Vol: 16, Issue: 1 2026

Page: 40-68

AI-Enabled Theory of Constraints: A Two-Case Study for Bottleneck Detection and Production Flow Improvement in Manufacturing

Dr. Thamer Okab Hawas

Received Date: 2025-11-27

Accepted Date: 2025-12-28

Published Date: 2026-01-29

http://doi.org/10.37648/ijtbm.v16i01.004

This research paper explores the role of artificial intelligence (AI) algorithms in bottleneck?detection and constraint management according to the Tank Theory of Constraints (TOC) in manufacturing. One ongoing management problem is that our constraints are often observed after the fact, i.e., after throughput has already been lost, simply because signals of downtime can be scattered among products, shifts,?operators and operating conditions. To close this gap, we switch to a two–case?study where we connect operational data and TOC-relevant, decision-ready information. In Case 1, production and downtime data are combined?to analyse constraint effect (e.g., total amount of downtime minutes, downtime share and efficiency loss) and pinpoint "vital few" causes of flow interruption via a Pareto-style loss analysis; predictive modelling is applied next for estimating downtime magnitude and ranking improvement actions. In Case 2, a predictive maintenance context is adopted to predict failure-induced perturbations endangering the constraint with a train-only balancing strategy and?an untouched test set leveraging real-world class imbalance and decision realism; explainability delivers most interesting risk drivers allowing for targeted preventive actions. Results indicate that AI algorithms can improve TOC practice by increasing bottleneck visibility, earlier actions and?prioritization of constraint-focused improvements systematic manner. The study contributes a business-oriented framework that would serve as an intermediate between AI outputs and executable TOC decisions?that safeguard throughput and stabilise the production flow.

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