7. Quality Planning, Assurance and Improvement

Introduction and Context

Customer experience is shaped not only by the final product or service but also by the consistency, speed, reliability, accessibility, and fairness of the processes through which value is delivered. A technically conforming product may still create dissatisfaction if it arrives late, is difficult to use, requires repeated contact with the organization, or fails to resolve the customer’s underlying need. Quality management therefore requires a deliberate system for planning quality, assuring conformance, and improving performance over time.

Quality planning defines what customers and other stakeholders require and designs processes capable of meeting those requirements. Quality assurance provides confidence that the planned processes are being followed and are capable of producing consistent results. Quality improvement seeks better performance by reducing variation, eliminating waste and defects, and redesigning processes when existing performance is inadequate. These activities are interdependent. Planning without assurance produces unstable execution; assurance without improvement can preserve mediocre performance; improvement without planning may optimize the wrong outcomes.

This chapter develops an integrated approach using the Juran Trilogy, PDCA/PDSA, Kaizen, DMAIC, root-cause analysis, Pareto analysis, cause-and-effect diagrams, FMEA, mistake-proofing, and standard work. The emphasis is not on treating these methods as isolated tools. Their value lies in selecting and combining them appropriately to prevent defects and improve customer outcomes.

Learning Objectives

By the end of this chapter, the learner should be able to:

1. Explain the relationship among quality planning, quality assurance, and quality improvement.

2. Apply the Juran Trilogy and PDCA/PDSA to organizational quality management.

3. Distinguish between reactive problem solving and proactive defect prevention.

4. Use Pareto analysis, cause-and-effect diagrams, root-cause analysis, FMEA, and mistake-proofing appropriately.

5. Compare Kaizen and DMAIC and select an improvement approach for a given situation.

6. Design standard work and control mechanisms that sustain improved performance.

7. Evaluate quality initiatives in terms of customer value, risk, ethics, and organizational capability.

Quality as a Designed and Managed Outcome

Quality is best understood as the degree to which a product, service, process, or experience satisfies explicit and implicit requirements. Explicit requirements may include delivery time, accuracy, safety, price, or technical specifications. Implicit requirements include ease of use, respectful treatment, transparency, and the expectation that the organization will correct problems fairly.

A useful distinction is between conformance quality and fitness-for-use quality. Conformance concerns whether output meets specified requirements. Fitness for use concerns whether the output actually serves the customer’s purpose. For example, a bank statement may be technically accurate but not fit for use if it is delivered after a customer’s tax-filing deadline. Both dimensions must inform quality planning.

Quality performance is also affected by variation. Common-cause variation arises from the normal design of a process, such as an inadequate information system or poorly balanced staffing model. Special-cause variation results from unusual events, such as a system outage, an incorrectly configured machine, or an untrained temporary employee. Treating every variation as an individual employee failure often leads to ineffective corrective action. Mature quality management asks whether the process itself makes error likely.

Important terminology includes the following:

• Defect: A failure to meet a requirement or intended use.
• Failure: The inability of a process, product, or service to perform its required function.
• Nonconformity: Non-fulfilment of a specified requirement.
• Variation: Difference in process or output performance over time or across units.
• Prevention: Action taken to stop a problem from occurring.
• Correction: Action taken to remove a detected nonconformity.
• Corrective action: Action taken to eliminate the cause of a nonconformity and prevent recurrence.
• Control: The policies, methods, measurements, and responsibilities used to keep performance within acceptable limits.
• Critical-to-quality (CTQ) characteristic: A measurable feature that is especially important to customer satisfaction or risk.

These concepts establish an essential principle: inspection can detect some failures, but quality must be built into the process through design, capability, control, and learning.

The Juran Trilogy: Planning, Control, and Improvement

Joseph M. Juran’s Trilogy presents quality management as three connected managerial processes: quality planning, quality control, and quality improvement. In many organizations, quality assurance operates through control activities, audits, reviews, and evidence that processes are capable and consistently followed.

Quality planning

Quality planning begins by identifying customers, including end users, internal recipients, regulators, suppliers, and other stakeholders. Their needs are translated into measurable requirements. The organization then designs products, services, and processes capable of meeting those requirements under actual operating conditions.

A planning sequence may include:

1. Identify internal and external customers.

2. Discover stated and unstated customer needs.

3. Convert needs into measurable specifications or CTQs.

4. Design the product, service, and delivery process.

5. Identify process resources, competencies, controls, and risks.

6. Establish measures and acceptance criteria.

7. Validate the design before full-scale implementation.

For example, “fast complaint handling” is not a sufficiently precise requirement. It may be translated into “90 percent of priority complaints receive an initial response within four working hours and a resolution or progress update within two business days.” The latter can be measured, analyzed, and improved.

Quality control and assurance

Quality control compares actual performance with standards and acts when performance deviates. It involves measurement, monitoring, inspection where appropriate, process checks, and corrective response. Quality assurance is broader: it creates confidence that quality requirements will be fulfilled through planned and systematic activities. It may include documented procedures, competence management, supplier controls, audits, validation, traceability, and management review.

Control should not be confused with excessive bureaucracy. A control is valuable when it reduces risk or provides useful information for decision-making. A form that is completed merely to satisfy an audit, without influencing behavior or outcomes, is a weak control.

Quality improvement

Improvement seeks a higher level of performance. It may involve reducing errors, shortening cycle time, improving accessibility, simplifying a customer journey, or redesigning a service. Improvement is usually focused on chronic losses: repeated rework, recurring complaints, avoidable delays, excessive variation, and poor first-time quality.

Juran’s concept of the “quality improvement project” is especially useful. A project should have a defined problem, a measurable baseline, a responsible team, a target, an analysis of causes, implemented changes, and a mechanism for holding the gains. Improvement becomes more effective when senior leaders remove barriers and allocate resources rather than merely exhorting employees to work harder.

PDCA/PDSA and the Logic of Learning

The PDCA cycle—Plan, Do, Check, Act—is a repeating method for learning and improving. The related PDSA formulation uses “Study” rather than “Check” to emphasize interpretation of results rather than simple comparison.

During Plan, the team defines the problem, understands the current process, identifies a proposed change, predicts its effect, and decides how evidence will be collected. During Do, the change is tested, preferably on a controlled or limited scale. During Check/Study, results are compared with the prediction, unintended consequences are examined, and learning is documented. During Act, the organization adopts, adapts, or abandons the change. A successful change becomes part of standard work and is monitored; an unsuccessful change generates a revised hypothesis.

PDCA is particularly valuable when knowledge is incomplete or when a low-risk experiment can reveal how a process behaves. It discourages large-scale implementation based on assumptions. However, PDCA becomes ineffective when teams skip measurement, confuse activity with learning, or declare success from a short-term improvement without checking sustainability.

Kaizen and DMAIC: Complementary Improvement Approaches

Kaizen refers to continuous, incremental improvement involving the people who perform and manage the work. It often addresses small sources of waste, inconvenience, or inconsistency. Examples include reorganizing supplies, simplifying a form, clarifying a work instruction, or changing the sequence of tasks. Kaizen develops problem-solving capability and ownership, but small improvements alone may not solve complex or deeply systemic problems.

DMAIC—Define, Measure, Analyze, Improve, Control—is a structured approach commonly associated with Six Sigma. It is appropriate when a process has a significant performance gap, reliable data can be obtained, and the causes of variation require disciplined analysis.

• Define: Establish the problem, customer impact, scope, objectives, and project responsibilities.
• Measure: Determine current performance, operational definitions, data sources, and measurement-system adequacy.
• Analyze: Identify and verify relationships between potential causes and performance.
• Improve: Develop, test, and implement solutions that address verified causes.
• Control: Standardize the improved process, monitor performance, and respond to deterioration.

Kaizen and DMAIC should not be treated as competing ideologies. A hospital may use DMAIC to reduce medication-administration errors across several departments, while using Kaizen events to improve the arrangement of one medication preparation area. The choice should depend on the scale, complexity, risk, and evidence available.

Finding Causes Rather Than Assigning Blame

Root-cause analysis

Root-cause analysis (RCA) is a systematic effort to identify underlying conditions that allow a problem to occur. A useful RCA distinguishes among the immediate cause, contributing factors, and systemic cause. If a customer receives the wrong invoice, the immediate cause may be an incorrect account selection. Contributing factors may include similar account names and time pressure. A systemic cause may be a billing interface that does not display sufficient identification information or require confirmation before release.

The “5 Whys” technique can expose deeper causes by repeatedly asking why a condition occurred. It is not a mechanical rule that every problem requires exactly five questions. Its quality depends on evidence and on avoiding unsupported assumptions. A cause should be verified through records, observation, data, or controlled testing.

Pareto analysis

Pareto analysis ranks categories of problems by frequency, cost, risk, or customer impact. A Pareto chart often reveals that a small number of categories account for a large proportion of the total loss, although the exact distribution varies. The tool helps teams prioritize rather than treating every problem as equally important.

The choice of classification is critical. Complaints may be grouped by product, failure mode, customer segment, process stage, or severity. A chart based only on frequency may understate rare but catastrophic failures. Consequently, Pareto analysis should be interpreted alongside severity, detectability, regulatory requirements, and customer harm.

Cause-and-effect diagrams

A cause-and-effect, or fishbone, diagram organizes possible causes of a defined effect. Common categories include people, methods, machines or technology, materials or information, measurement, and environment. The diagram is a thinking aid rather than proof of causation. Teams should use it to generate hypotheses, then test those hypotheses with evidence.

Effective facilitation matters. A dominant manager can cause the group to accept a preferred explanation prematurely. Including frontline employees, technical specialists, and customer-facing staff often reveals interactions that are invisible from one role’s perspective.

Failure Mode and Effects Analysis: Anticipating Risk

Failure Mode and Effects Analysis (FMEA) is a preventive method for examining how a process, product, or service might fail before harm occurs. For each failure mode, the team considers its effect, potential cause, existing controls, and relative priority. Traditional FMEA often scores severity, occurrence, and detection. Some organizations multiply these ratings into a risk priority number, but the resulting number should not be treated as an objective truth. A high-severity failure may deserve action even if its calculated score is not the highest.

A practical FMEA sequence is:

1. Define the process or design and its boundaries.

2. List process steps or functional requirements.

3. Identify possible failure modes.

4. Describe effects from the customer, operational, safety, and regulatory perspectives.

5. Identify causes and existing preventive or detection controls.

6. Evaluate risk using agreed criteria.

7. Assign actions, owners, and deadlines.

8. Reassess risk after actions are implemented.

FMEA is strongest when conducted during design or process introduction, not only after a failure. It also requires revision when technology, suppliers, regulations, customer expectations, or operating conditions change.

Mistake-Proofing and Standard Work

Mistake-proofing, or poka-yoke, designs a process so that an error is prevented or immediately detected. A connector that can be inserted in only one orientation, a software field that rejects an invalid date, and a barcode scan that verifies the correct item are examples. The best mistake-proofing prevents the error without depending on memory, vigilance, or heroic effort.

There are two broad forms. Prevention controls make the error impossible or highly unlikely. Detection controls identify the error before it reaches the customer. Detection is valuable, but prevention is generally stronger when feasible. Controls must also be evaluated for unintended consequences, such as creating workarounds, slowing critical service, or excluding customers with accessibility needs.

Standard work specifies the current best-known method for performing a process safely, efficiently, and consistently. It normally describes the sequence of work, expected time or timing, key quality points, materials or information required, and response to abnormal conditions. Standard work is not intended to suppress professional judgment. Rather, it provides a stable baseline from which deviations can be recognized and improvements can be tested.

A standard that is impossible to follow is not a control; it is a source of frustration and hidden nonconformance. Employees should participate in developing standards, receive appropriate training, and have a clear method for suggesting revision. Leaders must also ensure that staffing, equipment, and workload make compliance realistic.

Integrating the Methods in Practice

The tools described above form a sequence rather than a checklist. A typical improvement effort may begin with customer feedback, performance data, or a risk signal. The team defines the problem and establishes a baseline. Pareto analysis can focus attention on the most consequential failure categories. A process map and cause-and-effect diagram can generate possible causes. RCA and data analysis verify the important causes. FMEA can evaluate risks associated with proposed changes, while mistake-proofing and standard work can embed the solution. PDCA or DMAIC provides the overall learning and governance structure.

The sequence should remain flexible. In a high-risk product launch, FMEA may precede customer complaints. In a service recovery situation, immediate containment may be required before analysis is complete. In a mature process with frequent small irritations, Kaizen may be more appropriate than a major DMAIC project.

Measures should include both outcome and process indicators. Outcome measures may include complaint rate, customer effort, defect rate, retention, safety events, or first-contact resolution. Process measures may include queue time, adherence to standard work, verification completion, or percentage of transactions requiring rework. A balanced set prevents teams from improving an internal metric while damaging customer experience. For example, reducing average call duration may appear positive but could increase repeat contacts if issues are not resolved.

Illustrative Case Study: Reducing Errors in a Digital Claims Process

Background

The following is an illustrative case. A regional insurance provider receives motor-vehicle claims through a digital portal and contact centre. Customer satisfaction has declined because claimants frequently receive requests for documents they had already submitted. Processing delays have increased, and employees report spending substantial time reconciling records.

Situation and problem

An initial review shows that 14 percent of digitally initiated claims require at least one avoidable follow-up request. The organization’s leadership initially proposes additional employee training. However, the quality team defines the problem more precisely: “Reduce avoidable document follow-up requests for digitally initiated claims from 14 percent to below 5 percent within six months, without increasing claim fraud risk or average claim cycle time.”

Analysis

A Pareto analysis of 1,200 sampled cases shows that three categories account for most follow-up requests: unreadable photographs, documents uploaded under the wrong category, and files not linked to the correct claim record. A cross-functional cause-and-effect session identifies possible causes involving customer instructions, portal design, file standards, system integration, employee verification, and workload.

The team conducts RCA using transaction records and usability observations. It discovers that the portal permits submission before required metadata are completed. The document-category labels are technical and unclear to customers. In addition, a system integration delay means that some files are not visible in the employee’s work queue for several minutes. Employees, believing documents are missing, send requests prematurely.

An FMEA of the redesigned process identifies the most serious risk as a document being attached to the wrong claim. The team therefore prioritizes controls that verify identity and claim association rather than merely reducing the number of follow-up messages.

Approach and solution

Using PDCA, the organization tests several changes with one claim type. The portal introduces plain-language document categories, displays examples of acceptable files, and prevents submission when mandatory metadata are missing. A barcode or claim-specific digital token links uploaded documents to the appropriate record. The employee interface displays a clear “synchronization pending” status rather than showing the file as absent.

Standard work is revised so that employees wait for the defined synchronization period and check the exception queue before contacting the customer. A mistake-proofing control prevents a follow-up message from being sent when a document is present but not yet indexed. Supervisors monitor a daily dashboard showing avoidable requests, processing time, exception volume, and customer complaints.

Outcome

After the pilot, avoidable follow-up requests fall substantially compared with the baseline, while claim cycle time remains stable. The organization expands the changes after confirming that fraud-screening controls have not weakened. It also identifies a new issue: some customers using mobile devices cannot easily produce the required file format. A subsequent improvement adapts the upload function and provides an alternative assisted channel.

Learning points

The case demonstrates several principles. First, training alone would not have corrected a system-design problem. Second, customer experience and operational efficiency improved together because the team addressed the source of rework. Third, prevention and detection controls were combined to protect claim integrity. Finally, the solution was not considered complete until accessibility and unintended consequences had been examined.

Challenges, Risks, and Common Mistakes

Quality improvement can fail even when teams use recognized tools. Common mistakes include defining the problem as a solution, such as “employees need more training”; collecting data without an operational definition; relying on averages that conceal variation; confusing correlation with causation; and choosing a solution before understanding the process.

Another risk is overemphasis on easily measured outcomes. Customer trust, dignity, and perceived fairness may be damaged even when defect rates decline. Teams should therefore include qualitative evidence, complaint narratives, interviews, and direct observation where appropriate.

Improvement programs may also generate “metric gaming.” If employees are evaluated on speed alone, they may close cases prematurely or transfer difficult work. Governance should examine how measures influence behavior and should use a balanced set of indicators.

Sustainability is a further challenge. Improvements often disappear when a project team disbands, experienced employees leave, or demand changes. Control plans, ownership, training, audits, visual management, and periodic review are needed to preserve gains. Yet excessive auditing can create compliance behavior without real learning. The aim is an intelligent control system proportionate to risk.

Ethical, Regulatory, and Professional Considerations

Quality decisions can affect safety, privacy, access, employment, and financial well-being. Organizations must not reduce quality to cost reduction if the result transfers risk to vulnerable customers or frontline employees. FMEA and improvement prioritization should account for severity and equity, not only frequency or financial return.

Data used in quality analysis must be accurate, protected, and interpreted responsibly. Customer records should be minimized, access-controlled, and used for legitimate purposes. Automated analytics and AI may identify patterns in complaints or predict process failures, but models can reproduce historical bias or misclassify unusual cases. Human review remains important where decisions affect rights, safety, or access to essential services.

Professionals should distinguish evidence from assumption, document decisions, disclose limitations, and escalate risks that cannot be controlled locally. Regulatory requirements and professional standards may establish minimum controls, but compliance alone does not guarantee a good customer experience.

Digital Applications and Emerging Developments

Digital workflow systems can provide real-time process visibility, automated alerts, electronic standard work, and traceability. Process mining can reconstruct actual customer journeys from event logs and reveal rework loops or delays that are absent from formal process maps. Statistical dashboards can display trends, variation, and control limits, while text analytics can classify complaint themes.

AI-supported quality analysis can assist with anomaly detection, document classification, and prioritization of cases for review. Its use should be governed by data quality, explainability, cybersecurity, privacy, human oversight, and clear accountability. Automation should not simply accelerate a defective process. Before digitizing a control, the organization should understand the failure mode it is intended to address.

Emerging practice is moving toward integrated customer-experience and operational-quality management. Organizations increasingly combine journey analytics, employee experience data, service recovery information, and process performance rather than managing these as separate domains. The strategic challenge is to use technology to increase learning and reliability without reducing customers to data points or employees to compliance operators.

Best Practices for Managers and Improvement Teams

Effective practice rests on several principles:

• Begin with a customer-relevant problem and a measurable baseline.
• Involve process owners, frontline employees, technical experts, and affected customers where feasible.
• Separate containment from permanent corrective action.
• Use data to test causes rather than to decorate a predetermined conclusion.
• Prefer prevention and robust design over inspection and reminders.
• Select methods according to risk, complexity, scale, and available evidence.
• Make standards visible, teachable, realistic, and revisable.
• Measure both customer outcomes and process conditions.
• Evaluate unintended effects, equity, privacy, and accessibility.
• Assign ownership for sustaining gains and review performance after implementation.

Chapter Summary

Quality planning, assurance, and improvement are mutually reinforcing elements of a quality management system. The Juran Trilogy explains how organizations identify customer needs, control performance, and pursue higher levels of capability. PDCA/PDSA supports disciplined experimentation and learning, while DMAIC offers a structured approach for complex, data-intensive problems. Kaizen develops continuous participation through incremental change.

Pareto analysis helps prioritize significant problem categories; cause-and-effect diagrams organize possible causes; root-cause analysis tests underlying explanations; FMEA anticipates risk; mistake-proofing prevents or detects errors; and standard work sustains improved performance. These tools are most effective when integrated around customer outcomes rather than used mechanically. Ethical governance, sound measurement, employee involvement, digital capability, and attention to unintended consequences are essential to responsible improvement.

Key Terms

Quality planning: The process of identifying requirements and designing capable products, services, and processes.

Quality assurance: Planned and systematic activities that provide confidence that quality requirements will be fulfilled.

Quality control: Measurement and corrective action used to keep performance within defined requirements.

Quality improvement: Organized action to achieve better performance and reduce chronic losses.

Juran Trilogy: The framework of quality planning, quality control, and quality improvement.

PDCA/PDSA: A repeating cycle of planning, testing, studying results, and acting on learning.

Kaizen: Continuous, participative improvement through incremental changes.

DMAIC: Define, Measure, Analyze, Improve, and Control, a structured improvement methodology.

Root-cause analysis: Systematic investigation of underlying causes of a problem.

Pareto analysis: Ranking problem categories to identify those contributing most to loss or impact.

Cause-and-effect diagram: A structured diagram for organizing possible causes of an effect.

FMEA: A preventive analysis of potential failure modes, effects, causes, and controls.

Mistake-proofing: Process or product design that prevents errors or detects them immediately.

Standard work: The documented current best method for performing a process consistently.

Variation: Differences in process or output performance across time or units.

Common-cause variation: Variation arising from the normal design and operation of a process.

Special-cause variation: Variation resulting from an unusual or assignable event.

Critical-to-quality characteristic: A measurable feature strongly related to customer satisfaction, performance, or risk.

Corrective action: Action taken to eliminate the cause of a nonconformity and prevent recurrence.

Control plan: A defined set of measures, responsibilities, and responses used to sustain performance.

Review Questions

Short Answer Questions

1. Distinguish quality planning from quality assurance.

2. What is the purpose of the Study or Check stage in PDCA/PDSA?

3. Why is a Pareto chart useful in improvement work?

4. Define mistake-proofing and provide one example.

5. What is the difference between common-cause and special-cause variation?

Long Answer Questions

1. Explain the Juran Trilogy and show how its three elements support customer experience management.

2. Compare Kaizen and DMAIC in terms of purpose, scale, data requirements, and suitability.

3. Describe how root-cause analysis, cause-and-effect diagrams, and Pareto analysis can be used together.

4. Explain the stages of FMEA and discuss why severity should not be ignored when using risk scores.

5. Discuss the role of standard work and control plans in sustaining quality improvement.

Analytical / Application Questions

1. A service center reduces average handling time by 20 percent, but repeat contacts increase by 15 percent. Analyze the likely measurement problem and recommend a balanced evaluation approach.

2. A manufacturer discovers that a rare assembly error can cause a serious safety failure. Explain how FMEA and mistake-proofing should guide the response, even if the error has a low frequency.

3. A university’s online admissions system repeatedly asks applicants to upload documents they have already submitted. Design an improvement approach using at least four methods from this chapter and justify the order in which they would be applied.

22. Multiple Choice Questions with Answers

Question 1

Which activity most directly represents quality planning in the Juran Trilogy?

A. Auditing whether employees followed an existing procedure

B. Identifying customer needs and designing a capable delivery process

C. Sorting defects by frequency after they occur

D. Correcting an individual transaction error

Correct Answer: B. Identifying customer needs and designing a capable delivery process

Question 2

A team tests a revised appointment-reminder message with one clinic before wider implementation. Which PDCA/PDSA principle is being applied?

A. Replacing measurement with managerial judgment

B. Testing a change on a limited scale before adoption

C. Treating the first result as proof of permanent success

D. Eliminating the need for standard work

Correct Answer: B. Testing a change on a limited scale before adoption

Question 3

What is the principal purpose of Pareto analysis?

A. To prove that one cause is responsible for every defect

B. To rank problem categories and focus improvement effort

C. To define the exact sequence of standard work

D. To prevent every possible failure during product design

Correct Answer: B. To rank problem categories and focus improvement effort

Question 4

A fishbone diagram identifies unclear instructions as a possible cause of service errors. What should the team do next?

A. Treat the cause as confirmed immediately

B. Discard the diagram because it cannot prove causation

C. Collect evidence to test whether unclear instructions contribute to errors

D. Begin disciplinary action against employees

Correct Answer: C. Collect evidence to test whether unclear instructions contribute to errors

Question 5

Which situation is most appropriate for proactive FMEA?

A. Analyzing a process design before launching a new service

B. Ranking last month’s complaints after the service has ended

C. Replacing all process measures with customer opinions

D. Calculating employee attendance rates

Correct Answer: A. Analyzing a process design before launching a new service

Question 6

Which example best illustrates mistake-proofing?

A. Reminding employees to be more careful

B. Inspecting every output after completion without changing the process

C. Designing a software field that rejects an invalid account number

D. Publishing an annual quality slogan

Correct Answer: C. Designing a software field that rejects an invalid account number

Question 7

What is a major difference between Kaizen and DMAIC?

A. Kaizen is preventive, whereas DMAIC never uses data

B. Kaizen emphasizes incremental participation, whereas DMAIC structures complex data-based improvement

C. Kaizen applies only to manufacturing, whereas DMAIC applies only to services

D. Kaizen sustains gains, whereas DMAIC cannot include control activities

Correct Answer: B. Kaizen emphasizes incremental participation, whereas DMAIC structures complex data-based improvement

Question 8

A team defines a problem as “employees need more training” before examining process data. What common improvement mistake does this represent?

A. Using a customer-defined CTQ

B. Defining the problem as a solution

C. Applying prevention rather than detection

D. Separating outcome measures from process measures

Correct Answer: B. Defining the problem as a solution

Question 9

Why should a quality dashboard include both outcome and process measures?

A. To ensure that internal efficiency improvements do not damage customer outcomes

B. To avoid establishing any performance baseline

C. To replace root-cause analysis with visual reporting

D. To guarantee that every measure improves simultaneously

Correct Answer: A. To ensure that internal efficiency improvements do not damage customer outcomes

Question 10

Which control is most likely to sustain an improvement after a project team disbands?

A. A one-time announcement from senior management

B. A realistic standard-work instruction supported by ownership and monitoring

C. An assumption that experienced employees will remember the change

D. A higher target without additional process redesign

Correct Answer: B. A realistic standard-work instruction supported by ownership and monitoring