Academic year 2022 - 2023

•   Quality Tools MS00BW99-3002 13.09.2022-02.12.2022  5 credits  (YIQMS22V) +-
  Competence objectives of study unit

  After completing the course, the student is able to
  - define the main quality tools
  - choose a suitable software or programming environment for statistical analysis and use it.
  - do variance analysis, ANOVA
  - represent 2D and 3D data graphically
  - make non-linear fits and surface fits and use these to analyze data
  - do statistical Design of Experiments (DoE)

  Content of study unit

  -Cause-and-effect diagram
  -Check sheet
  -Control chart
  -Pareto chart
  -Stratification
  -Nonlinear curve fitting and surface fitting
  -Measurement errors
  -Analysis of variance, ANOVA
  -Design of Experiment, One factor at a time, Factorial Design, Response surface method
  -Evaluation of measurement processes

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  Teacher(s) in charge

  Patric Granholm

  Sari Airenne

  Teachers and responsibilities

  Sari Airenne, Patric Granholm

  Mode of delivery

  Face-to-face

  Learning material

  Sarah E. Burke and Rachel T. Silvestrini. The Certified Quality Engineer Handbook. ASQ Quality Press Milwaukee, Wisconsin 2017
  Data Analysis and Design of Experiment:
  Michael H. Herzog, Gregory Francis, Aaron Clarke. Understanding Statistics and Experimental Design - How to Not Lie with Statistics. Springer Open. 2019 ISBN 978-3-030-03498-6 ISBN 978-3-030-03499-3 (eBook) https://doi.org/10.1007/978-3-030-03499-3
  Franz Kronthaler, Silke Zöllner. Data Analysis with RStudio - An Easy going Introduction. Springer Spectrum and R Studio. 2021 ISBN 978-3-662-62517-0 ISBN 978-3-662-62518-7 (eBook) https://doi.org/10.1007/978-3-662-62518-7.
  Amitava Mitra, Fundamentals of quality control and improvement, 4th ed. John Wiley & Sons, Inc., Hoboken, New Jersey. 2016. ISBN 978-1-118-70514-8 (cloth)

  Learning methods

  - On-line videos and lectures
  - Independent exercises, quizzes
  - Group assignments
  - Individual assignments

  Objects, timing and methods of assessment

  Assessment will be based on the individual and group assignments submitted in the itslearning course platform.

  Teaching language

  English

  Timing

  13.09.2022 - 02.12.2022

  Enrollment date range

  01.06.2022 - 14.10.2022

  • 5 credits

  Group(s)

  • YIQMS22V

  Responsible unit

  Engineering and Business

  Degree Programme(s)

  Master of Engineering, Industrial Quality Management

  Assessment scale

  H-5

  Pedagogic approaches

  - Team-based learning in student groups and presentations (substance and communication skills)
  - Individual assignments
  - Independent working and information retrieval from reference material

  Student's schedule and workload

  Contact days and lectures: 16 hours (Online working)
  
  -Group work and assignments
  -Individual work and assignments

  Content scheduling

  - Define the main quality tools
  - Establish the quality tools for continuous improvement and problem-solving
  - Evaluate critically the applying of quality tools in practice
  - Nonlinear curve fitting and surface fitting
  - Measurement errors
  - Analysis of variance, ANOVA
  - Design of Experiment, One factor at a time, Factorial Design, Response surface method
  - Evaluation of measurement processes
  
  Online days: 30.9.2022 , 28.10.2022, 3.11.2022, 10.11.2022, 24.11.2022 and 2.12.2022
  Online lectures: 16 hours together (2 or 4 hours / day)

  Assessment criteria

  Basic learning outcomes are not met.
  Assignments are not returned or the quality is poor or incorrect.

  Assessment criteria – satisfactory (1-2)

  Assignments partly returned and the quality is weak.

  Assessment criteria – good (3-4)

  Assignments are returned and the quality is good.

  Assessment criteria – excellent (5)

  All assignments are returned and correctly solved. Solution are correctness are verified and critically evaluated.

•   Research and Development Methods MS00BW98-3002 01.09.2022-31.12.2022  5 credits  (YIQMS22V) +-
  Competence objectives of study unit

  After passing the course the student is able to:
  · search and utilize scientific literature
  · identify and describe the main research and development approaches and analysis methods
  · choose and apply an appropriate methodology for his/her work
  - choose a suitable software or programming environment for statistical analysis.
  - use the basic functionality of the chosen environment.
  - be aware of differences in data file formats and is able to open files for reading and writing.
  - perform basic data cleansing e.g. to treat outliers and missing data.
  - know the difference between data types.
  - do basic sampling descriptive statistics
  - graphically represent data.
  - make linear fits and linear regression analysis.
  - do statistical tests, Z-test, F-test and Student’s t-test

  Content of study unit

  Basics of research
  Qualitative and quantitative research methods
  Start of the Master´s Thesis project

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  Teacher(s) in charge

  Ulla Seppälä-Kaven

  Patric Granholm

  Sari Airenne

  Teachers and responsibilities

  Patric Granholm
  Ulla Seppälä-Kavén

  Mode of delivery

  Distance learning

  Learning material

  Qualitative research methods:
  Eriksson, P. & Kovalainen, A. 2008.Qualitative Methods in Business Research. London: SAGE Publications Ltd.
  Klenke, K., Martin, S. and Wallace, J.R. 2016. Qualitative Research in the Study of Leadership: Second Edition. Bingley: Emerald.
  Auerbach, C. & Silverstein, L.B. 2003. Qualitative Data : An Introduction to Coding and Analysis. New York: New York Univesity Press. ProQuest Ebook Central.
  Dey, I. 2005. Qualitative Data Analysis: a user-friendly guide for social scientists. London: Taylor & Francis.
  Ouantitative methods:
  Michael H. Herzog, Gregory Francis, Aaron Clarke. Understanding Statistics and Experimental Design - How to Not Lie with Statistics. Springer Open. 2019 ISBN 978-3-030-03498-6 ISBN 978-3-030-03499-3 (eBook) https://doi.org/10.1007/978-3-030-03499-3
  Sarah E. Burke and Rachel T. Silvestrini. The Certified Quality Engineer Handbook. ASQ Quality Press
  Milwaukee, Wisconsin 2017
  Franz Kronthaler, Silke Zöllner. Data Analysis with RStudio - An Easy going Introduction. Springer Spectrum and R Studio. 2021 ISBN 978-3-662-62517-0 ISBN 978-3-662-62518-7 (eBook) https://doi.org/10.1007/978-3-662-62518-7.

  Learning methods

  Qualitative research methods:
  - individual and team work
  - self- and peer-assessments
  - discussions
  - presentations
  Quantitative methods:
  - Individual and team work
  - Exercises
  - Presentations

  Objects, timing and methods of assessment

  Quantitative methods:
  - Individual and group assignments
  - Online exams and quick tests
  Qualitative methods:
  - Individual and group assignments

  Teaching language

  English

  Timing

  01.09.2022 - 31.12.2022

  Enrollment date range

  01.08.2022 - 02.09.2022

  • 5 credits

  Group(s)

  • YIQMS22V

  Seats

  5 - 35

  Responsible unit

  Engineering and Business

  Degree Programme(s)

  Master of Engineering, Industrial Quality Management

  RDI share

  3 credits

  Share of online studies

  5 credits

  Assessment scale

  H-5

  Pedagogic approaches

  Qualitative research methods:
  - individual and team assignments
  Quantitative methods:
  - individual and team assignments

  Student's schedule and workload

  Qualitative research methods:
  4 x 2 h online meetings + independent work individually and in groups
  research and analysis methods and tools - exercises
  Quantitative methods:
  4 x 2 h online meetings + independent work and exercises
  
  + Thesis seminars: presentations of thesis plans and acting as opponents

  Content scheduling

  Qualitative Research:
  Content:
  - different types of approaches and research principles
  - qualitative research and analysis methods: interviews, benchmarking, questionnaires with open questions, workshops / brainstorming, content analysis with coding
  - quality issues and ethics in the research, data management, IPR and GDPR
  - literature search, evaluation and use of the appropriate research methods for projects and master's thesis
  Ouantitative methods:
  -Basics of Probability Theory
  -Basics of Experiment Design
  -Data file formats and file reading.
  -Data cleansing, outliers and missing data
  -Graphical representation of data.
  -Curve fitting and linear regression
  -Sampling and descriptive statistics
  -Statistical tests, Z-test, F-test and Student’s t-test
  
  Master's Thesis plan and opposing - presentations 8.12.2022 8.00-16.00

  Assessment criteria

  - Quantity: Assignments are not completed to an acceptable level and or not returned assignments - instructions have not been followed
  - Quality: The minimum learning objectives are not met

  Assessment criteria – satisfactory (1-2)

  Communication and reflection are acceptable - assignments partly done according to the instructions

  Assessment criteria – good (3-4)

  Communication and reflection are good - assignments done according to the instructions

  Assessment criteria – excellent (5)

  Outstanding communication, reflection and application - assignments done according to the instructions and returned on time -by the deadline

Academic year 2023 - 2024

There is no implementations attached to this degree programme.