Developments in Acceptance Sampling Plans: A Systematic Review of Methodological Advancements

This systematic review examines a progress in Acceptance Sampling Plans (ASPs) research development from 2016 to 2026, highlighting the critical shift from static models toward dynamic, intelligent methodologies that enhance efficiency and decision‐making under uncertainty. Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines, we analyzed peer‐reviewed articles from Scopus and Web of Science, employing thematic analysis to categorize literature based on methodological evolution, optimization techniques, statistical advancements, and non‐classical uncertainty management. The review identifies several major developmental trends. Adaptive sampling schemes such as repetitive and group ASPs reduce required sample sizes by optimizing decision rules. Accelerated Life Testing (ALT) integrated with step‐stress modeling, ranked set sampling, and hybrid censoring schemes cuts test times for highly reliable products. Complex failure‐pattern modeling has evolved beyond traditional distributions to incorporate flexible semi‐parametric classes, lifetime percentiles, and process‐capability loss indices. Uncertainty frameworks, including Neutrosophic statistical interval methods and Bayesian‐Neutrosophic models, improve decision robustness under indeterminate data conditions. In general, this synthesis provides practitioners with guidance to reduce inspection time and costs, supporting adaptive quality control in modern manufacturing environments.