. Greasex Defects. What is the greatest barrier Kolb is facing?. Lack of quality awareness at the company and management emphasis on cost reduction. What should Kolb do?. Form quality council headed by Morganthol (general manager)Define clearly quality mission and objectives of the companyImmediate training in quality practicesForm quality improvement teamsbuy-incross-functional or specific areateams should be trained in quality practices.
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1. Hank Kolb Director of Quality Assurance
3. What is the greatest barrier Kolb is facing? Lack of quality awareness at the company and management emphasis on cost reduction
4. What should Kolb do? Form quality council headed by Morganthol (general manager)
Define clearly quality mission and objectives of the company
Immediate training in quality practices
Form quality improvement teams
cross-functional or specific area
teams should be trained in quality practices
6. Basic Forms of Statistical Sampling for Quality Control Sampling to accept or reject the immediate lot of product at hand (Acceptance Sampling).
Sampling to determine if the process is within acceptable limits (Statistical Process Control)
7. Statistical Fundamentals Statistical Thinking
Is a decision-making skill demonstrated by the ability to draw to conclusions based on data.
Why Do Statistics Sometimes Fail in the Workplace?
Regrettably, many times statistical tools do not create the desired result. Why is this so? Many firms fail to implement quality control in a substantive way.
8. Statistical Fundamentals Reasons for Failure of Statistical Tools
Lack of knowledge about the tools; therefore, tools are misapplied.
General disdain for all things mathematical creates a natural barrier to the use of statistics.
Cultural barriers in a company make the use of statistics for continual improvement difficult.
Statistical specialists have trouble communicating with managerial generalists.
9. Statistical Fundamentals Reasons for Failure of Statistical Tools (continued)
Statistics generally are poorly taught, emphasizing mathematical development rather than application.
People have a poor understanding of the scientific method.
Organization lack patience in collecting data. All decisions have to be made “yesterday.”
10. Statistical Fundamentals Reasons for Failure of Statistical Tools (continued)
Statistics are view as something to buttress an already-held opinion rather than a method for informing and improving decision making.
Most people don’t understand random variation resulting in too much process tampering.
11. Basic Forms of Variation Common (random) variation is inherent in the production process.
Assignable (nonrandom) variation is caused by factors that can be clearly identified and possibly managed.
12. Statistical Fundamentals Understanding Process Variation
Random variation is centered around a mean and occurs with a consistent amount of dispersion.
This type of variation cannot be controlled. Hence, we refer to it as “uncontrolled variation.”
The statistical tools discussed in this chapter are not designed to detect random variation.
13. Statistical Fundamentals Understanding Process Variation (cont.)
Nonrandom or “special cause” variation results from some event. The event may be a shift in a process mean or some unexpected occurrence.
Means that the variation we observe in the process is random variation. To determine process stability we use process charts.
14. Statistical Fundamentals Sampling Methods
To ensure that processes are stable, data are gathered in samples.
Random samples. Randomization is useful because it ensures independence among observations. To randomize means to sample is such a way that every piece of product has an equal chance of being selected for inspection.
Systematic samples. Systematic samples have some of the benefits of random samples without the difficulty of randomizing.
15. Statistical Fundamentals Sampling Methods
To ensure that processes are stable, data are gathered in samples (continued)
Sampling by Rational Subgroup. A rational subgroup is a group of data that is logically homogenous; variation within the data can provide a yardstick for setting limits on the standard variation between subgroups.
16. Statistical Process Control Charts
17. Process Control Charts Process Charts
Tools for monitoring process variation.
The figure on the following slide shows a process control chart. It has an upper limit, a center line, and a lower limit.
18. Process Control Charts
20. Process Control Charts Variables and Attributes
To select the proper process chart, we must differentiate between variables and attributes.
A variable is a continuous measurement such as weight, height, or volume.
An attribute is the result of a binomial process that results in an either-or-situation.
The most common types of variable and attribute charts are shown in the following slide.
21. Process Control Charts
22. Process Control Charts
23. Process Control Charts A Generalized Procedure for Developing Process Charts
Identify critical operations in the process where inspection might be needed. These are operations in which, if the operation is performed improperly, the product will be negatively affected.
Identify critical product characteristics. These are the attributes of the product that will result in either good or poor function of the product.
24. Process Control Charts A Generalized Procedure for Developing Process Charts (continued)
Determine whether the critical product characteristic is a variable or an attribute.
Select the appropriate process control chart from among the many types of control charts. This decision process and types of charts available are discussed later.
Establish the control limits and use the chart to continually improve.
25. Process Control Charts A Generalized Procedure for Developing Process Charts (continued)
Update the limits when changes have been made to the process.
26. Process Control Charts Understanding Control Charts
A process chart is nothing more than an application of hypothesis testing where the null hypothesis is that the product meets requirements.
An X-bar chart is a variables chart that monitors average measurement.
An example of how to best understand control charts is provided under the heading “Understanding Control Charts” in the textbook.
27. Process Control Charts X-bar and R Charts
The X-bar chart is a process chart used to monitor the average of the characteristics being measured. To set up an X-bar chart select samples from the process for the characteristic being measured. Then form the samples into rational subgroups. Next, find the average value of each sample by dividing the sums of the measurements by the sample size and plot the value on the process control X-bar chart.
28. Process Control Charts X-bar and R Charts (continued)
The R chart is used to monitor the variability or dispersion of the process. It is used in conjunction with the X-bar chart when the process characteristic is variable. To develop an R chart, collect samples from the process and organize them into subgroups, usually of three to six items. Next, compute the range, R, by taking the difference of the high value in the subgroup minus the low value. Then plot the R values on the R chart.
29. Process Control Charts
30. Example of x-Bar and R Charts: Required Data
31. Example of x-bar and R charts: Step 1. Calculate sample means, sample ranges, mean of means, and mean of ranges.
32. Example of x-bar and R charts: Step 2. Determine Control Limit Formulas and Necessary Tabled Values
33. Example of x-bar and R charts: Steps 3&4. Calculate x-bar Chart and Plot Values
34. Example of x-bar and R charts: Steps 5&6. Calculate R-chart and Plot Values
37. Process Control Charts Interpreting Control Charts
Before introducing other types of process charts, we discuss the interpretation of the charts.
The figures in the next several slides show different signals for concern that are sent by a control chart, as in the second and third boxes. When a point is found to be outside of the control limits, we call this an “out of control” situation. When a process is out of control, the variation is probably not longer random.
38. Process Control Charts
39. Process Control Charts
40. Process Control Charts
41. Process Control Charts
42. Process Control Charts Implications of a Process Out of Control
If a process loses control and becomes nonrandom, the process should be stopped immediately.
In many modern process industries where just-in-time is used, this will result in the stoppage of several work stations.
The team of workers who are to address the problem should use a structured problem solving process.
43. Process Control Charts X and Moving Range (MR) Charts for Population Data
At times, it may not be possible to draw samples. This may occur because a process is so slow that only one or two units per day are produced.
If you have a variable measurement that you want to monitor, the X and MR charts might be the thing for you.
44. Process Control Charts X and Moving Range (MR) Charts for Population Data (continued)
X chart. A chart used to monitor the mean of a process for population values.
MR chart. A chart for plotting variables when samples are not possible.
If data are not normally distributed, other charts are available.
45. Process Control Charts Other Control Charts (continued)
Moving Average Chart. The moving average chart is an interesting chart that is used for monitoring variables and measurement on a continuous scale.
The chart uses past information to predict what the next process outcome will be. Using this chart, we can adjust a process in anticipation of its going out of control.
46. Process Control Charts Control Charts for Attributes
We now shift to charts for attributes. These charts deal with binomial and Poisson processes that are not measurements.
We will now be thinking in terms of defects and defectives rather than diameters or widths.
A defect is an irregularity or problem with a larger unit.
A defective is a unit that, as a whole, is not acceptable or does not meet specifications.
47. Process Control Charts p Charts for Proportion Defective
The p chart is a process chart that is used to graph the proportion of items in a sample that are defective (nonconforming to specifications)
p charts are effectively used to determine when there has been a shift in the proportion defective for a particular product or service.
Typical applications of the p chart include things like late deliveries, incomplete orders, and clerical errors on written forms.
48. Process Control Charts np Charts
The np chart is a graph of the number of defectives (or nonconforming units) in a subgroup. The np chart requires that the sample size of each subgroup be the same each time a sample is drawn.
When subgroup sizes are equal, either the p or np chart can be used. They are essentially the same chart.
49. Example of Constructing a p-Chart: Required Data
50. Statistical Process Control Formulas: Attribute Measurements (p-Chart)
51. Example of Constructing a p-chart: Step 1
52. Example of Constructing a p-chart: Steps 2&3
53. Example of Constructing a p-chart: Step 4
54. Example of Constructing a p-Chart: Step 5
55. Process Capability Process Stability and Capability
Once a process is stable, the next emphasis is to ensure that the process is capable.
Process capability refers to the ability of a process to produce a product that meets specifications.
56. Process Capability Process limits
How do the limits relate to one another?
57. Process Capability Process Versus Sampling Distribution
To understand process capability we must first understand the differences between population and sampling distributions.
Population distributions are distributions with all the items or observations of interest to a decision maker.
A population is defined as a collection of all the items or observations of interest to a decision maker.
A sample is subset of the population. Sampling distributions are distributions that reflect the distributions of sample means.
59. Process Control Charts
60. Control Limits are based on the Standard Normal Distribution Curve
62. If the process capability of a normally distributed process is .084, the process is in control, and is centered at .550. What are the upper and lower control limits for this process?
66. Process Capability Index, Cpk
process mean = 1.0015
s = .001
LTL = .994
UTL = 1.006 Process Capability Index- Example
70. Process Control Charts Some Control Chart Concepts (continued)
Corrective Action. When a process is out of control, corrective action is needed. Correction action steps are similar to continuous improvement processes. They are
Carefully identify the problem.
Form the correct team to evaluate and solve the problem.
Use structured brainstorming along with fishbone diagrams or affinity diagrams to identify causes of the problem.
71. Process Control Charts Some Control Chart Concepts (continued)
Corrective Action (continued)
Brainstorm to identify potential solutions to problems.
Eliminate the cause.
Restart the process.
Document the problem, root causes, and solutions.
Communicate the results of the process to all personnel so that this process becomes reinforced and ingrained in the operations.
72. Process Control Charts Some Control Chart Concepts
How Do We Use Control Charts to Continuously Improve?
One of the goals of the control chart user is to reduce variation. Over time, as processes are improved, control limits are recomputed to show improvements in stability. As upper and lower control limits get closer and closer together, the process improving.
The focus of control charts should be on continuous improvement and they should be updated only when there is a change in the process.
73. Six Sigma Quality A philosophy and set of methods companies use to eliminate defects in their products and processes
Seeks to reduce variation in the processes that lead to product defects
The name, “six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs
74. Process Capability
75. Process Capability The Difference Between Capability and Stability?
Once again, a process is capable if individual products consistently meet specifications.
A process is stable if only common variation is present in the process.
76. Acceptance Sampling Acceptance Sampling
A statistical quality control technique used in deciding to accept or reject a shipment of input or output.
Acceptance sampling inspection can range from 100% of the Lot to a relatively few items from the Lot (N=2) from which the receiving firm draws inferences about the whole shipment.
77. Acceptance Sampling Purposes
Determine quality level
Ensure quality is within predetermined level
Less handling damage
Upgrading of the inspection job
Applicability to destructive testing
Entire lot rejection (motivation for improvement)
78. Acceptance Sampling Disadvantages
Risks of accepting “bad” lots and rejecting “good” lots
Added planning and documentation
Sample provides less information than 100-percent inspection
79. Acceptance Sampling
80. Acceptance Sampling Acceptance Sampling Fundamentals
Producer’s and Consumer’s Risk
Producer’s risk is the risk associated with rejecting a lot of materials that has good quality.
Consumer’s risk is the exact opposite. The risk associated with accepting a lot of materials that has bad quality.
81. Statistical Sampling Techniques Acceptable Quality Level (AQL)
The maximum percentage or proportion of nonconformities in a lot or batch that can be considered satisfactory as a process average.
Lot Tolerance Percent Defective (LTPD)
The level of poor quality that is included in a lot of goods.
82. Statistical Sampling Techniques n and c
The bottom line in acceptance sampling is that acceptance sampling plans are designed to give us two things: n and c, where
n = the sample size of a particular sampling plan
c = the maximum number of defective pieces for a
83. Statistical Sampling Techniques OC Curves
The operating characteristic (OC) curve provides an assessment of the probabilities of acceptance for a shipment, given the existing quality of the shipment.
84. Statistical Sampling Techniques
85. Statistical Sampling Techniques
86. Statistical Sampling Techniques Building an OC Curve
There are two ways to construct OC curves. The first uses the binomial distribution and the second, the Poisson distribution.
Estimating AQL and LTPD
OC curves can be used to estimate both AQLs and LTPDs. The figure on the next slide (Figure 9.8 in the text) shows an OC curve for a single sampling plan with n = 50 and c = 1.
87. Statistical Sampling Techniques
89. Robust Design
90. Robust Design Robustness = insensitive to noise variables
Systematically change control variables
Observe noise levels for each change
Determine response factor for each run
Choose best settings