Estimating Outgoing Quality Using the Quality Measurement Plan

Brush, Gary G.; Hoadley, Bruce; Saperstein, Bernard

doi:10.2307/1269842

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Thus, we should select a geometric Poisson CUSUM scheme with K ¼ 3. One of the criteria that is worth consideration is the cost associated with Type I and Type II errors (Brush, Hoadley, and Saperstein, 1990). Thus, the chosen CUSUM scheme for the geometric Poisson process is This particular geometric Poisson CUSUM scheme means that the average number of observations needed to detect the process change is 15, if the process mean shifts from 2.5 to 4.…”

Section: The Geometric Poisson Cusum Control Scheme a Numerical Examplementioning

confidence: 99%

Using CUSUM Control Schemes for Monitoring Quality Levels in Compound Poisson Production Environment: The Geometric Poisson Process

Chen

Randolph

Liou

2005

Quality Engineering

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Section: The Geometric Poisson Cusum Control Scheme a Numerical Examplementioning

confidence: 99%

Using CUSUM Control Schemes for Monitoring Quality Levels in Compound Poisson Production Environment: The Geometric Poisson Process

Chen

Randolph

Liou

2005

Quality Engineering

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“…It was observed in practice at some organizations that accepting or rejecting lots is not always a yes or no decision. When the lot sizes are small and the sample is thus a significant amount of the lot, the “good” units in the sample might be accepted, while the “bad” units in the sample and the uninspected units are rejected (Brush et al , 1990). Another company specifies that their small wood dowels be received with less than 0.01 per cent defects due to the automated process used to assembly them into cabinets.…”

Section: Introductionmentioning

confidence: 99%

A Sampling Policy for the Reduction of Quality Cost and Improvement of Accepted Percentage in Company L

Rothwell

Park

Kim

2009

Asian Journal on Quality

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The reduction of the time and resources spent inspecting product is critical to the success of Company L's continued resourcing efforts. The use of Mil‐Std and other sampling plans with acceptance numbers greater than zero usually results in increased inspection sizes and potential for controversy in inspection results between inspectors. The time and resources used to complete these outgoing inspections are directly related to the amount of product currently required to be inspected in order to determine the acceptance or rejection of a lot of finished goods. This paper proposes a new sampling policy that will allow Company L to reduce the size of outgoing inspections. The data used in the paper are from 2006 to 2007. It is a combination of Overseas Inspection reports from all suppliers as well as sales volumes for products sold to Company L's partner companies. There are currently over 80 suppliers that manufacture products for Company L. The major finding of this paper is that it is possible to reduce inspection size while still maintaining, or in most cases reducing, the risks associated with sample inspections. This will be accomplished by switching from the current Mil‐Std plan to a Zero acceptance number sampling plan.

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“…The actual consumer's risk can be either too small or too high than expected. The outgoing quality has been an important parameter since the producers are required to demonstrate that their products are meeting specified quality standard as required by the consumers (Brush, Hoadley and Saperstein 1990). Therefore, it is required to have the sampling plan which represents the final quality level received by customer.…”

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confidence: 99%

The Operating Characteristics Curve of the Acceptance Sampling of Type-a Based on Outgoing Percent Defective Lot

Dewi

Gunawan

Alamsjah

2022

Jambura J. Probab. Stat.

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The common type-A of Operating Characteristics (OC) curve measures the consumer’s risk through the incoming quality. However, the proportion of defective can alter after the sampling process; hence, the measure of consumer’s risk is better described by the outgoing quality or lot quality of post sampling inspection. A modified OC curve is developed based on the outgoing quality for two applicable cases; returned samples to the lot and non-returned or destructive samples. This research aims to develop the algorithm and evaluate the alternative acceptance sampling plan for isolated lots by outgoing percent defective. For the returned samples, the acceptance sampling requires less sample than that based on the common OC-curve and is seen as an opportunity for sampling size reduction. The number of reduced samples varies depending on the input parameters: Acceptance Quality Level (AQL), Rejectable Quality Limit (RQL), producer’s risk (α), consumer’s risk (β), and lot size (N). For the non-returned sample, more sample size (n) is required, even more than that of using the Binomial distribution’s sample size, which has been considered conservative.

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Estimating Outgoing Quality Using the Quality Measurement Plan

Cited by 12 publications

References 10 publications

Using CUSUM Control Schemes for Monitoring Quality Levels in Compound Poisson Production Environment: The Geometric Poisson Process

Using CUSUM Control Schemes for Monitoring Quality Levels in Compound Poisson Production Environment: The Geometric Poisson Process

A Sampling Policy for the Reduction of Quality Cost and Improvement of Accepted Percentage in Company L

The Operating Characteristics Curve of the Acceptance Sampling of Type-a Based on Outgoing Percent Defective Lot

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