QUALITY ENGINEERING
Academic Year 2026/2027 - Teacher: GIOVANNI CELANOExpected Learning Outcomes
This course introduces students to evidence-based quality engineering tools commonly used either in industry or service organizations. Emphasis is given to statistical tools for on-line monitoring and data analysis to implement quality control and to achieve continuous improvement. Process capability analysis and measurement system assessment are considered, as well. Course lectures and exam texts are in English language.
By the end of the course, students will be able:
Knowledge and Understanding:
- To handle the statistical toolbox of Six Sigma philosophy;
- To select acceptance sampling techniques for attributes;
- To discuss about measurement system assessment;
- To know how to implement a data analysis to make evidence-based decisions about quality control;
- To understand the insights behind the implementation of tools for quality monitoring with the goal of waste reduction.
Applying Knowledge and Understanding:
- To implement quantitative methods for quality control and improvement and evaluate related waste reduction;
- To use and interpret the statistical tools available within Microsoft Excel® and Minitab® software to carry out quality improvement.
- To be part of a team carrying out process improvement projects;
- To make evidence-based decision making about quality control problems;
Communication skills:
- Communicate findings of data analysis in structured presentations for a technical and non technical audience .
The skills acquired can be applied to quality management within organizations in the industrial and service sectors, in line with Goals 9, 11, and 12 of the United Nations 2030 Agenda for Sustainable Development.
The acquired skills can be applied within organizations operating in the industrial and service sectors, in alignment with Goals 9, 11, and 12 of the United Nations 2030 Agenda for Sustainable Development.
Course Structure
Lecture-Based Learning
Activities are designed to convey theoretical skills related to quantitative methodologies for quality engineering through:
- Presentation, analysis, and discussion of methods, with specific focus on their theoretical statistical foundations.
- Critical evaluation for selecting the appropriate quantitative method, also taking economic aspects into account.
- Weekly guided practical sessions dedicated to solving real-world and simulated case studies through the application of statistical formulas and models.
Interactive Learning
Activities are designed to foster active learning, analytical skills, and teamwork through:
- Resolution and discussion of applied case studies, utilizing the Minitab® software.
- Group gaming activities (with competing teams of 2-3 students) aimed at solving practical cases in the classroom. This approach is structured to enhance problem-solving skills, even for complex issues, and to develop soft skills in teamworking.
- Dedicated sessions for the winning teams to present and communicate their results, simulating brainstorming sessions in corporate environments.
Required Prerequisites
Prior knowledge acquired in Quality Management courses/modules covering content related to the implementation of ISO 9000 standards is important. A working knowledge of normal, binomial, and hypergeometric statistical distributions is highly beneficial.
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.30 of “Regolamento Didattico di AteneoDetailed Course Content
The course consists of 93 hours delivered over approximately 11 weeks of classes. Each week includes three 3-hour lectures. The module syllabus is divided as follows:
- STATISTICAL MODELS FOR QC. ACCEPTANCE SAMPLING FOR ATTRIBUTES
- STATISTICAL INFERENCE IN QUALITY CONTROL AND IMPROVEMENT
- HOW STATISTICAL PROCESS CONTROL WORKS
- VARIABLES AND ATTRIBUTES CONTROL CHARTS
- CAPABILITY ANALYSIS - MEASUREMENT SYSTEM ASSESSMENT
Detailed content and reference materials for each module are available in the "Course Schedule" section.
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. AIAG Measurement System Assessment - Reference Manual 4th Edition
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | QE1. STATISTICAL MODELS FOR QC. ACCEPTANCE SAMPLING FOR ATTRIBUTES. The definition of quality*. Quality management and its dimensions*. Role of Quality Engineering. Quality characteristics, Key Performance Indicators and specifications*. IS0 9001 standard structure and PDCA cycle*. 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. Describing variation with histograms, box and whiskers plots* and individual value plots. Data Visualization with Excel and Minitab. Feature relationships and correlation. Distribution Analysis. The normal distribution. Probability Plots*. The Anderson Darling test*. Fraction nonconforming calculation*. Quality control and process monitoring. Short term and long term variability. The DMAIC Process Steps for improvement projects*. Discrete distributions: Hypergeometric, Binomial*, Poisson Distributions. Acceptance sampling: Design of the single-sampling plan for attributes. Acceptable Quality Level (AQL) and Rejectable Quality Level (RQL). Rectifying inspection. Average Outgoing Quality (AOQ) and AOQL. Average Total Inspection (ATI). MIL STD 105 E** and ISO 2859 Series of standards**. Dodge Romig sampling plans**..The asterisk (*) indicates a prerequisite topic. A short summary will be provided during introductory classes of the course.The double asterisks (**) indicate a recommended topic for optional reading. | Text 1(Six Sigma):Ch.1, 1.4.1 p.28-32Ch.2 (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, 15-4 (Only reading), 15.5 (Only reading). |
| 2 | QE2. STATISTICAL INFERENCE IN QUALITY CONTROL AND IMPROVEMENT. 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, (t-test and z-test), Confidence Intervals. The p-value approach (exact calculation for the Z test and approximate calculation for the t test). The OC curve for the z-test: Choice of the Sample Size. Inference on the Variance of a Normal Distribution, (c2-test). Statistical Inference for Two Samples comparison in Quality Control. Inference on the ratio of Variances of a Normal Distribution, (F-test), Confidence Interval. Inference for a Difference in Means, (z-test and t-test), Confidence Intervals. The paired t-test. Comparing two populations. Inference on More Than Two Populations: Analysis of Variance (ANOVA). Multiple comparisons: the Fisher LSD test. Statistical inference in quality control with Minitab. | Text 1:Ch.4, Sections 4.1 to 4.5 |
| 3 | QE3. HOW STATISTICAL PROCESS CONTROL WORKS. Introduction. Chance and Assignable Causes. Statistical Basis of the Shewhart 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**. Note: As with the previous sections, the double asterisks (**) indicate a recommended topic for optional reading. | Text 1:Ch.5 |
| 4 | QE4. VARIABLES AND ATTRIBUTES CONTROL CHARTS. Introduction. Shewhart Control Charts for the sample mean Xbar and the range R. The operating characteristic curve. Computation of the performance for the Xbar control chart: in-control e out-of-control ARL. Shewhart Control Charts for Xbar and S. Control Charts with Variable Sample Size. The S^2 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). Implementation of control charts with Excel and Minitab. | Text 1:Ch.6, Sections 6.1 to 6.3.2,6.4 (to p.262)Ch.7, Sections 7.1, 7.2 (up to 7.2.2 p.304),7.3.1 (to p.314) |
| 5 | QE5. CAPABILITY ANALYSIS OF PROCESS - MEASUREMENT SYSTEMS. Introduction. Process Capability Ratios: Cp, Cpk. Process Capability Analysis with Control Charts. Capability analysis with Minitab. Measurement system analysis: reference value and resolution; location variation: bias, linearity, stability. Student's t test for location variation; width variation: definitions of precision error, repeatability, reproducibility. Testing for width variation, Gauge R&R study: and method with Excel. Part variation, Total variation. Gauge R&R Analysis with Minitab. | Ch.8, Sections 8.1, 8.3 to 8.3.2. Text 2: pp. 45-49, pp. 50-54, pp. 54-57, pp. 87-101, pp. 103-123. |
Learning Assessment
Learning Assessment Procedures
Students' acquired skills are evaluated through a written exam during the examination sessions, which consists of:
2-3 exercises (2/3 of the final grade): these simulate case studies where students must select the correct statistical quality control technique—also utilizing Minitab software—to analyze the available data and propose improvement strategies. The exam format is structured to assess the student's ability to search for and identify the most appropriate tool to develop an exploratory data analysis related to a specific quality characteristic.
2-3 open-ended questions (1/3 of the final grade): these cover predominantly theoretical topics developed during lectures and are aimed at evaluating the student's preparation and written communication skills.