Artificial Intelligence (AI) and its application Machine Learning (ML), are the latest hype technologies that are transforming the way we make decisions in the business world. As a part of its global enterprise solution, Tigers continues to invest in technology with a focus on predictive data anaylitics, disruption, and artificial intelligence.
Tigers spoke with Dr Karsten Leibold, a Management Consultant and Professor of Aviation Management, Transport & Logistics at IUBH – University of Applied Sciences. Dr Leibold has more than 25 years of experience in the air transport industry. His career track includes roles with Lufthansa, Roland Berger, McKinsey & Company, and airconomy aviation intelligence.
Together with buzzwords Internet of Things (IoT) and Big Data, these concepts have the potential to massively impact the quality of decision making by influencing the way companies derive information from their data.
And in contrast to other technologies before them, it is quite unlikely that these technologies will disappear in a trough of disillusionment soon.
Instead, it can be expected that AI and ML have the potential to allow many companies to reinvent at least parts of their business.
AI is not a new technology at all; its birth can be traced back to the introduction of computers and electronic data publishing at the beginning of the 1950s. However, it took six decades before AI and ML were able to leverage their full potential in today’s business world.
In the past, these technologies remained dormant because of a lack of available data, but in today’s world data is constantly collected, generated by any kind of transactions, sensors, social media interactions, etc. and made available in real time due to IoT. In the last two years only nearly the same amount of data was produced and stored as was in all previous years since records began.
The availability of this Big Data together with AI and ML is one of the most important developments companies are facing and consequently required to react upon to stay competitive.
This calls for the development of AI-enabled data analytics processes to bring the analytics transformation from Business Intelligence to Big Data on its way.
Instead of relying on sample or representative data with hypothetical conclusions, decision makers can nowadays count on the analysis of real time data down to the lowest level of detail.
To fully leverage the potential of AI every organization needs to identify for its business where this new technology can create the highest impact, which benefit can be expected, and which processes need to be changed or redesigned.
The possible spectrum of the usage of AI, ML and Big Data in the cargo industry is manifold and covers for instance areas like forecasting, capacity management, performance measurement, and predictive decision making. A few examples:
Improving the accuracy of forecasts by taking more data into consideration – internal as well as external – in real time. ML makes it possible to recognize patterns and to derive information that not long ago were considered to require human cognition
In the field of capacity management, the impact of an ad-hoc booking can be traced immediately and considered throughout every stage, with its impact on network profitability instantly ascertained
Performance measurement can be shifted from ex-post reporting to real-time performance optimization. Instead of reporting what went wrong during the last measurement cycle it is possible to identify disruption along the value chain instantly and act accordingly
Predictive decision-making, similar to predictive maintenance, constantly collects vital data from activities along the value chain to identify the current conditions of core processes. By early identification of anomalies and failure patterns, corrective actions can be taken long before severe problems occur
The digital transformation we are currently facing is definitively leading to digital disruptions.
Even though the cargo business is not a fully digitized industry, some core changes for its processes nevertheless are to be expected. Therefore, it is essential to develop the right AI strategy for your own organization, and ensure that the identified AI data processes are available shortly after.