Automotive companies usually incur 2% to 5% of their revenue on warranty costs. These escalate quickly when considering the scale at which automotive OEMs and suppliers operate. For the most part, these warranty costs are variable and unpredictable, so much so that they can potentially cripple a company’s bottom line.
And the issues don’t end here. The dynamic nature of the industry prevents appropriate earmarking for warranty claims.
IBM reports that while the global warranty claims across the world in 2018 amounted to $56 billion, the amount in reserves for warranty expenses stood at $115 billion.
Given this paradox of sorts – what can drive the efforts automotive companies put in to minimize the expense of warranty claims while simultaneously preparing for a future they can never predict? What can be done to strike an ideal balance between the two?
Data Analytics as the Answer
The answer lies in the holistic understanding of each variable that contributes to the warranty costs. More importantly, the knowledge of how these variables relate to each other can predict the overall financial costs and the scope of cash losses.
For example, the nature of a vehicle is essential when considering warranty costs. A vehicle with many moving parts or perhaps an engine that’s at the end of its life cycle can incur higher warranty costs and vice versa. Similarly, changes in the product demand can have significant implications in terms of warranty claims.
All-in-all, the notion and practice of holistic understanding are driven by data analytics because:
- It enables companies to reduce the risks of future warranty claims by identifying/removing predictable and controllable causes.
- It helps in benchmarking against competitor data and gives companies an idea of whether they are deviating from a preferred norm, of course.
Before getting into the details and the insights that data analytics can bring forth, it is essential to review the challenges automotive companies face in their day-to-day operations regarding warranty claims.
Warranty Cost Related Problems with Traditional Warranty Management
- No Foresight into the Patterns of Claims: Customarily, warranty claims are examined retrospectively, which means the warranty administrator only has knowledge of the claims after they have been realized. This delays corrective action and means higher costs because problems go unidentified for too long.
- No Foresight into the Demand of Claims: As a result of the lack of foresight, traditional warranty administrators are at the mercy of consumer demand. Without knowing in advance that there is going to be a high demand for repairs on a specific product, the automotive companies are not prepared to respond accordingly. This leads to resource shortages and inefficient service provision.
- Too Many False Positives: The ever-evolving agents exploit the outdated system of warranty claims management, making it hard to separate true positives from false alarms. The result is a build-up of false positives or the liquidation of useful data, which further leads to loss of assets and high costs.
- Manual Validation Process: The manual validation process carried out by traditional warranty administrators is a time-consuming and resource-intensive task, which generally involves manual inspection of every claim. This is an inefficient use of resources, mainly since claims data can be processed automatically to reduce the processing cost.
The Role of Data Analytics in Minimizing Warranty Costs
By putting data gathered from records and IoT devices to use, predictive analytics allows warranty administrators to predict flaw patterns and defects, reducing the amount of inspection work and resource allocation. More profoundly, predictive analytics helps:
- Drive Operational Efficiency –From optimizing the stocking and allocation of replacement parts to the development of optimal repair procedures, predictive analytics helps optimize the operation of activities related to claims and repairs.
- Identify Issues Early –It is possible to analyze warranty claims data and flag the product in question, and take action before there are more claims. This would mean that issues can be cleared up and resolved as soon as possible, minimizing the risk of defect, rendering the product more reliable, and optimizing warranty expenditures.
- Improve the Effectiveness of Claims –As each claim is put under the scrutiny of predictive analytics, the organization can develop more effective claims management strategies to resolve issues with fewer costs.
ML-Powered Warranty Management
With the help of machine learning, warranty administrators can streamline operational processes and eliminate manual tasks that consume time, resources, and money. More profoundly, warranty administrators can:
- Develop Automated Solutions –Administrators can leverage an automated solution that automatically finds patterns and faults in warranty claims data and proposes appropriate solutions for every claim. When a low-confidence case arises, the system can route the case to the concerned employee for manual intervention.
- Detect Fraudulent Claims –Machine learning is capable of detecting false positives and flagging claims as fraudulent or suspicious. This also helps to streamline claims resolution processes.
- Use Image Processing for Claims Analysis –With image processing, warranty administrators can take relevant images of defects and enable human-machine collaboration during the claims process. This can greatly reduce the time to process claims and ensure faster response times.
Automotive companies have a lot to gain by using data analytics in warranty costs management. ML and predictive analytics can analyze claim data and discover patterns that would affect the distribution of claims. They also allow warranty administrators to design more effective claims management strategies that minimize costs on resolving issues and flagging suspicious claims.
As such, automotive companies can embark on the journey of reducing verification and validation costs, ineffective manual processes, and scams. Most importantly, organizations are better positioned to minimize the impact of defect-related issues on the product while also ensuring that claims data is valuable and accurate.