Sometimes dealing with Warranty Analytics is difficult, that is why we are excited to share with you the benefits, analytical approaches, and real life examples of Warranty Analytics as part of the series Leveraging Analytics in Transportation to Create Business Value.
The manufacturer is obligated under warranty contract to repair or replace the asset without charging the customer. Hence, any opportunity to reduce the amount of field failures or complaints when asset is still under warranty has the ability to decrease the liability and increase profitability. Manufactures can also use Analytics to determine warranty terms and conditions to minimize their exposure.
The transportation owners and operators on the other hand, save maintenance costs if component failure is detected and a claim is made when a component is still under warranty. When condition monitoring capabilities exist, accurate information on health of the part is possible to be obtained in real-time. This helps timely replacement of the part while it is still under warranty.
Higher warranty costs are reflection of poor product quality and Analytics can help identify the reasons for poor product quality by analyzing claims data. The actionable insights obtained by Analytics translate into corrective action, thereby eliminating the root cause of the poor quality.
From a warranty provider’s perspective, the analytical techniques fall under two broad categories – early warning system, for detecting serious field reliability issues as early as possible, and subsequent root cause analysis techniques. The early warning systems are generally based on advanced statistical techniques which are sensitive to detect the trends indicating serious reliability problems. Once existence of a problem is evident, analytical techniques based on statistical quality control techniques, data/text mining and reliability analysis are used to perform a root cause analysis and obtain actionable insights from the data.
Real Life Examples
In all transportation segments Analytics can be effectely used to manage the exposure for the manufactures and improve the recovery by the owners and operators. One of the problems in terms of data is that many components are not tracked at the serial number level, especially the low cost consumable parts. In the rail industry there is recent regulation that mandate components, such as wheels, are tracked at the component level to manage the cost and reduce repair fraud.
Generally, it is easier to track the first time failures since the asset is put in to service brand new. Even for the high cost components such as engines that are tracked by the serial numbers complications arise when it comes to warranties. For example an engine consist of lower level subsystems such as fuel system, exhaust system, etc.
Some of these systems are not tracked by the serial numbers. Therefore one has to keep track of the subsequent replacement of subsystems by Analytical means considering parts used for the historical repairs. We have encountered similar practical problems in all modes of transportation and provided analytically based data management approaches to cope with the situation.
We hope to provide relevant insights and a fresh perspective on how analytics can make a profound impact on asset-intensive organizations. Stay tuned for our post next week on Labor Planning.