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

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

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

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

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

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

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

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

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

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

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

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

Process Capability Calculations Evaluation Cpk > 1.33 Definitely Capable 1.00 < Cpk < 1.33 Possibly Capable Cpk  1.00 Not Capable ETM 627 Fall 2014 Dr. Joan Burtner, Associate Professor of Industrial Engineering

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 > 1.67 (Capable) 1.33  Ppk  1.67 (Capable with tight control) Ppk <1.33 Not Capable Use cautiously ETM 627 Fall 2014 Dr. Joan Burtner, Associate Professor of Industrial Engineering

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

Acceptance Sampling Not Recommended Why not use sampling to collect data? (according to course text pp. 423-424) 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

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

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” www.quality.nist.gov Christensen, E.H., Coombes-Betz, K.M., and Stein, M.S. (2006). The Certified Quality Process Analyst Handbook. Milwaukee: ASQ Quality Press. Contact: Email: Burtner_J@Mercer.edu ETM 627 Fall 2014 Dr. Joan Burtner, Associate Professor of Industrial Engineering