Course detail
Statistical Process Control
FSI-XRP Acad. year: 2023/2024 Summer semester
The subject “Statistical Process Control” will familiarize students with the basic methods of process control, systemic and statistical analysis applicable in management of an organization and subordinate processes. Students will also understand the rules for identification of processes and selection of statistical variables for serial and piece production processes. The students will master the rules of data collection and sorting, their analysis and use for statistical process control.
Language of instruction
Czech
Number of ECTS credits
5
Supervisor
Entry knowledge
Knowledge of technology and materials. Knowledge of physics and applied statistics. Knowledge of quality management.
Rules for evaluation and completion of the course
The course unit credit requirements: Active participation in seminars, submission and presentation of analyses as assigned by the teacher.
The exam has both written and oral parts. Exam evaluation is graded on the ECTS grading scale: excellent (90-100 points), very good (80-89 points), good (70-79 points), satisfactory (60-69 points), sufficient (50-59 points), failed (0-49) points.
Attendance in lectures is recommended. The attendance at seminars is compulsory. In case of excused absence, the teacher may decide on an appropriate substitute assignment.
Aims
The first goal of “Statistical Process Control” is to familiarize students with the basic statistical methods of process control. Another goal is to teach students to use the fact, that real processes have a stochastic character, thus the rational approach to their management requires the application of statistical methods. The third goal is to teach students to apply statistical process control tools to standard and specific company processes and to devise appropriate improvement measures in the context of quality management system improvement.
The subject “Statistical Process Control” allows students to gain knowledge of methods of statistical process control as a part of complex quality management of a company. Students will also master identification of processes suited for statistical control. They will learn to apply individual methods of statistical quality control when solving problems, which may arise in manufacturing companies as well as service companies. Students will also learn to identify the key and supporting processes and to practically apply the methods of statistical quality control.
The study programmes with the given course
Programme N-KSB-P: Quality, Reliability and Safety, Master's, compulsory
Programme N-SLE-P: Foundry Technology, Master's, compulsory-optional
Programme RRTES_P: Risk Management of Technical and Economic Systems, Master's
specialization RRTS: Risk Management of Technical Systems, compulsory-optional
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
1. Processes in the product life cycle. Variability of processes. Statistical process control (SPC) methods.
2. Identification of different types of processes. Selection of statistical variables for process control. Statistical population and sample, characteristics of location and dispersion.
3. Collection of data, statistical tables and graphs. Theoretical
distributions and their use in SPC.
4. Histograms as quality management tools. Identification of systemic influences using histograms. Testing the fit of a theoretical distribution to measured data.
5. Cause and effect analysis. Ishikawa diagram.
6. Distinguishing critical and inconsequential causes – Pareto analysis.
7. Statistical process control. General rules for statistical control.
8. Statistical control by measurement. Control charts.
9. Process capability. Indices of short-term and long-term capability.
10. Gauge capability.
11. Statistical control by comparison. Control charts.
12. Use of regression and correlation analysis in process control.
13. Quality journal.
Computer-assisted exercise
26 hours, compulsory
Teacher / Lecturer
Syllabus
1. Descriptive statistics, basic use of statistical software.
2. Probability distributions – properties and uses.
3. Histograms, tests of good fit.
4. Cause and effect analysis – Ishikawa diagram.
5. Pareto analysis. Assignment 1.
6. Student presentations – assignment 1.
7. – 9. Control charts.
10. Process capability. Assignment 2.
11. Student presentations – assignment 2.
12. Gage capability. Assignment 3.
13. Student presentations – assignment 3. Course-unit credit.