Qm-statistical Control Charts

Statistical Control Charts      

Basic Concepts Mean Chart Range Chart C Chart P Chart NP Chart

Basic Concepts  

Control Charts form an integral part of production process. Samples taken continuously on a regular basis and data analysed statistically which will give a valued information.

Advantages 

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Anticipating trouble during production in the form of deterioration in quality of materials, properties or process characteristics and predicting well in time so that the causes can be identified and remedial or corrective action taken in time. Reduction in rejection rates thereby enhancing production. Reducing cost of inspection.

Advantages 

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Narrowing down the specifications, thus enabling higher quality of production without increasing cost of production. Allowing efficient use of materials. Reducing cost of production and affecting large savings. Providing sound & scientific altering for specification for high productivity and better economy. Fool proof method for past & present performance.

Mean Chart 

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Control limits are shown by two limits, one upper, and other lower, indicating that the distribution of points should not occur out side these two limits. If the tendency for the points to go out of the upper or lower limits persists there would be a problem of arising and the process going out of control. The control limits are called warning limits and the other action limits.

Mean Chart 

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If the points are dispersed within warning limits, the process is said to be stable and under control. If the points cross both limits, it shows real danger, warranting immediate action by stopping the process to prevent any damage.

Range Chart 

Range Charts are a set of control charts for variables data (data that is both quantitative and continuous in measurement, such as a measured dimension or time)

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The Range chart monitors the variation between observations in the subgroup over time.

Range Chart 

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Used when you can rationally collect measurements in groups (subgroups) of between two and ten observations. The charts' x-axis are time based, so that the charts show a history of the process. It is necessary to have data that is time-ordered; that is, entered in the sequence from which it was generated

C Chart

C Chart  

The final product is still useful but are with numbered defects. For example: steel sheets, wood furniture etc

P Chart 

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In this chart, we plot the percent of defectives (per batch, per day, per machine, etc.) as in the C chart. The control limits in this chart are not based on the distribution of rare events but rather on the binomial distribution (of proportions). Is mostly applicable to situations where the occurrence of defectives is not rare (e.g., we expect the percent of defectives to be more than 5% of the total number of units produced).

NP Chart 

Is used to determine if the rate of nonconforming product is stable, and will detect when a deviation from stability has occurred .

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There should only be an Upper Control Limit (UCL), and not a Lower Control Limit (LCL) since rates of nonconforming product outside the LCL is actually a good thing.

NP Chart 

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There is a difference between a "P Chart" and an "Np Chart". A P chart is one that shows the fraction defective (p), whereas the Np chart shows the NUMBER of defectives (Np). They are practically the same thing with the exception that an Np chart is used when the size of the subgroup (N) is constant, and a P chart is used when it is NOT constant.

NP Chart 

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STEP #1 - Collect the data recording the number inspected (N) and the number of defective products (Np). Divide the data into subgroups. Usually, the data is grouped by date or by lot numbers. The subgroup size (N) should be over 50, and it is strongly recommended you stick with the constant sample size of 100 for subgroups. STEP #2 - Record the number of defectives on a chart or spreadsheet, along with the subgroup size. STEP #3 - Record the number of