Steven Naron, Segev Wasserkrug
WSC 2007
Miele, a leading appliance manufacturer, was looking to optimize the ways in which it solves customer problems quickly and efficiently. A crucial part of this task is the precise diagnosis of faults before and during technician visits. A correct diagnosis allows technicians to bring with them the necessary parts and complete the repair with minimal time, effort, and spare parts. We created a system to help Miele optimize its service process based on statistics learned from historical data about technician visits; the data contained both structured and unstructured (textual) data that had to be combined to create a probabilistic model. We used a novel process in which a semantic model informed the creation of the probabilistic model as well as the analysis pipelines for the structured and unstructured data, combining expert knowledge with a large amount of heterogenous data. The results of our pilot study demonstrated a significant improvement in efficiency concomitant with an increase of an already very high first-fix rate.
Steven Naron, Segev Wasserkrug
WSC 2007
Arun Hampapur, Heng Cao, et al.
IBM J. Res. Dev
Sergey Zeltyn, Yariv N. Marmor, et al.
ACM TOMACS
Amit Fisher, Fabiana Fournier, et al.
SOLI 2007