What’s Wrong with My Dishwasher: Advanced Analytics Improve the Diagnostic Process for Miele Technicians

Segev Wasserkrug (), Martin Krüger (), Yishai A. Feldman, (), Evgeny Shindin () and Sergey Zeltyn ()
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Segev Wasserkrug: IBM Research – Haifa, 3498825 Haifa, Israel;
Martin Krüger: Miele Smart Home, 33332 Gütersloh, Germany;
Yishai A. Feldman,: IBM Research – Haifa, 3498825 Haifa, Israel;
Evgeny Shindin: IBM Research – Haifa, 3498825 Haifa, Israel;
Sergey Zeltyn: IBM Research – Haifa, 3498825 Haifa, Israel

Interfaces, 2019, vol. 49, issue 5, 384-396

Abstract: 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.

Keywords: diagnosis; Bayesian networks; text analysis; semantic model (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:49:y:2019:i:5:p:384-396

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