Objectivity Case Studies Digital Transformation of Paper-Based Processes for a Major European Airline

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	Digital Transformation of Paper-Based Processes for a Major European Airline Objectivity

Digital Transformation of Paper-Based Processes for a Major European Airline

Objectivity

Technology Category	Analytics & Modeling - Machine Learning Cybersecurity & Privacy - Intrusion Detection
Applicable Industries	Buildings Construction & Infrastructure
Applicable Functions	Quality Assurance
Use Cases	Counterfeit Product Identification Fraud Detection
Services	Testing & Certification
Challenge	The client, a major European airline, was grappling with a cumbersome document verification process. Their ground handling office was processing over 10,000 invoices a month, delivered by nearly 600 independent companies and contractors. These invoices came in different formats and languages, making the process time-consuming and requiring the involvement of multiple people and teams. This not only delayed data analysis and subsequent actions but also made the entire process prone to errors. The client sought to optimize this process and engaged Objectivity to develop a solution for automated verification of documents and easier error detection. The team was tasked with building a Proof of Concept (PoC) using Microsoft’s technology stack to validate documents from different sources and formats. Read More
About Customer	The client is a major European carrier, established in 1928, making it one of the world's oldest operating airlines. With a fleet of almost 100 aircraft, the client flies to over 120 destinations across Europe, Asia, and North America. The airline provides services to millions of passengers and cooperates with numerous business partners. Their ground handling office processes over 10,000 invoices a month, delivered by nearly 600 independent companies and contractors, making them a high-volume, high-traffic business with extensive data sets. Read More
Customer Name	Not disclosed Read More
Solution	Objectivity decided to use Azure Form Recognizer for document extraction. They built a tool for specific visualization of the structure of PDF files to test the results from the Form Recognizer against the actual structure of the invoices. Over 200 sample invoices were checked and collected as the reference data set for further testing. The team developed methods for pre- and post-processing of the extracted data and the correction of tabular data structure where needed. The solution was then tested and fine-tuned to achieve over 95% accuracy in data detection in the test set of invoices. The digitalization mechanism included initial pre-processing of documents, utilizing Azure Cognitive Services and the Form Recognizer to extract data, post-processing of the extracted data, and restoring previously missing data. The structure of tabular data was corrected where necessary using Machine Learning algorithms. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Cybersecurity & Privacy - Intrusion Detection

Applicable Industries

Buildings

Construction & Infrastructure

Applicable Functions

Quality Assurance

Use Cases

Counterfeit Product Identification

Fraud Detection

Services

Testing & Certification

Challenge

The client, a major European airline, was grappling with a cumbersome document verification process. Their ground handling office was processing over 10,000 invoices a month, delivered by nearly 600 independent companies and contractors. These invoices came in different formats and languages, making the process time-consuming and requiring the involvement of multiple people and teams. This not only delayed data analysis and subsequent actions but also made the entire process prone to errors. The client sought to optimize this process and engaged Objectivity to develop a solution for automated verification of documents and easier error detection. The team was tasked with building a Proof of Concept (PoC) using Microsoft’s technology stack to validate documents from different sources and formats.

About Customer

The client is a major European carrier, established in 1928, making it one of the world's oldest operating airlines. With a fleet of almost 100 aircraft, the client flies to over 120 destinations across Europe, Asia, and North America. The airline provides services to millions of passengers and cooperates with numerous business partners. Their ground handling office processes over 10,000 invoices a month, delivered by nearly 600 independent companies and contractors, making them a high-volume, high-traffic business with extensive data sets.

Customer Name

Not disclosed

Solution

Objectivity decided to use Azure Form Recognizer for document extraction. They built a tool for specific visualization of the structure of PDF files to test the results from the Form Recognizer against the actual structure of the invoices. Over 200 sample invoices were checked and collected as the reference data set for further testing. The team developed methods for pre- and post-processing of the extracted data and the correction of tabular data structure where needed. The solution was then tested and fine-tuned to achieve over 95% accuracy in data detection in the test set of invoices. The digitalization mechanism included initial pre-processing of documents, utilizing Azure Cognitive Services and the Form Recognizer to extract data, post-processing of the extracted data, and restoring previously missing data. The structure of tabular data was corrected where necessary using Machine Learning algorithms.

Impact #1	The developed PoC provided the client with a clear understanding of the potential improvements document digitalization could bring to their processes. It demonstrated how Microsoft technologies could meet their needs effectively. The PoC allowed the client's teams to explore automation possibilities for their invoice processing and proved that the designed solution could handle their extensive data sets with efficiency and accuracy. Once deployed to production, manual correction of data would only be needed for data marked as unreliable by the application. The automated transfer of data to the client's SAP system would significantly speed up the entire process and allow for extended data analysis. The PoC enabled the client to make informed decisions regarding the direction and scope of their process digitalization, in line with their business priorities.

Impact #1

The developed PoC provided the client with a clear understanding of the potential improvements document digitalization could bring to their processes. It demonstrated how Microsoft technologies could meet their needs effectively. The PoC allowed the client's teams to explore automation possibilities for their invoice processing and proved that the designed solution could handle their extensive data sets with efficiency and accuracy. Once deployed to production, manual correction of data would only be needed for data marked as unreliable by the application. The automated transfer of data to the client's SAP system would significantly speed up the entire process and allow for extended data analysis. The PoC enabled the client to make informed decisions regarding the direction and scope of their process digitalization, in line with their business priorities.

Benefit #1	Over 10,000 invoices processed monthly
Benefit #2	Nearly 600 independent companies and contractors' invoices handled
Benefit #3	Over 200 sample invoices checked and collected as the reference data set

Benefit #1

Over 10,000 invoices processed monthly

Benefit #2

Nearly 600 independent companies and contractors' invoices handled

Benefit #3

Over 200 sample invoices checked and collected as the reference data set

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Overview

Digital Transformation of Paper-Based Processes for a Major European Airline

Operational Impact

Quantitative Benefit