Maintenance, Reliability, and Asset Availability

Introduction

Transport assets—trucks, buses, rail vehicles, vessels, aircraft-support equipment, trailers, containers, forklifts, and terminal machinery—convert network plans into physical service. Their value is realized only when they are safe, legally compliant, economically usable, and available at the time and place required. Maintenance is therefore not merely a workshop activity. It is a strategic supply-chain capability affecting transport capacity, customer service, safety, fuel consumption, emissions, working capital, and total cost of ownership.

A fleet that is inexpensive to purchase but frequently unavailable may have a higher life-cycle cost than a more expensive fleet with superior reliability and maintainability. Conversely, excessive preventive maintenance can waste labor and parts, introduce maintenance-induced failures, and remove serviceable assets from operation. The central managerial problem is to design a maintenance system that balances risk, availability, cost, and operational demand.

This chapter develops that system. It explains maintenance strategies, reliability and availability measures, failure analysis, spare-parts planning, workshop control, warranty recovery, and the use of digital and analytical technologies.

Learning objectives

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

1. Distinguish preventive, predictive, condition-based, corrective, and proactive maintenance.
2. Calculate and interpret reliability, maintainability, availability, mean time between failures, and mean time to repair.
3. Select maintenance policies using failure consequences, failure behavior, and life-cycle cost.
4. Apply failure-mode and effects analysis, root-cause analysis, and criticality analysis.
5. Design spare-parts, workshop, workforce, and warranty-control processes.
6. Evaluate maintenance performance and identify limitations in maintenance data and digital systems.

1. Maintenance as an asset-management system

Maintenance comprises the technical, administrative, and managerial actions that retain or restore an asset so that it can perform a required function. Asset management is broader: it coordinates acquisition, operation, maintenance, renewal, disposal, risk, and financial decisions over the asset life cycle.

For a fleet vehicle, the required function is not simply “engine runs.” It may be “transport a specified payload over a route, within a scheduled time, safely and legally, at an acceptable cost.” A maintenance policy must therefore begin with the required function and performance standard.

Important terminology includes:

• Failure: loss of ability to perform a required function. A vehicle may fail completely, or it may suffer a functional failure such as reduced braking performance.
• Defect: a condition that can lead to failure or degraded performance.
• Downtime: time during which an asset is unavailable for its required function. It may include waiting for diagnosis, parts, labor, authorization, or testing.
• Planned downtime: downtime scheduled in advance, such as a service inspection.
• Unplanned downtime: downtime caused by an unexpected failure or urgent intervention.
• Maintainability: the probability that an asset can be restored to service within a specified time under stated conditions.
• Availability: the proportion of time an asset is capable of performing its required function.

Maintenance performance must be interpreted with operations. A vehicle unavailable during a low-demand period may have little service impact; the same outage during a peak delivery window may require subcontracting, route cancellation, or customer compensation.

2. Maintenance strategies and their appropriate use

2.1 Corrective maintenance

Corrective maintenance restores an asset after a defect or failure has been detected. It can be planned when a noncritical component is allowed to run until replacement, or unplanned when a sudden failure interrupts service.

Run-to-failure is rational when the item is inexpensive, failure has low safety and environmental consequences, no hidden failure is involved, and replacement is rapid. It is inappropriate for braking systems, steering, structural components, legally required safety equipment, or components whose failure causes disproportionate disruption.

2.2 Preventive maintenance

Preventive maintenance is performed at predetermined intervals based on time, mileage, engine hours, cycles, or calendar age. Examples include oil replacement, lubrication, filter renewal, inspection, and scheduled component replacement.

It is effective where deterioration is related to usage or age and where an intervention can reduce the probability of failure. Its weaknesses include unnecessary replacement, incorrect intervals, maintenance-induced defects, and the assumption that all units age in the same way.

2.3 Predictive and condition-based maintenance

Predictive maintenance uses measurements and analysis to estimate developing degradation and decide when intervention is needed. Condition-based maintenance (CBM) performs maintenance when evidence indicates that a defined condition threshold has been reached. Predictive maintenance is commonly treated as an analytical extension of CBM.

Typical techniques include oil debris analysis, vibration analysis, infrared thermography, brake-wear measurement, battery impedance testing, tire-pressure monitoring, telematics, and analysis of engine fault codes. A condition indicator must have a credible relationship with failure and must provide sufficient warning time for planning.

A useful CBM design asks four questions:

1. What failure mode is being detected?
2. What measurable condition changes before functional failure?
3. What threshold separates acceptable from unacceptable condition?
4. How much lead time is available for parts, labor, and operational rescheduling?

2.4 Proactive maintenance

Proactive maintenance removes causes of recurring failure rather than repeatedly repairing symptoms. Examples include improving contamination control, correcting misalignment, redesigning a weak bracket, changing supplier specifications, improving driver practices, or modifying a route that causes abnormal loading.

2.5 Reliability-centered maintenance

Reliability-centered maintenance (RCM) is a structured process that identifies required functions, functional failures, failure modes, effects, consequences, and suitable policies. Its key principle is that maintenance should be selected according to the consequences and behavior of failure, not according to habit or a generic calendar.

For each significant asset function, RCM considers whether a task is:

• condition-directed;
• time-directed;
• failure-finding for hidden functions;
• redesign-oriented; or
• deliberately corrective because no effective preventive task exists.

The decision should account for safety, regulatory compliance, operational continuity, environmental impact, quality, and cost.

2.6 Strategy selection matrix

No single strategy should be applied to an entire vehicle. A fleet maintenance program is a portfolio of policies for systems and failure modes.

3. Reliability, maintainability, and availability

3.1 Reliability

Reliability is the probability that an item performs its required function for a specified time under stated operating conditions. Let R(t) denote reliability at time t and T the random time to failure:

The corresponding failure distribution is F(t) = P(T ≤ t), so:

The hazard rate describes the instantaneous risk of failure conditional on survival to time t:

where f(t) is the probability density of failure time. The hazard rate is not itself a probability and may vary with age.

A commonly used special case is the constant-rate exponential model:

Here λ has units of failures per unit time. This model assumes a constant hazard rate and is most defensible over a period in which failures are approximately random and wear-out is not dominant. It should not automatically be used for tires, brake linings, batteries, or other strongly age- or usage-dependent components.

For the exponential model, mean time to failure is:

For a repairable asset, mean time between failures (MTBF) is often calculated as operating time divided by the number of relevant failures. MTBF is not necessarily the same as MTTF: MTTF commonly describes a nonrepairable item, while MTBF describes intervals between failures of a repairable system. Organizations must define what counts as a failure.

3.2 Maintainability

Maintainability is often modeled using repair time. If repair time M is exponentially distributed with repair rate μ, then:

where μ is measured in repairs per unit time and MTTR = 1/μ is mean time to repair. In practice, MTTR should include diagnosis, access, disassembly, repair, reassembly, testing, and—if measuring operational availability—waiting for parts, labor, authorization, and facilities. A separate “wrench time” measure is useful because it excludes logistical delay.

Maintainability improves through modular design, accessible components, standard tools, diagnostic systems, competent technicians, good documentation, and ready parts.

3.3 Availability

Availability is the probability or proportion of time an asset is able to perform its function. Inherent availability focuses on operating and corrective repair time, excluding logistics and preventive maintenance:

This formula assumes a stable repairable system, statistically meaningful average times, and alternating operating and repair states.

Operational availability includes preventive maintenance and logistics delay. A practical ratio is:

The denominator must be defined consistently. If a fleet reports availability using scheduled hours, the organization must specify whether approved planned maintenance, depot waiting, and external workshop delays are included.

Worked example. A delivery truck averages 1,200 operating hours between failures. Corrective repair averages 10 hours, while parts and queue delays average 14 hours. Its inherent availability is:

A_i = 1200/(1200 + 10) = 0.9917, or approximately 99.17%.

If operational downtime per cycle is 10 + 14 = 24 hours, operational availability based on the same cycle is:

A_o = 1200/(1200 + 24) = 0.9804, or approximately 98.04%.

The difference demonstrates why workshop queues and parts planning matter as much as repair skill.

3.4 Systems and fleet availability

For independent components in a series system, all components must function:

For two independent components in parallel, the system works if at least one works:

These formulas require independence and clearly defined system logic. Real transport systems may have common-cause failures, dependencies, standby behavior, and operational constraints, so simple multiplication can be misleading.

Fleet availability is not necessarily the arithmetic average of vehicle availability. If a route requires a minimum number of vehicles, the probability that enough vehicles are available should be modeled, or operational spare capacity should be measured directly.

4. Failure behavior and maintenance interval design

The traditional “bathtub curve” presents three conceptual regions: early failures caused by defects or installation problems, a relatively stable random-failure period, and wear-out failures. Modern reliability studies show that many components do not follow this pattern; some have largely random failures, while others show gradual degradation. Therefore, a fixed replacement interval is justified only when evidence demonstrates that age or usage materially changes failure probability.

Maintenance interval decisions should use:

• manufacturer guidance, adjusted for actual duty cycle;
• historical failure and removal data;
• operating environment, payload, temperature, terrain, and driver behavior;
• legal and safety requirements;
• condition-monitoring results;
• cost of inspection, replacement, and failure consequences.

The economic decision compares the cost of intervention with expected failure cost. Expected failure cost can be expressed conceptually as:

This is not a complete optimization formula; probabilities and consequences must be estimated carefully, and safety or regulatory constraints may prohibit choosing the lowest monetary option.

5. Failure analysis and maintenance improvement

5.1 Criticality analysis

Criticality ranks assets or failure modes according to consequence and likelihood. A practical classification may combine:

• safety and legal consequence;
• service disruption and recovery time;
• environmental consequence;
• product or cargo damage;
• repair cost and parts lead time;
• availability of substitute capacity.

A criticality score can support prioritization, but a numerical score must not conceal a catastrophic safety risk behind a low average probability. Safety-critical failures require controls even when rare.

5.2 Failure Mode and Effects Analysis

Failure Mode and Effects Analysis (FMEA) identifies how an item can fail, what causes the failure, what happens locally and operationally, and how current controls detect or prevent it. A conventional FMEA may rank severity, occurrence, and detectability. If a risk-priority-number method is used, the organization must define rating scales and recognize that multiplying ordinal scores can create misleading rankings. A high-severity failure should receive managerial attention even when its numerical product is not highest.

A concise FMEA record includes:

5.3 Root-cause analysis

Root-cause analysis begins after an event or recurring defect. Useful methods include the five-whys technique, fault-tree analysis, causal-factor charts, and evidence-based component examination. The analysis should distinguish immediate cause, contributing conditions, and systemic causes such as poor specifications, inadequate training, weak planning, or incentives that encourage unsafe shortcuts.

A corrective action is not validated merely because the same failure has not yet recurred. Effectiveness should be checked against a defined period, sample, or performance indicator.

5.4 Learning from defects

A closed-loop maintenance system records the failure, preserves evidence, identifies cause, implements action, and updates the maintenance plan. “No fault found” events deserve analysis because intermittent electrical faults, poor diagnostic processes, and reporting errors can consume substantial capacity.

6. Spare-parts planning and inventory control

Parts availability is a major determinant of operational availability. The objective is not to maximize stock, but to provide economically justified service protection.

Parts should be classified by criticality, demand pattern, lead time, substitution possibility, repairability, obsolescence risk, and value. A low-cost seal with a long overseas lead time may be more operationally critical than an expensive part held locally.

For an item with reasonably stable independent demand, a basic reorder point is:

where d is average demand per time unit, L is replenishment lead time in the same time unit, and SS is safety stock. If demand and lead time are variable and approximately independent, a common approximation is:

where z is the standard-normal service factor, σ_d is the standard deviation of demand per time unit, and σ_L is the standard deviation of lead time. This approximation assumes a demand distribution suitable for the chosen service factor, stable parameters, and independence between demand and lead time. Intermittent, lumpy spare-parts demand often violates these assumptions; simulation, bootstrapping, or specialized intermittent-demand methods may be more appropriate.

For a fleet with several identical vehicles, a pooling effect may reduce total stock relative to independent stocking at every location. However, transport time, emergency shipping, and geographic access must be included.

Parts governance should include approved suppliers, counterfeit-part controls, shelf-life management, serial and batch traceability, quarantine of suspect parts, cycle counting, and disposition of obsolete stock. Parts removed from vehicles should be tagged and analyzed rather than automatically discarded; some may be repairable, warranty-eligible, or evidence of a systemic problem.

7. Workshop and workforce management

A professional workshop is a flow system. Its performance depends on work identification, triage, planning, parts kitting, bay capacity, technician skills, tooling, quality control, and release procedures.

A robust work-order process includes:

1. defect report with asset identity, symptom, location, and operational consequence;
2. safety triage and authorization;
3. diagnosis and job planning;
4. parts and labor reservation;
5. controlled execution with inspection points;
6. functional test and road test where appropriate;
7. accurate labor, parts, cause, and failure-code capture;
8. release to operations and post-work review.

Important measures include schedule compliance, planned percentage of work, wrench time, repeat repair rate, first-time fix rate, backlog age, parts fill rate, mean time to repair, and maintenance cost per kilometer or operating hour. Metrics can produce perverse behavior: for example, pressure to reduce MTTR may encourage premature release, while maximizing wrench time may discourage diagnosis and planning. Metrics must therefore be balanced with safety, quality, repeat failures, and customer service.

Technician competence requires a matrix covering mechanical, electrical, hydraulic, high-voltage, diagnostic, safety, and regulatory skills. High-voltage electric vehicles, compressed natural gas systems, refrigerants, and lifting equipment require specific authorization and isolation procedures.

8. Warranty, supplier, and contractor controls

Warranty recovery is a maintenance-control process, not an administrative afterthought. The organization should capture in-service date, mileage or hours, component serial number, failure evidence, diagnosis, labor, parts, and supplier terms. A warranty claim should be submitted within contractual limits and linked to the work order.

Supplier performance should be reviewed using defect rate, response time, technical support, parts availability, repeat failures, claim acceptance, and total cost rather than purchase price alone. Contracts should define data ownership, diagnostic access, software updates, service-level expectations, recall communication, and responsibilities for consequential damage.

Contracted maintenance requires clear boundaries. The operator remains accountable for safety and regulatory compliance even when work is outsourced. Audits, competence checks, calibration controls, parts traceability, and independent release authority are appropriate for high-risk work.

9. Digital maintenance, telematics, and artificial intelligence

A computerized maintenance management system (CMMS) or enterprise asset-management system provides the backbone for asset registers, work orders, preventive schedules, parts, labor, costs, and history. Data quality is more important than system sophistication. Asset identifiers, failure codes, meter readings, and completion notes must be standardized.

Telematics can provide location, engine data, fault codes, harsh-braking events, fuel use, battery state, tire pressure, and utilization. These data can support condition monitoring and driver coaching, but raw alerts are not maintenance decisions. Alert thresholds should be validated against actual failures and false-positive workload.

Machine-learning models can estimate failure risk from sensor and maintenance histories. Their value depends on representative training data, stable operating conditions, explainability, and actionability. A highly accurate prediction that provides no feasible intervention window has limited operational value. Models may also reproduce historical bias—for example, labeling assets from a particular depot as high risk because that depot historically recorded defects more completely.

Governance should address cybersecurity, access control, privacy, data retention, algorithmic accountability, and human override. Predictive analytics should augment—not eliminate—professional judgment, especially for safety-critical decisions.

10. Life-cycle cost and maintenance decision-making

Life-cycle cost includes acquisition, financing, energy, tires, labor, parts, downtime, training, infrastructure, compliance, overhaul, and disposal. A simplified present-value expression is:

where C₀ is initial cost, C_t is cost in period t, r is the discount rate per period, n is the analysis horizon, and S_n is residual or salvage value at the end. Costs should be expressed in consistent monetary terms and assumptions about inflation, utilization, taxes, and residual value should be explicit.

Downtime cost may include lost contribution margin, substitute transport, penalties, missed delivery windows, cargo risk, and reputational effects. It should not be double-counted if already embedded in another cost category. A decision to replace an engine, increase spare vehicles, or adopt condition monitoring should compare total relevant life-cycle consequences, not only workshop expenditure.

11. Illustrative case: regional parcel fleet

Background

The following is a fictional illustrative case. Meridian Parcel Services operates 180 diesel vans from three depots. On-time delivery is declining, and vehicle availability averages 91%. Managers initially blame technicians, but maintenance records show that 38% of downtime is waiting for parts and 22% is waiting for diagnosis or authorization.

Problem

The fleet uses a uniform 10,000-kilometer service interval. The policy does not distinguish urban stop-start vans from highway vehicles. Failure descriptions are free text, preventive work is often deferred during peak periods, and warranty claims are inconsistent.

Analysis

A six-month data review normalizes failures by vehicle-kilometer and groups defects by system. Brake and tire work is frequent but usually planned. Electrical starting failures are less frequent but cause disproportionate route disruption. Five vans account for 31% of unplanned downtime. Interviews reveal weak battery testing, delayed parts approval, and repeated installation of the same aftermarket battery.

An FMEA identifies starting failure as high in operational consequence, moderately likely, and readily detectable through battery conductance testing and charging-system checks. Root-cause analysis finds that the battery specification is unsuitable for the vans’ auxiliary refrigeration loads and that terminal corrosion is not consistently recorded.

Approach and solution

Meridian implements a mixed strategy:

• condition-based battery and charging tests during scheduled inspections;
• a revised battery specification and approved supplier;
• a critical-spares list with local stock for batteries, belts, sensors, and selected electrical components;
• triage rules allowing depot supervisors to authorize defined low-value repairs;
• standardized failure codes and required meter readings;
• a weekly review of repeat failures and warranty eligibility;
• separate reporting of wrench time, parts waiting, and administrative waiting;
• a pilot telematics alert for charging voltage, with technician validation before intervention.

Preventive intervals are retained for safety inspections but adjusted for duty cycle. No vehicle is automatically removed merely because an alert occurs; the alert triggers a diagnostic check and a decision based on evidence.

Outcome

After nine months, the fictional case reports operational availability rising from 91% to 96%, parts-related downtime falling by 45%, and repeat starting failures falling by 60%. Maintenance cost per kilometer initially increases because inspections and better parts are funded, then stabilizes as emergency towing, route recovery, and premature battery replacement decline. The most important improvement is not a single technology; it is the integration of data, planning, parts, technical standards, and accountability.

Learning points

1. Availability loss often occurs outside the repair bay.
2. Uniform maintenance intervals can be inappropriate for heterogeneous duty cycles.
3. A condition-monitoring program requires a defined failure mode, threshold, and response.
4. Failure coding and warranty evidence create economic value.
5. Performance improvement should be assessed using service, cost, safety, and quality measures together.

12. Challenges, limitations, and governance

Maintenance data are frequently incomplete, inconsistent, and affected by reporting incentives. A rise in recorded defects may indicate worsening reliability, or it may indicate improved reporting. MTBF can be inflated if minor failures are excluded, and MTTR can be understated if waiting time is omitted. Comparisons across depots require common definitions, exposure measures, and operating context.

Regulatory obligations may govern inspections, brakes, tires, emissions, lifting equipment, dangerous goods, driver hours, and high-voltage isolation. Compliance records must be auditable. Safety decisions should follow a precautionary approach: financial optimization cannot justify operating outside legal or engineering limits.

Ethical maintenance practice includes truthful defect reporting, no falsification of inspection records, protection of technician and driver safety, proper disposal of oils and batteries, and responsible use of telematics data. Managers should not reward availability targets in ways that pressure employees to defer safety work or release unsafe vehicles.

Best-practice implementation framework

A mature maintenance program can be developed in stages:

1. Define asset functions, service requirements, and safety constraints.
2. Establish a controlled asset register and common failure taxonomy.
3. Segment assets and components by criticality and duty cycle.
4. Review failure history and select policies through RCM or structured analysis.
5. Set preventive and condition-based tasks with explicit triggers and responses.
6. Improve work-order planning, parts availability, skills, tools, and quality release.
7. Measure reliability, availability, maintainability, cost, safety, and customer impact.
8. Audit data quality, warranty recovery, supplier performance, and regulatory compliance.
9. Use pilot studies and feedback before scaling predictive technologies.
10. Reassess policies when equipment, routes, payloads, technology, or risk changes.

Chapter Summary

Maintenance is a life-cycle supply-chain capability that protects transport capacity, safety, service quality, and cost. Corrective, preventive, predictive, condition-based, and proactive strategies are complementary; the appropriate policy depends on failure behavior and consequences. Reliability describes the probability of surviving a specified period, maintainability describes restoration performance, and availability combines operating and downtime experience. MTBF and MTTR are useful only when their definitions and measurement boundaries are explicit.

Reliability-centered maintenance, criticality analysis, FMEA, and root-cause analysis provide structured methods for selecting tasks and eliminating recurring failures. Parts planning, workshop flow, technician capability, warranty recovery, and data quality strongly influence operational availability. Digital tools and AI can improve detection and decision-making, but they require validated signals, accountable governance, cybersecurity, and human judgment. Effective programs optimize life-cycle outcomes rather than minimizing workshop expenditure alone.

Key Terms

Asset availability: The proportion or probability of time an asset can perform its required function.

Corrective maintenance: Maintenance that restores an asset after a defect or failure.

Condition-based maintenance: Maintenance initiated by measured evidence of asset condition.

Criticality analysis: Ranking assets or failure modes by consequence and likelihood.

Downtime: Time during which an asset cannot perform its required function.

Failure mode: The specific manner in which a function can be lost.

Failure Mode and Effects Analysis (FMEA): A structured analysis of failure modes, causes, effects, controls, and actions.

Hazard rate: Instantaneous failure risk conditional on survival to a given time.

Inherent availability: Availability based primarily on operating and corrective repair time.

Maintainability: Probability of restoring an asset within a specified time and conditions.

Mean time between failures (MTBF): Average operating interval between defined failures of a repairable asset.

Mean time to repair (MTTR): Average time required to restore an asset, subject to the organization’s defined boundary.

Predictive maintenance: Use of condition data and analysis to anticipate maintenance needs.

Preventive maintenance: Maintenance performed at predetermined time, usage, or cycle intervals.

Proactive maintenance: Actions that remove or control underlying causes of failure.

Reliability: Probability that an asset performs its required function for a specified period.

Reliability-centered maintenance (RCM): A function- and consequence-based process for selecting maintenance policies.

Root-cause analysis: Systematic investigation of immediate, contributing, and underlying causes.

Safety stock: Inventory held to protect against demand or replenishment uncertainty.

Warranty control: Processes for documenting, submitting, and recovering eligible supplier-related costs.

Review Questions

Short Answer Questions

1. Distinguish preventive maintenance from condition-based maintenance.
2. Why is operational availability usually lower than inherent availability?
3. Define MTBF and explain why its measurement boundary matters.
4. Identify two situations in which run-to-failure may be appropriate.
5. What information should a useful maintenance work order contain?

Long Answer Questions

1. Explain how reliability-centered maintenance can be used to design a fleet maintenance program.
2. Discuss the advantages and limitations of predictive maintenance in transport operations.
3. Explain how spare-parts planning affects asset availability and life-cycle cost.
4. Compare FMEA and root-cause analysis, including when each is most useful.
5. Develop a balanced maintenance-performance framework for a regional vehicle fleet.

Analytical / Application Questions

1. A vehicle has an MTBF of 900 operating hours, corrective MTTR of 12 hours, and average logistics delay of 18 hours. Calculate inherent availability and an operational availability measure based on the stated cycle. Interpret the difference.
2. A depot experiences repeated alternator failures after installing auxiliary refrigeration equipment. Design an investigation using criticality analysis, FMEA, and root-cause analysis, and specify the data required.
3. A manager proposes replacing every battery at a fixed age despite evidence that failures are condition-dependent. Evaluate the proposal and recommend a defensible maintenance policy, including safety and warranty controls.

Multiple Choice Questions with Answers

Question 1

Which maintenance strategy is most appropriate when a component shows measurable degradation before functional failure and sufficient warning time exists?

A. Run-to-failure maintenance

B. Condition-based maintenance

C. Unplanned corrective maintenance

D. Administrative inspection only

Correct Answer: B. Condition-based maintenance

Question 2

Under a constant hazard-rate exponential model, which expression represents reliability at time t?

A. R(t) = 1 + e^(−λt)

B. R(t) = λt

C. R(t) = e^(−λt)

D. R(t) = 1 − λ/t

Correct Answer: C. R(t) = e^(−λt)

Question 3

A repairable vehicle has MTBF of 1,000 hours and MTTR of 20 hours. Its inherent availability is closest to:

A. 98.04%

B. 95.00%

C. 2.00%

D. 99.98%

Correct Answer: A. 98.04%

Question 4

Which measure normally includes parts and queue delays when the organization defines downtime broadly?

A. Wrench time

B. Operational availability

C. Component severity

D. Failure density only

Correct Answer: B. Operational availability

Question 5

What is the primary purpose of FMEA?

A. To calculate vehicle depreciation

B. To identify failure modes, effects, causes, and controls

C. To eliminate all corrective maintenance

D. To set driver compensation

Correct Answer: B. To identify failure modes, effects, causes, and controls

Question 6

In the reorder-point expression ROP = dL + SS, what does SS represent?

A. Scheduled service

B. System speed

C. Safety stock

D. Supplier standard

Correct Answer: C. Safety stock

Question 7

Which situation most strongly supports a proactive maintenance intervention?

A. A recurring failure has an identifiable underlying cause

B. A component is inexpensive and failure has no consequence

C. No failure evidence exists and no function is defined

D. A vehicle is already scheduled for disposal

Correct Answer: A. A recurring failure has an identifiable underlying cause

Question 8

Why can a high predictive-model accuracy fail to create operational value?

A. Accuracy is unrelated to data

B. The prediction may provide no feasible intervention window

C. Models always increase downtime

D. Technicians cannot use any measurements

Correct Answer: B. The prediction may provide no feasible intervention window

Question 9

Which item is essential for a valid maintenance interval decision?

A. A uniform interval for every asset regardless of duty cycle

B. Only the purchase price of the asset

C. Failure behavior, consequences, operating conditions, and evidence

D. The number of workshop bays alone

Correct Answer: C. Failure behavior, consequences, operating conditions, and evidence

Question 10

A fleet manager wants to reduce repeat repairs. Which action is most directly relevant?

A. Remove all failure codes from work orders

B. Perform root-cause analysis and verify corrective-action effectiveness

C. Increase planned downtime without diagnosis

D. Exclude warranty claims from maintenance records

Correct Answer: B. Perform root-cause analysis and verify corrective-action effectiveness