TFNs-based reliability centric analysis of automated slaughterhouse system under imprecise failure and repair rates

Automated slaughterhouse system is crucial in the modern food processing industry, where a system’s high reliability indicate high operational efficiency, safety and sustainability. However, to understand the behaviour of the different reliability measure of a complex system one must have precise data related to the different component failure and repair which is, most often, not easily available due to fluctuating operating and environment conditions. The aim of this manuscript is to analyse the performance of a smart slaughterhouse system. Smart slaughter-houses is revolutionising meat processing through automation, cleanliness and smart monitoring. In this paper, the workability of such a system is modelled in terms of a mathematical model through Trapezoidal Fuzzy Numbers (TFNs) to cater to the uncertainty in the failure and repair rates. Automated conveyor, smart stunning unit, robotic cutter, hygiene monitoring, and the quality control unit based on AI are the major components of the considered smart slaughter-house system. Authors used the TFNs-based reliability centric approach to estimate the reliability, availability, and mean time to failure (MTTF) of the considered slaughterhouse system, which take into consideration data uncertainty. The results of the presented work can be considered as a good reference for the planning, minimizes waste, optimize energy consumption (which is aligned to SDG-12), and operation of intelligent slaughterhouses which significantly contribute as decision support tool for designing sustainable and fault tolerant smart slaughter-house systems. Furthermore, the presented mathematical model emphasize to safer and reliable working conditions which is aligning with SDG.