With data science and AI through the crisis

by | 30 March 2020 | [at] News

The German economy is facing a deep recession. An insight into how data science and artificial intelligence can counteract this scenario.  

The Corona Pandemic and its consequences for business and daily life will lead to an economic crisis of enormous proportions. Sales collapse, supply chains are disrupted, foundations of the global economy fail or are destroyed. With a two-month shutdown, such dislocations could, according to one Scenario calculation of the ifo Institute lead to a reduction in the annual GDP growth rate of between 7.2 and 11.2 percentage points. 

Nevertheless, no one can currently predict exactly how long the coronavirus will have us in its grip and how the effects on the national and global economy with its complex interconnections will actually look like in the end. 

In addition, the impact on different sectors and individual companies varies greatly. What is clear is that in all sectors Strategies for overcoming the crisis need to be developed. 

Data as the key to digitisation

An essential component of these strategies is the more intensive use of data as part of an expansion of the digitalisation of companies and their processes.  

The enormous importance of data and its analysis is currently being demonstrated to us in the fight against the pandemic. Statistical models calculate the course of infections, and artificial intelligence is used to research new medicines and vaccinations. South Korea shows how the spread of the virus can be contained through intensive testing and the simultaneous collection and comparison of movement data. The importance of the extensive collection of data and its central storage is shown by the high number of unreported cases of infected persons in many countries, which is expressed in a much higher mortality rate compared to Germany. 

Data and its comprehensive analysis are also a central strategic building block in many areas of companies for overcoming the economic crisis. 

AI use cases in the crisis

Suppliers and supply chains must be continuously recorded and analysed, sales potentials and markets constantly recalculated and liquidity planning regularly adjusted. This requires the recording and merging of additional data sources and the Development of new analyses and AI models. 

The use of automation on the basis of artificial intelligence contributes significantly to the necessary efficiency increases and Cost reductions at. Studies show that crises, recessions and the accompanying threat to one's own business model have in the past repeatedly been the starting point for disruptive innovations that resulted in extensive increases in productivity. 

Solution portfolio for individual industries

At Alexander Thamm GmbH, we have developed a comprehensive portfolio of solutions for using data to address the Corona crisis, based on experience from more than 1,000 projects in all areas of the corporate value chain.

AI applications for business management and finance

There is currently great uncertainty about the duration of the crisis and its economic impact on one's own company. With an extensive Scenario planning the effects of different scenarios on the company can be modelled. Appropriate options for action can then be derived from this.

Real-time analyses and forecasts of liquidity risks can be used to improve liquidity management.n can be used. In addition, machine learning methods can be used to predict payment delays and defaults.

AI applications in production & logistics

The Corona crisis is affecting the entire supply chain. Many productions are currently at a standstill, suppliers are failing and orders are being cancelled. Relevant influencing factors and key figures must therefore be identified and tracked in real time. Supply chain analyses and forecasts help to be informed in good time about delivery bottlenecks, order changes or shipping problems and to be able to react quickly.

AI applications in service and sales

It is crucial for companies to retain their existing customers during and after the crisis.
Churn Prediction or the Forecasting customer churn can predict switching probabilities and switching times and identify reasons for switching. Through a targeted approach and individual offers, expensive, crisis-related customer churn can be prevented.

Numerous companies are currently overrun with customer enquiries regarding delivery dates, cancellation conditions or even financing options. In order to ensure a timely response and to make efficient use of existing personnel capacities, we can automated classifications of written requests can be used. Through Text Analytcis and NLP, queries can be prioritised and partially answered automatically.

The crisis as an opportunity for digitalisation

In this context, the crisis should also be seen, in part, as a Interpreting opportunity. Companies can now position themselves strategically for the post-recession period and in the area of digitalisation and data strategy set the course for a successful future. 

We, Alexander Thamm GmbH, also support you with the Development of individual data strategies, which are adapted to the respective company-specific challenges. Via tools such as virtual workshops and Hackathons we ensure that initiatives can be taken forward even in times of social distancing. 

We would be happy to support you in your efforts to overcome the crisis with data science and AI and become a data-driven company - contact us. 

Header image: Martin Sanchez by Unsplash

<a href="https://www.alexanderthamm.com/en/blog/author/joerg/" target="_self">JÖRG BIENERT</a>

JÖRG BIENERT

Jörg Bienert is partner and CPO of Alexander Thamm GmbH, Germany's leading company for data science and AI. At the same time, he is co-founder and chairman of the KI-Bundesverband e.V. and a member of the Advisory Board Young Digital Economy at the BMWI. Furthermore, he is a respected keynote speaker and is regularly featured in the press as a data & AI expert. After studying technical computer science and holding several positions in the IT industry, he founded ParStream, a Big Data start-up based in Silicon Valley that was acquired by Cisco in 2015.

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