Data-Driven Bottleneck Detection with Minimal Information in Manufacturing Systems
Abstract
Bottleneck detection is crucial for optimizing production systems, but many current methods in manufacturing environments rely on large amounts of process data that are difficult to obtain in real time. This paper explores a novel data-driven approach that uses minimal information to identify and analyze production bottlenecks. By combining minimal information with various activity cycle and queuing cycle methods, it maintains bottleneck detection accuracy while reducing data collection requirements.
Fields of Study & Subjects
Scheduling and Optimization Algorithms
Advanced Queuing Theory Analysis
Business Process Modeling and Analysis
Publication details
- Journal / source
- Journal of Computer Technology and Applied Mathematics
- Volume
- 3
- Issue
- 1
- Pages
- 71–78
- Publication date
- 5 January 2026