Purpose: This research proposed dependent sampling plan incorporating mixed acceptance criteria for entrance quality control of corn kernels, a primary material in the feed manufacturing industry. Mathematical models are involved to determine the alternative samplin plan based on allowable risk levels commonly applied in acceptance sampling practices.Design/methodology/approach: This research develops alternative dependent mixed sampling plans by solving two non-linear equations simultaneously and satisfying the conditions of two points on the operating characteristic (OC) curve. Each sampling plan is then assessed not only for producer’s (?) and consumer’s (?) risks, but also for total inspection time and financial viability utilizing the benefit-cost ratio (BCR). A goal-programming technique finds the best course of action, providing the best balance between operational costs, inspection time efficiency, and the necessary quality level for the related industry.Findings: By implementing the proposed framework from this research, entrance quality control at one of the feedmills owned by PT XYZ can be optimized by increasing time efficiency by 72.06%, supported by a promising benefit-cost ratio (BCR) of 9.59. Additionally, in this research can identified the potential to streamline the total number of quality criteria for corn kernels without compromising the ability to capture overall defect variance, using principal component analysis (PCA). With a producer's risk (?) of 2.00% and a consumer's risk (?) of 4.00%, the model PDMSP (p; 3; 1.17; 2; 4; 2) allowed for the integration of these findings and resulted in a much improved inspection process while preserving acceptable risk levels.Research limitations/implications: This research focuses solely on the the inspection of corn kernels in entrance quality control at PT XYZ. Future research could extend this approach to other types of raw materials used in the feed manufacturing industryOriginality/value: The majority of earlier research has only examined sampling strategies in terms of allowable risk. By including resource utilisation, this research aims to create a more thorough framework that will be more balanced and useful for implementation.
Anityasari et al. (Fri,) studied this question.