Acceptable quality level (AQL)

Acceptable quality level (AQL) specifies the maximum percentage or number of defective items in a batch that is still considered acceptable during inspection. It sets the threshold between acceptable and unacceptable quality based on a statistical sampling plan. This concept balances producer and consumer risks by establishing clear quality criteria. AQL, originally known as acceptable quality level, was redefined in 2008 as acceptable quality limit.
See also: Acceptance quality limit (AQL), Acceptance sampling plan, Appraisal cost.

Acceptance number

The acceptance number is the maximum number of defects allowed in a sample before a production lot is rejected. It converts quality criteria into a specific numerical limit used during inspections. Adjusting this number directly impacts the risk levels assigned to suppliers and customers.
See also: Acceptance sampling, AQL, Sampling plan.

Acceptable quality limit (AQL)

Acceptable quality limit (AQL) is the predetermined worst tolerable process average when a continuing series of lots is submitted for acceptance sampling. It establishes the quality boundary for a lot to be considered acceptable. AQL, originally known as acceptable quality level, was redefined in 2008 as acceptable quality limit.
See also: Acceptable quality level (AQL), Acceptance sampling.
Note: Often used interchangeably with Acceptable Quality Level.

Acceptance sampling

Acceptance sampling is a statistical method used to decide whether a lot of products should be accepted or rejected based on the quality of a sample. It minimizes the need for a 100% inspection by using a representative sample. This approach reduces inspection costs while maintaining product quality.
See also: Acceptance sampling plan, AQL, Acceptance number.

Acceptance sampling plan

An acceptance sampling plan details the procedure for selecting and testing a sample from a production lot. It defines parameters such as sample size, acceptance/rejection criteria, and risk acceptance levels. Such plans play a vital role in making objective quality decisions without inspecting every item.
See also: Acceptance sampling, Acceptance number, AQL.

Accuracy

Accuracy represents the closeness of a measured or reported value to its true value. It indicates the correctness of measurement systems and is crucial for ensuring reliable output.
See also: Precision, Measurement error, Reliability.
Note: Both accuracy and precision should be evaluated.

Action plan

An action plan is a detailed roadmap outlining the steps required to reach a specific quality improvement goal. It includes assigned responsibilities, timelines, and measurable objectives to ensure effective execution. This planning tool is essential for resolving issues and driving continuous process improvement.
See also: Corrective actions, Process improvement, Project management.

Activity network diagram

An activity network diagram visually maps out the sequence and interdependencies of tasks within a project. It helps identify the critical path and potential bottlenecks, allowing for effective time and resource management. This diagram is a crucial tool for planning and monitoring quality improvement projects.
See also: Critical path method (CPM), Gantt chart, Project scheduling.

Affinity diagram

An affinity diagram organizes a large number of ideas or data points into natural groupings based on their relationships. This tool is especially useful during brainstorming sessions to sort complex issues into manageable categories. It facilitates the identification of themes that can drive targeted improvements in quality.
See also: Brainstorming, Cause-and-effect diagram, Grouping techniques.

Alias structure

In the context of design of experiments, an alias structure defines the confounding relationships among effects—such as main effects and interactions—resulting from using fractional factorial designs. It outlines which effects are indistinguishably merged due to a reduced number of experimental runs, thereby guiding the interpretation of results. A clear alias structure is vital for identifying which factors can be independently estimated and which are confounded.

See also: Confounding, Resolution, Fractional Factorial Design.

Alternative hypothesis

An alternative hypothesis is a statistical claim that suggests there is a significant effect or difference, serving as a counterpart to the null hypothesis. It is the premise that a test seeks to provide evidence for during hypothesis testing. Rejecting the null hypothesis in favor of the alternative indicates a significant finding in quality data analysis.
See also: Null hypothesis, Hypothesis testing, Statistical inference.

American Association for Laboratory Accreditation (A2LA)

The American Association for Laboratory Accreditation (A2LA) is an organization that evaluates and accredits laboratories to ensure they meet established quality standards. Its accreditation process verifies technical competence and compliance with industry benchmarks. A2LA accreditation is recognized globally and supports consistent laboratory performance.
See also: ISO/IEC Accreditation, Laboratory quality, Certification bodies.

American Customer Satisfaction Index (ACSI)

The American Customer Satisfaction Index (ACSI) is a benchmarking tool that measures customer satisfaction across industries. It compiles survey data to generate scores that reflect consumer perceptions of product and service quality. Companies use ACSI insights to drive improvements and maintain competitive quality standards.
See also: Customer satisfaction, Net Promoter Score (NPS), Service quality indices.

American National Standards Institute (ANSI)

The American National Standards Institute (ANSI) coordinates the development of American national standards across many industries. It accredits standards organizations and ensures consistency, reliability, and safety in products and processes. ANSI plays a pivotal role in bridging industry practices with quality and safety benchmarks.
See also: ASTM, ISO, National standards.

American National Standards Institute-American Society for Quality (ANSI-ASQ)

ANSI-ASQ is a collaborative partnership that merges the standardization expertise of ANSI with the quality management focus of ASQ. This alliance promotes integration between national standards and best practices in quality improvement. It serves as a bridge to facilitate industry-wide adoption of consistent quality benchmarks.
See also: ANSI, ASQ, Quality standards.

American Society for Quality (ASQ)

The American Society for Quality (ASQ) is a global community of quality professionals dedicated to advancing best practices in quality management. It offers certification, training, and resources to support continuous improvement in various industries. ASQ is also influential in setting industry standards and disseminating quality-related research.
See also: ASQC, ANSI-ASQ, Quality management systems.
Note: ASQ certifications are highly regarded in the quality and reliability fields and are frequently included in professional development programs.

American Society for Quality Control (ASQC)

The American Society for Quality Control (ASQC) is the former name of today’s ASQ and historically focused on statistical quality control practices. Its legacy contributions helped shape modern quality management techniques and standards. Knowledge of ASQC remains important for understanding the evolution of quality methodologies.
See also: ASQ, Statistical process control (SPC).
Note: Many ASQ resources and literature still reference ASQC, making it a familiar term for quality professionals.

American Society for Testing and Materials (ASTM)

The American Society for Testing and Materials (ASTM) develops and publishes voluntary consensus technical standards for a wide range of materials, products, systems, and services. Its standards help ensure the quality, safety, and reliability of commercial goods and industrial processes. ASTM standards are widely used across industries worldwide.
See also: ANSI, ISO, NIST.

American Standard Code for Information Interchange (ASCII)

ASCII is a character encoding standard that assigns numeric codes to letters, digits, punctuation marks, and control characters used in electronic communication. It provides a common framework for data exchange in computers and other devices. ASCII plays a foundational role in data quality and digital communication systems.
See also: Unicode, EBCDIC.

Analyze phase

The analyze phase is a critical step in quality improvement methodologies, such as Six Sigma’s DMAIC process, where root causes of defects and process inefficiencies are identified. It utilizes data analysis, process mapping, and statistical tools to uncover underlying issues. Insights gained during this phase drive targeted corrective actions.
See also: Define phase, Improve phase, Control phase.
Note: Mastery of the analyze phase is indispensable for quality professionals, particularly those pursuing ASQ or Six Sigma certifications.

Andon

Andon is a visual alert system used in manufacturing environments to signal production anomalies, quality issues, or process disruptions. Often incorporating lights or digital displays, it immediately notifies operators and supervisors so that corrective actions can be undertaken promptly. This real-time communication tool enhances responsiveness and quality control.
See also: Andon board, Visual management systems.

Andon board

An Andon board is a display panel that shows real-time information about production status, quality alerts, and potential process deviations. It acts as a central communication tool to inform team members about issues that require immediate attention. The board helps in streamlining problem resolution and supporting continuous improvement efforts.
See also: Andon, Visual management.
Note: Integrating Andon boards into production environments promotes transparency and rapid response, which are key in high-performance quality systems.

AOQ (average outgoing quality)

AOQ, or average outgoing quality, measures the expected quality level of products shipped after passage through an acceptance sampling process. It considers both accepted lots and the residual quality after additional inspections. This metric helps organizations assess the overall performance of their sampling plans.
See also: AOQL, Acceptance sampling plan.
Note: Calculating AOQ is essential for balancing inspection costs with product quality assurance.

AOQL (average outgoing quality limit)

AOQL represents the highest average defect rate that may occur when using an acceptance sampling plan over many production lots. It defines a critical benchmark that indicates the worst-case scenario for product quality quality escape. Maintaining AOQL within acceptable limits is vital for ensuring consistent customer satisfaction.
See also: AOQ, Acceptance sampling.

Appraisal cost (quality cost)

Appraisal cost (quality cost) refers specifically to the investment in evaluating processes, systems, and products to verify conformance to quality requirements. It is one element of the broader quality cost model, alongside prevention and failure costs. Effective management of these costs contributes to overall process optimization and customer satisfaction.
See also: Appraisal cost, Total quality management (TQM).

Assignable cause

An assignable cause, sometimes called a special cause, is a factor that leads to a variation in a process and can be traced to a particular, identifiable reason. It indicates a departure from normal process behavior and typically requires investigation and corrective action. Recognizing assignable causes is essential in maintaining process control and quality stability.
See also: Common cause, Statistical process control (SPC).

Attribute data

Attribute data comprises categorical information that reflects the presence or absence of a characteristic, such as pass/fail or defective/nondefective classifications. It is typically counted rather than measured and is used in many quality sampling methods. Though less detailed than continuous data, attribute data is effective for quick quality assessments.
See also: Variable data, Defect count, Binary data analysis.

Attributes sampling

Attributes sampling is a quality inspection method that focuses on counting the number of defective items within a sample rather than measuring continuous characteristics. It is particularly useful for processes where items fall into simply classified categories (acceptable or not acceptable). This approach streamlines decision-making during quality inspections.
See also: Acceptance sampling, Attribute data, Sampling plan.

Audit

An audit is a systematic, independent review of processes, systems, or products to assess conformance with established quality standards and procedures. It identifies areas of non-compliance and opportunities for improvement. Audits are essential for driving continuous improvement and ensuring adherence to regulatory and internal standards.
See also: Audit plan, Audit criteria, Compliance audit.

Audit criteria

Audit criteria are the benchmarks, standards, policies, or requirements used to evaluate what is being audited. They provide a consistent basis for determining whether processes, systems, or products meet established quality and regulatory expectations. Clearly defined criteria ensure a focused and objective audit process.
See also: Audit plan, Audit finding, Compliance standards.

Audit finding

An audit finding is a documented result that identifies a nonconformity, deviation, or gap between actual performance and the audit criteria. It highlights strengths, weaknesses, or hazards that may require corrective action. These findings form the basis for further investigations and improvement initiatives.
See also: Audit observation, Corrective action, Nonconformance report.

Audit observation

An audit observation is a note or preliminary remark made by an auditor on an issue noticed during the audit, which may not immediately constitute a formal nonconformity. It provides additional insight into potential areas of improvement and can prompt further review. Observations often serve as early indicators of issues before they become critical.
See also: Audit finding, Audit report, Preventive action.

Audit plan

An audit plan is a comprehensive outline detailing the objectives, scope, criteria, methods, and schedule of an audit. It defines which areas, processes, or departments will be reviewed and how the audit will be conducted. A well-structured audit plan ensures that the audit is systematic, efficient, and effective.
See also: Audit program, Audit scope, Audit schedule.

Audit program

An audit program is a series of audits organized over a specified period to assess different areas or processes within an organization. It integrates individual audit plans into a coordinated strategy to ensure comprehensive review and continuous improvement. This systematic approach supports ongoing compliance and quality assurance.
See also: Audit plan, Audit schedule, Continuous improvement.

Audit scope

Audit scope defines the limits and extent of the audit, including the boundaries in terms of processes, departments, product lines, or geographical areas to be reviewed. It ensures that auditors focus on the most critical areas while avoiding unnecessary investigations. A clearly defined scope optimizes resource use and audit effectiveness.
See also: Audit plan, Audit program, Audit criteria.

Auditee

An auditee is the individual, team, or organization being assessed during an audit. They are responsible for providing access to documentation, processes, and evidence needed for the audit. Active cooperation from the auditee is essential for a productive audit and subsequent improvement actions.
See also: Auditor, Audit report, Audit process.
Note: Building a cooperative relationship between auditors and auditees can enhance the overall audit effectiveness.

Auditing

Auditing refers to the systematic process of objectively examining and evaluating an organization’s processes, systems, or products against predetermined criteria. It involves collecting evidence, analyzing findings, and addressing areas that deviate from standards. Auditing is a key tool for verifying quality management and driving corrective measures.
See also: Audit, Audit plan, Compliance audit.

Auditor

An auditor is a professional trained to conduct audits, assess compliance with standards, and identify areas for change or improvement. They analyze data, documentation, and processes to verify that quality systems meet the required criteria. Auditors provide independent insights essential for maintaining transparency and enhancing overall quality.
See also: Auditee, Audit, Audit plan.
Note: Maintaining neutrality and objectivity is paramount for auditors, a key principle stressed in quality management courses.

Average

Average refers to a central value used to summarize a set of data, commonly calculated as the mean. In quality control, the average is used to monitor process behavior and detect shifts or trends over time. It serves as a fundamental statistical tool to gauge overall performance.
See also: Mean, Central tendency, Variability.

Average chart (X-bar chart)

The average chart, often known as the X-bar chart, is a control chart used to monitor the mean values of subgroups over time in a process. It helps in identifying trends, shifts, or unusual variations that may indicate process instability. X-bar charts are a critical component of statistical process control practices.
See also: Control chart, R-chart, Process monitoring.

Average outgoing quality (AOQ)

Average outgoing quality (AOQ) quantifies the defect level in products after going through an acceptance sampling process and any subsequent inspections. It reflects the overall quality level that reaches the customer by averaging the effect of sampling decisions over time. AOQ is used to evaluate the efficiency of sampling plans in maintaining desired quality levels.
See also: AOQL, Acceptance sampling plan.

Average outgoing quality limit (AOQL)

Average outgoing quality limit (AOQL) represents the maximum average defect rate that can be achieved through an acceptance sampling plan, considering the worst-case conditions. It acts as a performance threshold, indicating the upper limit of defects that might be accepted over a long period. Managing AOQL is vital to ensuring that quality standards remain consistent.
See also: AOQ, Acceptance sampling.

Average run length (ARL)

Average run length (ARL) is the expected number of samples or data points collected on a control chart before an alarm is triggered signaling an out-of-control process. It provides an indication of a chart’s sensitivity and its effectiveness in detecting shifts in the process. A lower ARL typically reflects higher sensitivity to changes, but it may also incur more false alarms.
See also: Control chart, Statistical process control (SPC), X-bar chart.

Average sample number (ASN)

Average sample number (ASN) is the expected number of units that will be inspected in a sequential sampling plan before a final decision is reached regarding a lot’s acceptance or rejection. It balances the need for quality assurance with the desire to minimize inspection costs. This metric plays a central role in optimizing the efficiency of sampling plans.
See also: Sequential sampling, Acceptance sampling, Operating characteristic curve.

Average Total Inspection (ATI)

ATI aggregates the overall inspection effort by combining the costs of sampling inspections and full (100%) inspections, weighted by the probability of occurrence. It provides an estimation of the total resources required to maintain quality across the production process by accounting for variations in incoming quality.

See also: Appraisal cost, Inspection cost, Acceptance sampling.