Unlocking the Potential in Lost Bag: With Intelligent Automation Powered by Machine Learning

Human Vs Robots – Biggest Debate of Decade

Questions like – “What will men do when robots can do everything? “or “Will robots steal our jobs?” or  “Will  automation take away our livelihood “ – have been cluttering our minds. There is no scarcity of the predication that robots will replace human in all kinds of jobs.

But now at the Fag end of this decade, this is the time to de-clutter and see how human and robots will co-exist. Not only will they co-exist but they will also augment human race, economy and ease of living.  It is slowly getting expected that humans will have a role to work very closely with AI-equipped smart system, and it will become difficult to hand over important decisions to a robot. This means decision making will become hybrid with robots that is not supporting but augmenting every aspect of human decision making.

The Complex World of Airline Travel

In a complex world of Airline travel, it is not easy to have human and semi-intelligent system work in cohesion. There are times where this becomes very difficult.

Then how do we make it work? The need of the hour is to build a right handshake between human and robot. A robot is used to do all the routine and repetitive job of the human but it passes control to a human especially in a complex decision making environment where it is confused. This actually augments a human by taking out the laborious, routine and repetitive part of their job. It also provides humans with more time to focus on the niche and complicated part of the job.

How IGT Unlocked the Potential in a Lost Bag

Lost bags is a major reason for passenger dissatisfaction. About 25 million bags get misdirected or lost every year due to multiple reasons and the total cost of bag mishandling to the aviation industry stood at around 2.4 billion dollar.

At IGT, we came across a complicated process of identifying the root cause of baggage lost. The process was very manual in nature. IGT divided the process into 2 phases to break down the tasks and identified a solution to improve this process by using intelligent automation driven by ML. Furthermore, the solution is also capable of spotting lost luggage patterns and identifying weak points in the system, such as particular destinations or bag types that are more problematic than others.

This process was divided into 2 phases:

Phase 1:

This phase focuses upon traversing across many internal systems at an airline, acquiring multiple data sets and completing preliminary analysis & interpretation of data.

Phase 2:

This phase focuses on doing an in-depth analysis of each baggage lost case and assigning responsibilities to the station or carrier.

The first phase is laborious, menu-driven, routine execution, whereas the second phase requires intense data analysis and due diligence.

Machine Learning Driven Intelligent Automation

IGT provided ML-driven intelligent automation solution where Phase 1 was automated using RPA and AI, and Phase 2 was performed using Machine Learning algorithms. Human Wit and Intelligence is leveraged to approve/review decisions given by ML & refine these algorithms by providing continuous feedback to the system. This solution resulted in not only saving tremendous manual efforts by automating and using intelligent algorithms but also helped in identifying a pattern to address the weak point for baggage loss.

Intelligent Automation Model:

Hybrid X Platform:

IGT’s Data Acquisition framework was used for Data Acquisition

Custom ML Algorithm:

Multiple ML algorithms were evaluated to finalize the best

Feedback Loop for Continuous Refinement:

A feedback loop cycle was added for continuous refinement of ML Algorithm

Leveraged Human Intelligence:

Human-agent is given the importance he deserves

With this automated solution humans can now focus on more complicated cases and free up time for high value work to bring in overall success of the business. The analysis for baggage lost reasons patterns using machine learning will also in turn reduce the number of cases for baggage loss to achieve higher customer satisfaction and brand loyalty.


About the Authors:

Sooraj Sundararajan is a Senior Technical Architect at IGT Solutions’ Digital Travel Analytics Division. Carrying with him over 16years of experience in IT consulting across various domains,  Sooraj has worked extensively on the front edge of cloud and data technologies, designing and delivering cutting edge Data & Analytics Solutions. He can be reached at Sooraj.sundararajan@igtsolutions.com

Sandeep Gupta is an Architect working for Analytics – Centre of Excellence at IGT Solutions. He is an experienced professional in digital, data & analytics space with 15 years of rich experience in providing business solutions using data technologies such as Data Warehousing, Data Lakes, Data Science & Big Data Analytics. He can be reached at sandeep.gupta1@igt.com