1. Topics To Be Covered 1. Process Analysis for Continuous Improvement
2. Benchmarking
3. Process Improvement Approaches
4. Pareto Chart
5. Cause and Effect Diagram
6. Acceptance Sampling
7. Process Control Charts
8. Control Limits vs Specification Limits

2. Variability Types
Random Variability: Natural variations in the output of a process, created by countless minor errors. (not readily fixable)
Assignable Variations: A variation whose cause can be identified.
Equipment or Process cause.
Type of variability that is most commonly addressed.
-3 3
-2 2
-3 3
-2 2
Preferred

3. Areas of Waste Over production – making or doing more than required
Waiting – for info, materials, people, maintenance, etc
Transport – moving people or goods around or between sites
Poor process design – too many/too few steps, non standardization, inspection rather than prevention
Inventory – raw materials, WIP, finished goods, papers, electronic files
Motion – inefficient layouts or poor ergonomics at workstations or in offices.
Defects – errors, scrap, rework, non-conformance
Underutilized personnel resources and creativity – ideas that are not listened to skills that are not utilized

4. Continuous Improvement of
Process Analysis
Proactive vs Reactive
overall proactive is cheaper
Various Methods for Process Improvement
Kaizen
Process Based Management
Taguchi
General Steps for Process Improvement Model.
1. Define the problem in the context of the process
2. Identify, analyze & document the process
3. Measure current performance
4. Understand why the process is performing as it is.
5. Develop a strategy & implement the alternative chosen
6. Evaluate the results of the new process
7. Commit to continuous improvement of the process

5. Benchmarking
The search for industry “best practice” that lead to superior business performance.
Perspective companies to bench mark are typically leaders in their industry
Not necessarily in your industry.
Benchmarking steps:
1. Identify what is to be benchmarked.
2. Identify competitive companies
3. Determine data collection method, collect data
4. Determine current performance gap
5. Project future performance levels.
6. Communicate benchmarking findings; gain acceptance
7. Establish process improvement goals
8. Develop action plan
9. Implement specific actions & monitor progress
10. Recalibrate benchmarks; return to 1.

6. Process Improvement Approaches
Brainstorming
Generating ideas in an environment free of criticism & intimidation
4 basic principles
1. No criticism
2. No constraints
3. Build on other peoples ideas
4. Encourage participation
What are some obstacles to effective Brainstorming?
Process documentation:
Process mapping (flow charting)
Run Diagram (line chart)
Check sheets
Pareto charts
Cause and Effect diagrams

11. Pareto Chart
Typically a bar chart that shows the frequency of different types of defects in a
product or service.
Shows which problems may make the largest improvement if a solution is found
Separates vital problems from trivial ones.
Is tracking the number of occurrences for a specific fault the best measure for a
Pareto Chart? Why or why not?

12. Cause and Effect Diagram
Also called a Fishbone Diagram
Useful for communicating the potential causes of an out-of-control quality
characteristic and coordinating the choice of a corrective action.
1. List the concern or problem (effect)
2. Identify the factors influencing this concern.
3. List the reasons why this factor is influencing this concern.
4. Investigate potential solutions to remedy the concern.
Effect

13. Acceptance Sampling
A form of inspection applicable to lots or batches.
Purpose is to decide if a batch satisfies a predetermined standard.
Most useful for the following conditions:
Large # of items produced in short time
Cost consequences of passing defects is low
Destructive testing is required
Fatigue or boredom caused by inspecting large # of items leads to errors.
Sampling Plan
Specify the batch size
Specify a frequency
Specify an acceptance/rejection criteria
Methods
Single Sampling
Double Sampling
Multiple Sampling
Cost & time required typically define the plan

14. Statistical Sampling and Control
What types of product characteristics could we sample?
What are we trying to control?
Central Limit Theorem
Assumes if you have a large enough sample size then the distribution of the sample
mean will approximate a normal distribution, regardless of their true distribution.
Most say that is a large enough sample.
Use the Student t for less
Statistical sampling uses inferential statistics
The idea is that the sample taken describes the entire population.
Error Types
Type I (): The sample is incorrectly rejected when the batch is good.
(Producer’s Risk). Rejects the process as being out of control when it is in control.
Type II(): The sample is accepted though the batch is bad. (Buyer’s Risk)
Accepts the process as in control when it is not.
Selecting  and  is not trivial

15. SPC CHART
2.5
2.4
2.3
2.2
2.1
2.0
1.9
1.8
1.7
1.6
1.5 .
UCL . . . . . . _ X . . . .
LCL
Sample

16. Mean Control Charts: Variable Data
Process control charts designed to detect shifts in the mean value of a process.
If the process true mean of sample is unknown then use : =
X +/- Z x
X ; Grand Mean or average of the average observations and samples
 = process standard deviation
x =  / (n) 1/2 ; Standard deviation of distribution of sample mean
Hypothesis is that the process is in-control (Ho )
If process true standard deviation of sample is not known then use X-bar chart. =
Lower Control Limit: X - A2* R =
Upper Control Limit: X + A2 * R
Compares each sample mean to a Grand Mean
A2 found in table (pg 310) and are based on number of observations per samples.

17. Range Control Chart: Variable Data
If the process variance is unknown then use a R-chart. _
Lower Control Limit: R * D3
Upper Control Limit: R * D4
Compares each range of a sample to a mean of the range of other
samples.
D3 & D4 are found in a table (pg 310) and are based on the number of
observations per samples.
To adequately establish the variability of a process it is recommended to
use both mean and range control charts on a sample.
Accuracy
Dispersion

18. Mean Control Charts: Variable Data
Four samples of three observations each have been taken, with actual measurements (in centimeters) shown below. Construct three sigma mean and range charts and determine if corrective action is needed.
Sample
The standard deviation of the sample is known and is 0.6, construct three sigma mean chart. Use the above data.

19. Process Control Charts: Attribute Data
What is an Attribute?
Use when data is subjective and can be counted.
P chart is used when the number of both good and bad are known.
C chart is used when the number of bad parts only are known.
P Chart
Underlying sample distribution is the binomial
Monitors the proportion of defects generated in a process
Constructed and used similar to the mean
Difference is we use the percent defects in a population
LCL: P - 3p
UCL: P + 3 p
p = (( P (1-P) )/ n)1/2

20. Process Control Charts: Attribute Data
C Chart
Underlying sample distribution is the Poisson.
large sample sizes approximate a normal distribution)
To track the countable number of defects when the ample size is the same.
The mean number of defects per unit is C and standard deviation is (C)1/2
If C is unknown then use C = # of defects / # of samples.
If less than zero, use zero.
LCL: C - 3 (C) 1/2
UCL: C + 3 (C) 1/2

21. Attributes Problem
A town’s department of public works is concerned about adverse public reaction to a sewer project that is currently in progress. Because of this, The Commissioner of Public Works has authorized a weekly survey to be conducted of town residents. Each week, a sample of 100 residents is questioned on their feelings towards the project. The results to date are shown below. Analyze this data using an appropriate control chart with a 5% risk of Type I error. Is the community sentiment stable?
Week
Opposed
Construct the appropriate control chart for the observations listed below, and determine if the process is in control using two sigma limits.
Observation
Number of
Defects per unit

22. Control Limits vs Specification Limits
The quality control limits represent the natural variation produced by a process.
Does not guarantee the product meets the design specifications.
Must be able to define a way of telling if a process is capable to produce at a
given design specification.
Cp defines the relationship between control limits of a process and the
specification limits of design.
Cp = (USL - LSL)/6 
USL = upper spec limit
LSL = lower spec limit
 = Standard deviation of the process
 can be approximated by R/D2
Cp = 1 says process is barely capable
Cp > 1 says Process is capable
Cp < 1 says process is not capable
Usually strive for a Cp = 1.33 or better
Cp does not consider how well the process is centered within the limit

23. Control Limits: Cpk
Cpk is used to define if the process and the spec limit are centered.
Compute by
finding difference between each of the specification limits and the mean
identifying the smaller difference
divide difference by three standard deviations of the process.
Cpk = Min [ ((Upper spec - Process mean)/3),
((Process mean - Lower Spec)/3)]
Limitations of Capability Index
1. The process may not be stable.
capability index is meaningless.
2. The process output may not be normally distributed.
Inference about fraction of output that is not acceptable will be incorrect.
3. The process is not centered
if the Cp index is used a misleading result is given.

24. Control Limits Problems
Studies on a machine that molds plastic water pipe indicates that when it is injecting 1-inch diameter pipe, the process standard deviation is 0.05 inches. The one-inch pipe has a specification of 1-inch plus or minus 0.10 inch. What is the process capability index (Cp)?
Is the process capable?
The specification limit for a product is 8cm and 10 cm. A process that produces the product has a mean of 9.5cm and a standard deviation of 0.2cm. What is the process capability, Cpk?
Is the process capable?