An Interconnected Neuro-Fuzzy Architecture for Production-Line Performance Prediction in Industrial Manufacturing Systems

Abstract

Efficient operation of interconnected production systems requires accurate modeling of upstream–downstream interactions to support informed operational decision-making. This study proposes an interconnected Adaptive Neuro-Fuzzy Inference System (ANFIS) framework consisting of two sequentially linked ANFIS models representing the upstream manufacturing stage and the downstream packaging stage, where the output of the first model is incorporated as an input to the second model. The framework uses operational Key Performance Indicators (KPIs), including mean time between failures (MTBF), mean time to repair (MTTR), uptime, reject rate, short stops, and long stops, to capture the nonlinear relationships between manufacturing and packaging operations. Unlike conventional single-model approaches, the proposed methodology represents the manufacturing and packaging stages as interconnected neuro-fuzzy subsystems, explicitly modeling their operational dependencies while maintaining model interpretability. Following data preprocessing and conditioning, two ANFIS models were developed using real industrial data and integrated into a MATLAB/Simulink environment for scenario-based analysis. The developed ANFIS models achieved low testing errors and satisfactory predictive performance on real industrial data. Simulation-based analyses were subsequently conducted to evaluate the effects of stoppage behavior and maintenance-related improvements on production-line uptime. The results indicate that the proposed framework captures upstream–downstream production dependencies and provides an interpretable decision-support tool for evaluating operational improvement scenarios in manufacturing environments.