Microsoft Azure (Microsoft) Case Studies AI-Driven Transformation in Romanian Farming: A Case Study

Edit This Case Study Record

	AI-Driven Transformation in Romanian Farming: A Case Study Microsoft Azure (Microsoft)

AI-Driven Transformation in Romanian Farming: A Case Study

Microsoft Azure (Microsoft)

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Cities & Municipalities Education
Use Cases	Demand Planning & Forecasting Movement Prediction
Services	Data Science Services
Challenge	The Rural Investments Financing Agency (AFIR), a Romanian government agency, is responsible for supporting farmers and companies to access grants and an average of 2 billion euros yearly of European Union (EU) funding for rural development projects. However, a vital internal forecasting task to manage these grant projects required a lot of manual intervention by AFIR’s employees. This process was not only slow but also prone to inaccuracies. The organization was keen on increasing the accuracy of spending and financing forecasts, as this would not only help its users but also boost its own operational efficiency. The process was paper-heavy and complex, and according to Daniel Ifrim, IT Director at AFIR, it was 'far from optimized'. The team needed a solution that would transform this process into a fully paperless and online system, and reduce the time taken to produce a report with spending and financing forecasts. Read More
About Customer	The Rural Investments Financing Agency (AFIR) is a Romanian government agency based in Bucharest. It is the main government agency supporting Romanian farmers and companies to access grants and an average of 2 billion euros yearly of European Union (EU) funding for rural development projects. AFIR is an important part of the Romanian government, with its primary role being to get funding and financial support into the hands of Romanian farmers and other key stakeholders who need resources to properly develop and care for the countryside. The organization is large, with between 1,000 and 9,999 employees. Read More
Solution	AFIR decided to experiment with artificial intelligence (AI) after local Microsoft partner Genisoft responded to a tender process. Genisoft proposed a completely new prediction mechanism for payments, which was delivered on a combination of Microsoft Azure Synapse Analytics and Azure Machine Learning, Azure SQL, and Microsoft Power BI. This AI-based payment prediction mechanism offered many improvements, especially around more accurate forecasting. The AI-driven prediction removed the risks of errors that could impact the report accuracy. The transformation process took several months, but the result was a fully paperless and online system. The new system not only increased the accuracy of the spending and financing requirements forecast but also reduced the time taken to produce a report from ten days to just ten minutes. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Cities & Municipalities

Education

Use Cases

Demand Planning & Forecasting

Movement Prediction

Services

Data Science Services

Challenge

The Rural Investments Financing Agency (AFIR), a Romanian government agency, is responsible for supporting farmers and companies to access grants and an average of 2 billion euros yearly of European Union (EU) funding for rural development projects. However, a vital internal forecasting task to manage these grant projects required a lot of manual intervention by AFIR’s employees. This process was not only slow but also prone to inaccuracies. The organization was keen on increasing the accuracy of spending and financing forecasts, as this would not only help its users but also boost its own operational efficiency. The process was paper-heavy and complex, and according to Daniel Ifrim, IT Director at AFIR, it was 'far from optimized'. The team needed a solution that would transform this process into a fully paperless and online system, and reduce the time taken to produce a report with spending and financing forecasts.

About Customer

The Rural Investments Financing Agency (AFIR) is a Romanian government agency based in Bucharest. It is the main government agency supporting Romanian farmers and companies to access grants and an average of 2 billion euros yearly of European Union (EU) funding for rural development projects. AFIR is an important part of the Romanian government, with its primary role being to get funding and financial support into the hands of Romanian farmers and other key stakeholders who need resources to properly develop and care for the countryside. The organization is large, with between 1,000 and 9,999 employees.

Solution

AFIR decided to experiment with artificial intelligence (AI) after local Microsoft partner Genisoft responded to a tender process. Genisoft proposed a completely new prediction mechanism for payments, which was delivered on a combination of Microsoft Azure Synapse Analytics and Azure Machine Learning, Azure SQL, and Microsoft Power BI. This AI-based payment prediction mechanism offered many improvements, especially around more accurate forecasting. The AI-driven prediction removed the risks of errors that could impact the report accuracy. The transformation process took several months, but the result was a fully paperless and online system. The new system not only increased the accuracy of the spending and financing requirements forecast but also reduced the time taken to produce a report from ten days to just ten minutes.

Impact #1	The implementation of the AI-based payment prediction mechanism has significantly improved AFIR's operational efficiency. The process of producing a report with spending and financing forecasts, which used to be a complex, paper-heavy job, is now fully paperless and online. This has not only reduced the time taken to produce a report but also increased the accuracy of the forecasts. The AI-driven prediction mechanism has eliminated the risks of errors that could impact the report accuracy. Furthermore, the digital transformation has helped AFIR improve its ranking from 24th to the 3rd place in Europe in 2017. The organization is now better positioned to support Romanian farmers and companies to access grants and EU funding for rural development projects.

Impact #1

The implementation of the AI-based payment prediction mechanism has significantly improved AFIR's operational efficiency. The process of producing a report with spending and financing forecasts, which used to be a complex, paper-heavy job, is now fully paperless and online. This has not only reduced the time taken to produce a report but also increased the accuracy of the forecasts. The AI-driven prediction mechanism has eliminated the risks of errors that could impact the report accuracy. Furthermore, the digital transformation has helped AFIR improve its ranking from 24th to the 3rd place in Europe in 2017. The organization is now better positioned to support Romanian farmers and companies to access grants and EU funding for rural development projects.

Benefit #1	The time taken to produce a report with spending and financing forecasts reduced from ten days to just ten minutes.
Benefit #2	The accuracy of the spending and financing requirements forecast increased to 80 percent.
Benefit #3	The AI-driven prediction mechanism is used across 25,000 projects for efficient grants funding management.

Benefit #1

The time taken to produce a report with spending and financing forecasts reduced from ten days to just ten minutes.

Benefit #2

The accuracy of the spending and financing requirements forecast increased to 80 percent.

Benefit #3

The AI-driven prediction mechanism is used across 25,000 projects for efficient grants funding management.

Download PDF Version

Overview

AI-Driven Transformation in Romanian Farming: A Case Study

Operational Impact

Quantitative Benefit