Severity is a key component in risk assessment methodologies like Failure Mode and Effects Analysis (FMEA). It is defined as the seriousness of the effect of a potential failure mode. It quantifies the impact or consequence of a failure on the customer, downstream processes, or the system itself. A higher severity rating indicates a more critical impact, such as a safety hazard or complete loss of function, whereas a lower rating signifies a minor nuisance. Severity is evaluated independently of the likelihood of the failure occurring or being detected.

A. This definition describes Detection, which is an assessment of the ability of current controls to identify the cause or the failure mode before the item reaches the customer.

B. This definition describes Occurrence, which is the likelihood that a specific cause will result in the failure mode during the design life.

C. This is the formal definition of Risk as per the ISO 31000:2018 standard, which is a broader concept than severity. Severity is one component used to evaluate risk.

1. ASQ Quality Press. (2017). The Certified Quality Engineer Handbook (4th ed.). In Chapter 20

"Risk Management

" the FMEA process is detailed. Severity is defined as "an assessment of the seriousness of the effect of the potential failure mode...if it occurs." (p. 298).

2. Automotive Industry Action Group (AIAG) & Verband der Automobilindustrie (VDA). (2019). Failure Mode and Effects Analysis FMEA Handbook (1st ed.). The handbook defines Severity (S) as the "seriousness of the effect" and provides ranking tables based on the impact to the end-user or manufacturing plant. (pp. 37

102).

3. Stamatis

D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press. The text states

"Severity is the assessment of the seriousness of the effect of the potential failure mode on the next component

subsystem

system

or customer." (p. 55).

4. Purdue University

School of Mechanical Engineering. (n.d.). ME 463

Engineering Design - FMEA Lecture Notes. The course material defines Severity as "a rating of the seriousness of the effect(s) of the potential failure mode." This aligns with the concept of impact.

A control chart is a fundamental tool in statistical process control (SPC) specifically designed to monitor process performance over time. It plots data points in a time-ordered sequence, allowing for the analysis of trends, patterns, and process stability. The chart includes a center line representing the process average and upper and lower control limits. By plotting data chronologically, it helps distinguish between common cause variation (inherent to the process) and special cause variation (indicating a specific, identifiable problem), making it the ideal tool for communicating performance data in a time sequence.

A. Scatter diagram: This tool is used to analyze the relationship and correlation between two different variables, not to track a single performance metric over time.

B. Pareto chart: This chart ranks causes or problems by their frequency or impact in descending order, helping to prioritize issues, but it does not display data chronologically.

D. Flowchart: This is a diagram that visually represents the sequence of steps in a process or workflow; it does not plot or communicate numerical performance data.

1. Montgomery

D. C. (2020). Introduction to Statistical Quality Control (8th ed.). John Wiley & Sons.

Page 1: "The control chart is a graphical display of a quality characteristic that has been measured or computed from a sample versus the sample number or time. The chart contains a center line that represents the average value of the quality characteristic... Two other horizontal lines

called the upper control limit (UCL) and the lower control limit (LCL)

are also shown on the chart. These control limits are chosen so that if the process is in control

nearly all of the sample points will fall between them."

2. American Society for Quality (ASQ). (n.d.). Quality Tools A to Z: Control Chart. ASQ.org.

Definition Section: "The control chart is a graph used to study how a process changes over time. Data are plotted in time order."

3. Besterfield

D. H. (2013). Quality Control (8th ed.). Pearson Education

Inc.

Chapter 5

Section "Introduction

" p. 121: "A control chart is a graphic comparison of process performance data to computed 'control limits' drawn as limit lines on the chart... The data are plotted on the control chart in a time sequence."

The Detection (D) rating in a Failure Mode and Effects Analysis (FMEA) assesses the effectiveness of current controls in detecting a potential failure mode before the product reaches the customer. The rating scale is typically inverted, where a high number (e.g., 10) signifies a very low probability of detection, and a low number (e.g., 1) signifies a very high probability of detection.

In the provided FMEA table, the "No solder" failure mode has a Detection rating of 8. The other failure modes ("Too much solder," "Location of solder," and "Stringing solder") all have a Detection rating of 2. Since "No solder" has the highest D rating, it is the least likely to be caught by existing process controls, and therefore the most likely to be seen by a customer.

B. Too much solder: This option has a low Detection rating (D=2), indicating that current controls are highly effective at identifying this failure before shipment.

C. Location of solder: This option also has a low Detection rating (D=2), meaning it is very likely to be detected internally.

D. Stringing solder: With a low Detection rating (D=2), this failure mode has a high probability of being caught by the existing process controls.

1. Stamatis

D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press. In Chapter 5

"The FMEA Form

" the Detection ranking is explained. A high ranking (e.g.

9 or 10) is defined as "Absolute uncertainty the control will detect the failure mode

" while a low ranking (e.g.

1 or 2) means "The control is certain or almost certain to detect the failure mode" (pp. 119-121). This supports that the highest number (8) represents the failure most likely to escape detection.

2. AIAG & VDA. (2019). FMEA Handbook (1st ed.). Automotive Industry Action Group & Verband der Automobilindustrie. In Step 5: Risk Analysis

the evaluation of Detection is detailed. The rating tables (e.g.

Table D3 for Process Controls) show that a high rating like 8 corresponds to a "Low" detection opportunity

whereas a rating of 2 corresponds to a "High" detection opportunity. This confirms that the failure with D=8 is most likely to reach the customer.

3. Montgomery

D. C. (2020). Introduction to Statistical Quality Control (9th ed.). Wiley. Chapter 1

"Quality Improvement in the Modern Business Era

" discusses FMEA as a key engineering tool for risk assessment. The text explains that the Risk Priority Number (RPN) is a product of Severity

Occurrence

and Detection

where the Detection rating reflects the probability that the failure will not be detected before it reaches the customer (Section 1-4.3).

Reliability prediction is the quantitative estimation of a product's ability to function without failure. The universally accepted definition of reliability, which prediction aims to estimate, is "the probability that an item will perform its intended function for a specified interval under stated conditions." Statement II is a direct and accurate representation of this definition. It correctly identifies the core components of a reliability statement: probability, function, and time. The other statements describe related but distinct concepts.

I. The process of performance estimation: This is too general. Reliability is a specific aspect of performance (i.e., sustained performance over time), but "performance estimation" can also refer to other metrics like speed or efficiency at a single point in time.

III. The process of telling "how you can get there from here": This describes a planning, improvement, or corrective action process (a roadmap), not the act of forecasting or estimating a probability, which is the essence of prediction.

1. ASQ Quality Press: Breyfogle III

F. W. (2015). The Certified Quality Engineer Handbook (4th ed.). ASQ Quality Press. In Chapter 20

"Reliability and Maintainability

" reliability is defined as "the probability that an item will perform its intended function for a specified interval under stated conditions" (p. 367). This directly supports statement II as the core of reliability prediction.

2. Academic Publication: O'Connor

P. D. T.

& Kleyner

A. (2012). Practical Reliability Engineering (5th ed.). Wiley. Chapter 1

Section 1.1

defines reliability as "the probability that an item will perform a required function without failure under stated conditions for a stated period of time" (p. 1). This aligns perfectly with statement II.

3. University Courseware: University of Maryland

A. James Clark School of Engineering. ENME 489L: Reliability Engineering. The course materials define reliability as "the probability that a system will perform its intended function for a specified period of time under a given set of conditions." This standard academic definition validates statement II.

Change control is the formal, systematic process used to manage and track all modifications to a product, process, or system. The primary purpose of a change control system is to ensure that any proposed revisions are properly reviewed, evaluated for impact, approved, implemented, and documented. This structured approach guarantees that product revisions are captured and tracked effectively, maintaining product integrity, safety, and conformity to requirements. It is a fundamental component of configuration management and a critical element within regulated quality management systems.

A. work instruction control: This is a component of document control focused on managing instructions for specific tasks, not the overarching system for product revisions.

B. procedure control: This is a general term for managing any documented procedure; change control is a specific procedure for managing revisions.

C. batch control: This system is used for tracking and tracing specific production lots (batches) for traceability, not for managing design or process changes.

1. ASQ

The Certified Quality Engineer Handbook

4th ed.

ASQ Quality Press

2017

pp. 133-134. Chapter 8

"Product and Process Control

" details configuration management

stating

"A key element of configuration management is a formal change control system... to ensure that all proposed changes are properly evaluated

documented

and approved before implementation."

2. International Organization for Standardization (ISO)

ISO 9001:2015 Quality management systems — Requirements

Clause 8.5.6

"Control of changes." This clause mandates that an organization "shall review and control changes for production or service provision... to ensure continuing conformity with requirements."

3. Pyzdek

T.

& Keller

P. A.

The Six Sigma Handbook

5th ed.

McGraw-Hill Education

2018

Chapter 18

"Design for Six Sigma

" Section on "Configuration Management." The text describes change control as the process for "requesting

evaluating

approving

and implementing changes to the baseline."

Walter A. Shewhart is widely recognized as the originator of Statistical Process Control (SPC). While working at Bell Telephone Laboratories in the 1920s, he developed the control chart, the foundational tool of SPC. His work established the statistical basis for differentiating between common cause (chance) and special cause (assignable) variation in a process. This distinction allows for effective process monitoring and improvement. His 1931 book, Economic Control of Quality of Manufactured Product, laid the groundwork for the entire field of modern statistical quality control.

A. Deming: W. Edwards Deming was a protégé of Shewhart who significantly expanded upon and popularized SPC concepts, particularly in post-WWII Japan, but he did not originate them.

B. Feigenbaum: Armand V. Feigenbaum is credited with developing the concept of Total Quality Control (TQC), a systems-wide approach to quality, not the invention of SPC.

C. Juran: Joseph M. Juran contributed significantly to quality management with concepts like the Juran Trilogy and the application of the Pareto principle, but he was not the originator of SPC.

1. Montgomery

D. C. (2019). Introduction to Statistical Quality Control (8th ed.). Wiley. In Chapter 1

Section 1.2

"A Brief History of Quality Control and Improvement

" it is stated

"The key figure in this movement was Walter A. Shewhart... He developed the control chart in 1924 to control the variability of a manufacturing process." (p. 3).

2. American Society for Quality (ASQ). (n.d.). About ASQ: Walter A. Shewhart. ASQ. Retrieved from asq.org. The official ASQ biography states

"Walter A. Shewhart... was an American physicist

engineer and statistician

sometimes known as the father of statistical quality control."

3. Woodall

W. H. (2000). Controversies and contradictions in statistical process control. Journal of Quality Technology

32(4)

341-350. The introduction states

"The field of statistical process control (SPC) was founded by Walter A. Shewhart of Bell Telephone Laboratories in the 1920s." (p. 341). https://doi.org/10.1080/00224065.2000.11980013

4. Best

M.

& Neuhauser

D. (2006). Walter A Shewhart

1924

and the control chart. Quality & Safety in Health Care

15(1)

73–74. This article details Shewhart's development of the control chart

noting

"On 16 May 1924 Walter Shewhart... wrote a one page memo... In this memo he presented a diagram of a control chart." (p. 73). https://doi.org/10.1136/qshc.2005.017393

The ANSI/ASQ Z1.4 standard provides a systematic procedure for determining the sample size for attribute inspection. The process begins with identifying the size of the lot presented for inspection. Next, based on the specified General Inspection Level (Level II in this case), the user finds the corresponding sample size code letter from Table I of the standard. This code letter then directs the user to the appropriate master table (e.g., Table II-A for single sampling) to find the exact sample size. Therefore, the size of the lot to be inspected is the primary determinant for the sample size within a given inspection level.

B. size of the batch to be manufactured: The standard is applied to the inspection lot, which is the quantity submitted for inspection at one time, and may not be the same as the manufacturing batch size.

C. average size of production lots: The sample size is determined for the specific, current lot being inspected, not an average of historical lot sizes.

D. decision of the quality assurance manager: While a manager selects the overall plan and inspection level, the sample size for that plan is dictated by the standard's tables, which are based on lot size.

1. Griffith

G. K. (2018). The Certified Quality Engineer Handbook (4th ed.). ASQ Quality Press. Chapter 21

pp. 318-319

"Using the ANSI/ASQ Z1.4 Tables

" explicitly states the first step is to "Determine the lot size."

2. Montgomery

D. C. (2013). Introduction to Statistical Quality Control (7th ed.). John Wiley & Sons. Section 15.2.3

"The MIL-STD-105E

" describes the procedure: "The first step is to determine the lot size... Given the lot size and the inspection level

a sample size code letter is found..."

3. ANSI/ASQ Z1.4-2008 (R2018). Sampling Procedures and Tables for Inspection by Attributes. Section 9.2

"Sample size code letters

" and Table I

"Sample size code letters

" directly illustrate that the lot size and inspection level are the inputs used to determine the code letter for the sample size.

A criticality analysis is a systematic procedure used to identify and evaluate the potential severity of failures and their effects. This analysis directly classifies product or process characteristics based on the seriousness of a potential failure. Characteristics whose failure could result in hazardous or unsafe conditions for the user are designated as "safety" or "critical" characteristics. This classification is a primary output of tools like Failure Mode and Effects Analysis (FMEA) or Failure Mode, Effects, and Criticality Analysis (FMECA), which are forms of criticality analysis. This process determines the level of control and validation required for these specific characteristics.

A. An inspection plan: This document specifies how, when, and where to conduct inspections; it is created after a characteristic's criticality has been determined.

C. Standard work instructions: These are documents that detail the most efficient and safe way to perform a job; they implement controls but do not classify characteristics.

D. A control chart: This is a statistical process control (SPC) tool used to monitor the stability of a process over time, not to classify the nature of a characteristic.

1. Laman

S. A.

& Westcott

L. K. (Eds.). (2017). The Certified Quality Engineer Handbook (4th ed.). ASQ Quality Press. In Chapter 13

"Product and Process Design

" the section on Failure Mode and Effects Analysis (FMEA) explains that the Severity rating is used to assess the seriousness of the effect of a potential failure mode. A high severity rating (e.g.

9 or 10) is typically assigned to failures that "affect safe operation and/or involve noncompliance with government regulations

" which directly classifies a safety characteristic (pp. 208-209).

2. De Feo

J. A. (Ed.). (2017). Juran's Quality Handbook: The Complete Guide to Performance Excellence (7th ed.). McGraw-Hill Education. Chapter 14

"Design for Quality

" section 14.11

"Classification of Characteristics

" states that characteristics are classified by their degree of importance or criticality

with "critical" characteristics being those where failure could cause personal injury or catastrophic failure. This classification process is a form of criticality analysis.

3. Stamatis

D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press. Chapter 4

"The FMEA Procedure

" details how criticality analysis is an integral part of FMEA

used to prioritize failure modes based on the severity of their consequences

with safety being the highest concern (pp. 81-83).

An ISO-compliant quality system, such as one based on ISO 9001, requires an organization to determine the necessary competence for personnel whose work affects quality performance (Clause 7.2). The most effective and systematic method to achieve this is by conducting a formal needs analysis for each job category. This process involves defining the required knowledge, skills, and abilities for a role and then assessing the current competency levels of employees. The identified gap between the required and actual competence directly informs targeted and efficient training plans, ensuring alignment with the quality management system's objectives and regulatory requirements.

A. Allowing employees to identify their own training needs: This approach is subjective and incomplete; employees may not be fully aware of all organizational needs or required job competencies.

C. Requiring all employees to complete the same training courses: This "one-size-fits-all" method is inefficient and ineffective, as it fails to address the specific, role-based competency requirements.

D. Requiring supervisors to identify training for their employees: While a valuable input, relying solely on supervisors can lead to inconsistency and subjectivity, lacking the rigor of a systematic analysis.

1. International Organization for Standardization (ISO). (2015). ISO 9001:2015 Quality management systems — Requirements. Clause 7.2

"Competence

" states the organization must "determine the necessary competence of person(s) doing work under its control that affects the performance and effectiveness of the quality management system." This necessitates a structured analysis

not ad-hoc methods.

2. Goetsch

D. L.

& Davis

S. B. (2014). Quality Management for Organizational Excellence: Introduction to Total Quality (7th ed.). Pearson. In Chapter 10

"Education and Training

" the text emphasizes that a "job/task analysis" is a critical step to identify specific training needs for various positions to ensure competence.

3. Pyzdek

T.

& Keller

P. A. (Eds.). (2017). The ASQ Certified Quality Engineer Handbook (4th ed.). ASQ Quality Press. Chapter 6

"The Quality System

" explains that fulfilling the competence requirements of ISO 9001 involves a systematic process to identify gaps between required and existing skills

which is the definition of a needs analysis.

The success of any organization-wide quality initiative is fundamentally dependent on the commitment and active involvement of management at all levels. Top management provides the vision, strategic direction, and necessary resources (financial, human, technological). Middle and lower management translate this strategy into tactical actions and provide direct support to front-line employees. This comprehensive buy-in creates a culture where quality is a shared responsibility and a core value, ensuring that initiatives are sustained and integrated into daily operations rather than being viewed as temporary projects. Without this leadership foundation, all other efforts are likely to fail.

B. Leadership by quality assurance staff is vital for technical guidance, but without management authority and resource allocation, their influence is limited.

C. Cooperation from front-line workers is essential for execution, but this cooperation is most effectively fostered and sustained through supportive and committed leadership.

D. Deployment of statistical process control (SPC) is a powerful tool within a quality initiative, not the overarching factor that determines its success.

1. Gryna

F. M.

Chua

R. C. H.

& DeFeo

J. A. (2007). Juran's Quality Planning and Analysis: From Product Development Through Use (5th ed.). McGraw-Hill. In Chapter 4

"Companywide Assessment of Quality

" it is stated

"A major obstacle to companywide quality management is the lack of leadership by upper management" (p. 81). This underscores that management leadership is the primary enabler.

2. Deming

W. E. (2018). Out of the Crisis. The MIT Press. Deming's 14 Points for Management are a cornerstone of modern quality theory. Point 1

"Create constancy of purpose toward improvement

" and Point 7

"Institute leadership

" explicitly place the responsibility for creating a successful quality system on management

not on workers or specific departments (pp. 23-96).

3. Besterfield

D. H. (2013). Quality Improvement (9th ed.). Pearson. In Chapter 2

"Leadership

" the text emphasizes

"A key element of a successful TQM program is the leadership of top management... Top management must be personally and visibly committed to quality" (p. 31).

4. Saraph

J. V.

Benson

P. G.

& Schroeder

R. G. (1989). An instrument for measuring the critical factors of quality management. Decision Sciences

20(4)

810–829. This foundational academic study empirically identified "the role of top management leadership" as the most critical factor for successful quality management. (DOI: https://doi.org/10.1111/j.1540-5915.1989.tb01420.x)