Cash flow management and forecasting of closing date liquidity in the automotive sector

Cash flow can be further optimised by recognising correlations between invoice attributes and the period of payment outflow.

Machine learning model for automated prediction of processing time from invoice receipt to payment outflow based on 11.6 million invoices

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Identification of influencing factors that can be used in the next step for process optimisation

Improved predictability of the cash flow as at the reporting date

Challenge

The financial planning department of a premium car manufacturer would like to improve its liquidity planning in order to optimise cash management and identify potential liquidity risks ahead of time
After receipt of the invoice in the company, the timing of the cash outflow depends on various factors and thus leads to a more difficult planning of the actual cash flow on the reporting date

Solution

In the course of a use case workshop, factors influencing the duration of invoice processing were identified
In the subsequent hackathon, a machine learning model was developed together with the subject matter expert, which can predict the respective payment outflow to the exact day

Result

Development of a machine learning model that has an average deviation of only 2.6 days by using features (such as document type, payment term and calendar week)
The findings from the modelling can be used in the next step for process optimisation

Are you interested in your own use cases?

Challenge

An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.

Solution

There will be a interactive and Flexible application, including of different maps with two different views implemented.

Result

Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.

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