What is Automotive Analytics & its challenges?

Disruptions in this world are at the peaks and will continue the same momentum in the coming future. Every day, we are witnessing new sophisticated products, technologies, methodologies etc. which are heavily contributing to the changes in the industry. The Automotive sector is one such segment where the impact is considerably significant. In these current changing dynamics, if an OEM is not adding any value to their products, then, they would perish in no time. We have seen many big companies going down for this single reason. Automotive analytics helps companies optimize various factors that can be banked upon for successful survival and expansions. Digitalization, globalization & customer-centric market are posing challenges to Analytics in the Automotive sector. Not only that, the recent International treaties on climate changes, the increase in focus on safety requirements are to be considered for a sustainable survival of this sector.

Also, there are other challenges of the sector are, such as:

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How Analytics in Automotive sector helps solve the problem?

Per a survey on the manufacturing of a car, on an average, more than 30% of resource contribution is shared by the digital technology. This will only increase in future models and products. The automotive sector, like many other sectors, are susceptible to many turbulent challenges. There are always different modes/methods for solutions.

Automotive analytics contributes to the risk aversion and problem solutions & how they are done are as follows:

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Why should I Choose Innodatatics?

Our state-of-the-art analytical expertise will continuously strive to focus not only on the problems that the customers are facing, but also contributes significantly to RCA. We rely on our Robust analytical capabilities to derive actionable insights that help OEMs and other players of automotive sectors to take strategic decisions, to remain Competitive.

Our R&D team takes into consideration various aspects and expertise such as Customer analytics, sentiment analytics, Optimization techniques, product breakdown prediction models etc. into consideration, which augment innovations, thereby giving players in this sector a competitive edge in these dynamic market conditions.

Case Study 1:   Know on how Innovation is achieved using Sentiment Analytics
Case Study 2 :   How to predict vehicle maintenance based on usage of vehicle & health parameters of its components
Case Study 2 :   How to predict vehicle maintenance based on usage of vehicle & health parameters of its components
Case Study 3:   How to achieve Network Optimization View full Case study
Case Study 4 :   How to classify the component failure cause
Case Study 4 :   How to classify the component failure cause