrier, preventing ML from delivering tangible results.
In this blog, we explore why ML matters in logistics globally, why adoption in India remains limited, and why data fragmentation is the missing link that must be solved to unlock the full potential of machine learning.
Read on to know more.
Benefits of ML in Logistics
Improved Forecasting
ML analyzes historical data, trends, and external factors to predict demand accurately, helping reduce stockouts and excess inventory.
Dynamic Route Optimization
ML algorithms use real-time traffic, weather, and delivery data to find the most efficient routes, cutting fuel costs and reducing delivery times.
Predictive Maintenance
ML predicts vehicle and equipment failures before they occur. This minimizes downtime and lowers maintenance expenses.
Enhanced Visibility
ML continuously monitors shipment data to provide real-time updates and alerts for exceptions. This improves transparency across the supply chain.
Environmental Benefits
Optimized operations reduce miles traveled and fuel use. This lowers costs and supports sustainability goals.
Why ML Matters — A Global Perspective
UPS — Machine Learning-Driven Route Efficiency
UPS's ORION system uses machine learning to evaluate over 200,000 alternative route combinations for maximum efficiency. Since its launch in 2012, ORION has helped UPS save about 100 million miles and 10 million gallons of fuel annually, reducing both costs and carbon emissions.
DHL — AI in Last-Mile Delivery
DHL uses machine learning to optimize delivery routes and predict shipment arrival times. Furthermore, with route optimization, delivery accuracy reaches around 90–95%, boosting operational efficiency and customer satisfaction.
Amazon — AI and Robotics in Delivery
Amazon uses machine learning to forecast demand and optimize deliveries. Its AI models improve national forecasts by 10% and regional forecasts by 20%. The company operates over 1 million robots that sort, lift, and carry packages and use agentic AI to improve operational efficiency.
The Indian Logistics Landscape: Key Challenges
Low Global Logistics Efficiency
The World Bank's Logistics Performance Index (LPI) ranks India 38th globally, highlighting gaps in coordination, tracking, and information flow.
Highly Unorganized Ecosystem
Industry studies show that only about 10–15% of India's logistics market is organized, with the rest dominated by unorganized players such as small fleet owners and standalone operators.
Limited Digital Incentives
India has uneven adoption of digital tools, which restricts real-time data integration, visibility, and automation across logistics operations.
Furthermore, additional factors such as manual data handoffs, skills gaps, and high cost sensitivity among SMEs continue to reinforce fragmentation across the Indian logistics ecosystem.
Data Fragmentation: The Missing Link for ML
While we have identified structural issues, the core obstacle is data fragmentation. India's logistics sector is structurally fragmented.
Industry research shows that India's logistics sector suffers from fragmented data systems across transport, warehousing, customs, and last-mile delivery. This lack of integrated data limits visibility and efficient planning across the supply chain.
In India:
- Data is siloed across carriers, warehouses, customs, and last-mile delivery.
- Standards vary, with inconsistent formats and definitions.
- Updates are often delayed or incomplete.
- Legacy systems block automated data exchange and real-time ML insights.
The Path Forward: Data First, Algorithms Second
To unlock the full potential of machine learning and AI, Indian logistics must tackle data convergence before worrying about algorithms. Essential steps include:
- Standardized Data Models: Common fields and schemas across freight, warehousing, billing, and customs.
- Shared Platforms & APIs: Enable real-time data exchange between carriers, warehouses, and partners.
- Data Governance & Quality: Clear rules for data capture, cleaning, and ownership.
- Digital Skills & Training: Equip staff to use and manage modern digital systems effectively.
Conclusion
In conclusion, clean, integrated data forms the foundation for machine learning. Without it, AI-driven logistics remains aspirational. In India, predictive routing and demand forecasting cannot reach their full potential until data fragmentation is fixed. By prioritizing data first and algorithms second, logistics operations can achieve higher efficiency and better supply chain visibility.
It's time for Indian logistics leaders to act and invest in unified data systems today to unlock the full power of machine learning tomorrow.