Leveraging Unstructured Data to Detect Emerging Reliability Issues

TL;DR

This paper presents a text mining approach to analyze unstructured customer and technician data for early detection of reliability issues, enabling proactive service interventions.

Contribution

It introduces a novel method for analyzing unstructured text data to identify emerging reliability problems before failures occur.

Findings

Effective detection of reliability issues from unstructured data

Case study demonstrates practical application of the method

Proactive diagnostics can reduce failure-related costs

Abstract

Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, which generally describe symptoms, and technician comments, which outline diagnostics and treatment information. A legitimate customer complaint can eventually be tracked to a failure or a claim. However, there is a delay between the time of a customer complaint and the time of a failure or a claim. A proactive strategy aimed at analyzing customer complaints for symptoms can help service providers detect reliability problems in advance and initiate corrective actions such as recalls. This paper introduces essential text mining concepts in the context of reliability analysis and a method to detect emerging reliability issues. The application of the method is illustrated using a case study.