The automotive industry is undergoing a data revolution. As vehicles become increasingly connected, they generate a torrent of data that harbors the potential to dramatically enhance quality and service operations of vehicle manufacturers (OEMs). From earlier detection of production issues to predictions of individual vehicle and component failures to reduced service times, the potential for lower expenses, higher revenues and improved customer experience is enormous.
However, the sheer volume, complexity, heterogeneity, and the time-series nature of connected vehicle data present significant challenges to realizing this vision. While powerful Business Intelligence (BI) tools are necessary for doing so, they alone are not sufficient. The key to unlocking value lies in deploying a more sophisticated tool, high-dimensional time-series Artificial Intelligence (AI).
In this post, we'll explain what BI is, how it is typically used in quality processes, and why it is insufficient for leveraging connected vehicle data. We will then contrast the BI status quo with a modern system that leverages an AI-infused knowledge graph (Viaduct TSI Engine) and workflow to transform the quality process.
For the purpose of this discussion, we’ll talk about BI less as a singular technology or product than as an approach to data analysis that relies on the following capabilities:
While BI tools are required for any data analysis process, they are far from sufficient to deliver business impact on connected vehicle data. BI tools operate under a reactive model, i.e., they only respond to queries presented to them by users. BI tools fail to offer users the guidance they need to navigate the scale and complexity of connected vehicle data. This limitation creates major challenges that are difficult or impossible for users to overcome:
While BI tools are crucial building blocks for vehicle data analysis, without AI that is tightly integrated into business processes, this analysis will be slow and ineffective. AI systems, such as Viaduct’s patented Temporal Structural Inference (TSI) Engine, can change this. While AI is a broad category with a myriad of technologies, it is not merely an industry buzzword. Rather, we define AI as a tool that serves users by preemptively surfacing relevant information, enabling them to navigate complex environments with enhanced clarity and foresight. If BI is like a flashlight that can be used to interrogate data to uncover insights, AI serves the crucial role of telling a person where to shine their flashlight. It guides them to the right questions based on the context of the process they are executing and then provides answers that are relevant to the next action that should be taken.
Among the OEM processes being transformed by AI is quality, especially in post-production settings. Field quality refers to the process whereby teams of investigators monitor data coming from fleets of in-service vehicles, looking for patterns and evidence of systemic quality issues. Traditionally these investigators have relied heavily on manual review and analysis of warranty claims and service records to flag potential issues.
With the advent of connected vehicles, it is now possible to monitor fault and sensor data, giving investigators orders of magnitude more data to parse through. While BI platforms, even those designed to handle big data, might technically provide the flashlight required to see important patterns, without AI there is just no way a human investigator can efficiently explore the vast amount of data at their disposal. An AI solution tailored to the needs of finding systematic quality issues is a must-have.
Let’s see how the AI-based TSI Engine works in the field quality context as an example.
As an AI-based system, the TSI engine must first be primed with data to build up a knowledge graph. The steps involved in priming the system include (see figure 1):
The knowledge graph can be primed quickly and will deliver value, much like a BI platform can deliver value, just by making it possible for a human to access all their data in one place. But transformative value comes when users interact with the system as part of their process.
The quality process can be broken into four stages, each of which benefits in concrete ways from an AI-infused knowledge graph and workflow.
As an issue is investigated and refined the TSI engine takes input, learns from it, and starts a virtuous feedback loop with the investigative team. The TSI engine continues to suggest which correlations between telematics, service records, and vehicle attributes are most descriptive and predictive of the issue. User input helps the TSI engine narrow the boundaries of the search space so that it can run more sophisticated and computationally intensive algorithms that improve fidelity and accuracy of its suggestions.
As you can see in this example, AI is not an after-thought in the design of the field quality system. It is integral to every aspect of the workflow, and it turns a historically hunt-and-peck, wait-and-see exercise into a symbiotic barrage of proactive insights, questions and answers about an issue.
In summary, BI tools are adept at solving some of today’s quality challenges by helping OEMs integrate various data sources so vehicle and data experts don't have to work so hard to manually assemble, analyze, and present insights. However, future-proofing a quality process requires deploying purpose-built AI. By harnessing technology like Viaduct’s TSI Engine, OEMs can transform their organizations, converting their staff from data wranglers into problem solvers, finding issues faster, speeding up root cause analysis, and preventing vehicle failures before they occur!