Mining the relationship between production and customer service data for failure analysis of industrial products

- We propose a data mining process for failure analysis of industrial products.
- Failures are examined by a mashup of the production and customer service data.
- Interpretable visualization based on relative failure density is implemented.
- A case study is conducted using the data of real-world products.

Analyzing the causal relationships for failures of industrial products is necessary for manufacturers to prevent the occurrence of failures and enhance customer satisfaction. The data collected from each of the production and customer divisions can be a fruitful source for failure analysis. In this paper, we present a data mining process for efficient failure analysis of industrial products by a mashup of data collected from both divisions. The process consists of four main steps: problem definition, preprocessing, modeling, and visualization. Each step is designed to satisfy two constraints in order to be practically applied to industrial products. First, it has to be quick and incremental because the life cycle of most industrial products is not sufficiently long. Second, the insight derived from the process has to be easy to understand for domain experts since they are generally not familiar with data mining methodologies. A case study is conducted to demonstrate the effectiveness of the data mining process by using real-world data collected from a manufacturer in Korea.