How to learn from intelligent products; The structuring of incoherent field feedback data in two case studies

Abstract

A growing number of products - particularly highly innovative and intelligent products - are being returned by customers, while analysis shows that many of these products are in fact functioning according to their technical specifications. Product developers are recognizing the need for information that gives more detail about the reason for these product returns, in order to find the root cause of the problems. Traditionally a lot of information from the field is stored in departments like sales and service (helpdesks and repair centers). Combining these data sources with new field feedback data sources, like customer experiences on the internet and product log files, could provide product developers with more in-depth and accurate information about the actual product performance and the context of use. Case studies were done at two different industries: a company from consumer electronic industry and a company from professional medical system industry. The Maturity Index on Reliability (MIR) method, a method to assess capability for businesses to respond to product reliability related issues, was combined with Contextual Design, a method for user context mapping and requirement definition, and explored as a means to analyze the structure and capability of the current field feedback process in supporting the development of complex and innovative products. © 2009 Springer Berlin Heidelberg.