Total Quality Management

Transform Your Organization into a High-Performance Entity

Project Management & Leadership 5.0(52)11h 39m31 lectures2 sections

What you'll learn

Understand total quality management and its concepts
Analyze competitiveness and the cost of quality
Analyze the voice of the customer, VOC
Use Kano analysis and the house of quality
Apply PDCA to solve problems
Use quality tools such as Pareto and the cause-and-effect diagram
Apply control charts in Minitab
Measure process capability and analyze the measurement system, MSA
Connect quality management to risk management

About this course

This course explains total quality management, meaning its principles, its tools and ways to improve performance and customer satisfaction. It starts with an introduction to total quality management in four parts, then competitiveness analysis, a deeper look at the concept of quality management and the cost of quality. Next you learn the voice of the customer, VOC, and how to analyze it, Kano analysis and the house of quality, and problem solving with a practical application of PDCA. Then you go into quality tools: flowcharts, the cause-and-effect diagram, Pareto, the histogram and the scatter diagram, with the Minitab program. After that come the foundations of statistical quality and control charts, measuring process capability, measurement system analysis, MSA, then quality management and risk management, along with the workbook. It suits quality engineers and managers and people working in production and services. By the end, you will be able to use quality tools and statistical analysis to improve processes. Practical lessons follow each group of concepts to connect the idea to application on real data.

Expected outcomes

Analyze and improve processes using advanced tools.
Enhance your understanding of quality costs and customer voice.
Ability to manage risks and analyze data effectively.
Practical application of total quality concepts in the workplace.

Learner reviews

These reviews are from learners who bought the content and completed at least 50% of it — there are no reviews from anyone who hasn't gone through the material.

5.0

★★★★★

52 reviews

5★

4★

3★

2★

1★

Course content

Total Quality Management

30 lectures

Introduction to Total Quality Management (Part One)Introduces Total Quality Management and its origin in Japan after World War II, the roles of Deming, Crosby, and others in defining quality, Garvin's model, and the social and environmental dimensions of quality.27:39
Introduction to Total Quality Management (Part Two)Continues the introduction to Total Quality Management, presenting the contributions of Japanese scholars such as Ishikawa and Taguchi, the idea that quality covers all processes, reducing variation, control charts, and quality culture.16:18
Introduction to Total Quality Management (Part Three)Explains the five dimensions of Garvin's model for defining quality, including perceived quality and manufacturing-based quality, corporate social responsibility, the environmental definition of quality, and the evolution of customer needs.17:36
Introduction to Total Quality Management (Part Four)Answers why quality matters, the impact of dissatisfied customers, the evolution of quality from handcrafts to mass production, who is responsible for quality in total quality management, product quality dimensions, and involving interested parties.23:38
Competitive AnalysisExplains competitive analysis and evaluating an organization's quality management performance, starting from the customer and competitors, weighting product attributes through customer surveys, and quality system requirements such as data and strategic planning.30:07
In-Depth Look at Quality Management ConceptIntroduces Edward Deming and his achievements, such as the PDCA cycle and his book on the 14 points, then explains the elements of his System of Profound Knowledge: systems thinking, understanding variation, and psychology of human behavior.20:01
Cost of QualityExplains the cost of quality and Philip Crosby's idea that quality is free, distinguishing the cost of doing things right from the cost of poor quality, including internal and external failure, and how its indicators measure the quality system.22:48
Introduction to Voice of the Customer VOCIntroduces the Voice of the Customer (VOC) within Quality Function Deployment (QFD), covering why knowing the customer matters, profiling the customer and describing needs, Kano analysis, and maximizing customer value by closing the gap between need and delivery.25:46
Voice of the Customer VOC AnalysisExplains how to analyze the voice of the customer, from distinguishing specific from vague requests to turning general complaints into measurable quality requirements, with key buying factors, Kano analysis and the House of Quality.30:51
Kano and Quality House AnalysisExplains the Kano model, which classifies customer needs into basic, stated and delighting ones, then builds the House of Quality, a matrix linking customer wants to technical specifications across its phases through to production and the quality plan.19:24
Problem SolvingIntroduces problem-solving strategy in total quality management, defining a problem as the gap between the current and desired state, its causes such as missing standards and poor resource use, tools like FMEA, and the elements of a problem statement.24:39
Practical Application on PDCAA practical PDCA case study on production delays in a company, from defining the problem with data and finding causes, to planning, doing and checking results, then rolling out the solution and adopting the cycle as a process improvement method.19:02
Introduction to Quality Tools and FlowchartsIntroduces the seven basic quality tools with a focus on flowcharts: their symbols and decision points, the conditions for a step to add value for the customer, how to draw the chart and set process boundaries, with an input and output analysis example.32:52
Cause and Effect Diagram and ParetoExplains check sheets for collecting data, the cause-and-effect (fishbone) diagram with repeated whys to find root causes, and the Pareto chart for ranking problems and focusing on the vital few, including the steps to build it.20:59
Frequency Distribution and Scatter PlotExplains two of the seven quality tools: the frequency distribution (histogram) for understanding process behavior and how close the distribution is to normal, and the scatter diagram for seeing the relationship between two variables and whether they are correlated.26:59
Minitab 17 ProgramAn introduction to Minitab 17 and the parts of its interface: the command menus, the output window and the data worksheet, how to enter data and use it for quality tools such as the Pareto chart, including the data type it needs.15:42
Statistical Quality FoundationsExplains the foundations of statistical quality within statistical quality control: descriptive statistics, data types and scales, measures of central tendency such as mean, median and mode, standard deviation and outliers, with a Minitab application.25:07
Statistical Application on Minitab 17A hands-on Minitab 17 application of statistical quality basics: testing whether data follows the normal distribution using the p-value, reading outliers, calculating the share of output below a given limit, and producing descriptive statistics and reports.10:13
Control Maps (Part One)Introduces control charts created by Walter Shewhart at Bell Labs, explains population versus sample, and how to choose the chart type based on the data type and whether the sample size is constant, with examples such as the individuals chart.17:27
Control Maps (Part Two)Explains the X bar R chart for samples larger than one and the calculation of mean and range, importing data from Excel, interpreting chart patterns such as points outside limits, then charts suited to attribute data, defective units and defect counts.13:42
Practical Application of Control MapsA hands-on Minitab application on control charts: choosing the chart type based on how data is collected, single observations or subgroups, reading out-of-control tests such as a point beyond three sigma limits, and entering data in rows or columns.35:17
Measuring Process Capability (Part One)Answers whether process output meets customer needs once the process is confirmed to be under statistical control, explaining variation, common versus assignable causes, and the difference between short-term and long-term process capability.28:34
Measuring Process Capability (Part Two)Explains process capability indicators, including the stability index, how they are calculated for measured and attribute data against customer specification limits, and calculating DPMO and sigma level, with filling and manufacturing examples.38:35
Practical Application of Measuring Process CapabilityA hands-on Minitab application for measuring process capability: entering data, choosing the right quality tools and specification limits, and reading results such as the share of output above the upper limit, sigma level, and actual versus potential performance.11:52
Analysis of MSA Measurement System Capability (Part One)Explains measurement system analysis (MSA) and why measurement data must be trustworthy, the sources of variation from the measurement versus the process, and gauge accuracy, repeatability, and reproducibility between operators.42:14
Analysis of MSA Measurement System Capability (Part Two)Continues measurement system analysis with destructive testing, the criteria for evaluating a measurement system, calculating its variation share and acceptance thresholds, and reading the charts that show whether the problem lies with operators, parts or the gauge.22:51
Practical Application of Analysis of MSA Measurement System CapabilityA hands-on Minitab application of measurement system analysis: choosing the data type, setting up a randomized measurement experiment where operators measure parts without knowing them in advance, and reading charts that reveal an operator who differs from the others.13:09
Quality Management and Risk Management (Part One)Explains the link between quality and risk management, the adoption of risk-based thinking in ISO 9001 instead of preventive action, the elements of risk assessment (severity, probability, detectability), and terms such as failure.28:05
Quality Management and Risk Management (Part Two)Explains forming an FMEA team and the role of a sponsor, where FMEA is used across product design stages, a smoke detector example, and rating each risk by severity, occurrence and detection and multiplying them to get the risk priority number.20:13
Practical Application of Quality Management and Risk ManagementA practical FMEA application using a car oil change example: identifying the process step and failure modes such as the wrong oil, their effects, causes and current controls, then scoring and adding an improvement action that lowers the risk priority number.17:54