Data-Driven Bottleneck Detection with Minimal Information in Manufacturing Systems

Leede Frank

doi:10.70393/6a6374616d.333634

OPEN ACCESS |Research Article ||5 January 2026

Data-Driven Bottleneck Detection with Minimal Information in Manufacturing Systems

Leede Frank

University of California-Berkeley

LedeeFrank123@gmail.com

University of California-Berkeley, 94720, US.

* Corresponding Author¹: Leede Frank, E-Mail: LedeeFrank123@gmail.com

Publication

Accepted 2025 December 27 ; Published 2026 January 5

Journal of Computer Technology and Applied Mathematics, 2026, 3(1), 3007-4126.

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.

Keywords

Bottleneck Detection , Minimal Information , Manufacturing Systems , Queue Directed Graph .

Metadata

DOI:

10.70393/6a6374616d.333634

ARK:

ark:/40704/JCTAM.v3n1a09

Pages: 71-78

References: 28

Disciplines: Applied Mathematics

Subjects: Mathematical Modeling

Cite This Article

APA Style

Frank, L. (2026). Data-driven bottleneck detection with minimal information in manufacturing systems. Journal of Computer Technology and Applied Mathematics, 3(1), 71-78. https://doi.org/10.70393/6a6374616d.333634

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

1.

Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P., & Bokrantz, J. (2018). A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines. Computers & Industrial Engineering, 125, 533-544.

2.

Chen, Y. (2025). Leveraging LSTM Networks for Vehicle Stability Prediction: A Comparative Analysis with Traditional Models under Dynamic Load Conditions. Computing and Interdisciplinary Science, 1(2), 15-22.

3.

Wang, H. (2022). Supervised Learning for Complex Data (Doctoral dissertation, The University of North Carolina at Chapel Hill).

4.

Wang, H., Li, Q., & Liu, Y. (2024). Multi-response Regression for Block-missing Multi-modal Data without Imputation. Statistica Sinica, 34(2), 527.

5.

Yin, M. (2025). Predictive Maintenance of Semiconductor Equipment Using Stacking Classifiers and Explainable AI: A Synthetic Data Approach for Fault Detection and Severity Classification. Journal of Industrial Engineering and Applied Science, 3(6), 36-46.

6.

Sun, Y., & Ortiz, J. (2024). An ai-based system utilizing iot-enabled ambient sensors and llms for complex activity tracking. arXiv preprint arXiv:2407.02606.

7.

Yin, M. (2025). Data Quality Control in Semiconductor Manufacturing through Automated ETL Processes and Class Imbalance Handling Techniques. Journal of Industrial Engineering and Applied Science, 3(6), 13-22.

8.

Yin, M. (2025). A Data-Driven Approach for Real-Time Bottleneck Detection and Optimization in Semiconductor Manufacturing Using Active Period Method and Visualization. Academic Journal of Natural Science, 2(4), 19-26.

9.

Yin, M. (2025). Robust Bilevel Network-Flow Scheduling for Semiconductor Wafer Logistics under WLTP Uncertainty.

10.

Huang, S. (2025). Reinforcement Learning with Reward Shaping for Last-Mile Delivery Dispatch Efficiency. European Journal of Business, Economics & Management, 1(4), 122-130.

11.

Chen, Y. (2025). Interpretable Automated Machine Learning for Asset Pricing in US Capital Markets. Journal of Economic Theory and Business Management, 2(5), 15-21.

12.

Rudnitckaia, J., Venkatachalam, H. S., Essmann, R., Hruška, T., & Colombo, A. W. (2022). Screening process mining and value stream techniques on industrial manufacturing processes: process modelling and bottleneck analysis. IEEE Access, 10, 24203-24214.

13.

Huang, S. (2025). Prophet with Exogenous Variables for Procurement Demand Prediction under Market Volatility. Journal of Computer Technology and Applied Mathematics, 2(6), 15-20.

14.

Cao, S., Wang, J., & Tse, T. K. (2023). Life‐cycle cost analysis and life‐cycle assessment of the second‐generation benchmark building subject to typhoon wind loads in Hong Kong. The Structural Design of Tall and Special Buildings, 32(11-12), e2014.

15.

Pang, F. (2020, November). Research on Incentive Mechanism of Teamwork Based on Unfairness Aversion Preference Model. In 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME) (pp. 944-948). IEEE.

16.

Wang1a, J., Tim, K. T., Li, S., Chan1b, T. K., & Fung3c, J. C. (2023). A systematic comparison of the wind profile codifications in the Western Pacific Region. Wind and Structures, 37(2), 105-115.

17.

Chen, Y. (2025). Generative Diffusion Models for Option Pricing: A Novel Framework for Modeling Volatility Dynamics in US Financial Markets. Journal of Industrial Engineering and Applied Science, 3(6), 23-29.

18.

Wang, J., Cao, S., Tim, K. T., Li, S., Fung, J. C., & Li, Y. (2025). A novel life-cycle analysis framework to assess the performances of tall buildings considering the climate change. Engineering Structures, 323, 119258.

19.

Chen, Y. (2025). A Comparative Study of Machine Learning Models for Credit Card Fraud Detection. Academic Journal of Natural Science, 2(4), 11-18.

20.

Luo, M., Du, B., Zhang, W., Song, T., Li, K., Zhu, H., ... & Wen, H. (2023). Fleet rebalancing for expanding shared e-Mobility systems: A multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3868-3881.

21.

Wang, J., Tse, T. K., Li, S., & Fung, J. C. (2023). A model of the sea–land transition of the mean wind profile in the tropical cyclone boundary layer considering climate changes. International Journal of Disaster Risk Science, 14(3), 413-427.

22.

Zhang, Z., Li, S., Zhang, Z., Liu, X., Jiang, H., Tang, X., ... & Jiang, M. (2025). IHEval: Evaluating language models on following the instruction hierarchy. arXiv preprint arXiv:2502.08745.

23.

Lee, J. Y. J., Bonab, H., Zalmout, N., Zeng, M., Lokegaonkar, S., Lockard, C., ... & Wang, H. (2025, August). DocTalk: Scalable graph-based dialogue synthesis for enhancing LLM conversational capabilities. In Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue (pp. 658-677).

24.

Luo, M., Zhang, W., Song, T., Li, K., Zhu, H., Du, B., & Wen, H. (2021, January). Rebalancing expanding EV sharing systems with deep reinforcement learning. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 1338-1344).

25.

Zhang, K. (2025). Supply Chain Intelligent Rating System: A Study on Its Adaptability and Application in Multiple Industries. Journal of World Economy, 4(5), 20-26.

26.

Ding, Q. (2025). Development and Die-Cutting Process Optimization of High-Temperature-Resistant and Anti-Aging Barcode Substrate for Photovoltaic Modules. Innovation in Science and Technology, 4(8), 22-28.

27.

Wang, J., Golnary, F., Li, S., Weerasuriya, A. U., & Tse, K. T. (2024). A review on power control of wind turbines with the perspective of dynamic load mitigation. Ocean Engineering, 311, 118806.

28.

Jieyang, P., Kimmig, A., Dongkun, W., Niu, Z., Zhi, F., Jiahai, W., ... & Ovtcharova, J. (2023). A systematic review of data-driven approaches to fault diagnosis and early warning. Journal of Intelligent Manufacturing, 34(8), 3277-3304.

PUBLISHER'S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.