Research on dynamic bottleneck prediction methods in manufacturing workshops

Abstract To address the challenge of identifying bottleneck responsibilities when multiple bottlenecks arise in process workshops and predicting bottleneck trends before their occurrence, a novel dynamic prediction method based on the Markov chain is introduced. This approach effectively identifies all bottlenecks in a multi-bottleneck process shop and identifies the primary bottleneck unit with the highest level of responsibility, enabling anticipation of bottleneck shifts prior to their occurrence. Initially, the concept of bottleneck is elaborated, accounting for the intricate interplay among manufacturing processes, leading to the development of a static mathematical model for multi-bottleneck prediction incorporating parameters such as time, cost, and quality. This model evaluates the bottleneck severity of each manufacturing unit, facilitating the identification of all bottleneck units and the assignment of bottleneck responsibilities, thereby establishing primary and secondary bottlenecks. Subsequently, a bottleneck state transition matrix is formed based on historical bottleneck processes, initializing the state matrix through the process with the highest bottleneck responsibility in the static setting. Leveraging the Markov chain prediction technique, the method anticipates the location of bottleneck transitions preceding the occurrence of bottleneck drift. Finally, simulations were conducted in an authentic production line environment to validate the method’s efficacy. Results indicate a 9.1% increase in the main bottleneck coincidence rate compared to single-bottleneck prediction models. Moreover, the Markov chain prediction accurately forecasts bottleneck trends prior to drift occurrences, boasting an 86.4% accuracy rate. These findings underscore the applicability and guidance potential of the proposed method.