This module is broken up into three distinct sections: The first is dedicated to project management methodologies and is designed to supplement and support the final Project. The second part presents the student with an overview of pragmatic quality improvements in the modern business environment. Students will cover aspects of Statistical Process Control (SPC) and capability analysis for both variable and attribute data classifications focusing on performance metrics in each respective area. The third part covers process design and improvement with designed experiments covering factorial and fraction factorial experiments and analysis of variance analysis. Acceptance sampling is also covered in this section.
Introduction to Project Management: Project constraints, Project characteristics, Project life cycle phases, Project management processes, Project time management, Scheduling techniques, Mathematical Analysis. Agile and Waterfall techniques
Network Diagrams and Activity Sequencing: Activity sequencing (Activity on node) (Activity on arrow), Dummy activities, Critical path method, Element relationships, Leads and lags.
Resource Levelling, Project Crashing and PERT: Resource levelling, Project crashing, Programme evaluation and review technique (PERT), Project contingency.
Statistical Data Analysis: Application of Descriptive Statistics: calculation and interpretation of measures of dispersion, including the mean, mode, median and standard deviation. Inferential Statistics. Sample and population. The definition of probability. The definition and meaning of a random variable. Discrete and continuous probability distributions.
Introduction to Statistical Process Control (SPC): Quality definition, Six sigma, Quality costs, Stem and leaf plot, Cumulative frequency plot, Mean variance and standard deviation, Population versus sample data, Box plot applications, Basic SPC tools, Sample size and frequency, Analysis pattern of control charts.
SPC Control Charts: Variable charts, Mean and range charts, Process capability ratios, Precision and accuracy, Control versus specification limits, Attribute charts, Fraction nonconforming charts, Number nonconforming charts, Non conformities charts, Implementing control charts.
Process Design Improvement with Designed Experiments: Introduction to experimental design, Guidelines for designing experiments, Randomized single factor experiment, Residual analysis, Factorial experiments with several factors, Analysis of variance (ANOVA) analysis, The F-distribution approximation.
Acceptance Sampling: Approaches to acceptance sampling, Sampling plan classification, Lot stratifying, Acceptance sampling procedures, The operating characteristic curve, Lot tolerance percent defective, Limiting quality level, Producers and consumers risk.
