📊Probabilistic Decision-Making Unit 14 – Quality Control & Process Improvement

Key Concepts and Definitions

• Quality control involves techniques and activities aimed at maintaining the quality of products or services to meet customer requirements
• Process improvement focuses on identifying, analyzing, and improving existing processes to optimize performance, meet best practice standards or simply improve quality and the user experience for customers and end-users
• Statistical process control (SPC) is the application of statistical methods to monitor and control a process to ensure it operates at its full potential and produces conforming product
• A process is a series of steps and decisions involved in the way work is completed
• Process capability is the ability of a process to produce output that meets customer requirements and specifications
• Control charts are graphs used to study how a process changes over time and to monitor process variation
• Root cause analysis (RCA) is a systematic process for identifying the underlying causes of problems or events and an approach for responding to them
• Continuous improvement is an ongoing effort to improve products, services, or processes incrementally over time
• Quality management systems (QMS) are a collection of business processes focused on consistently meeting customer requirements and enhancing their satisfaction

Statistical Foundations for Quality Control

• Descriptive statistics such as measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation) are used to summarize and describe quality characteristics
• Inferential statistics involves using sample data to make inferences about a population
  • Hypothesis testing is used to determine whether a process change has occurred based on sample data
  • Confidence intervals estimate the range of values within which a population parameter is likely to fall
• Probability distributions (normal, binomial, Poisson) model the likelihood of different quality outcomes
• Sampling techniques (random sampling, stratified sampling, systematic sampling) are used to select representative samples from a population for quality inspection
• Statistical significance and p-values indicate whether an observed effect is likely due to chance or a meaningful difference
• The Central Limit Theorem states that the distribution of sample means approximates a normal distribution as the sample size gets larger, regardless of the population distribution shape

Process Capability Analysis

• Process capability compares the output of a process to the specification limits by using capability indices
• The most common indices are $C_p$, $C_{pk}$, $C_{pm}$, and $C_{pmk}$
  • $C_p$ measures the spread of the process relative to the specification limits assuming the process mean is centered
  • $C_{pk}$ measures the ability of the process to produce output within the specification limits, accounting for a shift in the process mean
  • $C_{pm}$ is similar to $C_p$ but also accounts for the deviation of the process mean from the target value
  • $C_{pmk}$ is a hybrid of $C_{pk}$ and $C_{pm}$, measuring the ability to produce within specification limits while penalizing deviation from the target
• A process is considered capable if the capability index exceeds a specified threshold (typically 1.33 or 1.67)
• Histograms and probability plots are used to visualize the distribution of process output and compare it to specifications
• Process capability sixpack includes a histogram, probability plot, and capability plot to provide a comprehensive view of process performance

Control Charts and Their Applications

• Control charts monitor a process over time to determine whether it is stable and predictable
• A process is considered in statistical control if all points fall within the control limits and there are no non-random patterns
• Common types of control charts include:
  • $\bar{X}$ and R charts for monitoring the mean and range of a continuous variable
  • $\bar{X}$ and S charts for monitoring the mean and standard deviation of a continuous variable
  • p and np charts for monitoring the proportion or number of defectives in a sample
  • u and c charts for monitoring the rate or count of defects per unit
• Control limits are typically set at $\pm$ 3 standard deviations from the center line, which represents the average value of the quality characteristic
• Out-of-control signals indicate that a process shift or non-random variation has occurred and requires investigation
• Control charts can be used for both variables data (measurable characteristics like length or weight) and attributes data (countable characteristics like number of defects)

Root Cause Analysis Techniques

• Root cause analysis (RCA) aims to identify the underlying causes of problems rather than just addressing symptoms
• The 5 Whys technique involves repeatedly asking "Why?" to drill down to the root cause of a problem
• Fishbone diagrams (Ishikawa diagrams) visually organize potential causes into categories such as materials, methods, machines, people, measurement, and environment
• Pareto analysis prioritizes problems or causes by frequency or impact, based on the principle that 80% of effects come from 20% of causes
• Failure Mode and Effects Analysis (FMEA) systematically identifies potential failure modes, assesses their risks, and prioritizes actions to mitigate them
  • Severity, Occurrence, and Detection are rated on a scale (usually 1-10) and multiplied to calculate a Risk Priority Number (RPN)
  • Failure modes with higher RPNs are prioritized for corrective action
• Fault tree analysis graphically represents the logical relationships between a failure (top event) and its contributing causes

Continuous Improvement Methodologies

• Lean focuses on maximizing value for the customer by minimizing waste in processes
  • The 8 wastes (DOWNTIME) are Defects, Overproduction, Waiting, Non-utilized talent, Transportation, Inventory, Motion, and Extra-processing
  • Value stream mapping visualizes the flow of materials and information to identify and eliminate waste
• Six Sigma is a data-driven approach to reduce variation and defects in processes
  • DMAIC (Define, Measure, Analyze, Improve, Control) is a structured problem-solving methodology used in Six Sigma projects
  • Six Sigma aims to achieve a defect rate of 3.4 defects per million opportunities (DPMO)
• Kaizen is a philosophy of continuous, incremental improvement involving all employees
  • PDCA (Plan-Do-Check-Act) cycle is a key tool in Kaizen for iterative improvement
  • Kaizen events are focused improvement projects typically lasting 3-5 days
• Total Quality Management (TQM) is a comprehensive approach that involves all employees in continuously improving processes, products, and services to meet customer needs
• Theory of Constraints (TOC) focuses on identifying and managing constraints (bottlenecks) that limit system throughput

Quality Management Systems

• A quality management system (QMS) is a formalized system that documents processes, procedures, and responsibilities for achieving quality policies and objectives
• ISO 9001 is an international standard that sets out the requirements for a QMS
  • It follows a process-based approach and emphasizes customer focus, leadership, engagement of people, process approach, improvement, evidence-based decision making, and relationship management
  • Organizations can be certified to ISO 9001 by an accredited third-party certification body
• Total Quality Management (TQM) principles are often incorporated into QMS
  • Customer focus, leadership, involvement of people, process approach, system approach to management, continual improvement, factual approach to decision making, and mutually beneficial supplier relationships
• QMS documentation typically includes a quality manual, quality policy, quality objectives, procedures, work instructions, forms, and records
• Audits are used to evaluate the effectiveness of the QMS and identify opportunities for improvement
  • Internal audits are conducted by the organization itself
  • External audits may be conducted by customers, certification bodies, or regulatory authorities

Real-World Case Studies and Applications

• Motorola pioneered the Six Sigma methodology in the 1980s to reduce defects in their manufacturing processes
  • Achieved a defect rate of 3.4 DPMO and saved billions of dollars
  • Six Sigma has since been adopted by many other companies across various industries (General Electric, Boeing, Ford)
• Toyota is known for its Toyota Production System (TPS) which focuses on lean principles and continuous improvement
  • Just-in-time (JIT) production, Jidoka (built-in quality), and Kaizen are key elements of TPS
  • Toyota has consistently been ranked among the top automotive companies for quality and efficiency
• 3M's quality management system emphasizes innovation and customer focus
  • New Product Vitality Index (NPVI) measures the percentage of sales from products introduced in the past five years
  • 3M's "15% rule" allows employees to spend up to 15% of their time on self-initiated projects
• Healthcare organizations use root cause analysis to investigate sentinel events (unexpected patient deaths or serious injuries)
  • Joint Commission requires accredited hospitals to conduct RCA and implement corrective actions to prevent recurrence
• Service industries (hotels, restaurants, call centers) use quality control techniques to monitor and improve customer satisfaction
  • Mystery shoppers or customer surveys are used to collect data on service quality attributes
  • Control charts and process capability analysis can be applied to service processes (wait times, call handling times, customer ratings)