ChallengeIf a fully loaded truck breaks down on the road, for example due to injector damage, the component failure has to be repaired, which incurs costs that usually have to be paid by the manufacturer. The late delivery leads to convention penalties and a downgrade in the quality ranking, which is bad for follow-up orders.
SolutionBased on the telematics data, fault memory entries and repair information, a data set is built up to predict the failures. The algorithm developed identifies patterns in the ECU data that can be used to distinguish healthy from failed vehicles. With the learned and validated pattern, predictions can be made for all vehicles in the future as to the probability of an injector failure.
ResultWith the current status, 92 % of injector failures can be correctly predicted. This leads to lower warranty costs in the long term, penalties for delay are prevented and follow-up orders can be secured. For this predictive maintenance project, Alexander Thamm GmbH received the Best of Consulting Award 2016 of the Wirtschaftswoche.
Are you interested in your own use cases?
An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.
There will be a interactive and Flexible application, including of different maps with two different views implemented.
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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