Perform Predictive Diagnostics and Analytics Across Your Fleet

In today's automotive industry, a significant challenge persists: the abundance of data with minimal actionable outcomes derived from it. This gap underscores the necessity of leveraging Artificial Intelligence (AI) and Machine Learning (ML) to transform raw data into decisive actions and outcomes. Why, then, is AI pivotal in automotive contexts?

The Significance of AI in Automotive

Despite the buzz surrounding AI, with 90% of large enterprises dabbling in it, only a third have realized its expected outcomes and benefits (McKinsey). This discrepancy points to the critical need for meticulous data wrangling—sifting through both internal and external, structured and unstructured data sources. The objective is to harness the right AI models and present the data in a manner that's not just understandable but actionable. This innovative approach to data is not widespread in the automotive sector, with only select companies, like App Orchid, leading the charge.

Speeding Up Value Creation for Automakers

The question of how quickly value can be scaled for automakers is pressing. Products like Sibros’ Deep Logger and integration with platforms such as App Orchid provide actionable insight quickly. Integration into vehicles can be achieved within 6 to 8 weeks, a stark contrast to the years it might take some OEMs. Rapid turnaround is crucial, especially when considering that some large automakers' in-house predictive maintenance models have only succeeded to a 60% threshold.

Transformative Use Cases

The efficiency of Sibros and App Orchid’s joint solutions is illustrated through various use cases. Let’s look at a scenario where 30 billion data points are collected from a fleet over the course of a week. The OEM can then query the AI about the fleet's performance. The response involves the immediate creation of dashboards that sift through the noise to highlight crucial data like faults detected, affected components, and the best solutions, thereby unpacking vast amounts of information from a single query.

Another use case from the driver's perspective is the ability to review all trips, offering insights into driving efficiency, habits, and potential areas for improvement. This feature is particularly beneficial for fleet managers looking to enhance vehicle utilization through driver training or incentive programs.

Moreover, the concept of reductive design comes into play when evaluating aspects such as charging information and battery discharge rates. This approach enables OEMs to implement cost-saving changes without compromising the user experience, further enriched by integrating external data like traffic and weather conditions to assess impacts on vehicle functionality and efficiency.

The Future of Automotive Innovation with AI and ML

In conclusion, the integration of AI and ML with tools like Deep Logger is transforming the landscape of predictive diagnostics and analytics in the automotive industry. By converting vast data into actionable insights, automakers can not only enhance operational efficiency but also pave the way for innovative solutions that meet the evolving demands of the market and consumers. Catch the exclusive recorded session from CES 2024, click here.