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Consulting & Analytics

Fixing India's Grape Economy

Data-driven model to improve farmer income & reduce wastage

Consulting & Analytics

IIT Roorkee

Problem Context

India’s grape industry has strong revenue potential but suffers from inefficiencies in supply chain, storage, and pricing systems. Farmers face unstable income despite high production capacity.
Farmers experience price crashes, high wastage (25–30%), and income instability due to fragmented supply chains, lack of storage infrastructure, and heavy dependence on export markets.

Approach

Analyzed multi-layer supply chain inefficiencies including logistics and storage gaps
Identified key risks such as price volatility, climate impact, and export dependency
Designed a cluster-based sourcing model using data-driven scoring
Integrated forecasting, storage, and export allocation into a unified system

Solution

Cluster-based sourcing model prioritizing high-yield and export-ready regions
Tech-enabled supply chain with demand forecasting and storage optimization
Farmer dashboard for price insights, storage decisions, and quality alerts
Circular economy model to reduce waste via processing and alternate markets

Impact

Increased farmer income from ~₹15L/ha to ₹18–20L/ha
Reduced wastage from 25–30% to ~10–15%
Decreased price volatility by ~40%
Improved overall efficiency and sustainability of the supply chain

Key Learnings

Agricultural challenges are primarily system design problems, not production issues
Data-driven decision-making can significantly improve rural income outcomes
Small optimizations in logistics and storage create large economic impact
End-to-end system thinking is critical for solving supply chain problems

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Fixing India's Grape Economy

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