<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p><bold>Background:</bold> Acceptance sampling is a statistical tool of quality control. Sampling plans and operating characteristic (OC) curves are very useful for conducting acceptance sampling and provide the quality manager with tools to evaluate the quality of a production run or shipment. There are developed different sampling plans, but common used in practise are single and double acceptance sampling plans. <bold>Objectives:</bold> The goal of the paper is to test if applying of single and double sampling plan can lead to statistically significant different conclusion about quality level of observed lot. <bold>Methods/Approach:</bold> Statistical tests of difference in proportions are used to test if there is some statistically significant difference in probabilities of lot fraction defectives between a single and a double sampling plan at the same levels of probability of acceptance. <bold>Results:</bold> The results of the analysis show that in some cases there is statistically significant difference. Namely, the quality manager should be careful when he chooses to use, instead of the first, the second sampling plan with different parameters because on that way he could make statistically significant different conclusion about quality level of observed lot. <bold>Conclusions:</bold> The paper shows that some intentional manipulations by using different sampling plans are possible.</p></abstract>

Background: Acceptance sampling is a statistical tool of quality control. Sampling plans and operating characteristic (OC) curves are very useful for conducting acceptance sampling and provide the quality manager with tools to evaluate the quality of a production run or shipment. There are developed different sampling plans, but common used in practise are single and double acceptance sampling plans. Objectives: The goal of the paper is to test if applying of single and double sampling plan can lead to statistically significant different conclusion about quality level of observed lot. Methods/Approach: Statistical tests of difference in proportions are used to test if there is some statistically significant difference in probabilities of lot fraction defectives between a single and a double sampling plan at the same levels of probability of acceptance. Results: The results of the analysis show that in some cases there is statistically significant difference. Namely, the quality manager should be careful when he chooses to use, instead of the first, the second sampling plan with different parameters because on that way he could make statistically significant different conclusion about quality level of observed lot. Conclusions: The paper shows that some intentional manipulations by using different sampling plans are possible.

Decision making based on single and double acceptance sampling plans for assessing quality of lots

Department of Statistics, Faculty of Economics and Business, University of Zagreb, Zagreb, Croatia

Business Systems Research Journal

Background: Acceptance sampling is a statistical tool of quality control. Sampling plans and operating characteristic (OC) curves are very useful for...

Decision making based on single and double acceptance sampling plans for assessing quality of lots

Publié en ligne: 19 sept. 2012

Pages: 27 - 40

DOI: https://doi.org/10.2478/v10305-012-0010-4

Mots clés
lot, single acceptance sampling plan, double acceptance sampling plan, operating characteristic curve, supplier's and buyer's risk

This content is open access.

Decision making based on single and double acceptance sampling plans for assessing quality of lots

Publié en ligne: 19 sept. 2012

Pages: 27 - 40

DOI: https://doi.org/10.2478/v10305-012-0010-4

Mots cléslot, single acceptance sampling plan, double acceptance sampling plan, operating characteristic curve, supplier's and buyer's risk

This content is open access.

Mots clés
lot, single acceptance sampling plan, double acceptance sampling plan, operating characteristic curve, supplier's and buyer's risk