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CQE test Format | CQE Course Contents | CQE Course Outline | CQE test Syllabus | CQE test Objectives

Topics in this body of knowledge (BoK) include subtext explanations and the cognitive level at which the questions will be written. This information will provide useful guidance for both the test Development Committee and the candidate preparing to take the exam. The subtext is not intended to limit the subject matter or be all-inclusive of that material that will be covered in the exam. It is meant to clarify the type of content that will be included on the exam. The descriptor in parentheses at the end of each entry refers to the maximum cognitive level at which the syllabu will be tested. A complete description of cognitive levels is provided at the end of this document

I. Management and Leadership (18 Questions) A. Quality Philosophies and FoundationsDescribe continuous improvement tools, including lean, Six Sigma, theory of constraints, statistical process control (SPC), and total quality management, and understand how modern quality has evolved from quality control through statistical process control (SPC) to total quality management and leadership principles (including Demings 14 points). (Understand)B. The Quality Management System (QMS)1. Strategic planningIdentify and define top managements responsibility for the QMS, including establishing policies and objectives, setting organization-wide goals, and supporting quality initiatives. (Apply) 2. Deployment techniques Define, describe, and use various deployment tools in support of the QMS such as

a. Benchmarking Define the concept of benchmarking and why it may be used. (Remember)b. Stakeholder Define, describe, and use stakeholder identification and analysis. (Apply)c. Performance Define, describe, and use performance measurement tools. (Apply)d. Project management Define, describe, and use project management tools, including PERT charts, Gantt charts, critical path method (CPM), and resource allocation. (Apply) 3. Quality information system (QIS) Identify and describe the basic elements of a QIS, including who will contribute data, the kind of data to be managed, who will have access to the data, the level of flexibility for future information needs, and data analysis. (Understand)

C. ASQ Code of Ethics for Professional ConductDetermine appropriate behavior in situations requiring ethical decisions. (Evaluate)D. Leadership Principles and TechniquesAnalyze various principles and techniques for developing and organizing teams and leading quality initiatives. (Analyze)E. Facilitation Principles and Techniques1. Roles and responsibilitiesDescribe the facilitators roles and responsibilities on a team. (Understand)2. Facilitation toolsApply various tools used with teams, including brainstorming, nominal group technique, conflict resolution, and force-field analysis. (Apply)F. Communication SkillsIdentify specific communication methods that are used for delivering information and messages in a variety of situations across all levels of the organization. (Analyze)G. Customer RelationsDefine, apply, and analyze the results of customer relation tools such as quality function deployment (QFD) and customer satisfaction surveys. (Analyze)H. supplier Management1. TechniquesApply various supplier management techniques, including supplier qualification, certification, and evaluation. (Apply)2. ImprovementAnalyze supplier ratings and performance improvement results. (Analyze)3. RiskUnderstand business continuity, resiliency, and contingency planning. (Understand)

I. Barriers to Quality ImprovementIdentify barriers to quality improvement, analyze their causes and impact, and implement methods for improvement. (Analyze)II. The Quality System (16 Questions)A. Elements of the Quality System1. Basic elementsInterpret the basic elements of a quality system, including planning, control, and improvement, from product and process design through quality cost systems and audit programs. (Evaluate)2. DesignAnalyze the design and alignment of interrelated processes to the strategic plan and core processes. (Analyze)B. Documentation of the Quality System1. Document componentsIdentify and describe quality system documentation components, including quality policies and procedures to support the system. (Understand)2. Document controlEvaluate configuration management, maintenance, and document control to manage work instructions and quality records. (Evaluate)C. Quality Standards and Other GuidelinesApply national and international standards and other requirements and guidelines, including the Malcolm Baldrige National Quality Award (MBNQA), and describe key points of the ISO 9000 series of standards. (Note: Industry-specific standards will not be tested.) (Apply)

D. Quality Audits1. Types of auditsDescribe and distinguish between various types of quality audits such as product, process, management (system), registration (certification), compliance (regulatory), first, second, and third party. (Apply)2. Roles and responsibilities in auditsIdentify and define roles and responsibilities for audit participants such as audit team (leader and members), client, and auditee. (Understand)3. Audit planning and implementationDescribe and apply the stages of a quality audit, from audit planning through conducting the audit. (Apply)4. Audit reporting and follow-upApply the steps of audit reporting and follow-up, including the need to verify corrective action. (Apply)E. Cost of Quality (COQ)Identify and apply COQ concepts, including cost categorization, data collection, reporting, and interpreting results. (Analyze)F. Quality TrainingIdentify and apply key elements of a training program, including conducting a needs analysis, developing curricula and materials, and determining the programs effectiveness. (Apply)III. Product, Process, and Service Design (23 Questions)A. Classification of Quality CharacteristicsDefine, interpret, and classify quality characteristics for new and existing products, processes, and services. (Note: The classification of defects is covered in IV.B.3.) (Evaluate)

B. Design Inputs and Review1. InputsTranslate design inputs such as customer needs, regulatory requirements, and risk assessment into robust design using techniques such as failure mode and effects analysis (FMEA), quality function deployment (QFD), Design for X (DFX), and Design for Six Sigma (DFSS). (Analyze)2. ReviewIdentify and apply common elements of the design review process, including roles and responsibilities of participants. (Apply)C. Technical Drawings and SpecificationsInterpret specification requirements in relation to product and process characteristics and technical drawings, including characteristics such as views, title blocks, dimensioning and tolerancing, and GD&T symbols. (Evaluate)D. Verification and ValidationInterpret the results of evaluations and tests used to verify and validate the design of products, processes and services, such as installation qualification (IQ), operational qualification (OQ), and process qualification (PQ). (Evaluate)E. Reliability and Maintainability1. Predictive and preventive maintenance toolsDescribe and apply the tools and techniques used to maintain and Excellerate process and product reliability. (Apply)2. Reliability and maintainability indicesReview and analyze indices such as MTTF, MTBF, MTTR, availability, and failure rate. (Analyze)3. Reliability modelsIdentify, define, and distinguish between the basic elements of reliability models such as exponential, Weibull, and bathtub curve. (Apply)

4.Reliability/Safety/Hazard Assessment ToolsDefine, construct, and interpret the results of failure mode and effects analysis (FMEA), failure mode, effects, and criticality analysis (FMECA), and fault tree analysis (FTA). (Evaluate)IV. Product and Process Control (25 Questions)A. MethodsImplement product and process control methods such as control plan development, critical control point identification, and work instruction development and validation. (Analyze)B. Material Control1. Material identification, status, and traceabilityDefine and distinguish between these concepts, and describe methods for applying them in various situations. (Analyze)2. Material segregationDescribe material segregation and its importance, and evaluate appropriate methods for applying it in various situations. (Evaluate)3. Material classificationClassify product and process defects and nonconformities. (Evaluate) 4. Material review boardDescribe the purpose and function of an MRB and evaluate nonconforming product or material to make a disposition decision in various situations. (Evaluate)C. Acceptance Sampling1. Sampling conceptsInterpret the concepts of producer and consumer risk and related terms, including operating characteristic (OC) curves, acceptable quality limit (AQL), lot tolerance percent defective (LTPD), average outgoing quality (AOQ), and average outgoing quality limit (AOQL). (Analyze)2. Sampling standards and plans Identify, interpret, and apply ANSI/ASQ Z1.4 and Z1.9 standards for attributes and variables sampling. Identify and distinguish between single, double, multiple, sequential, and continuous sampling methods. Identify the characteristics of Dodge-Romig sampling tables and when they should be used. (Analyze)3. sample integrityIdentify and apply techniques for establishing and maintaining sample integrity. (Apply)D. Measurement and Test1. Measurement toolsSelect and describe appropriate uses of inspection tools such as gage blocks, calipers, micrometers, and optical comparators. (Analyze)2. Destructive and nondestructive testsIdentify when destructive and nondestructive measurement test methods should be used and apply the methods appropriately. (Apply)E. MetrologyApply metrology techniques such as calibration, traceability to calibration standards, measurement error and its sources, and control and maintenance of measurement standards and devices. (Analyze)F. Measurement System Analysis (MSA)Calculate, analyze, and interpret repeatability and reproducibility (gage R&R) studies, measurement correlation, capability, bias, linearity, precision, stability and accuracy, as well as related MSA quantitative and graphical methods. (Evaluate)

V. Continuous Improvement (27 Questions)A. Quality Control ToolsSelect, construct, apply, and interpret the following quality control tools:1. Flowcharts2. Pareto charts3. Cause and effect diagrams4. Control charts5. Check sheets6. Scatter diagrams7. Histograms (Analyze)B. Quality Management and Planning ToolsSelect, construct, apply, and interpret the following quality management and planning tools:1. Affinity diagrams and force field analysis2. Tree diagrams3. Process decision program charts (PDPC)4. Matrix diagrams5. Interrelationship digraphs6. Prioritization matrices7. Activity network diagrams (Analyze)C. Continuous Improvement MethodologiesDefine, describe, and apply the following continuous improvement methodologies:1. Total quality management (TQM)2. Kaizen3. Plan-do-check-act (PDCA)4. Six Sigma5. Theory of constraints (ToC) (Evaluate)D. Lean toolsDefine, describe, and apply the following lean tools:1. 5S2. Value stream mapping3. Kanban4. Visual control5. Waste (Muda)6. Standardized work7. Takt time8. Single minute exchange of die (SMED) (Evaluate)E. Corrective ActionIdentify, describe, and apply elements of the corrective action process, including problem identification, failure analysis, root cause analysis, problem correction, recurrence control, and verification of effectiveness. (Evaluate)F. Preventive ActionIdentify, describe, and apply various preventive action tools such as error proofing/poka-yoke and robust design and analyze their effectiveness. (Evaluate)VI. Quantitative Methods and Tools (36 Questions)A. Collecting and Summarizing Data1. Types of dataDefine, classify, and compare discrete (attributes) and continuous (variables) data. (Apply)2. Measurement scalesDefine and describe nominal, ordinal, interval, and ratio scales. (Understand)3. Data collection methods Describe various methods for collecting data, including tally or check sheets, data coding, and automatic gaging and identify the strengths and weaknesses of the methods. (Apply)

4. Data accuracy and integrity Apply techniques that ensure data accuracy and integrity, and identify factors that can influence data accuracy such as source/resource issues, flexibility, versatility, inconsistency, inappropriate interpretation of data values, and redundancy. (Apply)5. Descriptive statisticsDescribe, calculate, and interpret measures of central tendency and dispersion (central limit theorem), and construct and interpret frequency distributions, including simple, categorical, grouped, ungrouped, and cumulative. (Evaluate)6. Graphical methods for depicting relationships Construct, apply, and interpret diagrams and charts such as stem-and-leaf plots, and box-and-whisker plots. (Note: Scatter diagrams are covered in V.A.) (Analyze)7. Graphical methods for depicting distributions Construct, apply, and interpret diagrams such as normal and non-normal probability plots.(Note: Histograms are covered in V.A.) (Analyze)B. Quantitative Concepts1. TerminologyDefine and apply quantitative terms, including population, parameter, sample, statistic, random sampling, and expected value. (Analyze)2. Drawing statistical conclusionsDistinguish between numeric and analytical studies. Assess the validity of statistical conclusions by analyzing the assumptions used and the robustness of the technique used. (Evaluate)3. Probability terms and concepts Describe concepts such as independence, mutually exclusive, multiplication rules, complementary probability, and joint occurrence of events. (Understand)C. Probability Distributions1. Continuous distributions Define and distinguish between these distributions such as normal, uniform, bivariate normal, exponential, lognormal, Weibull, chi square, Students t, and F. (Analyze)

2. Discrete distributions Define and distinguish between these distributions such as binomial, Poisson, hypergeometric, and multinomial. (Analyze)D. Statistical Decision Making1. Point estimates and confidence intervalsDefine, describe, and assess the efficiency and bias of estimators. Calculate and interpret standard error, tolerance intervals, and confidence intervals. (Evaluate)2. Hypothesis testingDefine, interpret, and apply hypothesis tests for means, variances, and proportions. Apply and interpret the concepts of significance level, power, and type I and type II errors. Define and distinguish between statistical and practical significance. (Evaluate)3. Paired-comparison testsDefine and use paired-comparison (parametric) hypothesis tests and interpret the results. (Apply)4. Goodness-of-fit tests Define chi square and other goodness-of-fit tests and understand the results. (Understand)5. Analysis of variance (ANOVA) Define and use ANOVAs and interpret the results. (Analyze)6. Contingency tablesDefine and use contingency tables to evaluate statistical significance. (Apply)E. Relationships Between Variables1. Linear regressionCalculate the regression equation for simple regressions and least squares estimates. Construct and interpret hypothesis tests for regression statistics. Use linear regression models for estimation and prediction. (Analyze)2. Simple linear correlation Calculate the correlation coefficient and its confidence interval and construct and interpret a hypothesis test for correlation statistics. (Analyze)3. Time-series analysisDefine, describe, and use time- series analysis, including moving average to identify trends and seasonal or cyclical variation. (Apply)F. Statistical Process Control (SPC)1. Objectives and benefitsIdentify and explain the objectives and benefits of SPC. (Understand)2. Common and special causes Describe, identify, and distinguish between these types of causes. (Analyze)3. Selection of variableIdentify and select characteristics for monitoring by control chart. (Analyze)4. Rational subgroupingDefine and apply the principles of rational subgrouping. (Apply)5. Control chartsIdentify, select, construct, and use various control charts, including X-R, X-s, individuals and moving range (ImR or XmR), moving average and moving range (MamR), p, np, c, and u. (Analyze)6. Control chart analysisRead and interpret control charts and use rules for determining statistical control. (Evaluate)7. Pre-control chartsDefine and describe these charts and how they differ from other control charts. (Understand)8. Short-run SPCIdentify and define short-run SPC rules. (Understand)

G. Process and Performance Capability1. Process capability studies Define, describe, calculate, and use process capability studies, including identifying characteristics, specifications and tolerances, developing sampling plans for such studies, and establishing statistical control. (Analyze)2. Process performance vs. specificationsDistinguish between natural process limits and specification limits, and calculate percent defective, defects per million opportunities (DPMO), and parts per million (PPM). (Analyze)3. Process capability indices Define, select, and calculate Cp, Cpk, Cpm, and Cr, and evaluate process capability. (Evaluate)4. Process performance indices Define, select, and calculate Pp and Ppk, and evaluate process performance. (Evaluate)H. Design and Analysis of Experiments1. TerminologyDefine terms such as dependent and independent variables, factors, levels, response, treatment, error, and replication. (Understand)2. Planning and organizing experimentsIdentify the basic elements of designed experiments, including determining the experiment objective, selecting factors, responses, and measurement methods, and choosing the appropriate design. (Analyze)3. Design principlesDefine and apply the principles of power and sample size, balance, replication, order, efficiency, randomization, blocking, interaction, and confounding. (Apply)4. One-factor experiments Construct one-factor experiments such as completely randomized, randomized block, and Latin square designs, and use computational and graphical methods to analyze the significance of results. (Analyze)5. Full-factorial experiments Construct full-factorial designs and use computational and graphical methods to analyze the significance of results. (Analyze)6. Two-level fractional factorial experimentsConstruct two-level fractional factorial designs and apply computational and graphical methods to analyze the significance of results. (Analyze)VII. Risk Management (15 Questions)A. Risk Oversight1. Planning and oversight Understand identification, planning, prioritization, and oversight of risk. (Understand)2. MetricsIdentify and apply evaluation metrics. (Apply)3. Mitigation planningApply and interpret risk mitigation plan. (Evaluate)B. Risk AssessmentApply categorization methods and evaluation tools to assess risk. (Analyze)C. Risk Control1. Identification and documentation Identify and document risks, gaps, and controls. (Analyze)2. Auditing and testingApply auditing techniques and testing of controls. (Evaluate)

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ASQ Engineer real questions

Use, Misuse, and Abuse of the device Failure Modes effects evaluation | CQE braindumps and Questions and Answers

those of us who work with scientific gadget software are well aware of the radiation remedy software catastrophe within the mid-1980s, during which patients obtained one hundred instances the supposed dose of radiation as a result of a utility defect. The incident triggered FDA-legislation of scientific equipment application.1 it's well centered that even the easiest application can have advanced and unpredictable habits, even when it is developed in the structured, systematic manner prescribed by way of the concepts and practices of up to date software engineering. hazards creep into the top-rated designs. Your best protection is a robust chance management method with valid choice making.2, three

comprehensive and proper risk evaluation is the groundwork of risk management for scientific gadget design. It always has been, and should proceed to be, important to the defense and effectiveness of scientific gadgets, as long as they continue to enhance new or modified products

scientific machine risk management

right through the late Nineteen Nineties, there turned into a identified need to codify engineering choicest practices for risk administration, so that they were accessible to the biggest viable medical gadget engineering viewers. the first comprehensive overseas consensus usual for risk administration of medical instruments (ISO 14971) become posted in 2000 and amended in 2003. A 2d edition turned into posted in 2007. ISO 14971 has been an FDA-identified chance administration consensus commonplace on account that 2001.four In 2005, the global Harmonization task drive (GHTF), a voluntary overseas group of representatives from scientific device regulatory authorities and alternate associations from Europe, the U.S., Canada, Japan, and Australia, issued its remaining counsel on implementation of chance management within a scientific machine best management equipment.5 each of those documents provide scientific equipment manufacturers with a structured, systematic strategy to risk management.

medical device chance management (figure 1) is a critical, besides the fact that children no longer satisfactory, part of scientific equipment design manage (figure 2).6, 7 Envision chance administration as such as iterative risk analyses, followed with the aid of comparison of the general residual hazards and era of a chance management file. chance evaluation proceeds iteratively (figure three) because the clinical gadget design evolves. It contains the following:

  • possibility identification. Is a possible supply of harm established or foreseen? (Foreseeable hazards consist of these linked to anticipated use, sudden use, misuse, and abuse. They include the entire range of use actions from acquisition via disposal.)
  • chance comparison. Do I definitely care?
  • risk mitigation. can i thoroughly manage the focused risk?
  • once the design is frozen, the acceptability of the typical residual risks is determined to evaluate no matter if the product is achievable.

    a variety of tools can be used for possibility analysis (determine 4). The tool chosen depends on two important components. First, will the possibility analysis be inductive (bottom-up) or deductive (properly-down)? 2d, what sort of data is available? Is the statistics aim (e.g., ancient, quantitative, failure price records from clinical or actuarial sources) or subjective (e.g., a superior bet, the use of knowledgeable opinions). relevant risk management is primary for medical machine innovation. for a lot of scientific machine producers commercializing new or enhanced contraptions, an economically in your price range inductive possibility evaluation will also be simplest accomplished with ordinarily subjective data. The choice is exhaustive scientific, container, and accelerated environmental trying out.

    Two critical features of each ISO 14971 and the GHTF information are as follows:

    top management, now not the design engineer or engineering manager, is answerable for incorporating effective chance administration into the first-rate administration of clinical instruments.5, eight

    Nowhere within the ISO ordinary or GHTF tips does it state that best a bottom-up chance analysis is necessary and ample; a combination of correct-down and backside-up analyses gives the greatest and most low-budget possibility insurance.

    The dFMEA

    The design (or gadget) failure modes outcomes evaluation (dFMEA) is an inductive chance analysis device that addresses design-related hazards to the end-person (e.g., the affected person, the patient’s family, and the affected person’s healthcare providers). Annex C and table E1 of ISO 14971:2007 provide a group of critical questions and a effortless record of risks that need consideration in an FMEA. Annex A of the GHTF counsel offers a easy tabulation of risk values for an FMEA, segregated via even if they're low, medium, or high. Given these, figure four suggests why the FMEA is a well-liked tool.

    there is a vital caveat concerning these convenient questions, tables, and possibility categories. they are everyday, now not comprehensive, and do not always all follow to a particular scientific device. The clear expectation is that the dressmaker will think seriously through what's and isn't principal to the protection and effectiveness of the scientific equipment and its clients.

    possibility is described in both ISO 14971:2007 area 2.16 and the GHTF assistance (area 2) because the “combination of the probability of prevalence of harm and the severity of that damage.” Severity of hurt (S) is how essential the hazard is (i.e., will it kill you or will it be easily annoying?). The likelihood or frequency of incidence (O) is an estimate of how frequently that hazard is anticipated to turn up. The product of these (S × O) is the useful measure of risk, and the numerical value is a chance prioritization quantity (RPN = S × O) that can be used to decide the order through which to mitigate these hazards (i.e., optimum first, lowest last). The RPN can also also be used as a mechanism to allocate the engineering substances required to contend with each and every chance. What they are attempting to achieve in the dFMEA is the administration of design possibility. Design risk originates from the certain design of a product, now not from its manufacture, distribution, or renovation.

    Nowhere within the regular or information is detectability (D) covered in the definition of possibility as a result of detection is not a part of possibility however a risk mitigation or risk handle measure. using detectability, which legitimately can be utilized in a technique failure modes analysis (pFMEA), is not permissible in a dFMEA. The ingress of detectability within the dFMEA may additionally have been the result of bewilderment the change between a pFMEA (building, manufacturing, distribution, or maintenance) and the dFMEA (ultimate chance to the end-person). the misconception has persisted in some quarters since it looks to offer a handy capacity of revealing applicable design risk. despite the fact, it doesn't. using detectability within the dFMEA isn't only using a faulty chance administration procedure in a quality management equipment; it's also using a flawed corporate determination-making system. Detectability doesn't exchange the specific design risk; best redesign can also accomplish that.

    Managing chance improved by detectability isn't managing design chance. The total aim of ISO 14971 is to supply a framework “to manipulate the risks linked to using scientific devices.”9 unless the conclusion-consumer can observe the certain chance in a adequate amount of time to steer clear of the chance and take into account the appropriate process for keeping off that specific chance, detectability is not even advantageous as a chance handle measure.


    chance contains severity of hurt (S) and frequency of incidence (O). do not consist of detectability (D) in a design FMEA because it corrupts the chance evaluation and reasons you to underestimate the precise possibility to the users of your medical machine. decreasing design chance requires changing the design, designing engineering controls, or, as a latest hotel, designing valuable warnings. yes, decreasing the possibility prioritization quantity (RPN = S × O) with detectability (RPN × D) certainly reduces your engineering workload and reduces your time to market, nonetheless it does nothing to in the reduction of the chance to sufferers, their members of the family, and their healthcare providers.


    1. S Casey, Set Phasers On Stun And different genuine testimonies Of Design, know-how, And Human Error, 2nd ed., (Santa Barbara, CA: Aegean, 1998).

    2. Mike W Schmidt, “The Use and Misuse of FMEA in chance evaluation,” clinical device and Diagnostic trade [online] March2004 [cited 24 February 2013]; purchasable from information superhighway:

    3. Stan Telson, “enhance Defensively: handle possibility and Predict consequences,” clinical device and Diagnostic business [online] may 2005 [cited 24 February 2013]; attainable from information superhighway:

    4. Federal Register, sixty six FR:23032, may additionally 7, 2001.

    5. GHTF/SG3/N15R8, “Implementation of chance administration ideas and activities inside a high quality administration device” (global Harmonization task Froce, 2005).

    6. Code of Federal rules 21 CFR 820.30

    7. ISO 13485:2003, “medical instruments—pleasant administration systems—necessities for Regulatory applications—part 7: cognizance requirements” (Geneva: foreign corporation for Standardization, 2003).

    8. ISO 14971:2007, “clinical gadgets—application of possibility administration to clinical instruments—part 3.2: management responsibilities” (Geneva: foreign organization for Standardization, 2007).

    9. ISO 14971:2007, “clinical contraptions—software of possibility management to scientific gadgets—Introduction” (Geneva: international company for Standardization, 2007).

    G.M. Samaras is a biomedical scientist and engineer presently in inner most practice (Pueblo, CO). expert as an electrical engineer, he has doctorates in physiology and industrial engineering. he is a licensed skilled engineer, board-certified human elements engineer, and an ASQ-certified fine engineer. Samaras has a few biomedical patents and publications in physiology and engineering. He has labored at CDRH as a reviewer and supervisor, become a scientific faculty and engineering graduate school professor, and established an biomedical engineering firm that he ran for a decade. reach him at [email protected] 

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