QUALITY ENGINEERING

At the end of the course the students should be able:

• to know the main quality principles and definitions;
• to join a quality assurance team implementing the ISO 9000 standards;
• to implement a process improvement project according to DMAIC metholodogy and Six Sigma principles;
• to perform a preliminary data analysis aimed at identifying quality issues in manufacturing/service processes;
• to implement statistical process monitoring tools;
• to perform process and measurement system capability analysis;
• to use the Minitab® software quality tools.

Learning is based on theory classes and practice sessions. Practice sessions are scheduled once per week and consist of solving numerical problems/case studies.

1. QUALITY IMPROVEMENT IN MODERN BUSINESS ENVIRONMENT (Week #1)
The Meaning of Quality and its Dimensions. Quality Improvement (QI). Quality Engineering Terminology: Quality Characteristics (CTQs). A Brief History of Quality Control and Improvement (QCI)*. Statistical Methods for QCI. Management Aspects of Quality Improvement: Quality Planning, Quality Assurance, Quality Control and Improvement. PDCA cycle. The Link between Quality and Productivity: first-time yield (FTY), first-pass yield (FPY). Supply Chain Quality Management*, Quality Costs*.

2. QUALITY PHILOSOPHIES AND MANAGEMENT STRATEGIES (Week #1)
ISO 9000 quality standards and their evolution, certification and accreditation, Audits, ISO 9000 and 9001 ver. 2015. Fundamental Concepts, Structure, Context of the Organization, Quality policy and objectives, Risk-based thinking. Maintaining and retaining documented information. Nonconforming and defective products: rework, repair, scrap. The Six Sigma Philosophy: Meaning of Six Sigma, The Six Sigma Roles, The DMAIC Process Steps. DFSS, Lean principles and Six Sigma. Case Studies*.

3. STATISTICAL MODELS FOR QCI (Week #2)
Describing variation and data: Histogram, Box Plot and Dot Plot. Discrete distributions: Hypergeometric, Binomial, Poisson Distributions. Continuous distributions: Normal, Lognormal, Exponential Distributions. Probability Plots. The Anderson Darling test. The Normal Approximation to the Binomial.

4. STATISTICAL INFERENCE IN QCI (Weeks #3-#5)
Statistics and Sampling Distributions. Sampling from a Normal, Binomial and Poisson Distribution. Point Estimation of Process Parameters. Statistical Inference for a Single Sample. Inference on the Mean of a Normal Distribution, (z-test and t-test), Confidence Intervals. The p-value approach. Inference on the Variance of a Normal Distribution, (chi2-test). Inference on a Population Proportion and Confidence Interval. The OC curve for the z-test: Choice of the Sample Size. Statistical Inference for Two Samples. Inference for a Difference in Means, (z-test and t-test), Confidence Intervals. Inference on the Variances of a Normal Distribution, (F-test), Confidence Interval. Comparing two populations. Inference on Two Population Proportions and Confidence Interval. Inference on More Than Two Populations: Analysis of Variance (ANOVA).

5. HOW STATISTICAL PROCESS CONTROL WORKS (Week #6-#8)
Introduction. Chance and Assignable Causes. Statistical Basis of the Control Chart. Choice of Control Limits. Sample Size and Sampling Frequency. The Run Length of a control chart. Rational Subgroups. Analysis of Patterns. Adding Sensitizing Rules to Control Charts. Phase I and II Implementation of Control Charts. The Rest of Magnificent Seven: Pareto Chart, Cause and Effect Diagram. Applications of SPC (reading).

6. VARIABLES AND ATTRIBUTES CONTROL CHARTS (Weeks #9-#10)
Introduction. Control Charts for Xbar and R. The operating characteristic curve. Computation of the performance for the Xbar control chart5. Control Charts for Xbar and s: Construction and Operation of Xbar and s Charts; the Xbar and s Control Charts with Variable Sample Size. The s^2 control chart. The Shewhart Control Chart for Individual Measurements. Shewhart Control Charts for Attributes. Control Charts for Fraction Nonconforming (p Charts). Selection of the sample size for a p control chart. Control Charts for number of nonconforming (np Charts). Control Charts for Nonconformities (only c Charts). Procedures with variable sample size.

7. CAPABILITY ANALYSIS OF PROCESS AND MEASUREMENT SYSTEMS (Weeks #11-#12)
Introduction. Process Capability Ratios: Cp, Cpk, Cpm. Process Capability Analysis with Control Charts. Confidence intervals (only Cp) and tests on process capability ratios*. Short term and long term variability. Measurement system analysis: reference value and resolution; location variation: bias, linearity, stability, testing for location variation; width variation: precision error, repeatability, reproducibility, testing for width variation, Gauge R and R study: Xbar and R method with Excel. Part variation, Total variation. Gauge R and R Analysis with Minitab.

*The topic is suggested for reading, but it is not included in the exam text.

1. D.C. Montgomery, “Statistical Quality Control”, 6^th edn or successive, Wiley. MAIN TEXT. The most widely used textbook about SQC in Universities teaching courses on Quality Engineering and in Companies implementing SQC tools.

2. C. Cochran, “ISO 9001: 2015 in plain English”, 2015, Paton Professional, ISBN: 978-1-932828-72-6. A recent guide to the implementation of the new ISO 9000: 2015.

3. K. Magnusson, D. Kroslid, B. Bergman, 2003, “Six Sigma: The Pragmatic Approach, Studentilitteratur, Sweden, ISBN: 9-789-144-028033. A professional text presenting the Six Sigma approach.

4. Q. Brook, “Lean Six Sigma & Minitab”, 4^th edn., 2014, OPEX Resources Ltd, ISBN-13: 978-0954681388. A very good text for professional use of Minitab in a lean Six Sigma context.