It is imperative for a product to remain competitive on all the above parameters. Time-to-market, costs of development, and defects in quality have to continually decrease over time, while feature competitiveness should be on the upswing.

In this article, we will focus on the important topic of quality defects and warranty issues, which have a significant impact on the other parameters. We will identify the technology multipliers available within the automotive industry that can manage and possibly prevent warranty costs from spinning out of control.

Today's automotive architecture is built towards connectivity
A look at the technologies incorporated into today's vehicle provides one with the realization that the vehicle itself is a physical extension of the automotive chain, connected to the enterprise at one end and increasingly able to report on the operating parameters within the vehicle at the other end. This connectivity between the vehicle-level operating parameters and the decision-making parameters at the organizational level is what will determine the monitoring and control aspect of the warranty management proposal.

Example: Tire pressure monitoring (TPM)
The case study of the Firestone Tire/Ford Explorer incidents is well known and is responsible for triggering the TPM system legislation that provides advance information to prevent tire-pressure-related safety incidents.

The same technologies can now be utilized to monitor and diagnose other key safety and warranty-related parameters such as engine pressure variations, vibration to detect manifold leakage or cylinder/piston wear, transmission gear anomalies, and so on.

Example: General Motors OnStar program
The case study of the GM OnStar initiative is a solid example of how vehicle information is connected to the organizational enterprise to provide real-time diagnostic information. Vehicle health information received from all the sensory electronics can be transferred through telematics to a receiving station, which can then process the data in comparison to diagnostic databases and appropriately direct to warranty related applications, or forward the data to parts suppliers.

Onboard diagnostics and OBD2
OBD2 (or OBDII) defines a communications protocol and a standard connecter to acquire data from passenger cars. It was required by U.S. EPA on all gasoline powered cars and light duty trucks manufactured for the U.S. after 1996 to help monitor/inspect vehicle emissions. Canada required it after 1998, and now a European standard (EOBD) is also enforced. Theoretically, the OBD2 could provide any data that any of the sensors are capable of reporting.

Generic data is mandated by legislation and comprises information such as calculated engine load, coolant temperature, data about short and long term fuel trim (whether the engine is adjusting lean or rich), fuel pressure, manifold air pressure, engine RPM, vehicle speed, ignition advance, intake air temperature, air flow, throttle position, and O2 sensor data. Extended data includes any data that is not in the above "generic" set. Almost every manufacturer provides many more data points that their own proprietary scan tools are capable of reading.

It is this extended data section that could be utilized towards the data requirements for warranty related diagnostics.

As can be seen from the figure below, a vehicle goes through many hands in its lifetime. The diagnostic data collection that is dependent on these many centers or owners is a logistical nightmare and therefore needs to be independent and centralized to be effective.

The figures below indicate two transactions that occur towards maintenance or warranty related activity. The first transaction (top) is initiated by the vehicle user through a scheduled visit to a service center, while the second is initiated by the OEM or parts supplier based on received data from the vehicle.

Root-cause identification is vital to lowering warranty costs
In terms of lowering warranty costs, predictably, root-cause identification remains the biggest challenge for the industry. This task has resulted in detection-to-correction cycles of up to 200 days—astonishing, when each day's delay in implementing an effective fix can cost $1 million per day. Generally speaking, 75% of the annual warranty expenses are borne by repetitive and chronic problems; these command most of the quality engineers' attention, and there is simply no time to detect causes for newly emerging defects.

For years, OEMs have chased the goal of immediacy of information on failures from the field in order to provide their plant and design teams with the best possible opportunity of a quick and accurate root-cause analysis and engineering fix. This immediacy takes two forms:

Directness of event reporting
Telematics and OBDs offer the huge advantage that they allow tracking of the fault as it happens. Once the workshop technician has performed—or attempted to perform—a fix, the opportunity is often lost to know and report exactly what the operating conditions were when the fault occurred.

One manufacturer even went as far as to implement a "Fast-Track" returns system whereby in the event of a fault developing on the engine within 6 months of model launch, the workshop was requested not to attempt a repair but to remove the entire engine and ship it back to the OEM! This action may have been expensive, but the engineers found it invaluable to have the conditions replicated as faithfully as possible, and the scheme was credited with contributing to major reductions in warranty cost.

Over and above the information gathered is the cost issue. Estimates range from $90 up to $500 for each occurrence of a part return, to bring the part back through the full reverse logistics and inspection cycle. The combination of telematics and diagnostics is an extremely powerful combination, allowing the information relating to the part failure to enter the engineering and quality analysis systems without the expense of returning (or even removing) the part.

The vast majority of quality analytics is based on warranty claims—and even then, despite the vast systems and volumes of data stored on them, merely on the structured data that supports the payment of the claim; most commonly part number, defect code, and repair cost information, which is neatly and easily tagged.

Reducing warranty further requires an enterprise to tap into the richness of information contained elsewhere as well as on the warranty claim. Inroads are being made regarding mining the vast unstructured data, such as information embedded in free-form text fields, given by customers about the symptom, and by technicians about the diagnosis and repair, which has hitherto been all but ignored.

The major prospect is for telematics which are capable of transmitting (near) real-time performance data into manufacturer systems—providing sources of information which are immediate, direct, and uncompromised in their ability to report things gone wrong.

Bearing in mind the necessity for all parties to tackle warranty concerns together as a consolidated exercise (of which Infosys has been an advocate as a premium member of the AUTOSAR open architecture development partnership), we would add that to be really useful, the data models from warranty-related diagnostics and telematics need to be standardized. If the data are going to be used for analytical early warning purposes (i.e. for the earliest possible identification of potential root causes) there is no time for the long-winded analyst-led interrogations that characterized processes of the past.

This standardization has to include owners, suppliers, dealers, service, and roadside assistance organizations, enabling early warning signals on components for one manufacturer to be compared to a similar component for others.

Put the data to work
Finally, getting the data into the extended enterprise more quickly is one problem that can be overcome by telematics, but that is only the start of the correction cycle—it only becomes a true early warning if it can be analyzed more quickly and reliably and the corresponding root cause identified.

An early warning system is the intelligence vital to achieve the maximum out of the data inputs, be these from the "traditional" sources such as warranty claims processing systems, or from modern, advanced sources such as on-board telematics, and in-workshop vehicle diagnostic systems.

Another important dimension of early warning is an "inference engine" based on statistical analysis of claims data to provide initial pointers to the failure patterns. This engine draws on the "reality" of vehicle systems, manufacturing processes, and their past failure trends, to highlight probable causes of failure and make intelligent forecasts of possible failures in the field. Such a "decision support system" can significantly assist quality analysts and planners alike in focusing on more value-added activities.

Robert Pritchard is a principal solutions consultant at Infosys Technologies and a member of the Infosys Warranty Management Practice. Rob has over 18 years of automotive industry and consulting experience with a variety of blue-chip automotive retail, Tier 1, and OEM companies, in the field of automotive warranty, aftermarket, technical service information, and dealer-systems. Rob holds an MBA from Henley Management College. He can be reached at Robert_Pritchard@infosys.com.

Uday H. Prabhu is a group project manager at Infosys Technologies and a member of the Product Lifecycle Engineering Solutions Practice focused on automotive and currently based out of Southfield, MI. He comes with 15 years of experience in the automotive electronics industry. He is part of the AUTOSAR group within Infosys. He can be reached at Udayh_Prabhu@infosys.com.

Raju Rampa is the practice lead for automotive suppliers at Infosys Technologies and is currently based in Southfield, MI. He can be reached at Raju_Rampa@infosys.com.