Lean Six Sigma Projects and Sector Applications

Introduction

Lean Six Sigma becomes valuable when it is treated not as a collection of tools but as a disciplined system for improving performance. Lean contributes speed, flow, simplicity and the removal of activities that do not create value. Six Sigma contributes analytical rigour, reduction of variation, control of defects and evidence-based decision-making. A Lean Six Sigma project therefore seeks a process that is faster, more reliable, more predictable and more valuable to customers and stakeholders.

Projects are the practical vehicle through which improvement is delivered. However, not every operational problem deserves a formal project. A project should be selected when the problem is strategically relevant, measurable, sufficiently important to justify resources, and likely to benefit from structured improvement. Poor project selection produces disconnected local gains, change fatigue and weak financial results. Good selection creates a portfolio of improvements linked to organisational strategy.

This chapter explains how to identify, prioritise and govern Lean Six Sigma projects, integrate Lean and Six Sigma methods, manage teams and risks, validate benefits, and adapt the approach to manufacturing, healthcare, logistics, public services and transactional work.

Learning Objectives

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

• explain how Lean and Six Sigma complement one another;
• identify and define a suitable improvement project;
• prioritise projects using strategic, customer, operational and financial criteria;
• construct a project charter and select an appropriate DMAIC approach;
• manage roles, stakeholders, risks, benefits and organisational change;
• distinguish hard savings, cost avoidance, capacity release and intangible benefits;
• adapt Lean Six Sigma methods to different sectors and process types; and
• evaluate limitations, ethical issues and contemporary digital developments.

1. The Integrated Lean Six Sigma Perspective

Lean is concerned primarily with the relationship between customer value and the flow of work. Typical forms of waste include defects, overproduction, waiting, unused human capability, transportation, inventory, motion and overprocessing. Lean improvement often begins with direct observation, process mapping, workplace organisation, visual management, standard work, pull systems and rapid experimentation.

Six Sigma is concerned primarily with process variation and the probability of producing an unacceptable result. It uses measurement, statistical reasoning, root-cause analysis, designed experimentation and control methods. Its familiar DMAIC cycle is:

1. Define the problem, customer requirements, scope and business case.
2. Measure current performance and establish trustworthy operational definitions.
3. Analyse evidence to identify and verify important causes.
4. Improve the process by developing, testing and implementing solutions.
5. Control the improved process so that performance is sustained.

The two approaches are complementary rather than interchangeable. A process may be slow because of queues and unnecessary movement, even when its output is statistically consistent. Conversely, a process may be fast on average but unreliable because of large variation. Lean addresses flow and responsiveness; Six Sigma addresses capability and consistency. In practice, the distinction is not absolute: stabilising a process is often necessary before flow can be improved, and removing delays may expose quality problems that require statistical analysis.

A useful project logic is:

Customer need → critical-to-quality requirement → process measure → verified cause → intervention → controlled outcome.

For example, a hospital patient may value timely discharge. The critical requirement could be discharge completed within 90 minutes after the decision. Measures might include median discharge time, the proportion exceeding 90 minutes, rework caused by missing medication information and waiting time at each hand-off.

Important performance measures

The following measures are frequently useful, but each must be defined for the particular process:

• Throughput: completed units per unit of time.
• Lead time: elapsed time from customer request to delivery.
• Cycle time: time required to complete a defined process step or unit.
• First-pass yield: proportion completed correctly without rework.
• Defect rate: defects divided by relevant opportunities or units.
• On-time delivery: proportion delivered by the agreed time.
• Work-in-process: items started but not yet completed.
• Process capability: the ability of a stable process to meet specification limits.

A commonly used relationship in flow analysis is Little’s Law:

where WIP is average work-in-process, TH is average throughput per unit of time, and LT is average lead time in the same time unit. The relationship assumes a reasonably stable system over the period considered: items enter and leave at approximately the same long-run rate, and averages are meaningful. For example, if a process completes 40 claims per day and has an average lead time of 3 days, average WIP is approximately 120 claims. The equation does not by itself identify waste or prove causation; it describes a system relationship.

For a stable process with an upper specification limit (USL) and lower specification limit (LSL), potential capability is often expressed as:

where σ is the within-process standard deviation. This index reflects spread only and assumes a stable, approximately normal process with meaningful specification limits. Centred capability is represented by:

where μ is the process mean. Capability indices should not be calculated for an unstable process, and specification limits must represent customer or engineering requirements rather than arbitrary control limits. A high capability value cannot compensate for an invalid measurement system or an unstable process.

2. From Problems to Well-Designed Projects

Improvement begins with a gap between current and required performance. A vague statement such as “the service is inefficient” is not a project definition. A useful problem statement describes what is happening, where and when it occurs, the magnitude of the gap, the effect on customers or the organisation, and what is not yet known about the cause.

A project is generally suitable when:

• the problem is important to customers, strategy, safety, compliance or cost;
• the process has an identifiable owner and a definable boundary;
• baseline data can be collected or created with reasonable effort;
• the cause is not already obvious and a structured investigation is justified;
• the scope can be completed within an agreed time; and
• the organisation can implement and sustain the proposed change.

A project is usually unsuitable when the solution is already mandated and only implementation remains, the issue requires major capital redesign beyond the project authority, the process is in crisis and needs immediate containment, or the problem is so broad that no baseline or boundary can be established.

The project charter

The charter is a governance document, not merely an administrative form. It establishes a common agreement among the sponsor, process owner and project team. It normally includes:

A strong goal might be: “Reduce average order-entry lead time from 48 hours to less than 24 hours by the end of quarter three, while maintaining an error rate below 1%.” It is more useful than “improve order entry” because it specifies measure, direction, target and constraint.

Project selection and prioritisation

A portfolio should balance strategic importance with feasibility. Common criteria include customer impact, safety or regulatory exposure, financial opportunity, frequency, urgency, process readiness, data availability, implementation complexity and alignment with strategic objectives.

A weighted scoring model can make assumptions explicit. If project i receives a score sᵢⱼ from 1 to 5 against criterion j, and criterion j has weight wⱼ, the weighted score is:

where the weights sum to 1. The score is a decision aid, not an objective truth. Criteria should be agreed before scoring, evidence should be recorded, and political influence should not be disguised as numerical precision. A low-scoring project may still be mandatory because of patient safety or legal compliance.

A practical prioritisation sequence is:

1. screen for safety, legal and ethical obligations;
2. remove projects with no accountable owner or unacceptable scope;
3. estimate customer and strategic value;
4. estimate opportunity and resource requirements;
5. assess data and implementation readiness;
6. score and rank the remaining candidates; and
7. review the proposed portfolio for balance, dependencies and capacity.

3. Executing a Lean Six Sigma Project

Define: establish purpose and voice of the customer

The Define phase should identify internal and external customers, translate needs into measurable requirements, and map the high-level process. Useful methods include stakeholder analysis, SIPOC (suppliers, inputs, process, outputs and customers), voice-of-customer interviews, complaint analysis and a high-level value-stream map.

The team should distinguish symptoms from problems. “Customers call repeatedly” is a symptom. A more analytical question is whether unclear instructions, system status, delayed processing or inconsistent decisions generate the calls. The team should also identify constraints such as legislation, clinical protocols, segregation of duties, cybersecurity or contractual conditions.

Measure: create credible evidence

Measurement begins with operational definitions. “Processing time” may mean calendar time, staffed time, touch time or elapsed time between two system events. Different definitions produce different conclusions. The team should specify the unit of analysis, start and stop points, inclusion rules, sampling period, data owner and treatment of missing values.

Measurement-system analysis is particularly important where people judge quality or where data are manually recorded. The question is not merely whether a measuring instrument is calibrated, but whether the whole measurement process is repeatable and reproducible. If two inspectors classify the same item differently, apparent process variation may actually be measurement variation.

Baseline analysis may include a run chart, Pareto analysis, process map, stratification by product or customer, and examination of distributions rather than averages alone. Averages can conceal queues, shifts, locations or vulnerable groups. Data should be protected from manipulation, and the team should document exclusions.

Analyse: connect causes to evidence

Cause-and-effect diagrams, five-whys analysis, process observation, failure mode and effects analysis (FMEA), hypothesis tests, regression and designed experiments may be appropriate. The method must match the question and data. A correlation is not automatically a root cause; a plausible explanation should be tested against process evidence.

FMEA supports proactive risk analysis. For each failure mode, the team considers the effect, cause, existing controls and potential actions. Traditional risk priority numbers multiply ratings, but multiplication can hide important differences and imply unjustified mathematical precision. Safety-critical failures should therefore be escalated by severity and legal or clinical significance even if their numerical score is modest.

Improve: design and test changes

Improvements should address verified causes and should be tested before full deployment. Lean interventions may include point-of-use storage, standard work, workload balancing, setup reduction, pull signals, error-proofing and removal of unnecessary approvals. Six Sigma interventions may include parameter optimisation, redesigned forms, control of influential inputs or a designed experiment.

A pilot should define the population, duration, success measures, constraints, responsible people and rollback conditions. Where feasible, the team can compare performance before and after implementation, or between pilot and comparison groups. It should account for seasonality, learning effects and concurrent changes.

Control: make the gain durable

Control is a management system, not the final graph in a presentation. A control plan should identify the critical measure, data source, sampling frequency, owner, reaction limit, escalation route and audit method. Standard operating procedures, training, visual controls, automated alerts and layered process audits may be used.

Control limits are calculated from process data and describe expected process variation; specification limits describe what customers or regulators require. They must not be confused. A point beyond a control limit signals unusual process behaviour, while a point beyond a specification limit signals a nonconforming output. A process can be statistically stable but consistently fail to meet specification, or appear capable while being unstable.

4. Financial Benefits and Business Governance

Improvement teams need a credible benefits case, but financial claims require disciplined validation. Important categories include:

• Hard savings: a verified reduction in expenditure, such as lower external purchasing or eliminated overtime, reflected in financial records.
• Cost avoidance: prevention of an expected future cost; it may not reduce the current budget.
• Capacity release: time or equipment made available for additional work, which becomes a financial benefit only if the capacity is used or costs are actually reduced.
• Revenue protection or increase: retained customers, fewer chargebacks or additional sales attributable to the change.
• Working-capital benefit: reduced inventory or receivables, subject to accounting treatment.
• Intangible benefits: safety, morale, experience, resilience, compliance and reputation.

A basic annualised labour benefit can be estimated as:

The assumptions must state whether “hours saved” represent paid hours eliminated, overtime avoided or merely time made available. If 500 overtime hours are avoided per quarter at a fully loaded cost of 24 currency units per hour, annual hard saving is 500 × 24 × 4 = 48,000 currency units, assuming the overtime would otherwise have occurred and the rate is appropriate.

Return on investment may be expressed as:

This simple measure requires a defined period and consistent treatment of one-time and recurring costs. Finance should validate the baseline, attribution, timing and treatment of benefits. Teams should never claim savings by simply multiplying every minute removed from a process by an hourly wage.

5. Teams, Leadership, Risk and Change

A typical governance structure includes an executive sponsor, a process owner, a project leader or Black Belt, subject-matter experts, data or improvement specialists, frontline representatives and a finance partner. The sponsor removes organisational barriers and approves resources. The process owner accepts the solution and owns sustained performance. Frontline workers contribute practical knowledge and must not be treated merely as sources of compliance.

Effective teams establish decision rights, meeting routines, escalation rules, data access and psychological safety. Resistance may reflect rational concerns about workload, professional identity, safety or previous failed initiatives. Listening, co-design, transparent evidence and visible leadership are more effective than labelling sceptics as resistant.

A risk register should identify the risk, cause, consequence, likelihood, impact, existing controls, owner, mitigation and review date. Risks may include patient or customer harm, data privacy, service interruption, supplier failure, cyber vulnerability, labour relations, regulatory non-compliance and benefit shortfall. A change-impact assessment should examine who will perform each task differently, what skills are needed, and how unintended consequences will be detected.

Ethical governance requires respect for people, confidentiality, fairness and informed use of data. In healthcare and public services, efficiency must never be pursued by denying necessary care or creating inequitable access. Automated decisions require explainability, bias testing, human oversight and an appeal route. Improvement data should be used to understand systems rather than to punish individuals for defects produced by poorly designed work.

6. Sector Applications

Manufacturing

Manufacturing projects frequently combine value-stream mapping, standard work, setup reduction, total productive maintenance, mistake-proofing, statistical process control and capability analysis. A line with long changeovers may have large batches, high inventory and delayed response to demand. A setup-reduction project separates internal activities, which require the machine to be stopped, from external activities, which can be completed while it runs. Six Sigma analysis may then address dimensional variation or defect causes.

The main risks are optimising one machine while constraining the whole flow, confusing inspection with quality assurance, and pursuing utilisation at the expense of customer lead time. Safety, traceability and validated process parameters must remain protected.

Healthcare

Healthcare is a complex socio-technical system in which demand is variable, work is urgent and outcomes are partly clinical rather than purely operational. Suitable projects include reducing emergency-department waiting, improving medication reconciliation, reducing specimen errors, shortening discharge delays and increasing operating-room readiness.

Value must be defined with clinicians and patients. A shorter consultation is not necessarily better care. Measures should include clinical outcomes, readmissions, harm, equity, patient experience and staff workload alongside time and cost. Process mapping should include decision points, information availability, hand-offs and interruptions. Changes require clinical governance, privacy protection and often formal approval.

Logistics and supply chains

Logistics projects may target order accuracy, dock-to-stock time, picking travel, delivery reliability, inventory accuracy, claims processing or returns. Lean methods expose movement, waiting and batching; Six Sigma methods identify variation in demand, scanning, carrier performance or replenishment parameters. Little’s Law can help relate warehouse WIP, throughput and order lead time, but demand seasonality and unstable arrival rates must be considered.

A local reduction in warehouse labour may increase transport cost, damage or late deliveries. Therefore the unit of analysis should include end-to-end service and total cost, not only departmental productivity.

Public services

Public-sector processes include licensing, benefits administration, inspections, permitting and public enquiries. Customers may be citizens with unequal resources, and the service may have legal obligations rather than a conventional market price. Projects can reduce unnecessary hand-offs, clarify forms, improve first-time completeness and create transparent case-status information.

Efficiency must not become an excuse for excluding complex cases. Measures should include accessibility, fairness, statutory compliance, appeal outcomes and distributional effects. Data definitions need particular care because a “completed” case may mean an administrative closure rather than a satisfactory public outcome.

Transactional and knowledge work

Transactional processes such as invoice approval, procurement, insurance claims, recruitment and IT service management often suffer from queues, rework, unclear decision rules and excessive email. Process mining can reconstruct actual digital flows from event logs, revealing loops and waiting that workshops may miss. However, event logs reflect system design and may omit informal work.

Automation is appropriate when rules are stable, inputs are structured and exceptions are manageable. Standardising a bad process and automating it can accelerate waste. Teams should first remove unnecessary steps, clarify ownership and test exception handling.

7. Illustrative Case: Reducing Hospital Discharge Delay

Background. This illustrative case concerns a 250-bed hospital whose medical wards experienced delayed discharge. The project was sponsored by the chief operating officer and owned by the medical director and head of nursing.

Problem. Although clinicians made discharge decisions before noon for many patients, the average elapsed time from decision to physical discharge was 4.1 hours. Families complained about uncertainty, beds were unavailable for incoming patients, and staff reported repeated phone calls. The project excluded clinical eligibility decisions and focused on the administrative and coordination process after the decision.

Define and measure. The team created an operational definition: elapsed time began when the electronic discharge decision was entered and ended when the patient left the ward. A sample of 180 discharges was stratified by ward and weekday. The median time was 3.2 hours, while the mean was 4.1 hours, indicating a long tail. Process observation showed waiting for medication preparation, transport, final documentation and family communication. A data review also found that 22% of records lacked a complete discharge information field at the time of the decision.

Analyse. A Pareto analysis suggested that medication waiting and transport coordination accounted for most recorded delay, but interviews showed that work was often started late because no clear trigger notified the relevant teams. The team tested the relationship between missing information and delay rather than assuming that staffing alone was the cause. It found that incomplete fields were associated with additional clarification cycles.

Improve. The hospital piloted a standard discharge checklist, an electronic alert triggered by the decision entry, a morning multidisciplinary huddle and a defined medication cut-off for routine cases. Complex cases retained a clinical escalation route. Transport requests were submitted using a standard priority category. The pilot ran for six weeks on one ward while a similar ward continued its usual process.

Outcome. In the pilot ward, the mean elapsed time fell from 4.1 to 2.6 hours and the proportion discharged within three hours increased from 38% to 67%. The comparison ward showed no similar improvement during the same period. Medication errors and readmissions did not increase, although the team continued monitoring these measures because six weeks was too short to establish long-term clinical safety. The operational gain was not counted as a cash saving; it was reported as improved bed capacity and reduced waiting, with finance separately assessing whether additional admissions could be accommodated safely.

Control and learning. The electronic alert, checklist compliance, median discharge time, 90th percentile time, medication incidents and readmissions were placed on a monthly control review. Ward managers owned the process, and a quarterly audit examined whether staff bypassed the checklist for appropriate reasons. The case demonstrates that a Lean intervention can improve flow, while Six Sigma discipline ensures that definitions, comparison evidence, variation and unintended outcomes are considered. It also shows why a healthcare project must not define success by speed alone.

8. Technology, AI and Contemporary Developments

Digital tools expand the evidence available to improvement teams. Process mining uses event timestamps to identify actual paths, rework loops and waiting. Internet-of-Things sensors can support condition monitoring and traceability. Statistical dashboards can detect shifts earlier than periodic reports. Digital twins and simulation can test capacity or layout changes before implementation.

Artificial intelligence may assist with classification, demand forecasting, anomaly detection, document extraction and identification of likely causes. It should be treated as a decision-support capability, not as proof of causation. Models can reproduce historical bias, drift as conditions change, or optimise a narrow metric at the expense of safety and equity. Governance should include data quality checks, model validation, access control, explainability appropriate to the risk, human review and monitoring of disparate effects.

Contemporary Lean Six Sigma is therefore increasingly data-enabled but remains socio-technical. A dashboard cannot repair unclear ownership, unsafe incentives or poor process design. The strongest projects combine frontline observation, statistical evidence, domain expertise and responsible digital governance.

Challenges and Limitations

Lean Six Sigma projects can fail through weak sponsorship, overlarge scope, unreliable data, premature solution selection, poor change management or insufficient control. DMAIC is not a rigid sequence: teams may return to measurement after discovering an invalid operational definition, or revisit scope when evidence reveals a system-level dependency. Not every problem requires advanced statistics, and not every variation problem can be solved locally.

There are also conceptual limitations. Waste categories can encourage teams to label necessary professional work as waste. Capability indices can mislead when distributions are non-normal or unstable. Weighted scores can create false confidence. Financial benefits may be difficult to attribute in changing environments. Finally, local optimisation can damage the wider system. For these reasons, project review should ask not only “Did the metric improve?” but also “For whom, at what cost, with what risks, and for how long?”

Best-Practice Checklist

A professionally governed project should:

1. link the project to strategy and a real customer or stakeholder need;
2. define the problem without assuming its cause;
3. establish operational definitions and verify the measurement system;
4. examine flow, variation, quality, safety and equity together;
5. involve process owners and frontline experts;
6. test changes on a controlled and appropriately sized basis;
7. validate financial claims with finance;
8. document risks, dependencies and unintended consequences;
9. transfer ownership through a control plan; and
10. review results after implementation rather than declaring success at pilot completion.

Chapter Summary

Lean Six Sigma integrates Lean’s concern with value, flow and speed with Six Sigma’s concern with variation, defects and evidence. Projects should be selected through a disciplined assessment of strategic importance, customer impact, risk, financial opportunity, feasibility and readiness. A project charter establishes the business case, problem, goal, scope, measures, roles and governance.

DMAIC provides a structured route from definition to sustained control. Reliable operational definitions and measurement systems precede analysis; verified causes precede improvement; and a control plan follows implementation. Financial benefits must distinguish hard savings, cost avoidance, capacity release and intangible value. Team effectiveness depends on clear sponsorship, process ownership, frontline participation, psychological safety and responsible change management.

Sector adaptation is essential. Manufacturing may emphasise flow, equipment and capability; healthcare must balance efficiency with clinical outcomes and safety; logistics requires end-to-end service and total-cost thinking; public services require fairness and statutory compliance; and transactional work benefits from process mining and careful automation. Digital tools and AI can strengthen analysis, but they do not replace human judgement, ethical governance or process ownership.

Key Terms

Lean: An approach focused on customer value, flow and elimination of non-value-adding activity.

Six Sigma: A data-based approach focused on reducing variation and defects.

DMAIC: Define, Measure, Analyse, Improve and Control.

Project charter: An approved document defining a project’s purpose, scope, goals and governance.

Critical-to-quality requirement: A measurable feature that is important to customer or stakeholder satisfaction.

Value-stream map: A representation of material and information flow across a process.

Operational definition: A precise rule for how a measure is observed, recorded and interpreted.

First-pass yield: The proportion of units completed correctly without rework.

Little’s Law: The relationship WIP = TH × LT for a stable flow system.

Process capability: The ability of a stable process to meet specification requirements.

Control limit: A statistically derived boundary used to detect unusual process behaviour.

Specification limit: A customer, engineering, contractual or regulatory requirement.

FMEA: Failure mode and effects analysis, a structured method for examining potential failures.

Process owner: The person accountable for the ongoing performance and control of a process.

Hard saving: A verified reduction in expenditure or resource cost.

Cost avoidance: Prevention of an anticipated future cost without necessarily reducing the current budget.

Capacity release: Time or resource made available for additional work.

Process mining: Analysis of event-log data to reconstruct and evaluate actual process paths.

Poka-yoke: Mistake-proofing that prevents or immediately detects error.

Control plan: A documented method for monitoring, reacting to and sustaining process performance.

Review Questions

Short Answer Questions

1. How do Lean and Six Sigma contribute different but complementary perspectives to improvement?
2. What information should be included in a project charter?
3. Why must control limits be distinguished from specification limits?
4. Define hard savings, cost avoidance and capacity release.
5. Why are operational definitions important in the Measure phase?

Long Answer Questions

1. Explain how an organisation should identify and prioritise a portfolio of Lean Six Sigma projects.
2. Describe the DMAIC cycle and show how Lean methods may be integrated within each phase.
3. Discuss the roles of the sponsor, process owner, project leader and frontline staff in project governance.
4. Compare the application of Lean Six Sigma in manufacturing, healthcare and public services.
5. Evaluate the benefits and limitations of using process mining and AI in improvement projects.

Analytical / Application Questions

1. A claims process completes 40 claims per day and has average lead time of 3 days. Use Little’s Law to estimate average WIP, state the assumptions, and explain what additional evidence is needed before recommending a change.
2. A project removes 500 overtime hours per quarter at a fully loaded cost of 24 currency units per hour. Calculate the annualised hard saving and identify two reasons why the finance function might reject the claim.
3. Design a project charter outline for reducing delayed laboratory test results in a hospital. Include the problem statement, goal, scope, measures, stakeholders, risks and control approach.

Multiple Choice Questions with Answers

Question 1

Which statement best describes the primary contribution of Lean Six Sigma integration?

A. It replaces all operational judgement with statistical software.

B. It combines flow and waste reduction with variation and defect reduction.

C. It focuses exclusively on reducing labour cost.

D. It applies only to high-volume manufacturing.

Correct Answer: B. It combines flow and waste reduction with variation and defect reduction.

Question 2

Which project statement is most suitable for a Lean Six Sigma charter?

A. Improve customer service as much as possible.

B. The process is poorly managed by staff.

C. Reduce order-entry lead time from 48 hours to less than 24 hours while maintaining error rate below 1%.

D. Install a new software system immediately.

Correct Answer: C. Reduce order-entry lead time from 48 hours to less than 24 hours while maintaining error rate below 1%.

Question 3

A stable process completes 40 claims per day and has an average lead time of 3 days. What is its approximate average WIP?

A. 13.3 claims

B. 37 claims

C. 120 claims

D. 240 claims

Correct Answer: C. 120 claims

Question 4

Which statement correctly distinguishes a control limit from a specification limit?

A. A control limit is set by the customer, while a specification limit is calculated from process data.

B. Both terms mean exactly the same thing.

C. A control limit detects unusual process behaviour, while a specification limit represents a requirement.

D. Specification limits are used only for financial benefits.

Correct Answer: C. A control limit detects unusual process behaviour, while a specification limit represents a requirement.

Question 5

Which activity belongs primarily to the Measure phase?

A. Establishing operational definitions and validating data quality

B. Selecting a final solution before collecting evidence

C. Launching a full-scale intervention without a baseline

D. Closing the project before control measures are defined

Correct Answer: A. Establishing operational definitions and validating data quality

Question 6

Which is the best example of capacity release rather than a confirmed hard saving?

A. A supplier invoice is permanently eliminated.

B. Overtime payments are verified to have fallen.

C. Employees spend less time on a task but remain fully paid and are reassigned to additional work.

D. A contractual fee is removed from the budget.

Correct Answer: C. Employees spend less time on a task but remain fully paid and are reassigned to additional work.

Question 7

Why should healthcare improvement projects include measures beyond speed?

A. Time measures are never useful in healthcare.

B. Clinical outcomes, safety, patient experience and equity may be affected by process changes.

C. Financial measures are prohibited in healthcare.

D. Healthcare processes have no measurable variation.

Correct Answer: B. Clinical outcomes, safety, patient experience and equity may be affected by process changes.

Question 8

What is the principal purpose of a control plan?

A. To identify the original project sponsor

B. To monitor performance, define reactions and sustain improvement

C. To replace all standard operating procedures

D. To calculate a project’s weighted selection score

Correct Answer: B. To monitor performance, define reactions and sustain improvement

Question 9

Which risk is particularly associated with automating a poorly designed transactional process?

A. Automation always increases variation to infinity.

B. The process may perform waste faster and propagate errors at scale.

C. Automation prevents the collection of event-log data.

D. Automation eliminates the need for exception handling.

Correct Answer: B. The process may perform waste faster and propagate errors at scale.

Question 10

Which statement about process capability indices is correct?

A. They should be calculated without regard to process stability.

B. They compare process spread and centring with specification limits under stated assumptions.

C. They are identical to control limits.

D. They prove that a process has no defects.

Correct Answer: B. They compare process spread and centring with specification limits under stated assumptions.