{"title":"Warranty Repairs Reimagined through Machine Learning and Statistical\u0000 Pattern Recognition (Part 1)","authors":"Jody Hand, Sawyer Hall, Michael Carr, Jeremy Worm","doi":"10.4271/2024-01-5071","DOIUrl":"https://doi.org/10.4271/2024-01-5071","url":null,"abstract":"The automotive industry has been funding warranty repair work for many decades.\u0000 The most common vehicle warranty is 3 years or 36,000 miles [1]. Original equipment manufacturers (OEM)\u0000 in North America have dealers record all the work completed and submit claims\u0000 for the work that qualifies for warranty reimbursement [2]. The OEM reviews the request and pays dealers for the\u0000 work performed. In addition to payments, the database is also used to complete\u0000 quality analysis for the vehicles. Often the software being used by dealerships\u0000 is old and not designed for quality analysis. Reviewing all the warranty work\u0000 done can be an arduous task. OEMs can receive 100,000 or more claims each day.\u0000 To speed up the analysis process the OEMs will divide the repair work into\u0000 sections based on the segment of the vehicle requiring work. This categorization\u0000 allows the OEMs to spread the work across many experts in the company. But what\u0000 does the OEMs do when the problem cannot be located at the dealership? The\u0000 dealer still requires payment for the time they spent trying to find the issue.\u0000 This is often categorized as TNF (trouble not found). This type of work without\u0000 a resolution can account for a sizable percentage of warranty costs! It can be\u0000 as high as 38% depending on the manufacturer. It can also affect customer\u0000 satisfaction and customer loyalty. In fact, one of the most common complaints\u0000 submitted to NHTSA (National Highway Traffic Safety Administration) is “dealer\u0000 unable to locate problem” [3]. To make\u0000 matters worse, most customers end up returning to the dealer multiple times\u0000 before the issue is, if ever, located, and fixed [4]. So how can we find a better way to analyze these warranty claims\u0000 and improve the customer experience while decreasing the cost for the OEM? This\u0000 problem can be improved through machine learning and statistical pattern\u0000 analysis.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":" 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}