Software : MRP Systems : Operations Manager : Quality Control

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Quality Control
Operations Manager
ACCEPTSA 
Overview

Acceptance sampling (ACCEPTSA)

Acceptance sampling is a procedure for screening lots of incoming material. We decide whether to accept or reject the entire lot based on the results of a sample. A sampling plan is defined by two parameters: sample size and acceptance number. The acceptance number is the maximum number of allowable defects in the sample. If the sample contains this number or fewer defects, the lot is accepted without further inspection. If the sample contains more than the maximum number of defects, the lot is rejected and a 100% inspection is conducted.

The sample size and acceptance number determine the risks faced by the producer and consumer of the lot. The producer's risk is the probability that a "good" lot will be rejected by the sampling process. Lots are defined as good if they contain no more than a certain level of defectives called the acceptable quality level (AQL). The consumer's risk is the probability that a "bad" lot will be accepted. Lots are called bad if they contain more than a certain level of defectives called the lot tolerance percent defective (LTPD). Using the binomial distribution, the ACCEPTSA worksheet computes the producer's and consumer's risks, given the lot size, sample size, acceptance number, AQL, and LTPD.

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Operations Manager
MR-CHART 
Overview

Control chart for mean and range (MR-CHART)

The basic idea in all quality control charts is to select a sample from a production process at equal intervals of time and record some quality characteristic. The most common quality characteristic is the mean of each sample. If the process is under control, the series of sample means should vary about the population mean in a random manner. That is, we should expect some natural variation in any process and there should be no real assignable cause to this variation. If the process is in control, almost all sample mean values should fall within control limits, almost always defined as the mean plus or minus 3 standard deviations. The standard deviation is a measure of the variation of a process. If all sample observations are constant, the standard deviation is zero; as variation increases, the standard deviation grows. The control charts do not measure the standard deviation directly. Instead, the range (high value minus low value) of each sample is used as a simpler measure of variation. To establish control limits, the range is automatically converted to a standard deviation.

It is important to understand that the control chart is a management-by-exception tool. If a sample mean falls outside the control limits, there is a very small probability that this happened due to randomness or chance alone. In fact, with control limits set at 3 standard deviations, the probability is less than 1% that the sample mean occurred due to chance. There is a very large probability, more than 99%, that the sample mean is due to an assignable cause and an investigation should be conducted.

The control charts in SOM are classified as either variable or attribute charts. Variables are measurements on a continuous scale such as inches or pounds. What types of variables can be monitored with the variables control charts? Anything that can be measured and expressed in numbers, such as temperature, dimension, hardness number, tensile strength, weight, viscosity, etc. Variables are monitored in the MR-CHART worksheet for the mean and range of samples and in the I-CHART for individual observations. Attributes are discrete data such as the number of items in the sample that are defective or the number of defects in one unit of product. The P-CHART and CU-CHART models are available for attributes data.

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Operations Manager
I-CHART 
Overview
Control chart for individual observations (I-CHART)

The I-CHART is another variables control chart used to monitor individual observations (samples of one each) rather than larger samples. The I-CHART is most used in cases where a considerable period of time elapses between opportunities to collect quality observations. For example, there only be one quality observation per shift in operations with long lead times. Although the I-CHART is usually identified with manufacturing applications, it is widely used in casinos to monitor card, dice, and roulette games. The individual observations entered in the chart are typically the net cash profits recorded when a dealer settles his or her account with the house at the end of a work period.

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Operations Manager
P -CHART 
Overview

Control chart for percent defective (P-CHART)

P-CHART is the most versatile and popular control chart. To use P-CHART, quality inspectors classify sample items into two groups: good or bad. This can mean defective or non-defective, conforming or non-conforming to specifications, acceptable or unacceptable, or other definitions in which there are only two categories of results.

Brookshire Cookware Corporation, a producer of pots and pans located in Brookshire , Texas , uses P-CHART worksheets to monitor the quality of component parts received from its Mexican suppliers. At the end of each business day, Brookshire faxes a copy of each P-CHART to its suppliers. This feedback helps maintain a good working relationship with suppliers and alerts them to potential problems.

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Operations Manager
CU-Chart 
Overview

Control chart for number of defects (CU-CHART)

Occasionally, product classification as merely good or bad is not enough and variable measurements do not apply. For example, in evaluating the quality of a new automobile, there could be many defects but it would be misleading to classify the entire automobile as unacceptable. The solution in situations like this is another attributes chart, the CU-CHART ), which monitors the number of defects per inspection unit. In general, the inspection unit is usually expected to have some defects and we wish to know whether the number of defects is excessive. CU-CHART is also valuable when dimensions or units of measure complicate quality assessments. For example, suppose that a coil of steel is 100 meters long and contains 7 lamination defects. What is the defect rate? It could be 7/100 = 7%. But the defects are small, each perhaps a centimeter in length. There are 10,000 centimeters in the coil so the defect rate becomes 0.07%. We could also compute the square centimeters in the area of the coil and compute yet another defect rate. The only sensible way around this problem with dimensions is to state quality in terms of total number of defects per inspection unit.

Three conditions must be satisfied to use CU-CHART. First, the definition of an inspection unit must be constant from one time period to the next. Second, there must be a very large number of opportunities for defects to occur in each unit produced. Third, the probability that a defect will occur at any particular location in each unit must be very small.

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Operations Manager
LIMIT 
Overview

Control limit calculator (LIMIT)

LIMIT, is a calculator for control limits in MR-CHART, P-CHART, and CU-CHART. All control limits are based on 3 standard deviations. If you want different limits, edit the control limit formulas.

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