ANIRUDDH SHARMA
ANIRUDDH SHARMA
ANIRUDDH SHARMA
Crop Portfolio Monitoring Platform
Crop Portfolio Monitoring Platform
Designing an enterprise platform to digitize India’s crop yield estimation process - transforming manual methods into a scalable, data-driven, and transparent system.
Designing an enterprise platform to digitize India’s crop yield estimation process - transforming manual methods into a scalable, data-driven, and transparent system.
Context
Munich Re, a global reinsurance leader, has been advancing agriculture insurance in India through better portfolio monitoring and claim validation. With the Government’s YES-Tech Guidelines under PMFBY introducing satellite-based crop yield estimation, a major gap emerged - the lack of a digital platform to implement these guidelines.
Munich Re, a global reinsurance leader, has been advancing agriculture insurance in India through better portfolio monitoring and claim validation. With the Government’s YES-Tech Guidelines under PMFBY introducing satellite-based crop yield estimation, a major gap emerged - the lack of a digital platform to implement these guidelines.
My Role
My Role
Product Design, Analysis, Research
Product Design, Analysis, Research
Team
2 Product Managers
8 Engineers
2 Product Managers
8 Engineers
Timeline
~ 50 Weeks
~ 50 Weeks
OBJECTIVE
Enhance Munich Re’s agriculture insurance portfolio management by leveraging geospatial data and YES-Tech yield models.
Enhance Munich Re’s agriculture insurance portfolio management by leveraging geospatial data and YES-Tech yield models.
Problem Statement
Crop yield estimation was slow, manual, and error-prone and no digital tool existed to run the newly introduced YES-Tech models.
Crop yield estimation was slow, manual, and error-prone and no digital tool existed to run the newly introduced YES-Tech models.
Solution
YES-Tech Workbench – Configure, compute, and qualify yield models.
YES-Tech Explorer – Map-based outputs with layers, charts, and role-based access.
Data Explorer – Integrated historical & real-time satellite/weather datasets.
User Management – Role-based onboarding for multiple insurers and clients.
Scalable Design – Modular system built for nationwide adoption.
Results
40%
40%
faster claim validation compared to traditional processes
60+
districts covered across multiple states in the pilot phase
1st
digitised YES-Tech platform in India.
understanding basics
🌾 What is CCE ?
Crop Cutting Experiment (CCE) = the traditional way to measure crop yield.
Crop Cutting Experiment (CCE) = the traditional way to measure crop yield.
🧩 How Does CCE Work?
Select a random small plot of land (say 5m × 5m) in a farmer’s field.
Harvest all the crops from that plot.
Weigh the harvest → get yield for that sample plot.
Use a formula to scale up to per-hectare yield for the entire field/region.
Select a random small plot of land (say 5m × 5m) in a farmer’s field.
Harvest all the crops from that plot.
Weigh the harvest → get yield for that sample plot.
Use a formula to scale up to per-hectare yield for the entire field/region.
Old Method
Why It’s a Problem
Manual, time-consuming
Limited samples → prone to errors
Delays insurance claim processing
Why It’s a Problem
Manual, time-consuming
Limited samples → prone to errors
Delays insurance claim processing
Old Method
🌾 What is YES-Tech?
YES-Tech = Yield Estimation System based on Technology
YES-Tech = Yield Estimation System based on Technology
🧩 How Does YES-Tech Models Work?
Combine satellite-based estimates with manual CCEs
Create a blended yield value that is more reliable
Aim: gradually reduce dependence on manual, error-prone methods
Combine satellite-based estimates with manual CCEs
Create a blended yield value that is more reliable
Aim: gradually reduce dependence on manual, error-prone methods
New System
Why YES-Tech Matters
✅ Accurate yield estimates
✅ Faster insurance claim processing
✅ Transparency and fairness
✅ Builds trust between farmers, insurers, and govt.
Why YES-Tech Matters
✅ Accurate yield estimates
✅ Faster insurance claim processing
✅ Transparency and fairness
✅ Builds trust between farmers, insurers, and govt.
YES-Tech is like a fitness tracker for agriculture - replacing guesswork with satellite-driven, standardised yield measurements.
YES-Tech is like a fitness tracker for agriculture - replacing guesswork with satellite-driven, standardised yield measurements.
New System
research
Uncovering the Gap
Our research showed: despite YES-Tech guidelines, no digital tool existed in India. This ambiguity gave us the opportunity to build something entirely new. Through two design thinking workshops with the client, we identified the key problems.
Our research showed: despite YES-Tech guidelines, no digital tool existed in India. This ambiguity gave us the opportunity to build something entirely new. Through two design thinking workshops with the client, we identified the key problems.
Who is the end user?
Through our research and design thinking workshops, we identified the key personas who interact with crop yield data and insurance workflows.
Through our research and design thinking workshops, we identified the key personas who interact with crop yield data and insurance workflows.
Risk Assessor
Looks at data to spot risks in crop portfolios
Shares risk categories with managers and underwriters
Looks at data to spot risks in crop portfolios
Shares risk categories with managers and underwriters
Underwriter
Uses risk data to decide insurance pricing
Creates detailed reports supported by satellite insights
Uses risk data to decide insurance pricing
Creates detailed reports supported by satellite insights
Claims Validator
Reviews farmer claims and checks them against crop data
Ensures payouts are accurate and fair
Reviews farmer claims and checks them against crop data
Ensures payouts are accurate and fair
research
Insights from users & research
Manual & Slow Estimation
Manual & Slow Estimation
Yield calculation relied on Crop Cutting Experiments, which were time-consuming and error-prone.
Yield calculation relied on Crop Cutting Experiments, which were time-consuming and error-prone.
No Digital Tool for YES-Tech
No Digital Tool for YES-Tech
Government guidelines existed, but there was no platform to run SPM/CHF models.
Government guidelines existed, but there was no platform to run SPM/CHF models.
Low Transparency & Delayed Claims
Low Transparency & Delayed Claims
Fragmented workflows caused delays, disputes, and reduced trust between farmers, insurers, and government.
Fragmented workflows caused delays, disputes, and reduced trust between farmers, insurers, and government.
user Journey
Current User Journey
problem breakdown
Problems to solve
Yield estimation was manual, slow, and prone to errors.
Yield estimation was manual, slow, and prone to errors.
Heavy reliance on Crop Cutting Experiments (CCE) without technology support.
Heavy reliance on Crop Cutting Experiments (CCE) without technology support.
No digital tool existed to run YES-Tech models (SPM, CHF, AI/ML).
No digital tool existed to run YES-Tech models (SPM, CHF, AI/ML).
solutioning
Early attempts to understand workflow
My first step was to sketch and prototype early visual design concepts.
These weren’t final solutions but conversation starters - they helped me and the team understand how models might be configured and run.
The visuals were key in discussions with data scientists, product managers, and clients - revealing what wouldn’t work and exposing conceptual gaps in the workflow.
My first step was to sketch and prototype early visual design concepts.
These weren’t final solutions but conversation starters - they helped me and the team understand how models might be configured and run.
The visuals were key in discussions with data scientists, product managers, and clients - revealing what wouldn’t work and exposing conceptual gaps in the workflow.
process
Improved User Flow
process
Core Modules
After several back-and-forth iterations and joint sessions, we narrowed down the platform structure to two core modules:
After several back-and-forth iterations and joint sessions, we narrowed down the platform structure to two core modules:
process
Early explorations
A few early explorations helped me identify the overall navigation structure and uncover key gaps in the concept.
A few early explorations helped me identify the overall navigation structure and uncover key gaps in the concept.
solution
The Final Outcomes : Highlight
YES-Tech Workbench
YES-Tech Workbench
YES-Tech Workbench
yes-tecH workbench
Raw Data
In this step, clean the data and initiate raw data preparation by adding raw data for a specific combination of region, season, and source configuration.
In this step, clean the data and initiate raw data preparation by adding raw data for a specific combination of region, season, and source configuration.
yes-tecH workbench
Model Configuration & Computations
Model Configuration → Users can create and experiment with multiple setups by tweaking parameters.
Model Computation → Users can run different methods (like blending models through ensemble or applying correction factors), review outputs, and qualify the best model by locking it.
Model Configuration → Users can create and experiment with multiple setups by tweaking parameters.
Model Computation → Users can run different methods (like blending models through ensemble or applying correction factors), review outputs, and qualify the best model by locking it.
yes-tecH explorer
Map Visualisation & Analysis
Analyse outputs with map views and charts to make informed decisions.
Analyse outputs with map views and charts to make informed decisions.
other modules
Sneak-Peak
Impact
India’s first digitised YES-Tech platform
Praised by both clients and MNCFC (guideline creators) for automation & flexibility
Praised by both clients and MNCFC (guideline creators) for automation & flexibility
Positioned as the benchmark solution for insurers, govts, and tech partners
Positioned as the benchmark solution for insurers, govts, and tech partners
Successfully onboarded 10+ clients and 100+ users across 60 districts.
Successfully onboarded 10+ clients and 100+ users across 60 districts.
Learnings
Designing in ambiguity
Designing in ambiguity
Working on a first-of-its-kind platform taught me how to structure workflows when no prior reference exists.
Working on a first-of-its-kind platform taught me how to structure workflows when no prior reference exists.
Systems thinking
Systems thinking
Small design choices (like parameters or navigation) had ripple effects across the entire model workflow, reinforcing the need for holistic design.
Small design choices (like parameters or navigation) had ripple effects across the entire model workflow, reinforcing the need for holistic design.
Cross-functional collaboration
Cross-functional collaboration
Alignment with data scientists, PMs, & engineers was essential to bridge gaps b/w design, guidelines, and backend feasibility.
Alignment with data scientists, PMs, & engineers was essential to bridge gaps.
Alignment with data scientists, PMs, & engineers was essential to bridge gaps b/w design, guidelines, and backend feasibility.
Simplifying complexity
Simplifying complexity
Translating government guidelines and scientific models into a clear, intuitive experience.
Translating government guidelines and scientific models into a clear, intuitive experience.
Scalability mindset
Scalability mindset
Designing not just for one client, but for a platform that can scale across insurers and states, strengthened my ability to think long-term in enterprise product design.
Designing not just for one client, but for a platform that can scale across insurers and states, strengthened my ability to think long-term in enterprise product design.