Statistical Quality Control

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Concept of statistical quality control.:

Concept of statistical quality control. 1 REVOLUTIONPHARMD.COM THIS PPT IS CONTRIBUTED BY SAI KUMAR KATAM

CONTENTS:

Introduction Quality control in Production System Control Charts Quality Control in Services Conclusion Contents 1 CONTENTS 2 REVOLUTIONPHARMD.COM

Introduction :

Introduction Statistical quality control (SQC) is a method of visually monitoring manufacturing processes. With the use of control charts and collecting few but frequent samples, this method can effectively detect changes in the process that may affect its quality. Under the assumption that a manufactured product has variation and this variation is affected by several process parameters, when SQC is applied to "control" each parameter the final result trend to be a more controlled product. SQC can be very cost efficient, as it usually requires collection and charting data already available, while "product control" requires accepting, rejecting, reworking and scrapping products that already went through the whole process. 3 REVOLUTIONPHARMD.COM

QC Throughout Production Systems:

QC Throughout Production Systems Raw Materials, Parts, and Supplies Production Processes Products and Services Inputs Conversion Outputs Control Charts and Acceptance Tests Control Charts and Acceptance Tests Control Charts Quality of Inputs Quality of Outputs Quality of Partially Completed Products 4 REVOLUTIONPHARMD.COM

Control Charts:

Control Charts Primary purpose of control charts is to indicate at a glance when production processes might have changed sufficiently to affect product quality. If the indication is that product quality has deteriorated, or is likely to, then corrective measure is taken. If the indication is that product quality is better than expected, then it is important to find out why so that it can be maintained. Use of control charts is often referred to as statistical process control (SPC). 5 REVOLUTIONPHARMD.COM

Types of Control Chart:

Types of Control Chart Variables Control Charts The quality characteristics are variables and numerical values can be obtained for each Examples: average length, average diameter, average tensile strength, average resistance, average service time. Attributes Control Charts The quality characteristics are the proportion of nonconforming items, the number of nonconformities in a unit, and the number of demerits per unit. 6 REVOLUTIONPHARMD.COM

Control Charts for Variables:

Inspection of the units in the sample is performed on a variable basis. The information provided from inspecting a sample of size n is: Sample mean, x, or the sum of measurement of each unit in the sample divided by n Range, R, of measurements within the sample, or the highest measurement in the sample minus the lowest measurement in the sample Control Charts for Variables 7 REVOLUTIONPHARMD.COM

Control Charts for Variables:

In this case two separate control charts are used to monitor two different aspects of the process’s output: Central tendency Variability Central tendency of the output is monitored using the x-chart. Variability of the output is monitored using the R-chart. Control Charts for Variables 8 REVOLUTIONPHARMD.COM

x-Chart:

x-Chart The central line is x, the sum of a number of sample means collected while the process was considered to be “in control” divided by the number of samples. The 3 s lower control limit is x - AR The 3 s upper control limit is x + AR Factor A is based on sample size. = = = 9 REVOLUTIONPHARMD.COM

R-Chart:

R-Chart The central line is R, the sum of a number of sample ranges collected while the process was considered to be “in control” divided by the number of samples. The 3 s lower control limit is D 1 R. The 3 s upper control limit is D 2 R. Factors D 1 and D 2 are based on sample size. 10 REVOLUTIONPHARMD.COM

3s Control Chart Factors for Variables:

3 s Control Chart Factors for Variables Control Limit Factor Control Limit Factor Sample for Sample Mean for Sample Range Size n A D 1 D 2 2 1.880 0 3.267 3 1.023 0 2.575 4 0.729 0 2.282 5 0.577 0 2.116 10 0.308 0.223 1.777 15 0.223 0.348 1.652 20 0.180 0.414 1.586 25 0.153 0.459 1.541 Over 25 0.45+.001 1.55-.0015n 11 REVOLUTIONPHARMD.COM

Example: Variable Control Chart:

Example: Variable Control Chart Harry Coates wants to construct x and R charts at the bag-filling operation for Meow Chow cat food. He has determined that when the filling operation is functioning correctly, bags of cat food average 50.01 pounds and regularly-taken 5-bag samples have an average range of .322 pounds. 12 REVOLUTIONPHARMD.COM

Example: Variable Control Chart:

Example: Variable Control Chart Sample Mean Chart x = 50.01, R = .322, n = 5 UCL = x + AR = 50.01 + .577(.322) = 50.196 LCL = x - AR = 50.01 - .577(.322) = 49.824 13 REVOLUTIONPHARMD.COM

Example: Variable Control Chart:

Example: Variable Control Chart 14 REVOLUTIONPHARMD.COM

Quality Control in Services:

Quality Control in Services In all services there is a continuing need to monitor quality Control charts are used extensively in services to monitor and control their quality levels With automation, inspection and testing can be so inexpensive and quick that companies may be able to increase sample sizes and the frequency of samples, thus attaining more precision in both control charts and acceptance plans. 15 REVOLUTIONPHARMD.COM

Conclusion:

Conclusion Quality cannot be inspected into products. Processes must be operated to achieve quality conformance; quality control is used to achieve this. Statistical control charts are used extensively to provide feedback to everyone about quality performance. 16 REVOLUTIONPHARMD.COM

Conclusion:

Conclusion Where 100% inspection and testing are impractical, uneconomical, or impossible, acceptance plans may be used to determine if lots of products are likely to meet customer expectations. The trend is toward 100% inspection and testing; automated inspection and testing has made such an approach effective and economical. 17 REVOLUTIONPHARMD.COM

Thank You!:

Thank You! 18 REVOLUTIONPHARMD.COM

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