About this course

Standards for quality assurance and quality work, quality systems, quality tools and statistical methods, quality data, quality costs, quality improvement and quality management in practical application within various industries, for example aviation, hydropower and nuclear power. An introduction to Six Sigma will also be given.

Learning outcome

Having passed the course, students should know standards for quality assurance and quality work and the main principles behind quality management and system control, as well as be able to use techniques, data and methods within quality management, quality improvement.

  • Learning activities

    The course will be taught through a combination of lectures, academic discussions, laboratory exercises and term papers with homework.

  • Teaching support

    Student groups can get access to guidance in lessons, dedicated lessons for group work as well as feedback on written partial submissions.

  • Syllabus

    Announced in the first lecture.

  • Assessment method

    Final written examination, 3 hours, which is evaluated pass/not pass.

    Written exam Karakterregel: Passed / Not Passed Hjelpemiddelkode: B1 Calculator handed out, no other aids

  • About use of AI

    Exam: K1 - No use of AI

    Term paper: K2 - Specified use of AI

    Academic work in this course involves the use of various sources, methods, and tools to generate new insights. Artificial intelligence (AI) is one of the tools students may utilize. Using AI requires that the student remains in control of the process and has insight into AI’s strengths, limitations, and implications for industrial applications. Students bear full responsibility for recommendations and decisions partially influenced by the use of AI. When using AI, students must still demonstrate professional effort, critical thinking, the ability to explain selected solutions and the processes behind them, as well as ensure the quality of AI-generated partial results through professional insight, thoroughness, and ethical diligence.

    The use of AI must comply with the guidelines for the use of artificial intelligence (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme

    The external examiner participates with the internal examiner in the design of the examination papers and the examiner's guidance. The external examiner checks the internal examiner's assessment of a random sample of candidates as a calibration at certain intervals in accordance with the department's grading guidelines.

  • Mandatory activity

    Mandatory attendance at laboratory exercises and group presentations. Mandatory submissions, which are assessed as approved/not approved.

  • Teaching hours

    Lectures: 35 hours. Exercises: approx. 12 hours. Attendance is expected.

  • Admission requirements

    Minimum requirements for entrance to higher education in Norway (generell studiekompetanse)