Optimum design of an efficient variables sampling system for validating process yield with Six-Sigma quality requirement and creation of a cloud-computing too

Six Sigma quality levels have become well-known process yield targets in supply chain channels. To meet this high-yield requirement, the variables tightened-normal-tightened sampling system (VTSS) operates a dynamic rule-switching strategy between sampling plans, becoming a flexible and economical method for practitioners to verify products. Existing VTSSs based on the process yield index are only designed to adjust sample sizes in tightened and normal inspections. In this paper, a VTSS with alterable acceptance standards is developed. We derive the proposed VTSS's operating characteristic function and integrate it with the producer's and consumer's yield-and-risk requirements to construct an optimisation model for the determination of the optimal system design. After conducting a series of investigations into the performance between the proposed VTSS system with the existing VTSS system with alterable sample sizes, we concluded the proposed VTSS could reduce the average sample size by more than 50% and has a steeper operating characteristic shape, which indicates superior cost-efficiency and discriminative power. Moreover, we designed a cloud-computing tool to build an open-access platform to help practitioners implement our proposed VTSS easily and efficiently. Finally, the practicality and applicability of the proposed VTSS are illustrated through an industrial case. [Received: 15 December 2022; Accepted: 25 August 2023]