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Statirstical Process Control in manufacturing
1.
© 2007 Wiley Chapter
6 - Statistical Quality Control
2.
© 2007 Wiley Learning
Objectives Describe Categories of SQC Using statistical tools in measuring quality characteristics Identify and describe causes of variation Describe the use of control charts Identify the differences between x-bar, R-, p-, and c-charts Explain process capability and process capability index Explain the term six-sigma Explain acceptance sampling and the use of OC curves Describe the inherent challenges in measuring quality in service organizations
3.
© 2007 Wiley Three
SQC Categories Statistical quality control (SQC) is the term used to describe the set of statistical tools used by quality professionals SQC encompasses three broad categories of; Traditional descriptive statistics e.g. the mean, standard deviation, and range Acceptance sampling used to randomly inspect a batch of goods to determine acceptance/rejection Does not help to catch in-process problems Statistical process control (SPC) Involves inspecting the output from a process Quality characteristics are measured and charted Helpful in identifying in-process variations
4.
© 2007 Wiley Sources
of Variation Variation exists in all processes. Variation can be categorized as either; Common or Random causes of variation, or Random causes that we cannot identify Unavoidable e.g. slight differences in process variables like diameter, weight, service time, temperature Assignable causes of variation Causes can be identified and eliminated e.g. poor employee training, worn tool, machine needing repair
5.
© 2007 Wiley Traditional
Statistical Tools Descriptive Statistics include The Mean: measure of central tendency The Range: difference between largest/smallest observations in a set of data Standard Deviation: measures the amount of data dispersion around mean Data distribution shape: normal or bell shaped or skewed n x x n 1 i i Mean 1 n X x σ Deviation Standard n 1 i 2 i
6.
© 2007 Wiley Distribution
of Data Normal distributions Skewed distribution
7.
© 2007 Wiley SPC
Methods-Control Charts Control Charts show sample data plotted on a graph with CL, UCL, and LCL Control chart for variables are used to monitor characteristics that can be measured, e.g. length, weight, diameter, time Control charts for attributes are used to monitor characteristics that have discrete values and can be counted, e.g. % defective, number of flaws in a shirt, number of broken eggs in a box
8.
© 2007 Wiley Setting
Control Limits Percentage of values under normal curve Control limits balance risks like Type I error
9.
© 2007 Wiley Control
Charts for Variables Use x-bar and R- bar charts together Used to monitor different variables X-bar & R-bar Charts reveal different problems In statistical control on one chart, out of control on the other chart? OK?
10.
© 2007 Wiley x x x x n 2 1 zσ x LCL zσ x UCL sample each w/in ns observatio of # the is (n) and means sample of # the is ) ( where n σ σ , ...x x x x
x k k Constructing a X-bar Chart: A quality control inspector at the Cocoa Fizz soft drink company has taken three samples with four observations each of the volume of bottles filled. If the standard deviation of the bottling operation is .2 ounces, use the below data to develop control charts with limits of 3 standard deviations for the 16 oz. bottling operation. Center line and control limit formulas Time 1 Time 2 Time 3 Observation 1 15.8 16.1 16.0 Observation 2 16.0 16.0 15.9 Observation 3 15.8 15.8 15.9 Observation 4 15.9 15.9 15.8 Sample means (X-bar) 15.875 15.975 15.9 Sample ranges (R) 0.2 0.3 0.2
11.
© 2007 Wiley Solution
and Control Chart (x-bar) Center line (x-double bar): Control limits for±3σ limits: 15.92 3 15.9 15.975 15.875 x 15.62 4 .2 3 15.92 zσ x LCL 16.22 4 .2 3 15.92 zσ x UCL x x x x
12.
© 2007 Wiley X-Bar
Control Chart
13.
© 2007 Wiley Control
Chart for Range (R) Center Line and Control Limit formulas: Factors for three sigma control limits 0.0 0.0(.233) R D LCL .53 2.28(.233) R D UCL .233 3 0.2 0.3 0.2 R 3 4 R R Factor for x-Chart A2 D3 D4 2 1.88 0.00 3.27 3 1.02 0.00 2.57 4 0.73 0.00 2.28 5 0.58 0.00 2.11 6 0.48 0.00 2.00 7 0.42 0.08 1.92 8 0.37 0.14 1.86 9 0.34 0.18 1.82 10 0.31 0.22 1.78 11 0.29 0.26 1.74 12 0.27 0.28 1.72 13 0.25 0.31 1.69 14 0.24 0.33 1.67 15 0.22 0.35 1.65 Factors for R-Chart Sample Size (n)
14.
© 2007 Wiley R-Bar
Control Chart
15.
© 2007 Wiley Second
Method for the X-bar Chart Using R-bar and the A2 Factor (table 6-1) Use this method when sigma for the process distribution is not known. Control limits solution: 15.75 .233 0.73 15.92 R A x LCL 16.09 .233 0.73 15.92 R A x UCL .233 3 0.2 0.3 0.2 R 2 x 2 x
16.
© 2007 Wiley Control
Charts for Attributes – P-Charts & C-Charts Use P-Charts for quality characteristics that are discrete and involve yes/no or good/bad decisions Number of leaking caulking tubes in a box of 48 Number of broken eggs in a carton Use C-Charts for discrete defects when there can be more than one defect per unit Number of flaws or stains in a carpet sample cut from a production run Number of complaints per customer at a hotel
17.
© 2007 Wiley P-Chart
Example: A Production manager for a tire company has inspected the number of defective tires in five random samples with 20 tires in each sample. The table below shows the number of defective tires in each sample of 20 tires. Calculate the control limits. Sample Number of Defective Tires Number of Tires in each Sample Proportion Defective 1 3 20 .15 2 2 20 .10 3 1 20 .05 4 2 20 .10 5 2 20 .05 Total 9 100 .09 Solution: 0 .102 3(.064) .09 σ z p LCL .282 3(.064) .09 σ z p UCL 0.064 20 (.09)(.91) n ) p (1 p σ .09 100 9 Inspected Total Defectives # p CL p p p
18.
© 2007 Wiley P-
Control Chart
19.
© 2007 Wiley C-Chart
Example: The number of weekly customer complaints are monitored in a large hotel using a c-chart. Develop three sigma control limits using the data table below. Week Number of Complaints 1 3 2 2 3 3 4 1 5 3 6 3 7 2 8 1 9 3 10 1 Total 22 Solution: 0 2.25 2.2 3 2.2 c c LCL 6.65 2.2 3 2.2 c c UCL 2.2 10 22 samples of # complaints # CL c c z z
20.
© 2007 Wiley C-Control
Chart
21.
© 2007 Wiley C-Chart
Example: The number of weekly customer complaints are monitored in a large hotel using a c-chart. Develop three sigma control limits using the data table below. Week Number of Complaints 1 3 2 2 3 3 4 1 5 3 6 3 7 2 8 1 9 3 10 1 Total 22 Solution: 0 2.25 2.2 3 2.2 c c LCL 6.65 2.2 3 2.2 c c UCL 2.2 10 22 samples of # complaints # CL c c z z
22.
© 2007 Wiley C-
Control Chart
23.
© 2007 Wiley Process
Capability Product Specifications Preset product or service dimensions, tolerances e.g. bottle fill might be 16 oz. ±.2 oz. (15.8oz.-16.2oz.) Based on how product is to be used or what the customer expects Process Capability – Cp and Cpk Assessing capability involves evaluating process variability relative to preset product or service specifications Cp assumes that the process is centered in the specification range Cpk helps to address a possible lack of centering of the process 6σ LSL USL width process width ion specificat Cp 3σ LSL μ , 3σ μ USL min Cpk
24.
© 2007 Wiley Relationship
between Process Variability and Specification Width Three possible ranges for Cp Cp = 1, as in Fig. (a), process variability just meets specifications Cp ≤ 1, as in Fig. (b), process not capable of producing within specifications Cp ≥ 1, as in Fig. (c), process exceeds minimal specifications One shortcoming, Cp assumes that the process is centered on the specification range Cp=Cpk when process is centered
25.
© 2007 Wiley Computing
the Cp Value at Cocoa Fizz: three bottling machines are being evaluated for possible use at the Fizz plant. The machines must be capable of meeting the design specification of 15.8-16.2 oz. with at least a process capability index of 1.0 (Cp≥1) The table below shows the information gathered from production runs on each machine. Are they all acceptable? Solution: Machine A Machine B Machine C Machine σ USL-LSL 6σ A .05 .4 .3 B .1 .4 .6 C .2 .4 1.2 1.33 6(.05) .4 6σ LSL USL Cp 0.33 6(.1) .4 6σ LSL USL Cp 0.25 6(.2) .4 6σ LSL USL Cp
26.
© 2007 Wiley Computing
the Cpk Value at Cocoa Fizz Design specifications call for a target value of 16.0 ±0.2 OZ. (USL = 16.2 & LSL = 15.8) Observed process output has now shifted and has a µ of 15.9 and a σ of 0.1 oz. Cpk is less than 1, revealing that the process is not capable .33 .3 .1 Cpk 3(.1) 15.8 15.9 , 3(.1) 15.9 16.2 min Cpk
27.
© 2007 Wiley ±6
Sigma versus ± 3 Sigma Motorola coined “six-sigma” to describe their higher quality efforts back in 1980’s Six-sigma quality standard is now a benchmark in many industries Before design, marketing ensures customer product characteristics Operations ensures that product design characteristics can be met by controlling materials and processes to 6σ levels Other functions like finance and accounting use 6σ concepts to control all of their processes PPM Defective for ±3σ versus ±6σ quality
28.
© 2007 Wiley Acceptance
Sampling Definition: the third branch of SQC refers to the process of randomly inspecting a certain number of items from a lot or batch in order to decide whether to accept or reject the entire batch Different from SPC because acceptance sampling is performed either before or after the process rather than during Sampling before typically is done to supplier material Sampling after involves sampling finished items before shipment or finished components prior to assembly Used where inspection is expensive, volume is high, or inspection is destructive
29.
© 2007 Wiley Acceptance
Sampling Plans Goal of Acceptance Sampling plans is to determine the criteria for acceptance or rejection based on: Size of the lot (N) Size of the sample (n) Number of defects above which a lot will be rejected (c) Level of confidence we wish to attain There are single, double, and multiple sampling plans Which one to use is based on cost involved, time consumed, and cost of passing on a defective item Can be used on either variable or attribute measures, but more commonly used for attributes
30.
© 2007 Wiley Operating
Characteristics (OC) Curves OC curves are graphs which show the probability of accepting a lot given various proportions of defects in the lot X-axis shows % of items that are defective in a lot- “lot quality” Y-axis shows the probability or chance of accepting a lot As proportion of defects increases, the chance of accepting lot decreases Example: 90% chance of accepting a lot with 5% defectives; 10% chance of accepting a lot with 24% defectives
31.
© 2007 Wiley AQL,
LTPD, Consumer’s Risk (α) & Producer’s Risk (β) AQL is the small % of defects that consumers are willing to accept; order of 1-2% LTPD is the upper limit of the percentage of defective items consumers are willing to tolerate Consumer’s Risk (α) is the chance of accepting a lot that contains a greater number of defects than the LTPD limit; Type II error Producer’s risk (β) is the chance a lot containing an acceptable quality level will be rejected; Type I error
32.
© 2007 Wiley Developing
OC Curves OC curves graphically depict the discriminating power of a sampling plan Cumulative binomial tables like partial table below are used to obtain probabilities of accepting a lot given varying levels of lot defectives Top of the table shows value of p (proportion of defective items in lot), Left hand column shows values of n (sample size) and x represents the cumulative number of defects found Table 6-2 Partial Cumulative Binomial Probability Table (see Appendix C for complete table) Proportion of Items Defective (p) .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 n x 5 0 .7738 .5905 .4437 .3277 .2373 .1681 .1160 .0778 .0503 .0313 Pac 1 .9974 .9185 .8352 .7373 .6328 .5282 .4284 .3370 .2562 .1875 AOQ .0499 .0919 .1253 .1475 .1582 .1585 .1499 .1348 .1153 .0938
33.
© 2007 Wiley Example
6-8 Constructing an OC Curve Lets develop an OC curve for a sampling plan in which a sample of 5 items is drawn from lots of N=1000 items The accept /reject criteria are set up in such a way that we accept a lot if no more that one defect (c=1) is found Using Table 6-2 and the row corresponding to n=5 and x=1 Note that we have a 99.74% chance of accepting a lot with 5% defects and a 73.73% chance with 20% defects
34.
© 2007 Wiley Average
Outgoing Quality (AOQ) With OC curves, the higher the quality of the lot, the higher is the chance that it will be accepted Conversely, the lower the quality of the lot, the greater is the chance that it will be rejected The average outgoing quality level of the product (AOQ) can be computed as follows: AOQ=(Pac)p Returning to the bottom line in Table 6-2, AOQ can be calculated for each proportion of defects in a lot by using the above equation This graph is for n=5 and x=1 (same as c=1) AOQ is highest for lots close to 30% defects
35.
© 2007 Wiley Implications
for Managers How much and how often to inspect? Consider product cost and product volume Consider process stability Consider lot size Where to inspect? Inbound materials Finished products Prior to costly processing Which tools to use? Control charts are best used for in-process production Acceptance sampling is best used for inbound/outbound
36.
© 2007 Wiley SQC
in Services Service Organizations have lagged behind manufacturers in the use of statistical quality control Statistical measurements are required and it is more difficult to measure the quality of a service Services produce more intangible products Perceptions of quality are highly subjective A way to deal with service quality is to devise quantifiable measurements of the service element Check-in time at a hotel Number of complaints received per month at a restaurant Number of telephone rings before a call is answered Acceptable control limits can be developed and charted
37.
© 2007 Wiley Service
at a bank: The Dollars Bank competes on customer service and is concerned about service time at their drive-by windows. They recently installed new system software which they hope will meet service specification limits of 5±2 minutes and have a Capability Index (Cpk) of at least 1.2. They want to also design a control chart for bank teller use. They have done some sampling recently (sample size of 4 customers) and determined that the process mean has shifted to 5.2 with a Sigma of 1.0 minutes. Control Chart limits for ±3 sigma limits 1.2 1.5 1.8 Cpk 3(1/2) 5.2 7.0 , 3(1/2) 3.0 5.2 min Cpk 1.33 4 1.0 6 3 - 7 6σ LSL USL Cp minutes 6.5 1.5 5.0 4 1 3 5.0 zσ X UCL x x minutes 3.5 1.5 5.0 4 1 3 5.0 zσ X LCL x x
38.
© 2007 Wiley Chapter
6 Highlights SQC can be divided into three categories: traditional statistical tools (SQC), acceptance sampling, and statistical process control (SPC). SQC tools describe quality characteristics, acceptance sampling is used to decide whether to accept or reject an entire lot, SPC is used to monitor any process output to see if its characteristics are in Specs. Variation is caused from common (random), unidentifiable causes and also assignable causes that can be identified and corrected. Control charts are SPC tools used to plot process output characteristics for both variable and attribute data to show whether a sample falls within the normal range of variation: X-bar, R, P, and C- charts. Process capability is the ability of the process to meet or exceed preset specifications; measured by Cp and Cpk.
39.
© 2007 Wiley Chapter
Highlights (continued) The term six-sigma indicates a level of quality in which the number of defects is no more than 3.4 parts per million. Acceptance sampling uses criteria for acceptance or rejection based on lot size, sample size, and confidence level. OC curves are graphs that show the discriminating power of a sampling plan. It is more difficult to measure quality in services than in manufacturing. The key is to devise quantifiable measurements.
40.
© 2007 Wiley Chapter
6 Homework Hints 6.4: calculate mean and range for all 10 samples. Use Table 6-1 data to determine the UCL and LCL for the mean and range, and then plot both control charts (x-bar and r-bar). 6.8: use the data for preparing a p-bar chart. Plot the 4 additional samples to determine your “conclusions.” 6.11: determine the process capabilities (CPk) of the 3 machines and decide which are “capable.”
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