1. Measurement: Assessment and Metrics Westcott CH. 15
Presented by Dr. Joan Burtner
Certified Quality Engineer
Associate Professor of
Industrial Engineering and Industrial Management

2. Dr. Joan Burtner, Associate Professor of Industrial Engineering
Overview
Process Measurement as a Management Function
Project Management Metrics
Human Aspects of Data Gathering
Statistical Analysis
Theory of Variation
Process Capability
Acceptance Sampling
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

3. Process Measurement as a Management Function
“Effective management of an organization depends on defining, gathering, and analyzing information that provides feedback on current performance as well as projecting future needs.” ETM627 Course Text p. 416
“Analysis refers to extracting larger meaning from data and information to support evaluation, decision making, and improvement.” Baldrige National Quality Program 2005
“Statistics is the science of turning data into information” ETM627 Course Text p. 417
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

4. Typical Project Management Metrics
Schedules met
Resources used
Costs versus budget
Project objectives met
Risks identified and eliminated or mitigated
Earned value analysis (planned vs. actuals)
Customer satisfaction
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

5. Human Aspects of Data Gathering
Perception that excessive data collection and development of multiple metrics is:
A reflection of management’s obsession with numbers
Not necessarily helpful in producing a better product or service
Perception that organization is more interested in data collection than task performance
Lack of understanding of the connection between what workers produce and the metrics by which management assesses performance
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

6. Dr. Joan Burtner, Associate Professor of Industrial Engineering
Statistical Analysis
Central Tendency
Mean
Median
Mode
Variation or Spread
Range
Standard Deviation
Variance
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

7. Probability Distributions
Widely-used distributions
Normal
Exponential
Weibull
Poisson
Binomial
Negative Binomial
Hypergeometric
Graphs, Functions, and Applications
See page 429 of course text
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

8. Advanced Statistical Methods for Managers
Basic Hypothesis Testing
One Sample t or Z Tests
Two Sample t or Z Tests
Advanced Hypothesis Testing
Design of Experiments (ANOVAs)
Regression (Simple, Multiple, Non-linear)
Visualization
Response Surface
Evolutionary Operation (EVOP)
Incremental search for more optimal points on a response surface
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

9. Dr. Joan Burtner, Associate Professor of Industrial Engineering
Theory of Variation
Common Cause
Stable and predictable causes of variation
Inherent in all processes
Managers, not workers, are responsible for common cause variation
Special Cause
Unexpected or abnormal causes of variation
May result in sudden or extreme departures from normal
May also result in gradual shifts (trends)
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

10. Dr. Joan Burtner, Associate Professor of Industrial Engineering
Control Chart Types
Control Charts
Variables – based on continuous data
X bar and R (mean and range)
Attributes - based on discrete data
P (proportion)
C (count)
Example of R Chart:
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

11. Control Chart Calculations for Variables Charts
Xbar and R Control Chart Constants
Control Chart Calculations n
d 2
A 2
D 3
D 4 2
1.128
1.88
3.267 3
1.693
1.023
2.575 4
2.059
0.729
2.282 5
2.326
0.577
2.115 6
2.534
0.483
2.004 7
2.704
0.419
0.076
1.924
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

12. Dr. Joan Burtner, Associate Professor of Industrial Engineering
Process Capability
Analysis conducted on processes that have been shown to be “in-control”
Only common cause variation in range
Only common cause variation in mean
Two standard measures Cp
Compares variability of process to specifications
Cpk
Is the process sufficiently centered?
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

13. Process Capability Calculations
Evaluation
Cpk > Definitely Capable
1.00 < Cpk < Possibly Capable
Cpk  Not Capable
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

14. Dr. Joan Burtner, Associate Professor of Industrial Engineering
Cpk vs. Ppk
Ppk: potential process capability
Process validation stage of a new product launch
Assumption:
small number of parts produced
data does not include the normal variability
Evaluation
Ppk > (Capable)
1.33  Ppk  (Capable with tight control)
Ppk <1.33 Not Capable
Use cautiously
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

15. Dr. Joan Burtner, Associate Professor of Industrial Engineering
Acceptance Sampling
Definition: Acceptance sampling is the process of sampling a batch of material to evaluate the level of nonconformance relative to a specified quality level.
Incoming product
Product moved from one process to another
Types of samples
Random
Stratified
Sampling Decision Process Figure 15.3 in course text
D number of defective items (not number of defects)
n sample size
C acceptance number
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

16. Acceptance Sampling Not Recommended
Why not use sampling to collect data? (according to course text pp )
Customer requires 100% inspection
Relatively small number of items or services allows for ‘economical’ 100% inspection
The inspection method is built into production so that no defectives can be shipped
Self-inspection by trained operators is sufficient for the nature of the product produced
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

17. Dr. Joan Burtner, Associate Professor of Industrial Engineering
Sampling Plans
Standards
ANSI/ASQC Z1.4 which replaces MIL-STD 105
ANSI/ASQC Z1.9 for variables
Potential errors (uncertainty risk)
Producer’s Risk “the probability of not accepting a lot, the quality of which has a designated numerical value representing a level that is generally desirable”
Consumer’s Risk “the probability of accepting a lot, the quality of which has a designated numerical value representing a level that is seldom desirable”
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering

18. Dr. Joan Burtner, Associate Professor of Industrial Engineering
References / Contact
Course Text:
Westcott, R.T., Ed. (2006). Certified Manager of Quality/Organizational Excellence Handbook (3rd ed.). Milwaukee: ASQ Quality Press.
Additional Sources
“Baldrige National Quality Award Criteria”
Christensen, E.H., Coombes-Betz, K.M., and Stein, M.S. (2006). The Certified Quality Process Analyst Handbook. Milwaukee: ASQ Quality Press.
Contact:
ETM 627 Fall 2014
Dr. Joan Burtner, Associate Professor of Industrial Engineering