# Quality-Aware Resource Model Discovery

- Type: Journal article
- URL: https://www.gyunam.com/publication/2021-applied-sciences/
- Date: 2021-06-21
- Authors: Minsu Cho, Gyunam Park, Minseok Song, Jinyoun Lee, Euiseok Kum
- Venue: Applied Sciences
- DOI: https://doi.org/10.3390/app11125730
- PDF: https://www.gyunam.com/publication/2021-applied-sciences/paper.pdf
- Research lines: [Process discovery and diagnostics](https://www.gyunam.com/research-line/process-discovery-and-diagnostics/index.md)
- Tags: Journal Article, Prediction, DSS

## Abstract

Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems.

## Practical problem

In semiconductor fabs, hospitals and service operations the quality of the outcome depends heavily on which machines or staff handle each step. Quality engineers know this but have no systematic way to see which paths through hundreds of resources lead to good or bad results. Process mining tools describe control flow and sometimes time or cost, but quality is rarely treated as context and resource networks are not linked to it. When the resource network is drawn for a long process with many machines per step, it has thousands of nodes and arcs and cannot be read, so the insight stays hidden in the data.

## Research questions

1. Can superior and inferior resource paths be identified from real-life manufacturing event logs annotated with case quality?
2. Does simplifying the resource model based on the statistical effect of each resource on quality make the model easier to understand?

## Contributions

- A five-step method for discovering quality-aware resource models: resource-oriented transition system discovery, superior and inferior case identification, integrated model construction, statistical analysis of resources, and model simplification.
- Formalizes case-annotated transition systems and the superior-case ratio, inferior-case ratio, between-sum and between-difference measures used to rank and color states and arcs.
- An algorithm that classifies cases as superior, inferior or moderate using a Kolmogorov-Smirnov normality test followed by mean and standard deviation or median and median absolute deviation bands.
- A statistical procedure per activity (ANOVA with linear contrasts, or Kruskal-Wallis with Mann-Whitney U tests) that labels resources as superior, inferior or moderate, with three merging rules for simplification.
- A Python tool with a graphical user interface covering import, preprocessing, model mining, statistical analysis, interactive filtering and highlighting, and export.

## Key findings

- On a Korean semiconductor manufacturing log of about 15,000 wafers and 118 analyzed activities (selected from more than 500 with domain experts), a weight of 0.85 separates 20 percent superior and 20 percent inferior cases and yields an integrated model with 120 vertical activity lines.
- Simplification with all significance thresholds at 0.05 and merging type 1 reduces the model from 1,773 to 504 nodes (71.6 percent) and from 11,973 to 1,078 arcs (91 percent).
- In the most complex activity (the 51st line) the number of states drops from 93 to 12, with 6 resources flagged as producing statistically significant high quality and 5 as producing low quality.
- Limitation: the method still needs user-set parameters (weights, thresholds) and only explains past resource-quality relations; it does not predict the quality of a running case or operate online.