History and Concepts of Quality Control

History of Quality Control

• Frederick Taylor introduced scientific management principles in 1900 as mass production industries grew.
• Walter Shewhart of Bell Laboratories developed the statistical control chart in 1924, marking the formal beginning of statistical quality control.
• Harold Dodge and Harry Romig developed statistically based acceptance sampling as an alternative to 100% inspection in the late 1920s.
• Statistical quality control methods were widely used in Western Electric (manufacturing arm of Bell Laboratories) in the mid-1930s.
• World War II greatly expanded the use and acceptance of statistical quality-control concepts in manufacturing.

Quality Definition

• Quality is defined as "fitness for use".
• This definition is useful when a product/service does not meet customer specifications or is not performing adequately.
• Quality is critical for organizational success, growth, and competitiveness.

DMAIC Methodology - Define

• DMAIC (Define, Measure, Analyze, Improve, Control) is a structured methodology for quality improvement projects.
• Define stage identifies the problem or opportunity for improvement, defines clear objectives, and establishes a project charter.
• Customer voice (VOC) is crucial for understanding and addressing customer needs and expectations.

DMAIC Methodology - Measure

• Measure stage quantifies the problem or opportunity in objective terms by collecting data on key process input, output, and performance variables.
• Critical to quality (CTQ) characteristics are identified and measured to determine the impact on customer satisfaction.
• Data analysis tools such as stem-and-leaf plots, histograms, and probability distributions are used to summarize and analyze data.

DMAIC Methodology - Analyze

• Analyze stage seeks to understand the root causes of the problem or opportunity by identifying the key drivers of process variability.
• Statistical tools like control charts are used to identify patterns and trends in the data.
• Cause-and-effect diagrams (also known as fishbone diagrams) provide a structured approach for identifying potential root causes.

DMAIC Methodology - Improve

• Improve stage develops and implements solutions to address the root causes identified in the analysis stage.
• Brainstorming, solution selection matrices, and pilot studies are used to evaluate potential solutions.
• Design experiments are often conducted to determine the most effective solutions and understand the interaction between different factors.

DMAIC Methodology - Control

• Control stage establishes processes and mechanisms to maintain the improvements achieved in the improvement phase.
• Key elements include control charts, standard operating procedures, communication plans, and implementation plans.
• Process control plans document and monitor the effectiveness of the new process to ensure that quality standards are met.

Bank Service Quality Improvement Example

• This example illustrates how DMAIC can be applied to improve service quality in a bank.
• Project objectives focused on improving teller and customer work stations and staff training.
• A designed experiment using customer satisfaction surveys evaluated the impact of different workstations and training programs.

Tollgate Questions

• Tollgate questions are important for project review and closure.
• They ensure progress aligns with the project charter, objectives are met, and control plans are in place.

On-Time Delivery Improvement Example

• This example focuses on achieving 100% on-time delivery for a manufacturing company.
• Key challenges include supplier quality issues and process delays.
• Data analysis methods like stem-and-leaf plots and histograms are used to identify problem areas.

Control Chart Design

• Control charts are critical for monitoring process performance and detecting variations.
• Control limits are chosen based on the desired balance between Type I and Type II errors.
• Sample size and sampling frequency are determined based on factors like the size of the shift we are trying to detect and economic feasibility.

Types of Control Charts

• Variables control charts are used for continuous quality characteristics like dimension, volume, or weight.
• Attributes control charts are used for discrete quality characteristics like defects or defects per unit.

Process Capability Analysis

• Process capability analysis assesses the ability of a process to meet customer specifications.
• It helps determine whether the process is capable of producing products or services that consistently meet customer requirements.
• Cp and Cpk are common metrics used in process capability analysis.

Explore the significant milestones in the history of quality control, from Frederick Taylor's scientific management to the development of statistical quality control. Understand the definition of quality as 'fitness for use' and learn about the DMAIC methodology crucial for enhancing organizational success and competitiveness.