1. Foundations and Evolution of Six Sigma

Introduction and Context

Six Sigma is one of the most influential approaches to quality and process improvement developed in modern industry. It is commonly associated with statistical measurement, defect reduction, disciplined problem solving, and financial accountability. Yet Six Sigma is more than a collection of statistical tools. It is a management philosophy that connects customer requirements, process performance, data-based decision-making, organizational leadership, and continuous improvement.

The term sigma refers to the standard deviation of a process output. In statistical language, a process operating at a higher sigma level produces fewer defects because its outputs vary less in relation to the limits specified by customers or designers. In managerial language, Six Sigma represents an aspiration toward extremely consistent performance. The objective is not merely to inspect defective products after production, but to understand and improve the processes that create those products or services.

Six Sigma emerged from the quality movement that included statistical quality control, total quality management, just-in-time production, reliability engineering, and the improvement philosophies of thinkers such as Walter A. Shewhart, W. Edwards Deming, Joseph M. Juran, Philip B. Crosby, and Kaoru Ishikawa. Its distinctive contribution was to integrate these traditions into a highly structured, project-based system with defined roles, quantitative metrics, and strong executive sponsorship.

For postgraduate students of Total Quality Management, Six Sigma is important because it illustrates how quality philosophy can be translated into operational decisions. It also raises significant questions: What counts as a defect? How should variation be measured? How can customer expectations be converted into technical requirements? When is a statistical improvement meaningful to the business? How should organizations balance standardization with innovation? These questions provide the foundation for studying Six Sigma in subsequent chapters.

Learning Objectives

After studying this chapter, the learner should be able to:

1. Explain the historical development and managerial significance of Six Sigma.

2. Distinguish common Six Sigma concepts, terminology, and performance measures.

3. Explain variation, defects, yield, sigma levels, DPMO, and cost of poor quality.

4. Describe the DMAIC framework and the roles commonly used in Six Sigma deployment.

5. Relate Six Sigma to TQM, continuous improvement, Lean, and other quality philosophies.

6. Apply basic Six Sigma measures to practical manufacturing and service situations.

7. Evaluate the organizational, ethical, technological, and managerial conditions required for successful implementation.

From Statistical Quality Control to Six Sigma

The intellectual roots of Six Sigma lie in the recognition that quality must be designed into processes rather than achieved solely through final inspection. Shewhart’s work on control charts established the distinction between common-cause variation, which is inherent in a process, and special-cause variation, which arises from identifiable and unusual factors. This distinction remains central to Six Sigma: a process cannot be improved intelligently until its sources of variation are understood.

During the post-war period, Deming emphasized systems thinking, reduction of process variation, leadership responsibility, and the importance of learning rather than blaming workers. Juran focused on the quality trilogy of planning, control, and improvement, while Crosby popularized the idea that quality means conformance to requirements and that prevention is preferable to detection. Ishikawa advanced cause-and-effect analysis, quality circles, and organization-wide participation.

Japanese manufacturing organizations demonstrated that quality improvement could be integrated with employee involvement, supplier partnerships, standardized work, and continuous improvement. Total Quality Management subsequently broadened quality from a technical department’s responsibility to an organizational philosophy involving customers, processes, people, suppliers, and leadership.

Six Sigma developed within this historical context. Motorola formalized the approach in the 1980s in response to intense competition and customer expectations for highly reliable products. The approach gained international attention when General Electric adopted it as a corporate management system during the 1990s. GE’s deployment emphasized executive leadership, project selection, training, measurement, and financial results. This helped transform Six Sigma from a quality-control initiative into a business improvement method.

Later developments combined Six Sigma with Lean principles. Lean emphasizes flow, customer value, waste elimination, and speed; Six Sigma emphasizes reduction of variation and defects. The resulting Lean Six Sigma approach seeks both process efficiency and process capability. Contemporary organizations also use Six Sigma ideas in healthcare, banking, information technology, logistics, government, education, and public services, although the metrics and definitions of defects must be adapted to the context.

The Central Logic of Six Sigma

The logic of Six Sigma can be expressed as a chain:

Customer requirements → measurable process characteristics → data collection → analysis of variation → targeted improvement → control and learning.

A customer may express a requirement in broad terms such as “fast service,” “accurate billing,” or “safe product.” Six Sigma requires that such expectations be translated into measurable critical-to-quality characteristics. For example, “fast service” may become a transaction completion time of no more than five minutes for 95 percent of customers. This translation is difficult and consequential: a poorly defined requirement produces misleading measurements and inappropriate improvement actions.

Six Sigma projects normally address a problem that is important to customers and significant to the organization. A project should have a defined scope, a baseline measure, a responsible team, a timetable, and an expected benefit. The aim is not to collect data for its own sake. Data are used to make better decisions about where a process is failing, why it is failing, and which intervention is likely to produce sustainable improvement.

Variation: the Problem Behind Inconsistency

Variation is the difference among outputs produced by a process or among repeated observations of the same process. No real process produces identical outcomes indefinitely. Variation may occur in dimensions, cycle times, transaction values, service quality, or patient outcomes.

Common-cause variation results from the normal design and operation of a process: machine capability, standard methods, material properties, environmental conditions, or the organization of work. Special-cause variation results from unusual events such as a damaged tool, incorrect software configuration, untrained temporary staff, or an unexpected supplier problem.

The distinction has managerial implications. If a process is affected mainly by common causes, asking individual employees to “try harder” is unlikely to produce lasting improvement. The system itself must be redesigned. If a special cause is present, it should be identified and removed without unnecessarily changing a stable process. Confusing the two types of variation can increase instability rather than improve quality.

Variation may be measured through statistics such as range, variance, and standard deviation. The mean describes the central tendency of a process, while the standard deviation describes the spread of observations around that mean. A process may have low variation but still produce defects if it is centered incorrectly; conversely, a process may be centered near the target but have excessive spread. Six Sigma therefore considers both location and dispersion.

Requirements, Specifications, and Defects

A voice of the customer is the expressed or inferred need of a customer or stakeholder. The voice of the customer must be converted into measurable requirements. A specification limit is the boundary established by a customer, regulator, designer, contract, or internal standard. The upper specification limit and lower specification limit define the acceptable range for a characteristic.

A defect is a failure to meet a specified requirement. A defective unit is a product or service unit containing one or more defects. These terms should not be confused. One invoice may contain several defects, while a unit may be defective because of only one defect. A clear operational definition is therefore essential. “Late delivery,” for example, should specify the agreed delivery date, time zone, permissible tolerance, and treatment of partial shipments.

A critical-to-quality characteristic, often abbreviated CTQ, is a measurable characteristic that is important to customer satisfaction or compliance. CTQs may include dimensions, response time, accuracy, reliability, safety, completeness, or confidentiality. A Six Sigma project should define its CTQs before selecting measures and analytical methods.

Sigma Levels, Yield, and DPMO

A sigma level expresses how well a process performs relative to the limits of acceptable performance. Under a simplified normal-distribution interpretation, a process with a wider separation between its mean and the nearest specification limit has a lower probability of producing defects. The term “Six Sigma” is often associated with approximately 3.4 defects per million opportunities, based on the conventional assumption of a 1.5-sigma long-term shift in the process mean. This figure is a convention used in Six Sigma practice, not a universal law of nature.

The 1.5-sigma shift has been debated academically and should be explained transparently. Short-term capability calculations and long-term performance claims are not identical. Organizations should state which convention, time horizon, and assumptions they use rather than presenting a sigma figure as an absolute fact.

Yield

Yield is the proportion of output that meets requirements. If 950 of 1,000 units pass without defect, the yield is:

Yield = conforming units ÷ total units = 950 ÷ 1,000 = 95 percent.

Two yield concepts are especially useful. First-pass yield is the proportion that meets requirements without rework, repair, or rerouting. Rolled throughput yield is the probability that a unit will pass through all process steps without a defect. If three sequential steps have first-pass yields of 0.98, 0.95, and 0.97, the rolled throughput yield is:

0.98 × 0.95 × 0.97 = 0.90307, or approximately 90.3 percent.

This example shows why a process can appear satisfactory at each individual stage but produce disappointing overall performance when defects accumulate across the workflow.

Defects per Unit, DPO, and DPMO

A defect per unit measure indicates the average number of defects observed per unit. A unit can have more than one defect, so this measure is not identical to the defective-unit rate.

Defects per opportunity (DPO) is calculated as:

DPO = number of defects ÷ (number of units × opportunities per unit).

Defects per million opportunities (DPMO) standardizes the result:

DPMO = DPO × 1,000,000.

Suppose 2,000 forms are processed, each with five possible defect opportunities, and 25 defects are found. The total opportunities are 10,000. DPO is 25/10,000 = 0.0025, and DPMO is 2,500. The calculation is meaningful only if the number of opportunities is defined consistently. Inflating or understating opportunities can make performance appear better or worse without changing the process.

DPMO is valuable when comparing processes with different numbers of opportunities, but it has limitations. It assumes that opportunities are identified reliably and that each opportunity is appropriately counted. It may also conceal the severity of defects: a minor typographical error and a life-threatening safety failure should not be treated as equivalent merely because each counts as one defect. For high-risk industries, severity, detectability, and regulatory significance must supplement DPMO.

Cost of Poor Quality

Cost of poor quality (COPQ) is the economic consequence of failing to meet requirements. It includes more than scrap and rework. The traditional quality-cost framework divides costs into prevention, appraisal, internal failure, and external failure.

Prevention costs include training, process design, mistake-proofing, supplier development, and preventive maintenance. Appraisal costs include inspection, testing, audits, and measurement-system evaluation. Internal failure costs arise before delivery, such as scrap, rework, retesting, delays, and excess inventory. External failure costs occur after delivery and may include warranty claims, returns, complaints, recalls, legal exposure, compensation, lost goodwill, and customer switching.

Six Sigma generally supports shifting resources from failure and excessive appraisal toward prevention and process improvement. A project that reduces defects but increases hidden administrative work or creates safety risks has not necessarily improved quality. Financial validation should therefore include direct savings, avoided costs, capacity release, revenue protection, and the costs of implementation.

COPQ also has a strategic dimension. Repeated service failures can weaken trust and damage a brand even when the immediate financial loss is difficult to calculate. Conversely, not every variation should be eliminated. Improvement has an economic optimum: the organization should determine whether the cost of reducing a defect is justified by its customer, operational, regulatory, and strategic consequences.

DMAIC and the Architecture of Improvement

For improving an existing process, the most recognized Six Sigma framework is DMAIC: Define, Measure, Analyze, Improve, and Control. DMAIC is not a rigid checklist; it is a logic for reducing the risk of premature conclusions.

Define establishes the business problem, customer impact, project objective, scope, stakeholders, and expected benefits. A project charter commonly records these elements. A weak problem statement proposes a solution, such as “install new software.” A stronger statement describes an observable performance gap, such as “the percentage of accurate first-time claims submissions has declined from 96 percent to 88 percent over six months.”

Measure establishes operational definitions, validates the measurement process, identifies the process baseline, and determines the scale of the problem. Measurement-system analysis is important because unreliable measurement can create apparent variation or hide real variation. Process mapping, data stratification, capability analysis, and checks for data integrity may be used at this stage.

Analyze seeks evidence about root causes. Teams distinguish symptoms from causes and test possible explanations using appropriate qualitative and quantitative tools. A cause-and-effect diagram may organize hypotheses; Pareto analysis may reveal concentration of problems; statistical tests, regression, or designed experiments may be appropriate when relationships require formal evaluation. Root-cause claims should be supported by data rather than authority or intuition alone.

Improve develops, tests, and implements solutions. Possible interventions include redesigning work, reducing handoffs, changing settings, standardizing methods, mistake-proofing, modifying supplier requirements, or automating a reliable task. Pilot testing is valuable because a solution can improve one measure while harming another, such as speed improving at the expense of accuracy.

Control ensures that gains persist. Control plans, standard operating procedures, visual management, training, response rules, audits, and statistical process control may be used. Control does not mean freezing a process permanently; it means maintaining intended performance while enabling evidence-based future improvement.

For new products or processes, Design for Six Sigma approaches such as DMADV—Define, Measure, Analyze, Design, Verify—may be more suitable than attempting to improve a fundamentally flawed existing design.

Roles, Infrastructure, and Leadership

Six Sigma deployments often use martial-arts-inspired role titles. A Champion links organizational strategy with project selection and removes barriers. A Master Black Belt provides advanced technical and coaching expertise. A Black Belt leads complex improvement projects, while a Green Belt supports or leads projects part time. A Yellow Belt or trained process participant may contribute practical knowledge and local improvement activity. Titles vary among organizations; competence and accountability matter more than labels.

Leadership is not limited to approving training. Senior managers must select projects that matter, allocate time and data access, review progress, resolve cross-functional conflicts, and recognize evidence-based results. Middle managers are particularly important because they translate project recommendations into operating routines. Without managerial ownership, Six Sigma may become a temporary analytical exercise rather than a management system.

Project selection should consider strategic alignment, customer impact, feasibility, urgency, risk, data availability, and financial significance. A portfolio containing only easy projects may produce visible local gains but little strategic value. A portfolio containing only very ambitious projects may overwhelm the organization and erode confidence.

Six Sigma in Relation to TQM, Lean, and Continuous Improvement

Six Sigma and TQM share several principles: customer focus, prevention, process orientation, employee involvement, leadership responsibility, fact-based decisions, and continuous improvement. TQM is generally broader and more philosophical, emphasizing an organization-wide culture and the integration of quality into every function. Six Sigma is typically more structured, metric-driven, and project-centered. It provides a disciplined mechanism for converting quality aspirations into measurable improvement work.

Continuous improvement is the broader commitment to repeatedly improve products, services, and processes. Kaizen represents an approach based on frequent, often incremental improvements involving people close to the work. Six Sigma projects may be larger, more analytical, and cross-functional, but they need not replace daily improvement. Mature organizations use both: local teams address routine opportunities while formal Six Sigma projects address significant or complex problems.

Lean and Six Sigma also complement one another. Lean asks where customer value is delayed or diluted by waste, such as waiting, excess movement, overproduction, or unnecessary processing. Six Sigma asks why process outcomes vary and how defects can be reduced. Lean may expose the problem of long queues; Six Sigma may help determine the causes of variation in processing time. Their combination must be purposeful rather than a fashionable relabeling of unrelated activities.

The relationship may therefore be represented as follows: TQM supplies a broad quality philosophy; continuous improvement supplies an enduring organizational habit; Lean emphasizes value and flow; Six Sigma supplies disciplined variation reduction and evidence-based project management. These approaches overlap but are not interchangeable.

Applications Across Sectors

In manufacturing, Six Sigma may address dimensional variation, supplier defects, equipment reliability, yield loss, or changeover performance. In healthcare, potential projects include reducing medication errors, improving laboratory turnaround time, or reducing appointment delays. Definitions of defects must account for patient safety and clinical complexity; statistical improvement cannot justify unsafe standardization.

In banking and insurance, projects may concern application-processing time, error rates, complaints, fraud-screening delays, or claims accuracy. In information technology, useful CTQs include system availability, response time, escaped defects, incident resolution, and data integrity. In public services, the “customer” may include citizens, regulated entities, employees, and vulnerable groups, and quality must include equity, accessibility, legality, and transparency.

Digital technologies now support automated data capture, process mining, real-time dashboards, workflow analytics, and anomaly detection. Machine-learning systems can identify patterns in large data sets, but they do not eliminate the need for sound problem definition, reliable data, causal reasoning, and human governance. Correlation discovered by an algorithm is not automatically a root cause. Automated decisions also require attention to bias, explainability, privacy, cybersecurity, and accountability.

Illustrative Case: Reducing Errors in a University Admissions Process

Background

The following is a fictional illustrative case. A university’s postgraduate admissions office processes approximately 18,000 applications annually. Applicants frequently report that decisions are delayed and that requests for missing documents are inconsistent.

Situation and Problem

The office reports that only 82 percent of complete applications receive an initial eligibility decision within ten working days. An internal review finds repeated data-entry errors, unclear responsibility for document verification, and variation in how staff interpret equivalent evidence. The project team defines a defect as an application that either receives an incorrect eligibility classification or exceeds the ten-day service requirement after all required documents have been received.

Analysis

The team maps the process from application receipt to decision. It separates applications by programme, intake period, document type, and staff group. The data show that delays are concentrated in two programmes with unusually complex prerequisite checks. A Pareto analysis indicates that missing prerequisite verification and duplicate manual entry account for most defects. Interviews initially suggest that staff training is the main problem, but further analysis reveals that the online form permits inconsistent course descriptions and that the workflow does not display ownership of pending cases.

The team also examines the measurement system. Different employees have been recording the “received date” differently, making baseline comparisons unreliable. After an operational definition is agreed, historical data are corrected for a representative sample.

Approach and Solution

During the Improve phase, the university introduces a controlled list of prerequisite subjects, removes one redundant data-entry step, assigns electronic case ownership, and creates an escalation rule for ambiguous applications. A pilot is run for one intake period. Staff are trained in the revised standard, but training is treated as a supporting action rather than the sole solution. A daily dashboard displays aging cases, error categories, and first-pass completion.

In the Control phase, the office establishes a control plan. Supervisors review a sample of decisions weekly, and the process owner receives an alert when the proportion of cases exceeding ten working days rises above the agreed threshold. The university also reviews whether applicants from different educational backgrounds are experiencing unequal rates of requests for additional evidence.

Outcome

In the pilot, on-time initial decisions rise from 82 percent to 94 percent, while incorrect classifications decline. The improvement is not attributed simply to faster work; it results from clearer requirements, reduced handoffs, improved measurement, and visible ownership. The university postpones a proposed expensive software replacement because the main causes were process design and inconsistent definitions rather than inadequate computing capacity.

Learning Points

The case illustrates several Six Sigma principles. A service defect must be operationally defined. Measurement-system problems can distort the baseline. Root causes may lie in process design rather than employee effort. Customer experience and equity considerations should accompany efficiency measures. Finally, a project should be controlled after implementation so that initial gains do not disappear.

Challenges, Limitations, and Professional Responsibilities

Six Sigma can fail when it becomes a certification exercise, a search for impressive numerical targets, or a substitute for leadership. Common mistakes include beginning with a preferred solution, selecting a project with no meaningful customer or business impact, using unreliable data, confusing correlation with causation, counting defects inconsistently, and ignoring process changes outside the project boundary.

A narrow focus on measurable characteristics can also create dysfunctional behavior. Employees may optimize the metric while degrading the broader customer experience. For example, a call centre may reduce average handling time by ending conversations prematurely. Balanced measures, customer feedback, outcome measures, and ethical review are therefore necessary.

Managers should avoid using sigma levels or DPMO to rank fundamentally different processes without considering defect severity, opportunity definitions, demand patterns, and risk. In regulated or safety-critical environments, compliance and harm prevention take precedence over convenient aggregate statistics.

Ethically, quality data should be accurate, traceable, and protected from manipulation. Employees should not be punished for reporting defects or near misses; concealment prevents learning and can endanger customers. Personal, health, financial, or educational data used in improvement projects must be handled lawfully and securely. Algorithmic tools should be assessed for discriminatory effects and should not obscure responsibility for decisions.

Current Directions and Best Practice

Contemporary Six Sigma increasingly combines statistical analysis with process mining, cloud-based collaboration, Internet of Things sensors, robotic process automation, and advanced analytics. These technologies make it possible to observe process behavior more continuously, but they also increase the volume and complexity of data. The discipline of defining CTQs, validating measures, and investigating causes remains essential.

Effective practice generally follows several principles:

• Select projects from important customer, risk, cost, or strategic problems.
• Define defects and opportunities before calculating performance.
• Validate the measurement system and protect data integrity.
• Separate common-cause from special-cause variation.
• Match analytical methods to the type and quality of data.
• Involve process employees and affected customers.
• Verify financial and operational benefits independently.
• Design controls and ownership before declaring success.
• Balance speed, cost, quality, safety, equity, and employee impact.
• Treat Six Sigma as part of a wider improvement system rather than as an isolated program.

Chapter Summary

Six Sigma evolved from statistical quality control, systems thinking, TQM, and related quality philosophies. Its central concern is the reduction of process variation and defects through customer-defined requirements, reliable measurement, analysis of causes, disciplined improvement, and control. Important measures include yield, first-pass yield, rolled throughput yield, defects per unit, DPO, DPMO, sigma level, and COPQ. Each measure is useful only when its definitions and assumptions are made explicit.

DMAIC provides a structured framework for improving existing processes, while DMADV is suited to designing new processes or products. Six Sigma uses specialized roles, project charters, analytical tools, and leadership governance to connect improvement with organizational results. It complements rather than replaces TQM, Lean, Kaizen, and continuous improvement. Successful application requires attention to customer value, data quality, financial impact, ethics, safety, equity, and sustainability. Digital technologies can strengthen analysis, but they cannot replace sound definitions, causal reasoning, and responsible management.

Key Terms

Sigma: A statistical unit representing one standard deviation of process variation.

Six Sigma: A disciplined, data-based approach for reducing defects and process variation while improving customer and business performance.

Variation: Differences in process outputs or performance over time or across units.

Common-cause variation: Variation inherent in the normal design and operation of a process.

Special-cause variation: Variation arising from an identifiable, unusual, or non-routine factor.

Defect: Failure to meet a specified requirement.

Defective unit: A unit containing one or more defects.

Critical-to-quality characteristic: A measurable characteristic important to customer satisfaction, compliance, or performance.

Specification limit: A boundary defining acceptable performance for a characteristic.

Yield: The proportion of output that conforms to requirements.

First-pass yield: The proportion passing a process step without rework or repair.

Rolled throughput yield: The probability that a unit passes all process steps without a defect.

DPO: Defects per opportunity, calculated by dividing defects by total opportunities.

DPMO: Defects per million opportunities.

COPQ: Costs resulting from failure to meet requirements, including internal and external failure costs.

DMAIC: Define, Measure, Analyze, Improve, and Control; a framework for improving existing processes.

DMADV: Define, Measure, Analyze, Design, and Verify; a framework for designing new processes or products.

TQM: An organization-wide philosophy of customer-focused, continuous quality improvement.

CTQ: Abbreviation for critical-to-quality characteristic.

Kaizen: A philosophy of continuous, often incremental, improvement involving people throughout the organization.

Review Questions

Short Answer Questions

1. Why is variation central to Six Sigma thinking?

2. Distinguish a defect from a defective unit.

3. What is the difference between DPO and DPMO?

4. Why must a measurement system be validated before process analysis?

5. State the five phases of DMAIC.

Long Answer Questions

1. Explain the historical evolution from statistical quality control and TQM to Six Sigma.

2. Discuss the meaning and limitations of sigma levels, including the conventional 1.5-sigma shift.

3. Explain yield, first-pass yield, rolled throughput yield, and DPMO with suitable examples.

4. Compare Six Sigma with TQM, Lean, and Kaizen, identifying areas of complementarity and difference.

5. Describe how leadership, project selection, roles, and control mechanisms influence Six Sigma implementation.

Analytical / Application Questions

1. A process has 1,500 units, four defect opportunities per unit, and 18 observed defects. Calculate DPO and DPMO, and explain one limitation of your result.

2. Three process stages have first-pass yields of 0.97, 0.96, and 0.94. Calculate rolled throughput yield and explain why it is lower than each individual stage yield.

3. A manager proposes replacing an information system because service delays have increased. Using DMAIC logic, identify the evidence that should be collected before approving the investment and explain how COPQ could inform the decision.

22. Multiple Choice Questions with Answers

Question 1

Which statement best expresses the central logic of Six Sigma?

A. Inspect every finished unit and remove all nonconforming output

B. Translate customer requirements into measures, analyze variation, improve causes, and sustain results

C. Reduce staffing levels until process costs reach a minimum

D. Replace human judgment with automated statistical software

Correct Answer: B. Translate customer requirements into measures, analyze variation, improve causes, and sustain results

Question 2

A process has 2,000 units, five defect opportunities per unit, and 25 defects. What is its DPMO?

A. 250

B. 1,250

C. 2,500

D. 12,500

Correct Answer: C. 2,500

Question 3

Which situation is most likely an example of common-cause variation?

A. A one-time damaged machine tool

B. An incorrectly configured software update

C. The normal spread created by the current process design

D. A temporary employee entering data in the wrong field

Correct Answer: C. The normal spread created by the current process design

Question 4

Why is rolled throughput yield useful?

A. It measures only the final inspection failure rate

B. It estimates the probability of passing all process steps without defects or rework

C. It counts the number of specification limits in a process

D. It converts COPQ directly into a sigma value

Correct Answer: B. It estimates the probability of passing all process steps without defects or rework

Question 5

Which DMAIC phase primarily establishes the baseline and validates the measurement system?

A. Define

B. Measure

C. Improve

D. Control

Correct Answer: B. Measure

Question 6

A project reports a high sigma level, but its defect opportunities were defined inconsistently across departments. What is the main concern?

A. The process must necessarily have high common-cause variation

B. The sigma level may not support a valid comparison

C. The project should skip the Analyze phase

D. The process has no customer requirements

Correct Answer: B. The sigma level may not support a valid comparison

Question 7

Which cost is an external failure cost?

A. Preventive maintenance before production

B. Operator training

C. Inspection of incoming material

D. Warranty claims after the product reaches customers

Correct Answer: D. Warranty claims after the product reaches customers

Question 8

What most clearly distinguishes Six Sigma from TQM?

A. Six Sigma rejects customer focus

B. TQM uses only inspection, whereas Six Sigma uses only prevention

C. Six Sigma commonly provides a more project-based, metric-driven improvement structure within a broader quality philosophy

D. TQM applies only to manufacturing and Six Sigma only to services

Correct Answer: C. Six Sigma commonly provides a more project-based, metric-driven improvement structure within a broader quality philosophy

Question 9

A team discovers that employees record the service start date differently. What should it do first?

A. Purchase new software immediately

B. Create and apply a clear operational definition

C. Increase the target sigma level

D. Remove the employees who recorded the data

Correct Answer: B. Create and apply a clear operational definition

Question 10

Why should DPMO be supplemented by severity and risk information in safety-critical work?

A. DPMO cannot be calculated for service processes

B. All defect opportunities have identical consequences

C. A numerical defect count may treat minor and catastrophic failures as equivalent

D. Safety-critical processes never contain variation

Correct Answer: C. A numerical defect count may treat minor and catastrophic failures as equivalent