Modelling KPIs of multi-product manufacturing systems prone to failures, scrap, and multiple reworks

The evolving demands of modern industries have driven companies to implement flexible manufacturing systems capable of handling multiple product types. Nevertheless, the majority of studies conducted thus far to assess the performance of manufacturing systems are limited to single-product due to the complexity of handling multi-product environments. Moreover, the majority of these researches rarely consider the rework of non-compliant parts as a base for sustainable manufacturing. This paper presents a novel methodology to address the problem of assessing the performance of multiple-product manufacturing systems considering random breakdowns, scrap, and multiple rework attempts. A discrete-time Markov chain model is proposed to analyze the stochastic behavior of the manufacturing system. Analytical formulations are developed allowing to evaluate several key performance indicators such as throughput, effectiveness, availability, quality ratio, yield, scrapped parts, and required quantities of raw parts. A comprehensive simulation model mimics the stochastic and dynamic character of these flexible manufacturing systems is also designed. Extensive simulation experiments are carried out to validate the proposed analytical models across various production scenarios and system configurations, demonstrating the accuracy and robustness of the proposed approach and the analytical models.