The course discusses advanced experimental design methods, statistical process monitoring, and reliability analysis. These methods are used for fact-based decision-making in the realm of operations management.
The course builds upon foundational quantitative methods introduced in the quality management course at the first-cycle level. Areas of sequential experimentation, encompassing screening, characterisation, and optimisation in the Design of Experiments field are covered. Additionally, the course explores response surface methodology and experimental designs tailored for intricate industrial conditions, particularly addressing challenges posed by randomisation difficulties.
The course progresses to an in-depth exploration of advanced univariate control charts for variable and attribute data. The course also introduces multivariate control charts, which combine simultaneous surveillance of multiple variables. Given the prevalence of precise process data in manufacturing and service environments, the course addresses complexities arising from serial dependence (time dependence) and deviations from the normal distribution assumption. Furthermore, connections between process monitoring and control engineering solutions are addressed. Reliability is a focal point, emphasising data-driven methods for reliability and system analysis.
The course integrates laboratory assignments, employing problem-based learning through collaborative group tasks. Students engage with advanced statistical analysis software, encompassing commercial tools and open-source alternatives such as R or Python.
The course discusses advanced experimental design methods, statistical process monitoring, and reliability analysis. These methods are used for fact-based decision-making in the realm of operations management.
The course builds upon foundational quantitative methods introduced in the quality management course at the first-cycle level. Areas of sequential experimentation, encompassing screening, characterisation, and optimisation in the Design of Experiments field are covered. Additionally, the course explores response surface methodology and experimental designs tailored for intricate industrial conditions, particularly addressing challenges posed by randomisation difficulties.
The course progresses to an in-depth exploration of advanced univariate control charts for variable and attribute data. The course also introduces multivariate control charts, which combine simultaneous surveillance of multiple variables. Given the prevalence of precise process data in manufacturing and service environments, the course addresses complexities arising from serial dependence (time dependence) and deviations from the normal distribution assumption. Furthermore, connections between process monitoring and control engineering solutions are addressed. Reliability is a focal point, emphasising data-driven methods for reliability and system analysis.
The course integrates laboratory assignments, employing problem-based learning through collaborative group tasks. Students engage with advanced statistical analysis software, encompassing commercial tools and open-source alternatives such as R or Python.
