With a major European multinational that designs and manufactures household appliances. Moxoff has developed an early warning algorithm that supports the company in identifying and managing issues in a timely manner.
Description and Benefits
Identifying and managing defects in production batches is of great complexity. The process between the reporting of individual defects by maintenance technicians in various countries and the collection and analysis of a sufficiently large number of similar reports can take a very long time. It is only at the end of this process that the quality control personnel report any production problems to be investigated. Prior to Moxoff work, this time could take up to 24 months.
Moxoff has created a text mining tool for the automatic classification of anomalies, through the extraction of textual information from maintenance reports compiled by technicians. This information feeds an early warning algorithm. An app was then implemented to support quality control activities and the definition of strategies for product improvement.
The application in detail
The whole application has general menus that allow users to perform in-depth analysis by choosing the production site, the product type, the production batch, the faulty component and the geographical market.
Unlike other standard software packages, Moxoff solution is tailored to the internal processes of the company and allows to detect problems only 4 months after the release on the market.
The developed solution lends itself to integration with service intervention analysis, to identify fraud attempts, such as replacement of non-faulty parts or interventions beyond the granted warranty period.
- Less 50% detection time, reduction of problem detection time to 4 months, compared to 8 to 24 months with standard software tools
- Multilingual interpretation, the text mining algorithm provides automatic classification of fault types from texts in 4 different languages
- Speeding up the root cause analysis of problems (from day to a few minutes), the application allows to identify the main causes of malfunctioning through a 5/10-minute analysis. Previously, analyses took 5-10 days
