Impact of the total measurement error on sampling plan for bulk materials – An optimal sampling plan under ToS framework

Abstract

Acceptance sampling plans are used in quality control between a producer and a buyer. However, in the literature of acceptance sampling for bulk materials, the term “sampling” refers to obtaining simple random samples in the traditional statistical sense, which may lead to heavy financial losses. In contrast, the Theory of Sampling (ToS) defines sampling as the collection of physical samples based on a thorough understanding of the material concerned. The goal of this research is to investigate the influence of incorrect sampling procedures on acceptance sampling in a mining value chain. To achieve this, the representative sampling approach of ToS was utilized, total measurement error was measured, and possibilities for improving sampling procedures were identified. A variable Quick Switch Sampling plan (QSS) based on the Beta distribution is applied. A nonlinear optimization model is used to estimate the plan parameters, which minimizes the necessary average sample number (ASN) while meeting the producer and consumer quality levels and maximum allowable risks using real-life data. Finally, A collaborative quality program is suggested for a solid producer-buyer relationship by using a correct sampling facility of the producer.