If it seems like the number of auto recalls continues to grow, you aren’t mistaken. Although recalls have an impact on a company’s bottom line—and potential future sales—most automotive companies don’t use advanced predictive analytics to help prevent, prepare for and manage recalls.


New Volkswagen Chief Executive Matthias Mueller recently announced the company will launch a recall for cars effected by its diesel emissions crisis in January and complete the fix by the end of next year. But that’s in Europe. There still is no timing for a U.S. recall. Other recent recall announcements include 470,000 Hyundai Sonata sedans in the U.S. for faulty engine parts, and Fiat Chrysler Automobiles announced it’s recalling 7,810 Jeeps in the U.S. to update software for radios to prevent hacking.


The growing number of recalls isn’t news to consumers. Car owners report a 40 percent increase in recalls compared to the second quarter of 2014, which—along with rising prices—damages driver satisfaction, according to the American Customer Satisfaction Index (ACSI).


“While it’s true that all cars are now much better than they were 10 to 20 years ago, it’s alarming that so many of them have quality problems,” says Claes Fornell, ACSI Chairman and founder. “The number of recalls is at an all-time high. This shouldn’t happen with modern manufacturing technology and has negative consequences for driver safety, costs and customer satisfaction.”


What’s surprising is that although 42 percent of the auto executives responding to a recent survey expect more industry recalls in 2015 and 2016, only eight percent of their companies use advanced predictive analytics to help prevent, prepare for and manage recalls, according to a study from Deloitte. Furthermore, almost one-quarter (23 percent) of the respondents to the poll, “Recall Readiness and Management in the Automotive Industry,” report their companies have no operational product safety and recall anticipatory analytic capabilities.


Today’s vehicles are among the highest quality ever produced from a safety and reliability standpoint. At the same time, innovations in technology have accelerated such that manufacturers can now identify emerging safety and quality issues much sooner. Nonetheless, as Derek Snaidauf, Deloitte Advisory senior manager in advanced analytics, Deloitte Transactions and Business Analytics LLP, notes, many automakers still take a manual, rearview-mirror approach to vehicle quality and safety. Leading OEMs, on the other hand, are starting to adopt innovative analytic capabilities such as proactive sensing for early issue identification and command centers for campaign management, he says.


“By cross-source correlating internal and external data sources, employing specialized advanced analytics and leveraging interactive visualizations, these companies can improve customer satisfaction, vehicle safety and brand perceptions,” Snaidauf says. “They also can realize significant reductions in their total cost of quality spend.”


One of the biggest challenges to OEM-supplier collaboration in preventing, preparing for and managing recall-related events was ineffective communication channels, cited by 21 percent of the survey respondents. Regardless of the reason for the recall, most respondents (90 percent) indicated that recalls impact working relationships between suppliers and original equipment manufacturers (OEMs).


“Because the stakes are so high in recall management, it now makes even more sense for traditional automakers and those within their supply chains—particularly new industry entrants—to consider investing in predictive analytics capabilities that can help detect trouble earlier,” says Bruce Brown, principal and U.S. automotive and off-highway consulting practice leader, Deloitte Consulting LLP. “Once in place, those competencies can also facilitate richer collaboration and communication between involved parties in times of investigation or crisis.”


Whether your company is in an automotive supply chain or not, what are your thoughts on the use of predictive analytics capabilities? If your company is in an automotive supply chain, what are the main impediments to adopting such technology?