Maintaining records, allotting task cards, and extracting detailed information from the records is a laborious task for the aviation maintenance team. The amount of time and resources taken to run the maintenance process and update the records leave the aviation maintenance team wondering if only an automation system exists to help them. 

A leading aviation leasing entity came to RecoSense with the same requirement. They needed an intelligent automation system that could analyze the incoming work pack, split the work packs into task cards. Post completion to process/validate the task cards for quick engine despatch there by optimizing TAT’s. 

When AI Improves Efficiency & Productivity

RecoSense is one of the fast-growing AI-powered data intelligence solution providers. With the proprietary NLP engine and efficient machine learning & intelligent deep learning system, RecoSense’s AI solution helps organizations better handle tons of data, structure them, and get better insights accurately in a shorter time frame. 

• Automated Compliance
• Reduced Turn Around Time (TAT)
• Plugging of Revenue Leakage
• Accelerating Process with higher accuracy

The Current Process & The Challenge

When the aviation leasing entity receives the engine or other major aviation component, a work pack containing thousands of pages regarding the tasks to be performed will be given. Then the maintenance team has to manually extract the data point and add it to the MRO software and Planner excel sheet. 

Similarly, all the updated task cards from various technicians during the engine/other major aviation component visit must be collected. Then the task cards must be manually scanned, reviewed, and clubbed together as one whole set for engine despatch. 

Since various human resources are used at each stage, the consistency of the end result varies. It takes months to plan the resources for maintenance, and the team has to wait for the entire set of task cards to arrive to complete the engine despatch process. 

Using legacy systems caused high operational costs, manual errors, and revenue leakage for the leading aviation leasing entity. To overcome the shortcomings, the entity required –

1. Automation for analyzing the incoming Work Packs.
2. Automation in processing the completed Task cards.

An overview of AI-based MRO/M&E automation

While the legacy system takes months to complete and despatch an engine, the AI system can perform the same tasks in a relatively shorter duration. Once the aircraft maintenance team collects the flight records, the records will be fed into the AI system. Later the AI system will-

• Extract data from the records
• Analyze the data and correlate them
• Execute the desired automation task

The task card may contain unstructured data like handwritten data, stamp seals, signatures, etc.; This is where the cognitive AI engine & Natural Language Processing (NLP) engine kicks in. Irrespective of the format, language, and information structure, the NLP engine extracts the proper information without any errors. 

The AI system uses deep learning to analyze the extracted data and get progressively higher-level features from the data. These extracted data will be used to perform data classification and correlation, which will help the AI system to automate the desired task, such as extracting the data and updating the info on MRO software. 

Let’s see how RecoSense’s AI-based solution helped the leading aviation leasing entity accomplish its goal.

DocuSense – AI-based Document Intelligence  for analysis of incoming Work Packs

The goal is to leverage NLP’s full potential to process the work packs and split work packs in a logical order based on the Task card number. Later the extracted data points should be uploaded to the MRO software & Planning excel sheets. Typical process followed on receipt of Work Pack is:

1. Print the entire Work Pack.
2. Segregate the Work Pack into individual Task cards.
3. Populate the Planning XLS.
4. Update Task Card Data into MRO Software.

All this work is manually done by the Planning Engineer. A senior resource doing manual work is an anachronism in today’s world.

Thankfully the DocuSense AI platform from RecoSense is powerful enough to perform the complex task of processing the docs and extracting all types of structured and unstructured data. Since the Docusense platform handles thousands of data regularly using high-end Optical Character Recognition (OCR), the system can effortlessly identify the key entities and tag them.

For the leading aviation leasing entity, RecoSense’s AI-platform process the work pack containing thousands of pages, split the work packs into task cards, and update the Planning XLS. This results into optimising the Planning Engineers time and reduce that TAT.

DocuSense for completed Task Card processing

The main goal here is how to automate the information processing on the completed task card and support the Shopfloor Team.

Here also, RecoSense’s cognitive AI system with NLP, OCR, and deep learning engines is used to extract the proper information. In this automation process, the key entities are to find and as well as to validate the data. The key aspects that DocuSense handles:

1. If a job card is missing a stamp or signature, it will be flagged and alerts sent to appropriate personnel.

2. . If any other compliance data is missing, it will also be flagged and alerts raised.

3. As the completed task cards are updated into DocuSense the system keeps a log and once all Task Cards are in the system it will automatically trigger task card compilation to manage compliance at time of dispatch.

4. While Analysing the Task Cards required information can be automatically updated into the MRO Solution. 

• End result is reduced TAT with enhanced compliance.

How did the AI platform for Process Automation benefit?

Since most of the laborious tasks like transcoding the work packs, assigning task cards, extracting & reviewing completed task cards, and updating the info on the MRO software & Planning excel sheet are automated, the leading aviation leasing entity managed to achieve-

• Optimize & improve the Engine turnaround time(TAT)
• Reduced processing time
• Reduced the error rate
• Reduced operation cost
• Increased consistency   
• Virtual platform for technical service issues
• Enhanced Compliances

The AI-solution benefits are multi-fold

Apart from splitting Work Pack tasks, validating Task cards, and keeping the maintenance audit intact, AI can be used in a wide range of automation tasks in the aviation industry. 

Predictive analytics is a critical use case where the AI system can forecast energy consumption, raw materials, resources, cost, time & effort, profitability, etc. so that the vendors and entities can easily preplan the operations and save time & money. 

Another important benefit is Centralized data intelligence system. All the collected and processed data will be in a centralized hub which makes accessibility easier. Plus, these data can also be used for predictive analysis as well. 

The power of deep learning can unveil many potential insights that mostly get overlooked. With the AI system, an aviation-specific knowledge graph can be prepared using specific ontology and co-relations between key entities. Knowledge graphs will also help vendors and other organizations to extract complex information within a fraction of the time. 

The leading aviation leasing entity was able to automate analyzing the work packs and processing the completed task cards using the sensibly designed and trained Docusense platform. The AI-automation system has reduced the time taken for maintenance and delivery of the aircraft engine/other major components to fewer weeks compared to the previous legacy system, which took them several months to accomplish the task.