Usulan Acceptance Sampling Plan Untuk Tape Yarn Produk Geotex 250 Studi Kasus: PT. Unggul Karya Semesta - Bogor

Perencanaan penerimaan sampel merupakan salah satu aplikasi system pengendalian kualitas yang merancang suatu teknik pengambilan sampel dengan jumlah sampel dan batas spesifikasi yang telah ditentukan. Untuk membuat rancangan ini terlebih dahulu ditentukan nilai untuk beberapa variabel, yaitu probabilitas menolak lot yang baik (producer\u27s risk), probabilitas menerima lot yang buruk (consumer\u27s risk), rata-rata lot berkualitas baik, rata-rata lot berkualitas buruk, dan standar deviasi proses. Variabel-variabel ini akan mempengaruhi perhitungan untuk menentukan jumlah sampel dan batas spesifikasi penerimaan sampel. Hasilnya berupa suatu rancangan di mana sampel yang akan diinspeksi adalah sejumlah 13 benang plastik (tape yarn), kemudian diukur kekuatan per deniernya (tenacity). Jika rata-rata tenacity sama dengan atau lebih dari 5,9 gram/D maka lot diterima, dan jika rata-rata tenacity kurang dari 5,9 gram/D maka lot ditolak. Kata kunci: pengendalian kualitas, perencanaan penerimaan sampel, perencanaan sampling untuk karakteristik variabel, batas spesifikasi tunggal, standar deviasi. Acceptance sampling plan is an application of quality control system that creates a sampling technique with certain sample size and specification limit. To plan this, it has to determine the value of variables, such as probability of rejecting good lot (producer\u27s risk), probability of accepting poor lot (consumer\u27s risk), good average quality, poor average quality, and process standard deviation. These variables will determine the sample size and acceptance specification limit. The result is a plan which the inspected sample size is 13 tape yarns, then its strength per denier (tenacity) is measured. If the sample average is equal to or more than 5,9 gram/D, accept lot; otherwise, reject lot

Sampling Plans for Control-Inspection Schemes Under Independent and Dependent Sampling Designs With Applications to Photovoltaics

The evaluation of produced items at the time of delivery is, in practice, usually amended by at least one inspection at later time points. We extend the methodology of acceptance sampling for variables for arbitrary unknown distributions when additional sampling infor- mation is available to such settings. Based on appropriate approximations of the operating characteristic, we derive new acceptance sampling plans that control the overall operating characteristic. The results cover the case of independent sampling as well as the case of dependent sampling. In particular, we study a modified panel sampling design and the case of spatial batch sampling. The latter is advisable in photovoltaic field monitoring studies, since it allows to detect and analyze local clusters of degraded or damaged modules. Some finite sample properties are examined by a simulation study, focusing on the accuracy of estimation

Spatial aggregation of time-variant stream water ages in urbanizing catchments

Date of Acceptance: 25/03/2015 Acknowledgements The help of Clara F. Soulsby with the sampling is gratefully acknowledged.Peer reviewedPostprin

Multilevel Fixed and Sequential Acceptance Sampling: The R Package MFSAS

Multilevel acceptance sampling for attributes is used to decide whether a lot from an incoming shipment or outgoing production is accepted or rejected when the product has multiple levels of product quality or multiple types of (mutually exclusive) possible defects. This paper describes a package which provides the tools to create, evaluate, plot, and display the acceptance sampling plans for such lots for both fixed and sequential sampling. The functions for calculating cumulative probabilities for several common multivariate distributions (which are needed in the package) are provided as well.

Tables of joint probabilities useful in evaluating mixed acceptance sampling plans

Tables of joint probabilities useful in evaluating mixed acceptance sampling plan

Acceptance sampling plan for multiple manufacturing lines using EWMA process capability index

The problem of developing a product acceptance determination procedure for multiple characteristics has attracted the quality assurance practitioners. Due to sufficient demands of consumers, it may not be possible to deliver the quantity ordered on time using the process based on one manufacturing line. So, in factories, product is manufactured using multiple manufacturing lines and combine it. In this manuscript, we present the designing of an acceptance sampling plan for products from multiple independent manufacturing lines using exponentially weighted moving average (EWMA) statistic of the process capability index. The plan parameters such as the sample size and the acceptance number will be determined by satisfying both the producer's and the consumer's risks. The efficiency of the proposed plan will be discussed over the existing sampling plan. The tables are given for industrial use and explained with the help of industrial examples. We conclude that the use of the proposed plan in these industries minimizes the cost and time of inspection. Smaller the sample size means low inspection cost. The proposed plan for some non-normal distributions can be extended as a future research. The determination of sampling plan using cost model is also interested area for the future research. ? 2017 The Japan Society of Mechanical Engineers.11Ysciescopu

Emperical Tests of Acceptance Sampling Plans

Acceptance sampling is a quality control procedure applied as an alternative to 100% inspection. A random sample of items is drawn from a lot to determine the fraction of items which have a required quality characteristic. Both the number of items to be inspected and the criterion for determining conformance of the lot to the requirement are given by an appropriate sampling plan with specified risks of Type I and Type II sampling errors. In this paper, we present the results of empirical tests of the accuracy of selected sampling plans reported in the literature. These plans are for measureable quality characteristics which are known have either binomial, exponential, normal, gamma, Weibull, inverse Gaussian, or Poisson distributions. In the main, results support the accepted wisdom that variables acceptance plans are superior to attributes (binomial) acceptance plans, in the sense that these provide comparable protection against risks at reduced sampling cost. For the Gaussian and Weibull plans, however, there are ranges of the shape parameters for which the required sample sizes are in fact larger than the corresponding attributes plans, dramatically so for instances of large skew. Tests further confirm that the published inverse-Gaussian (IG) plan is flawed, as reported by White and Johnson (2011)

PENERIMAAN DIRI SESEORANG SEBAGAI ORANG DENGAN HIV/AIDS (ODHA) DI MALANG (Study Di Yayasan Cahaya Kasih Peduli WPA Turen)

People with HIV/AIDS self-acceptance of their status is a complex problem for people with HIV/AIDS. One of the problems of people living with HIV/AIDS in self-acceptance occurs at the Cahaya Kasih Care Foundation WPA Turen. In the process of self-acceptance, people with HIV/AIDS often experience problems. This study aims to describe the problems experienced by people with HIV/AIDS and the process of people with HIV/AIDS in accepting their status. This study uses a qualitative approach and a case study type of research. Data collection techniques used are observation, interviews, and documentation. The research subject determination technique used was purposive sampling from Sugiyono. While the data validity technique used is a credibility test. The results of the study show that in self-acceptance people with HIV/AIDS experience psychological, physical and social problems. Meanwhile, in the process of self-acceptance of people living with HIV/AIDS, they experience the following stages: aversion, curiosity, tolerance, allowing, friendship/awakening

Acceptance-Rejection Sampling with Hierarchical Models

Hierarchical models provide a flexible way of modeling complex behavior. However, the complicated interdependencies among the parameters in the hierarchy make training such models difficult. MCMC methods have been widely used for this purpose, but can often only approximate the necessary distributions. Acceptance-rejection sampling allows for perfect simulation from these often unnormalized distributions by drawing from another distribution over the same support. The efficacy of acceptance-rejection sampling is explored through application to a small dataset which has been widely used for evaluating different methods for inference on hierarchical models. A particular algorithm is developed to draw variates from the posterior distribution. The algorithm is both verified and validated, and then finally applied to the given data, with comparisons to the results of different methods