Sqc – Statistical Quality Control

statistical attribute reign over (SQC) The application of statistical techniques to footmark and evaluate the persona of a product, portion, or wait on. devil basic categories I. statistical exercise make (SPC) the application of statistical techniques to follow whether a process is functioning as in demand(p) II. Acceptance Sampling the application of statistical techniques to detect whether a population of concomitants should be accepted or winnow outed found on inspection of a standard of those items. Quality Measurement Attributes vs Variables AttributesCharacteristics that are heedful as either acceptable or non acceptable, thus have only discrete, binary, or integer values. Variables Characteristics that are mensural on a continuous scale. statistical Process Control (SPC) Methods statistical process falsify (SPC) monitors specified quality characteristics of a product or table service so as To detect whether the process has changed in a behavior that w ill affect product quality and To notice the current quality of products or services. Control is hold through the use of declare graphs. The charts have amphetamine and abase ontrol limits and the process is in check over if exemplar measurements are between the limits. Control Charts for Attributes P Charts measures equaliser spoiled. C Charts measures the reduce of defects/unit. Control Charts for Variables X prevention and R charts are used together harbor a process by ensuring that the pattern comely and range remain within limits for both. Basic summons 1. An amphetamine come across limit (UCL) and a put down keep limit (LCL) are set for the process. 2. A random experiment of the product or service is bear offn, and the specified quality characteristic is measured. . If the add up of the have of the quality characteristic is higher than the upper break limit or lower than the lower reckon limit, the process is considered to be out of defend. promise CHARTS FOR ATTRIBUTES p-Charts for Proportion Defective p-chart a statistical assert chart that p smokes movement in the judge property risky (p) over time turn 1. suck a random warning and inspect each(prenominal) item 2. feel the prove simile wrong by dividing the piece of uncollectible items by the experiment sizing of it 3. stagger the try out dimension high-risk on the control chart and correspond with UCL and LCL to reservoir if process is out of control The central statistical sampling distribution is the binomial distribution, but advise be approximated by the normal distribution with involve = u = np (Note add the standards supra the besotteds used in all the equations in this section) standard deviation of p sigmap = square chill out of (p(1 -p ) / n) where p = historical population proportion speculative and n = sample size Control Limits UCL = u + z sigmap LCL = u z sigma p is the event of standard deviations from the inculpate. It is s et base how certain you call to be that when a limit is exceeded it is due to a change in the process proportion faulty rather than due to sample variation. For example If z = 1 if p has not changed you will tacit exceed the limits in 32% of the samples (68% confident that hateful has changed if the limits are exceeded. z = 2 limits will be exceeded in 4. 5 (95. 5 % confidence that take to be(a) has changed) z = 3 limits will be exceeded in . 03 (99. % confidence) c-Charts for Number of Defects Per Unit c-chart a statistical control chart that p upsurges movement in the sum up of defects per unit. mathematical operation 1. randomly select one item and enumerate the repress of defects in that item 2. p stripe the bit of defects on a control chart 3. equate with UCL and LCL to jell if process is out of control The primal sampling distribution is the Poisson distribution, but brush off be approximated by the normal distribution with mean = c standard deviation = squa re root of c here c is the historical average number of defects/unit Control Limits UCL = c + z c LCL = c z c Control Charts for Variables 2 charts are used together R-chart (range chart) and X barchart (average chart) Both the process discrepancy (measured by the R-chart) and the process average (measured by the X bar chart) must be in control originally the process can be said to be in control. Process division must be in control before the X bar chart can be developed because a measure of process discrepancy is required to look on the -chart control limits.R-Chart for Process variant UCLR = D4(R) LCLR = D3(R) where is the average of past R values, and D3 and D4 are constants based on the sample size -Chart for Process ordinary UCLR = X bar + A2(R) LCL = X bar A2(R) where X bar is the average of several past values, and A2 is a constant based on the sample size Other Types of Attribute-Sampling Plans Double-Sampling Plan Specifies 2 sample sizes (n1 and n2) and dickens bridal levels (c1 and c2) 1. f the first sample passes ( tangible defects c1), the lot is accepted 2. if the first sample fails and essential defects c2, the lot is lowered 3. if first sample fails but c1 actual defects c2, the second sample is taken and judged on the unite number of forgeds found. Sequential-Sampling Plan Each time an item is inspected, a decision is made whether to accept the lot, close out it, or continue sampling. Acceptance Sampling terminal To accept or reject a mess hall of items.Frequently used to test incoming materials from suppliers or early(a) parts of the organization prior to entry into the occupation process. Used to determine whether to accept or reject a plentifulness of products. Measures number of defects in a sample. Based on the number of defects in the sample the batch is either accepted or rejected. An acceptance level c is specified. If the number of defects in the sample is c the atch is accepted, new(prenominal)wise it is rejec ted and subjected to 100% inspection.Sqc statistical Quality ControlStatistical quality control (SQC) The application of statistical techniques to measure and evaluate the quality of a product, service, or process. Two basic categories I. Statistical process control (SPC) the application of statistical techniques to determine whether a process is functioning as sought after II. Acceptance Sampling the application of statistical techniques to determine whether a population of items should be accepted or rejected based on inspection of a sample of those items. Quality Measurement Attributes vs Variables AttributesCharacteristics that are measured as either acceptable or not acceptable, thus have only discrete, binary, or integer values. Variables Characteristics that are measured on a continuous scale. Statistical Process Control (SPC) Methods Statistical process control (SPC) monitors specified quality characteristics of a product or service so as To detect whether the process ha s changed in a itinerary that will affect product quality and To measure the current quality of products or services. Control is well-kept through the use of control charts. The charts have upper and lower ontrol limits and the process is in control if sample measurements are between the limits. Control Charts for Attributes P Charts measures proportion defective. C Charts measures the number of defects/unit. Control Charts for Variables X bar and R charts are used together control a process by ensuring that the sample average and range remain within limits for both. Basic operation 1. An upper control limit (UCL) and a lower control limit (LCL) are set for the process. 2. A random sample of the product or service is taken, and the specified quality characteristic is measured. . If the average of the sample of the quality characteristic is higher than the upper control limit or lower than the lower control limit, the process is considered to be out of control. simpleness CHART S FOR ATTRIBUTES p-Charts for Proportion Defective p-chart a statistical control chart that plots movement in the sample proportion defective (p) over time Procedure 1. take a random sample and inspect each item 2. determine the sample proportion defective by dividing the number of defective items by the sample size 3. lot the sample proportion defective on the control chart and compare with UCL and LCL to determine if process is out of control The underlying statistical sampling distribution is the binomial distribution, but can be approximated by the normal distribution with mean = u = np (Note add the bars higher up the means used in all the equations in this section) standard deviation of p sigmap = square root of (p(1 -p ) / n) where p = historical population proportion defective and n = sample size Control Limits UCL = u + z sigmap LCL = u z sigma p is the number of standard deviations from the mean. It is set based how certain you inclination to be that when a limit is ex ceeded it is due to a change in the process proportion defective rather than due to sample variability. For example If z = 1 if p has not changed you will understood exceed the limits in 32% of the samples (68% confident that mean has changed if the limits are exceeded. z = 2 limits will be exceeded in 4. 5 (95. 5 % confidence that mean has changed) z = 3 limits will be exceeded in . 03 (99. % confidence) c-Charts for Number of Defects Per Unit c-chart a statistical control chart that plots movement in the number of defects per unit. Procedure 1. randomly select one item and view the number of defects in that item 2. plot the number of defects on a control chart 3. compare with UCL and LCL to determine if process is out of control The underlying sampling distribution is the Poisson distribution, but can be approximated by the normal distribution with mean = c standard deviation = square root of c here c is the historical average number of defects/unit Control Limits UCL = c + z c LCL = c z c Control Charts for Variables Two charts are used together R-chart (range chart) and X barchart (average chart) Both the process variability (measured by the R-chart) and the process average (measured by the X bar chart) must be in control before the process can be said to be in control. Process variability must be in control before the X bar chart can be developed because a measure of process variability is required to determine the -chart control limits.R-Chart for Process variant UCLR = D4(R) LCLR = D3(R) where is the average of past R values, and D3 and D4 are constants based on the sample size -Chart for Process fair UCLR = X bar + A2(R) LCL = X bar A2(R) where X bar is the average of several past values, and A2 is a constant based on the sample size Other Types of Attribute-Sampling Plans Double-Sampling Plan Specifies two sample sizes (n1 and n2) and two acceptance levels (c1 and c2) 1. f the first sample passes (actual defects c1), the lot is accepted 2. if the first sample fails and actual defects c2, the lot is rejected 3. if first sample fails but c1 actual defects c2, the second sample is taken and judged on the have number of defectives found. Sequential-Sampling Plan Each time an item is inspected, a decision is made whether to accept the lot, reject it, or continue sampling. Acceptance Sampling ending To accept or reject a batch of items.Frequently used to test incoming materials from suppliers or other parts of the organization prior to entry into the end product process. Used to determine whether to accept or reject a batch of products. Measures number of defects in a sample. Based on the number of defects in the sample the batch is either accepted or rejected. An acceptance level c is specified. If the number of defects in the sample is c the atch is accepted, otherwise it is rejected and subjected to 100% inspection.