QUALITY ENGINEERING
Academic Year 2023/2024 - Teacher: GIOVANNI CELANOExpected Learning Outcomes
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 analyses;
- to use the Minitab® software quality tools and to know the R software basics.
Learning is based on theory classes and practice sessions. Practice sessions are scheduled once per week and consist of solving numerical problems/case studies.Course Structure
Learning is based on theory classes and practice sessions. Practice sessions are scheduled once per week and consist of solving numerical problems/case studies.
Required Prerequisites
Passing the course: Programmazione e Controllo della Produzione
Attendance of Lessons
Class attendance is mandatory. A roll call is done at the beginning of each class.
Students should attend at least 70% of scheduled classes, Point. 3.3, Regolamento Didattico CLM Ingegneria Gestionale. Reduced attendance is considered for students enrolled into categories described by Art. 27 of “Regolamento Didattico di AteneoDetailed Course Content
This course introduces students to quality management principles and quality engineering tools commonly used either by industry or service organizations to maintain their Quality Management Systems. Emphasis is given to statistical tools for data monitoring and analysis to guarantee quality control and to achieve continuous improvement.
During the QE course students are introduced to the Minitab® software suite for smart quality data analysis and to the basics of R software environment.
Textbook Information
1. D.C. Montgomery, “Statistical Quality Control”, 6th 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”, 4th edn., 2014, OPEX Resources Ltd, ISBN-13: 978-0954681388. A very good text for professional use of Minitab in a lean Six Sigma context.
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | 1. QUALITY IMPROVEMENT IN MODERN BUSINESS ENVIRONMENT. 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*.Reference Files: QE_QI_MBE_2324.pdf (Lectures slides)TextExercisesSolutions: ch01_LinkQualProdExSol.pdf | Text 1:Ch.1, Section 1.1 to 1.3, 1.4.3 |
| 2 | 2. ISO 9000 QUALITY MANAGEMENT STANDARD. ISO 9000 quality standards and their evolution. Certification and Accreditation, Audits. The ISO 9000-2015 standard: fundamental concepts, quality management principles, terms and vocabulary. Documented Information. Nonconforming and defective products: rework, repair, scrap. The ISO 9001-2015 standard: Structure, Process approach, PDCA cycle in ISO 9001, Risk-based thinking, Context of the Organization, Quality policy and objectives, Organizational Knowledge. Performance evaluation. Improvement.Reference files: QE_ISO9000_2324.pdf (Lectures Slides), ISO_9001_2015_Description.pdf, ISO9001_ExampleQualityPolicy.pdf, ISO9001_ExampleQualityObjectives, ISO9001_ImprovementCycle.pdf. | Text 2:Ch.1, Ch.2, Ch.4 (p.53-61), Ch.9 (only reading) |
| 3 | 3.SIX SIGMA AND STATISTICAL MODELS FOR QC (Weeks #2-#3)The Six Sigma Philosophy: The Six Sigma Roles and hierarchy, Meaning of Six Sigma. Statistical tools for quality control: Sampling from a population. Exploratory Data Analysis. Data summarization. The summary statistics: mean sample standard deviation, quantiles. Data Visualization. Describing variation with histograms, box and whiskers plots and individual value plots. Data Visualization with Excel and Minitab. Feature relationships. Distribution Analysis. The normal distribution. Fraction nonconforming calculation. Quality control and process monitoring. Short term and long term variability. The sigma quality level. The DMAIC Process Steps for improvement projects. Lean principles and Six Sigma. Case Studies*. Discrete distributions: Hypergeometric, Binomial, Poisson Distributions. An application to acceptance sampling of lots: Design of a single-sampling plan. Continuous distributions: Lognormal, Exponential Distributions. Probability Plots. The Anderson Darling test3.1. The Normal Approximation to the Binomial. Reference files: QE_SixSigma_QC_1_2324.pdf, QE_SixSigma_QC_2_2324.pdf (Lectures Slides), *SixSigmaProcessImprovAndCaseStudies.pdf, 3.1Ch03_SupplMaterial.pdf. (AD test Lectures Slides), QE_Dataset_SixSigma_QC.xlsx (Datasets)Tables: NormalDistributionTable_1.pdf, NormalDistributionTable_2.pdf. Suggested text exercises: 3.1-3.26, 3.29-3.50. TextExercisesSolutions: ch03.pdf. Past exams exercises: 1, 10, 11. | Text 1(Six Sigma):Ch. 1, 1.4.1 p.28-32. Ch. 2 (only reading). Text 3(Six Sigma):Ch.8 (DMAIC Description).Ch.9 (only reading)Text 1:Ch.3, Sections 3.1.2 to 3.3.3, 3.4, 3.5.3Ch 15. Sections 15.1-15.2.3. |
| 4 | 4. STATISTICAL INFERENCE IN QCI. 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, (c2-test). 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. The paired t-test4.1. Inference on the Variances of a Normal Distribution, (F-test), Confidence Interval. Comparing two populations4.2. Inference on More Than Two Populations: Analysis of Variance (ANOVA). Multiple comparisons: Tukey and LSD test4.3. Reference files: 4.1PairedTtest.pdf, 4.2Ch04_SupplMaterial.pdf, 4.3MultipleComparisonsTests.pdf. Suggested exercises: 4.1-4.21, 4.29, 4.31, 4.33-4.35, 4.37-4.40, 4.43-4.51. | Text 1:Ch.4, Sections 4.1 to 4.5 |
| 5 | 5. HOW STATISTICAL PROCESS CONTROL WORKS. 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 chart5.1. 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). Reference files: 5.1Ch05_06_SupplMaterial.pdf. Suggested exercises: 5.1-5.33. | Text 1:Ch.5 |
| 6 | 6. VARIABLES AND ATTRIBUTES CONTROL CHARTS. Introduction. Control Charts for and . The operating characteristic curve. Computation of the performance for the control chart6.1. Control Charts for and : Construction and Operation of and Charts; the and Control Charts with Variable Sample Size. The control chart. 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 Nonconformities (only c Charts). Procedures with variable sample size. Reference files: 6.1Ch05_06_SupplMaterial.pdf, Suggested exercises: 6.1-6.10,6.15-6.61, 7.1-7.31,7.36-7.60. | Text 1:Ch.6, Sections 6.1 to 6.3.2,6.4 (to p.262), Ch.7, Sections 7.1, 7.2, 7.3.1 (to p.314) |
| 7 | 7. CAPABILITY ANALYSIS OF PROCESS AND MEASUREMENT SYSTEMS. Introduction. Process Capability Ratios: Cp, Cpk. Process Capability Analysis with Control Charts. Confidence intervals (only Cp) and tests on process capability ratios*. Measurement system analysis: reference value and resolution7.1; location variation: bias, linearity, stability7.2, testing for location variation7.3; width variation: precision error, repeatability, reproducibility7.4, testing for width variation, Gauge R&R study: and method with Excel7.5,*. Part variation, Total variation. Gauge R&R Analysis with Minitab. Reference files: Ch08_SupplMaterial.pdf, AIAG_MSA_Reference_Manual_4th_Edition.pdf: 7.1(pp. 45-46), 7.2(pp. 50-54), 7.3(pp. 87-101), 7.4(pp. 54-57), 7.5(pp. 103-123)CapabilityAnalysisWithMINITAB.pdf, GaugeRandR_StudyWithMINITAB.pdf., Suggested exercises: 8.1-8.16, 8.23-8.27. | Text 1:Ch.8, Sections 8.1, 8.3 to 8.3.2.Text 4:p.56-69, p.77-92. |