For many years, manufacturers have been trying to outperform the competition by offering up to five warranties on a car, for example, overusing the warranty argument in their marketing. Moreover, warranty costs have gone through the roof and repeated callbacks have ended up tarnishing the image of many companies. Therefore, it is obvious that their favorite integrated management software is not going to solve the problem, as it requires the integration of data from the entire company and all third parties involved (suppliers, distributors, repair companies, etc.). A data warehouse is the only way to handle such integration and actively steer warranty and quality.
The stakes are high, as they represent more than 2% of the turnover of companies such as General Motors, Caterpillar and John Deere. In the past decade, the number of callbacks has gone up four-fold, with 2011 being an all-time record year, which in turn makes analyzing faults increasingly difficult. In the car manufacturing industry, the average number of defects is 133 for every 100 vehicles, while callback campaigns still last an average of 250 days. Finally, the reserves that companies need to protect themselves against the risks of a faulty product they sell are about € 300 per vehicle a year. The problem is the same in other industries, especially high technology suppliers.
Today, companies need to reduce related costs and warranty reserves, while improving their products and processes if they are to provide better service to their clients and indirectly protect their market share. In concrete terms, quality-related historical data should support the process of diagnosing recurring breakdowns (per part and manufacturing series) and costs should be passed on to the parties responsible (namely external suppliers). As well as best practices, low performance levels and training needs must be identified, while conclusions should be reached for designing and manufacturing new products.
As for the data warehouse, information from a wide variety of systems needs to be integrated in order to be detailed enough to support in-depth analysis of the various parties involved. Moreover, it should allow for quick analysis of a large volume of data and not be restricted when analyzing information from different functions. Implementing such an approach must be carried out progressively and, in the beginning, priority should be given to improving the search for the source of problems in order to speed up warning processes, restricting and targeting callbacks and improving the comprehension of incurred costs.
The problems of such a project are not technical in nature, since in this field it is important to make the right decision when preparing a company data warehouse in order to avoid the impasse of a data mart approach. Feeding, modeling and implementing various data mining and capturing tools are relatively simple projects that can easily be carried out and monitored. The real challenge lies in cross-company team-work where everyone needs to work together. This includes the brand image and communication unit, the different client-oriented services (before, during and after sales), buyers, manufacturers and financial services, which all need to define a company-wide warranty policy together and allow time for things to solidify.
Many of Teradata’s clients, such as Ford, Western Digital and Whirlpool are leaders in this approach and preliminary projects show that a first iteration can easily reduce warranty costs by 5%. Through contacts with leading clients, the Teradata manufacturing team has acquired solid experience and an excellent ability in supporting potential clients with their decisions, providing them with a well-adjusted, concrete offer which includes specific data modeling and analytical applications as well as our well-known infrastructure solutions (MPP server, Teradata database, etc.) Find more information on this topic on Teradata.com.