TECNOLOGIA E SISTEMI DI PRODUZIONE
Academic Year 2026/2027 - Teacher: GIOVANNI CELANOExpected Learning Outcomes
The module provides students with content related to basic quantitative concepts and methods used to implement a quality management system. Particular emphasis is placed on the study of quality systems management according to the ISO 9001 standard and its application within the industrial sector. The principles of managing improvement projects according to the Six Sigma approach are also described.
Furthermore, the module aims to provide students with introductory statistical skills for monitoring business processes using graphical tools and statistical distributions. In particular, Microsoft Excel and Minitab® software are used for exploratory data analysis in the field of quality.
By the end of the module, students will be able to:
Knowledge and Understanding:
- Know the principles of quality and their related definitions;
- Know the structure, principles, and contents of the ISO 9000 standards for quality management systems;
- Know the basic principles of the Six Sigma philosophy;
- Know how to implement an exploratory data analysis based on supporting statistical techniques in order to make evidence-based decisions within quality control.
- Adopt a process-oriented problem-solving approach aimed at continuous improvement according to a Plan-Do-Check-Act (PDCA) logic;
- Use and interpret the basic tools of exploratory data analysis available within Microsoft Excel® and Minitab® software;
- Participate as part of a working team in the implementation of a quality management system according to ISO 9000 standards;
- Participate as part of a working team in the implementation of an improvement project following the DMAIC methodology approach and Six Sigma principles.
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.
Course Structure
Direct Teaching:
Activities focus on the delivery of theoretical knowledge and reference regulatory frameworks through:
- Presentation, analysis, and discussion of theoretical and methodological content.
- Critical review and guided commentary on reference standards and technical regulations (ISO 9000 and industry-specific standards).
- Weekly guided tutorials dedicated to solving quantitative problems and applying statistical formulas and models.
Interactive Learning:
Activities are designed to stimulate active learning, analytical skills, and teamwork through:
- Resolution and discussion of real-world scenarios applied to industrial engineering, utilizing Microsoft Excel and Minitab® software.
- Collaborative group activities (composed of 2–3 students) to complete in-class practical exercises. This format is structured to enhance process-oriented problem-solving skills and develop soft skills in teamwork.
If the course is delivered in blended or remote mode, appropriate adjustments may be made to the above, in order to ensure consistency with the syllabus.
Required Prerequisites
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 CL Ingegneria Industriale. Reduced attendance is considered for students enrolled into categories described by Art.30 of “Regolamento Didattico di AteneoDetailed Course Content
The module consists of 29 hours delivered over approximately 10 weeks of classes. Each week includes one 3-hour lecture. The module syllabus is divided as follows:
- INTRODUCTION TO QUALITY
- ISO 9000 SERIES OF STANDARDS
- QUANTITATIVE TOOLS FOR QUALITY CONTROL
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 o successiva, Wiley.
2. Norme ISO 9000-2015 e ISO 9001-2015
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | 1. INTRODUCTION TO QUALITY. (2 hours: Theory. 5 hours: Practice). The definition of quality and its dimensions. The quality cycle. From expected to perceived quality. Quality engineering terminology. Quality characteristics and service performance indicators. History of quality control and continuous improvement. Statistical methods for quality control. Deming's PDCA cycle. Quality costs. First-pass yield (FPY) and First-time or throughput yield (FTY). | |
| 2 | 4. QUANTITATIVE TOOLS FOR QUALITY CONTROL. (8 hours: Theory and Teamworks). Quality characteristics, Key Performance Indicators and specifications. 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. Discrete distributions: Hypergeometric and binomial distributions with application to quality control. The normal distribution. Probability Plots. The Anderson Darling test. Specifications and fraction nonconforming calculation. Quality control and process monitoring. Short term and long term variability. The DMAIC Process Steps for improvement projects. ISO technical documents about statistical techniques for quality: ISO/TR 10017-2021 e ISO/TR 18532:2009 | |
| 3 | 3. QUANTITATIVE TOOLS FOR QUALITY CONTROL. (4 hours: Thepre. 10 hours: practice with exercises and software). Quality characteristics, Key Performance Indicators and specifications. 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. Specifications and fraction nonconforming calculation. Quality control and process monitoring. Short term and long term variability. Process stability. The DMAIC Procedure Steps for improvement projects. |
Learning Assessment
Learning Assessment Procedures
Passing the exam for the "Quality" module is subject to a written test, which includes practical exercises and open-ended questions on theoretical topics. When solving the practical exercises, students are required to critically apply basic methodologies to solve quality control problems. The theoretical questions aim to assess the students' ability to describe, with clarity and proper terminology, the content related to ISO 9000 quality management standards and manufacturing process variability. The score accounts for a maximum of one-third of the final grade for the overall course exam.
Learning assessment may also be carried out on-line, should the conditions require it.
To ensure equal opportunities and in compliance with current laws, interested students may request a personal interview in order to plan any compensatory and/or dispensatory measures based on educational objectives and specific needs. Students can also contact the CInAP (Centro per l’Integrazione Attiva e Partecipata — Servizi per le Disabilità e/o i DSA) referring teacher within their department (https://www.cinap.unict.it/content/referenti).