Integrated economic-statistical design of production, quality control and maintenance for continuous flow processes subject to multiple assignable causes

Abstract Joint optimization of production, quality control and maintenance offers substantial potential for cost savings and improvements in operational efficiency in manufacturing systems. However, existing integrated models are predominantly tailored to discrete piece-part manufacturing and suffer from three key limitations: they assume only a single assignable cause, rely on static quality control schemes that are insensitive to moderate-to-small process shifts, and employ design frameworks ill-suited for continuous flow manufacturing—where sampling is constrained by laboratory logistics rather than discrete production units. To address these gaps, this study develops an integrated model for continuous flow processes with multiple assignable causes, jointly optimizing production run length, a variable sampling interval X ̅ chart and condition-based maintenance. A developed genetic algorithm is utilized to minimize the expected total cost (including quality loss, inspection, maintenance, setup, and inventory holding costs) per production cycle while satisfying statistical quality constraints. Comparative analyses demonstrate that the proposed model achieves average cost savings of 6.08% compared to conventional static control charts and 10.82% compared to models assuming only a single assignable cause, under identical baseline parameter settings. Finally, Taguchi’s design of experiments is applied to explore parameter sensitivity, revealing that the in-control quality loss cost and variable inspection cost have significant effects on the total cost, while pairwise interactions between preventive maintenance cost, false alarm cost, and fixed inspection cost also exert non-negligible effects.