en کنترل کیفیت Quality Control پژوهشي Research <div style="text-align: justify;"><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><i><span lang="EN-IN" style="font-size:12.0pt"><span new="" roman="" style="font-family:" times="">The quality of pharmaceutical devices determines an entity&#39;s effectiveness in fulfilling its intended functions for patients. Statistical quality control entails the utilization of statistical methodologies to oversee and enhance the quality of processes and products. In the realm of the pharmaceutical industry, Reliability Acceptance Sampling Plans (RASP) entail inspecting a sample of items to ascertain whether the entire batch adheres to prescribed quality benchmarks, thereby curbing inspection expenses while upholding quality assurance of medical devices. The special Type of Double Sampling (STDS) plan constitutes a subset of reliability acceptance sampling plans that employ dual sampling stages to ascertain the batch acceptance or rejection of products or materials. This article develops a new methodology by employing the Special Type of Double Sampling plan under Exponential&ndash;Poisson (EP) distribution for the mean life when the product life test is truncated at a specified time.</span></span></i><m:omath><m:ssub><m:ssubpr><span style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><span style="font-style:italic"><m:ctrlpr></m:ctrlpr></span></span></span></m:ssubpr><m:e><i><span lang="EN-IN" style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><m:r>t</m:r></span></span></i></m:e><m:sub><i><span lang="EN-IN" style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><m:r>0</m:r></span></span></i></m:sub></m:ssub></m:omath><span lang="EN-IN" style="font-size:11.0pt"><span style="line-height:107%"><span calibri="" style="font-family:"><span style="position:relative"><span style="top:4.0pt"> </span></span></span></span></span><i><span lang="EN-IN" style="font-size:12.0pt"><span new="" roman="" style="font-family:" times="">&rsquo;. The minimum sample size &lsquo;n&rsquo; required to guarantee the attainment of the designated mean life &lsquo;</span></span></i><m:omath><m:ssub><m:ssubpr><span style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><span style="font-style:italic"><m:ctrlpr></m:ctrlpr></span></span></span></m:ssubpr><m:e><i><span lang="EN-IN" style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><m:r>&beta;</m:r></span></span></i></m:e><m:sub><i><span lang="EN-IN" style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><m:r>0</m:r></span></span></i></m:sub></m:ssub></m:omath><span lang="EN-IN" style="font-size:11.0pt"><span style="line-height:107%"><span calibri="" style="font-family:"><span style="position:relative"><span style="top:4.0pt"> </span></span></span></span></span><i><span lang="EN-IN" style="font-size:12.0pt"><span new="" roman="" style="font-family:" times="">&rsquo; within a predetermined consumer&#39;s risk. The Operational Characteristic (OC) function values are acquired based on varying tiers of quality and the minimum mean ratio of the real mean lifetime.</span></span></i><m:omath><i><span lang="EN-IN" style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><m:r>&beta;</m:r></span></span></i></m:omath><span lang="EN-IN" style="font-size:11.0pt"><span style="line-height:107%"><span calibri="" style="font-family:"><span style="position:relative"><span style="top:4.0pt"> </span></span></span></span></span><i><span lang="EN-IN" style="font-size:12.0pt"><span new="" roman="" style="font-family:" times="">&rsquo; to the designated mean lifetime &lsquo;</span></span></i><m:omath><m:ssub><m:ssubpr><span style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><span style="font-style:italic"><m:ctrlpr></m:ctrlpr></span></span></span></m:ssubpr><m:e><i><span lang="EN-IN" style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><m:r>&beta;</m:r></span></span></i></m:e><m:sub><i><span lang="EN-IN" style="font-size:12.0pt"><span cambria="" math="" style="font-family:"><m:r>0</m:r></span></span></i></m:sub></m:ssub></m:omath><span lang="EN-IN" style="font-size:11.0pt"><span style="line-height:107%"><span calibri="" style="font-family:"><span style="position:relative"><span style="top:4.0pt"> </span></span></span></span></span><i><span lang="EN-IN" style="font-size:12.0pt"><span new="" roman="" style="font-family:" times="">&rsquo; under the designated producer&#39;s risk are developed. Moreover, to enhance the comprehension of the proposed methodology, a numerical illustration accompanied by a real-world scenario that is applicable to the pharmaceutical industry is presented.</span></span></i></span></span></span></div> Reliability,Special Type of Double Sampling plan,Exponential – Poisson distribution,Truncated life test,Pharmaceutical Industry. 43 53 http://ijiepr.iust.ac.ir/browse.php?a_code=A-10-3091-1&slc_lang=en&sid=1 Kaviyarasu Velappan kaviyarasu@buc.edu.in 1800319475328460014968 1800319475328460014968 No Department of Statistics, Bharathiar University, Coimbatore, Tamilnadu , India Nagarajan Arumugam nagarajan.statistics@buc.edu.in 1800319475328460014969 1800319475328460014969 Yes Department of Statistics, Bharathiar University, Coimbatore, Tamilnadu , India