Monday, December 15, 2014

Big Data and The Future of Vehicle Diagnostics
(an excerpt)

Introduction


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


Conclusion


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 cwalker@carlisle-co.com


Monday, December 1, 2014

2014 Fall NASB: Express Service Metrics – A Recap
by Eliza Johnson

At our recent North America Service Benchmark (NASB) fall meeting, one of the key topics was Express Service Metrics—the tracking of performance metrics and KPIs related specifically to Express Service.


At this point, most OEMs either have, or are planning to implement soon, an Express Service program. As Express Service becomes more common, it is becoming an integral part of many dealerships and a way to manage dealer service business. However, the rise of Express Service also drives a shift in the dealer’s staffing strategy, capacity, and profitability model. As these programs both enable and require the dealership to handle service in a very different way, we need to look at them with a critical eye separately from the main service drive.


Most OEMs report that Express Service dealers perform better than dealers that do not offer Express Service. As such, they are tracking a variety of metrics to measure and assess these programs. Currently, most OEMs are focused on volume and customer satisfaction measures including number of ROs, sales volume, sales by commodity, and overall customer satisfaction. These are all fairly common and are being used to understand the level of Express adoption and whether it is making customers happy. Retention metrics are common as well, although these are all a little different (retention overall, by vehicle age, by commodity, etc.). Fewer OEMs are tracking profitability and operations/efficiency metrics. And while many OEMs have developed dashboards and other express-specific reporting tools, these help them manage the business at the dealer level, but don’t identify what is best-in-class.


As it stands, OEMs aren’t aligned in how they are tracking Express Service programs—and this lack of consistency means there is no coherent way to benchmark Express operations. Establishing a common set of benchmarks and gaining dealer and industry acceptance is critical to understanding the long term success of Express Service programs. And, in driving change at the dealership to best support Express Service, these standardized metrics need to be socialized and integrated into dealership processes (accounting, etc.) and training. The key here is to identify what is best-in-class for Express Service and what high performance looks like. To do so, we need comparable data. As a result, Carlisle spent time at the most recent NASB meeting rationalizing metrics to make benchmarking Express Service a reality


During the meeting, we discussed potential metrics in the areas of Volume, Retention, Customers, Satisfaction, Staff, and Operations. The goal was to confirm a small set (10-15) of metrics to be established immediately and discuss future additions. Below is a summary of the outcome and the initial set of measurements that will serve as an industry benchmark:



This represents a starting set of metrics, which most OEMs are able to track and agree are key to assessing and managing the Express business. As a starting point, this allows us to understand how Express volume compares amongst OEMs vis-à-vis standard repair service, the impact on the customer experience and retention, and staff productivity and consistency. While this is a starting point, it will be important to integrate additional productivity measures as well as profitability measures in the future—OEMs will need to lay the groundwork to collect the data necessary. Additionally, as these metrics become standardized, we can introduce segmentation to assess, on a deeper level, distinctions for vehicle age and type.


Bottom Line: Express Service is now an industry standard and OEMs believe these programs are performing well. However, there is little standardization within the industry regarding how to measure these programs. Assessing performance long term will require robust reporting and an established set of benchmarking metrics across OEMs. By adopting and measuring these metrics, OEMs can assess themselves and manage and track dealer improvement plans.