Dealers pride themselves on the quality and accuracy of service they provide. They know that they are the genuine service option, and that their technicians are highly trained and supported by the manufacturer’s diagnostic information. Yet, industry-wide benchmarking indicates that some manufacturers see fixed-right-first-time (FRFT) metrics as low at 90% – one of every ten vehicles leaves the service lane without being fully repaired. Needless to say, this can hurt customer retention and brand loyalty. In fact, “My vehicle is fixed right the first time” was the fifth most important selection criteria for consumers in Carlisle & Company’s 2014 Consumer Sentiment Survey (Figure 1).
In Carlisle’s industry-wide Technician Survey, technicians estimated that they spent roughly one-third of their time on diagnostic work (Figure 2); in many cases this is billed directly to the customer. While this time is necessary to properly repair a car, it significantly reduces a technician’s efficiency. Vehicles are also becoming more complex; their interdependent systems require more advanced diagnostic tools.
In short, FRFT rates and technician efficiency won’t improve until diagnostics improve. Improving these metrics requires making higher quality and intuitive diagnostic tools/systems available to technicians. New big data analytics models, such as Artificial Neural Networks, could improve the speed and accuracy of repair identification immensely.
READ ENTIRE ARTICLE 2014 Big Data and The Future of Vehicle Diagnostics
The goal of improving existing diagnostic processes is to increase FRFT rates, enhance service capacity, increase customer pay sales, reduce warranty costs, and, ultimately, drive customer retention. This paper presents a big data approach that can successfully utilize the heuristic, “experiential” knowledge within the dealer network as an effective strategy to reach that objective.
At the enterprise level, the data would be particularly useful in identifying potential recalls based on systems and parts with high failure or error rates. The data would also stimulate engineering and service process improvements. For the supply chain, the data from onboard diagnostic feeds could be used to help forecast parts sales, predict demand, and anticipate forward parts deployment. These predictive analytics would help the OEM identify potential, impending failures, and notify the customer to get their car repaired before it even breaks. The information could also be used for targeted, timely marketing of maintenance intervals and regular service.
There are many barriers to overcome to achieve full implementation: integration of the data, its security, and its ownership. For this reason, Carlisle believes that this topic represents an area that could benefit from industry collaboration. A well implemented connected diagnostic process would improve not only our vehicles but the customer experience, technician efficiency, and shop profitability.
If you are interested in participating in this collaborative effort or have more questions please contact Chad Walker at firstname.lastname@example.org