DMAIC
DMAIC is a data-driven improvement cycle used to enhance and control existing processes. It stands for:
- Define
- Measure
- Analyze
- Improve
- Control
This methodology is widely applied within Six Sigma projects to reduce defects and improve process performance.
See also: Six Sigma, Process Improvement
DMADV
DMADV is a structured approach for designing new processes or products at Six Sigma quality levels. It stands for:
- Define
- Measure
- Analyze
- Design
- Verify
Unlike DMAIC, which focuses on process improvement, DMADV is used when existing processes need to be developed from scratch.
See also: DFSS, Six Sigma
DOE (Design of Experiments) / DoE
DOE stands for Design of Experiments, a systematic method to plan, conduct, analyze, and interpret controlled tests to evaluate the factors that may influence a process or quality outcome. It enables the identification of cause–effect relationships and the optimization of performance.
See also: Statistical Analysis, Process Optimization
DFSS (Design for Six Sigma)
DFSS, or Design for Six Sigma, is a methodology focused on designing products or processes that meet Six Sigma quality standards from the start. It emphasizes understanding customer needs and aligning design parameters to achieve low defect levels.
See also: DMADV, New Product Development
DPMO (Defects Per Million Opportunities)
DPMO is a quality metric that quantifies process performance by calculating the number of defects per million opportunities. It standardizes defect counts across different processes for consistent improvement tracking.
See also: Sigma Level, Process Capability
Delighter
A delighter is an unexpected feature or attribute of a product or service that exceeds customer expectations, thereby providing a competitive edge and enhancing overall customer satisfaction—even though it may not be a stated requirement.
See also: Customer Satisfaction, Quality Attributes
Delphi Method
The Delphi method is a structured communication technique that gathers expert opinions through multiple rounds of questionnaires. After each round, a facilitator provides an anonymized summary of the experts' views, encouraging convergence toward a consensus decision.
See also: Forecasting, Consensus Building
Deming Cycle (PDSA Cycle)
The Deming cycle, also known as the Plan-Do-Study-Act (PDSA) cycle, is an iterative four-stage problem-solving model used for continuous process improvement. Its steps are:
- Plan: Identify an opportunity and plan for change.
- Do: Implement the change on a small scale.
- Study: Analyze the results.
- Act: Decide on necessary adjustments and standardize improvements if successful.
See also: Continuous Improvement, Quality Management
Deming Prize
The Deming Prize is a prestigious quality award established by the Japanese Union of Scientists and Engineers (JUSE) in honor of W. Edwards Deming. It recognizes organizations worldwide for outstanding achievements in Total Quality Management (TQM) and continuous improvement.
See also: TQM, Quality Awards
Dependent Variable
In experiments and data analysis, the dependent variable is the outcome or response that is measured. Its value is presumed to depend on or be affected by changes in one or more independent variables.
See also: Independent Variable, Experimental Design
Deploy Phase
The Deploy phase refers to the stage in a Six Sigma or quality improvement project where the solutions or improvements developed are implemented into the live production or operational environment.
See also: Implementation, Roll-Out
Deployment
Deployment is the process of transitioning a new system, process, or improvement from the development or pilot phase to full operational use. It involves proper planning, training, and support to ensure smooth operation in the field.
See also: Go-Live, Implementation
Design Resolution
Design resolution is the finalization and approval step in the design process where all technical specifications, customer requirements, and risk mitigations have been addressed and confirmed before proceeding to production.
See also: Product Design, Risk Management
Detection (in FMEA)
In Failure Modes and Effects Analysis (FMEA), detection is a rating that assesses the process’s ability to identify a potential failure mode before it reaches the customer. A lower detection capability signals a higher risk that a defect might escape notice.
See also: FMEA, Risk Priority Number (RPN)
Detection Risk
Detection risk is the probability that a defect or failure mode will go undetected by the quality control or inspection processes. In both FMEA and statistical sampling, it helps determine how much reliance can be placed on the existing detection mechanisms.
See also: Type II Error, FMEA
DFMEA (Design FMEA)
DFMEA stands for Design Failure Modes and Effects Analysis. It is a systematic method for evaluating potential failure modes within a product design, assessing their effects on performance, and prioritizing risk mitigation actions before mass production begins.
See also: Process FMEA, Risk Assessment
Diagnosis
Diagnosis in the context of quality management is the analytical process of determining the root cause of a process variation or defect. It involves data collection, analysis, and testing of hypotheses to pinpoint issues accurately.
See also: Root Cause Analysis, Problem Solving
Diagnostic Journey
The diagnostic journey refers to the sequence of steps, tests, and evaluations a customer, patient, or process undergoes to identify and understand a problem or condition. In quality and healthcare, it emphasizes the pathway toward reaching an accurate diagnosis.
See also: Process Mapping, Customer Experience
Diploma in Quality
A Diploma in Quality is an educational qualification focusing on quality management principles, tools, and practices. It typically covers topics such as Six Sigma, Lean, Total Quality Management (TQM), and various quality improvement methodologies, preparing professionals for roles in quality assurance and process improvement.
See also: Quality Certification, Continuous Improvement
Discrete Data
Discrete data consists of countable, distinct values or observations that cannot be subdivided meaningfully. Examples include the number of defects, items produced, or the count of occurrences.
See also: Continuous Data, Data Types
Discrete Distribution
A discrete distribution is a statistical probability distribution that describes the likelihood of outcomes for a discrete random variable. Common examples include the binomial distribution and the Poisson distribution.
See also: Probability Distribution, Discrete Data
Dispersion
Dispersion refers to the extent of variability or spread in a set of data values. It indicates how much the data points differ from the central tendency and is commonly measured by range, variance, or standard deviation.
See also: Variability, Standard Deviation
Distribution (Statistical)
A statistical distribution represents how values of a random variable are spread or distributed across possible outcomes. It can be characterized by parameters such as mean and variance and may take the form of continuous (normal, exponential) or discrete (binomial, Poisson) distributions.
See also: Probability, Statistical Analysis
Double Sampling
Double sampling is a two-phase sampling technique where an initial sample is taken and analyzed, and if the results are inconclusive, a second round of sampling is performed. This approach can improve accuracy in decision making while optimizing inspection costs.
See also: Sampling Methods, Quality Control
Double Sampling Plan
A double sampling plan is a specific quality control procedure that outlines two stages of sampling for lot acceptance. The first sample is used to make a preliminary decision; if that decision falls within an indeterminate range, a second sample is evaluated to reach a final conclusion on acceptance or rejection.
See also: Acceptance Sampling, Statistical Quality Control
Downtime
Downtime is the period when a system, machine, or process is not operating due to planned maintenance, unexpected failures, or other interruptions. It is a critical metric in operations management as it directly affects productivity and efficiency.
See also: Uptime, Process Performance
Durability
Durability refers to the ability of a product or material to withstand wear, pressure, or damage over time under normal usage conditions. It is a key quality attribute that reflects a product’s longevity and reliability over its intended lifecycle.
See also: Reliability, Life Cycle