Real-Time Bottleneck in Serial Production Lines With Bernoulli Machines: Theory and Case Study

In production systems, the bottleneck is typically understood as the operation that affects the system performance in the strongest manner. Identification and mitigation of bottleneck are among the most important problems in production systems research and practice. The current literature offers numerous methods for the identification of production bottlenecks. Most of these methods focus on steady-state system behavior under constant parameters, and the corresponding mitigation approach is typically to prioritize the improvement of that particular operation. This applies when the improvement cannot be dynamically reallocated to other operations in the system, and the improvement is to be fixed to an operation for a long period of time. In this article, we attempt to study the bottleneck problem in a dynamic environment. Especially, we consider serial production lines with finite capacity buffers and the Bernoulli reliability machines and use a control-theoretic approach to formulate the bottleneck identification and mitigation problem as a state-based feedback control problem with the objective being the maximization of steady-state throughput. Computation formulas and procedures are developed to calculate the performance metrics of such systems with dynamic bottleneck control. Properties of the optimal control policies are investigated, and a computationally efficient algorithm is developed to obtain an effective real-time bottleneck indicator. Numerical experiments are used to demonstrate the efficacy of the proposed method, and a case study is presented to illustrate its application. Note to Practitioners —It is very common in manufacturing practice that operators, engineers, and managers need to make decisions on-the-fly to ensure efficient production operations. One of the key tasks involved is to identify real-time bottleneck (RTBN). This is typically carried out based on the practitioners’ intuition and experience. With the advances in sensing, computing, and communication technologies emerging in the Industry 4.0 era, manufacturers now have the capability to collect, process, and visualize an enormous amount of data in real time. This provides a foundation for more rigorous real-time production management and control. The research described in this article is intended to provide such a tool for RTBN identification in production lines and, thus, guide timely decision-making on the factory floor for smarter production activities.