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Lufthansa

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Offering Lufthansa

Project Details

The goal of the project is to assist Lufthansa Airlines in their complete digital transformation journey. This involves overhauling their existing solution to align with the latest technology stack and design practices, ultimately empowering them with an AI-driven solution.

Industry

Aviation

Core Technologies

Machine Learning, NER, Deep Learning, Transformer Models, Python

Problem

Lufthansa Airlines had a holistic DRM solution in place that helped them manage the records of assets traded between lessees and lessors.
The existing DRM solution faced several challenges, including an outdated technology stack, exorbitant cloud expenses, elevated operational costs, and inefficiencies.
Each lessor, in our context the airlines, needed to maintain records of aircraft maintenance during the lease period, which had to be presented to the relevant regulatory body.
The count of records, such as routine maintenance, inspections, repairs, component replacements, compliance documentation, and job cards, was around a thousand for a single aircraft. This impacted storage costs, audit costs, and efforts to ensure the correct documents were mapped to the right category.
The main challenge, apart from cost, was binding the records to the correct category and maintaining the sequence of records uploaded from the M&E section.

Our Approach

To start the digital transformation journey, BigOhTech actively conducted primary research on each persona and performed a thorough tech audit to identify avenues for technology advancement and controlling cloud expenses.
The entire digital strategy was designed with the outcome of refining technical debt, considering easy wins and maximum impact on top priorities.
On the functional aspect, the product and design backlog were designed according to current customer needs and the latest design trends. AI-enabled solutions were integrated to facilitate quick audits and reduce manual intervention.
After conducting the infrastructure audit, a strategy was devised to reduce cloud expenditures and curb the month-on-month proportional increment in cloud costs.
The entire M&E system was revamped to allow ground engineers to easily update relevant records against maintenance activities they have completed.
An AI-driven solution was implemented to facilitate the grouping and categorization of documents uploaded by the M&E staff.

Benefits

Customer onboarding became faster. It used to take 4-5 months, but now it will only take one month.
Our infrastructure audit helped us devise a plan to reduce infrastructure and cloud costs by nearly 25%.
Using the audit, we were able to identify technical, functional, and design tasks that were easy to achieve and could have a significant impact on UX.