9. Service Lifecycle, Asset Support and Closed-Loop PLM

Introduction and Context

Product lifecycle management is incomplete when it ends at product release or shipment. For many engineered products, the longest and most consequential phase begins after delivery. Aircraft engines, industrial robots, medical equipment, wind turbines, vehicles, machine tools, and process plants generate value through years of operation. During this period, the manufacturer and the asset owner must manage maintenance, upgrades, spare parts, technical information, warranties, failures, safety obligations, and eventual retirement. These activities constitute the service lifecycle and form an essential extension of PLM.

The service phase is also the richest source of evidence about product behaviour in real operating environments. Design assumptions are tested against actual loads, operator practices, environmental conditions, misuse, maintenance quality, and ageing mechanisms. A closed-loop PLM system captures this operational knowledge, interprets it, and returns it to engineering, manufacturing, procurement, quality, and future product planning. The result is not merely better after-sales service; it is a learning system in which every product generation is informed by the experience of previous generations.

Service lifecycle management therefore connects the digital product definition with the physical asset in the field. It requires configuration control, service bills of materials, maintenance strategies, reliability analysis, warranty management, field-data analytics, spare-parts planning, and end-of-life governance. At postgraduate level, the subject must be understood both as an operational discipline and as a strategic capability.

Learning Objectives

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

1. Explain the service lifecycle and its relationship with the broader product lifecycle.

2. Distinguish engineering, manufacturing, and service bills of materials.

3. Develop maintenance information and select appropriate maintenance strategies.

4. Interpret reliability, maintainability, availability, warranty, and field-performance measures.

5. Design processes for spare-parts support, configuration control, and asset history.

6. Explain how operational data is returned to product development through closed-loop PLM.

7. Evaluate digital, analytical, ethical, and organisational challenges in service lifecycle management.

8. Apply these principles to an industrial asset support problem.

The Service Lifecycle as a Product-Lifecycle Domain

The service lifecycle begins when a product is commissioned and continues through operation, inspection, maintenance, repair, modification, upgrade, decommissioning, and disposal or remanufacture. Its boundaries are not always identical to those of ownership. A manufacturer may provide support after sale, while a third-party service organisation may perform maintenance. Consequently, service PLM must manage relationships among the original equipment manufacturer, asset owner, operators, maintenance providers, suppliers, regulators, and recyclers.

A useful way to view the lifecycle is as a chain of linked states:

design definition → manufactured configuration → delivered configuration → operating condition → maintained or modified configuration → retired configuration.

Each transition creates information that must be recorded. The delivered configuration may differ from the original design because of approved substitutions or customer-specific options. A repaired asset may contain a replacement component with a different serial number, revision, or performance characteristic. A software update may alter operating behaviour without changing the physical assembly. If these changes are not controlled, maintenance personnel may use incorrect instructions, warranty decisions may be disputed, and reliability analysis may be based on misleading populations.

Service PLM thus treats the product as a continuously evolving configuration rather than as a static object. The central question is not simply, “What was designed?” but also, “What was built, what was delivered, what is installed now, and what has happened to it?”

Essential Information Objects in Service Support

Service Bills of Materials

A service bill of materials (service BOM or SBOM) is the product structure used to support inspection, maintenance, repair, replacement, and technical communication. It may differ substantially from the engineering BOM. The engineering BOM expresses design intent and relationships among components. The manufacturing BOM expresses how the product is assembled and may include tooling, consumables, and production operations. The service BOM is organised around maintainable units, access panels, replacement assemblies, kits, diagnostic boundaries, and field procedures.

For example, a designer may define a hydraulic actuator through seals, bearings, a piston, and a housing. A service organisation may replace the actuator as a line-replaceable unit and therefore represent the internal parts only in an overhaul structure. The service BOM must also identify required tools, lubricants, safety precautions, torque values, inspection limits, compatible alternatives, and serialisation requirements.

A mature service BOM is configuration-aware. It relates part numbers to effectivity dates, product serial numbers, software versions, approved alternatives, retrofit status, and regulatory applicability. It may support several views: exploded illustrations for technicians, diagnostic views for remote support, inventory views for planners, and asset-history views for reliability engineers.

Technical Publications and Maintenance Information

Maintenance information translates product knowledge into executable work. It includes installation and removal instructions, inspection criteria, troubleshooting trees, wiring diagrams, lubrication schedules, calibration procedures, safety warnings, fault codes, and acceptance tests. Effective information is task-oriented: it identifies prerequisites, access requirements, tools, parts, sequence, expected results, and restoration requirements.

Poorly controlled maintenance information creates technical and legal risk. An obsolete torque value or incomplete isolation instruction can cause equipment damage or injury. Accordingly, service publications should be released through controlled change processes and linked to the product configuration to which they apply. When a design change alters a maintenance task, the associated publication, training material, spare-parts list, and diagnostic logic must be reviewed together.

Asset and Configuration Records

An asset record identifies a particular physical instance of a product. It may contain the serial number, owner, location, commissioning date, build standard, installed options, software version, accumulated operating hours, maintenance history, failures, modifications, and current condition. This information supports traceability and allows engineers to distinguish population-level trends from asset-specific behaviour.

Configuration management is especially important for long-lived assets. A field unit may contain components from several design revisions, some of which are interchangeable and some of which are not. The record must preserve as-maintained configuration, not merely as-designed configuration. Digital signatures, barcode or RFID identification, and service applications can reduce transcription errors, but technology does not replace disciplined data governance.

Maintenance Strategy and Asset Reliability

Maintenance is an intervention intended to preserve or restore an asset’s ability to perform its required function. The appropriate strategy depends on failure behaviour, consequences, detectability, economics, safety, and operational context.

Corrective maintenance occurs after a failure has occurred. It can be acceptable for inexpensive, non-critical items whose failure has limited consequences and whose replacement is rapid. It is undesirable when failure creates safety hazards, extensive secondary damage, or long downtime.

Preventive maintenance is performed at predetermined intervals or usage levels. Time-based replacement is appropriate when deterioration is strongly related to age or usage. However, arbitrary replacement intervals can waste useful life or introduce maintenance-induced failures.

Condition-based maintenance uses measurements or inspections to determine whether intervention is required. Vibration, temperature, oil debris, electrical signatures, pressure, acoustic emissions, and performance trends may reveal degradation before functional failure.

Predictive maintenance applies models or statistical techniques to estimate future condition or remaining useful life. It is not simply the installation of sensors; it requires valid failure modes, meaningful data, reliable thresholds, and a decision process that connects prediction with work execution.

Reliability-centred maintenance (RCM) provides a structured method for selecting maintenance tasks. It begins with required functions, identifies functional failures, determines failure modes and effects, and evaluates the consequences of each failure. Tasks are selected only when they are technically feasible and economically or operationally justified. Failure mode and effects analysis (FMEA) and failure mode, effects, and criticality analysis (FMECA) are commonly used inputs, although the resulting priority number should not replace engineering judgement.

Reliability describes the probability that an item performs its required function for a specified time under stated conditions. Maintainability concerns the probability that a failed item can be restored within a specified time using stated procedures and resources. Availability combines these ideas with logistics and downtime. A simplified steady-state relationship is:

Availability ≈ MTBF / (MTBF + MTTR)

where MTBF is mean time between failures and MTTR is mean time to repair. In practice, achieved availability also reflects waiting for diagnosis, technicians, permits, tools, and spare parts. Improving reliability alone may not improve operational availability if repair logistics remain weak.

A service lifecycle approach therefore evaluates the complete support system. Design decisions should consider access, modularity, standardisation, diagnostic coverage, replaceability, safe isolation, and the cost of downtime. Design for serviceability is a form of design for lifecycle value.

Warranty, Field Performance, and Feedback Quality

Warranty is both a commercial commitment and an information system. A claim may indicate a genuine design defect, a manufacturing variation, an installation error, misuse, inadequate maintenance, or a documentation problem. Treating every claim as equivalent produces misleading conclusions. Warranty analysis should classify claims by failure mode, product configuration, operating exposure, location, supplier, repair action, and time in service.

Important measures include claim rate, cost per unit, time to first failure, repeat repair rate, no-fault-found rate, service response time, first-time-fix rate, and customer downtime. The no-fault-found category deserves special attention: it may indicate intermittent faults, inadequate diagnostic capability, incorrect problem descriptions, or an ineffective repair process.

Field-performance analysis should combine reliability data with exposure. Ten failures among products operating 10,000 hours are not directly comparable with ten failures among products operating 100 hours. Useful analysis uses operating hours, cycles, kilometres, throughput, environmental severity, or another relevant measure. Statistical techniques such as trend analysis, Weibull analysis, survival analysis, Pareto classification, and proportional-hazard models can support investigation, but their validity depends on accurate event definitions and complete data.

Warranty decisions also require governance. A manufacturer should define authority levels, evidence requirements, escalation rules, and root-cause responsibilities. Rapid customer support is valuable, but premature replacement without evidence can conceal systemic problems. Conversely, excessive investigation before restoring a critical asset can damage the customer relationship. Effective processes separate immediate containment from later causal analysis.

Spare Parts, Logistics, and Support Economics

Spare-parts management balances availability against inventory investment, obsolescence, storage cost, and risk of stockout. The decision is more complex than forecasting ordinary product demand because service demand is often intermittent, failures may be rare, and a single missing component can immobilise a high-value asset.

Parts may be classified by criticality, demand frequency, lead time, repairability, commonality, and obsolescence exposure. A critical low-demand part may justify local stock, pooled inventory, supplier agreements, or a repair-and-return loop. Service kits can reduce search and preparation time by grouping parts, seals, fasteners, and consumables for a defined task. Approved alternates and interchangeable parts should be represented explicitly, with restrictions based on configuration and performance.

Support planning can use installed-base data, reliability estimates, fleet utilisation, lead-time distributions, and repair turnaround time. For repairable parts, the support system must track the part’s serial number, condition, location, repair status, and next approved use. This creates a closed logistics loop rather than a one-way flow from supplier to customer.

Lifecycle cost is a central decision criterion. Acquisition price may be small compared with energy use, scheduled maintenance, unscheduled downtime, technician labour, software support, and disposal. A design that is inexpensive to manufacture but difficult to inspect may impose greater total cost on the operator. Service PLM makes these consequences visible during design reviews.

Closing the Loop from Field to Design

Closed-loop PLM is the systematic return of operational knowledge to earlier lifecycle activities. The loop is effective only when information is transformed into decisions. A field report becomes valuable when it is linked to a product configuration, analysed against exposure, assigned to an accountable function, and used to change a design, process, maintenance task, or support policy.

A typical feedback workflow has the following logic:

1. Capture: collect service events, sensor readings, technician observations, warranty claims, parts consumption, and customer feedback.

2. Contextualise: link each event to asset identity, configuration, operating conditions, maintenance history, and software version.

3. Validate: remove duplicates, resolve inconsistent codes, assess data quality, and distinguish symptoms from confirmed failure modes.

4. Analyse: identify trends, abnormal populations, recurring causes, and economic or safety significance.

5. Decide: determine whether containment, redesign, supplier correction, publication revision, training, recall, or no action is appropriate.

6. Implement: release approved engineering changes, update service information, modify inventory and maintenance plans, and communicate the change.

7. Verify: monitor whether the intervention reduces recurrence and produces the expected operational result.

This loop crosses organisational boundaries. Product engineering may own design changes, service engineering may own maintenance instructions, quality may lead corrective action, supply chain may manage supplier problems, and customer service may manage communication. A governance board or equivalent cross-functional process is needed to prevent field information from remaining isolated in service departments.

Digital twins can support this loop by maintaining a digital representation of an individual asset and its evolving condition. However, a digital twin is not automatically a complete or accurate model. Its usefulness depends on sensor quality, data continuity, model validity, cybersecurity, and clarity about which decisions the model is intended to support. Machine-learning systems may detect patterns that are difficult to specify manually, but they require representative data and human oversight, particularly where false alarms can cause unnecessary maintenance or missed alarms can create safety risks.

An Illustrative Case: Wind-Turbine Gearbox Support

Background

Consider a fictional manufacturer, NorthWind Systems, that supplies medium-scale wind turbines under long-term service agreements. The turbines operate in coastal and inland locations with different humidity, temperature, turbulence, and maintenance accessibility. The gearbox is a critical subsystem because failure causes extended downtime and requires specialised lifting equipment.

Situation and Problem

During the fourth year of operation, the manufacturer observes an increase in gearbox warranty claims. The initial reports appear unrelated: some mention abnormal vibration, others elevated oil temperature, and several describe metallic debris in oil samples. The service BOM contains the gearbox assembly, but the installed bearing revision and lubrication-circuit configuration are not consistently recorded in the asset database. Spare bearings are available, but repair kits differ between revisions.

Analysis

The company first separates immediate operational containment from root-cause investigation. Turbines showing severe vibration are placed under inspection. The data team then links oil-analysis results, vibration trends, operating hours, gearbox revision, ambient conditions, and maintenance records. The analysis shows that failures are concentrated in one bearing revision installed during a defined production period and are more frequent in turbines exposed to high humidity combined with longer inspection intervals.

A review of service records reveals two additional problems. First, a maintenance instruction allows a sampling method that produces inconsistent debris measurements. Second, technicians sometimes install a visually similar bearing kit that is not approved for the affected gearbox revision. Thus, the field problem has a design contribution, a measurement problem, and a configuration-control problem.

Approach and Solution

NorthWind implements a coordinated response. The engineering team approves an improved bearing and defines its effectivity. Service engineering revises the service BOM to show the bearing revision, approved kit, installation tools, and inspection limits. The maintenance publication is changed to standardise oil sampling and add a vibration acceptance criterion. The asset database is corrected through a serial-number campaign, and technicians use barcode scanning to confirm component identity.

For high-risk turbines, the manufacturer introduces condition-based monitoring using vibration and oil-temperature trends. The parts group establishes a repairable bearing pool and prepositions kits at regional service centres. Warranty claims are coded by failure mode rather than by general component category. A monthly review compares recurrence, downtime, false alarms, and cost.

Outcome

The immediate containment reduces the risk of catastrophic gearbox damage, while the configuration correction prevents incompatible kits from being installed. Over subsequent maintenance cycles, the manufacturer observes fewer repeat failures and more consistent diagnostic results. The important achievement is not merely the improved bearing; it is the establishment of a feedback process connecting field evidence, configuration management, technical publications, parts logistics, and product development.

Learning Points

The case demonstrates that a field failure is rarely only a component problem. The effective solution may involve design revision, maintenance intervals, diagnostic methods, technician training, data quality, and spare-parts control. It also shows why reliability analysis requires exposure and configuration information. Without those links, the company might have replaced parts without discovering the interaction between bearing revision, environment, and maintenance practice.

Challenges, Risks, and Common Mistakes

One common mistake is treating service information as a secondary output of engineering. Service personnel need a different level of decomposition, different terminology, and different task logic. A technically correct engineering drawing may still be unusable for field repair.

Another risk is poor master-data quality. Duplicate part numbers, inconsistent failure codes, missing serial numbers, and unrecorded substitutions undermine analytics. Data governance should define ownership, validation rules, controlled vocabularies, retention periods, and correction procedures.

Organisations may also overinvest in sensors while underinvesting in work processes. A prediction has little value if no approved task, part, technician, or operating decision follows it. Similarly, predictive models can create alarm fatigue when thresholds are not calibrated to business and technical consequences.

Cybersecurity is increasingly significant. Connected assets may expose operational technology, customer data, intellectual property, and safety-critical controls. Access rights, secure communication, software signing, vulnerability management, incident response, and separation of monitoring from control functions should be considered throughout the asset lifecycle.

End-of-life decisions create additional challenges. Products may be retired because of technical obsolescence, poor economics, regulation, safety concerns, or lack of support. Responsible end-of-life planning includes decommissioning instructions, hazardous-material control, data erasure, recovery of valuable materials, remanufacturing feasibility, and communication of support termination. A product should not be designed for indefinite operation if essential parts or software cannot be supplied safely, but retirement should also not create avoidable waste.

Ethical, Regulatory, and Professional Considerations

Service organisations have a duty to communicate safety-critical limitations clearly. Concealing recurring failures, manipulating warranty classifications, or delaying a field action to protect short-term commercial interests can expose users and the public to unacceptable risk. Engineers must distinguish commercial confidentiality from information that must be disclosed for safety or regulatory compliance.

Traceability is essential in regulated sectors such as aerospace, medical devices, rail, energy, and automotive applications. Records must support investigations, approved modifications, calibration status, and accountability. Data collected from connected products also raises privacy questions. Location, usage patterns, operator identity, and maintenance behaviour may be commercially or personally sensitive. Collection should be proportionate, protected, and governed by clear agreements.

Professional judgement remains necessary when analytical evidence is incomplete. A low observed failure rate does not prove low risk if exposure data are missing or if failures are underreported. Conversely, a statistical correlation does not by itself establish causation. Engineers should document assumptions, uncertainty, decision authority, and verification evidence.

Current Developments and Best Practices

Service PLM is moving toward integrated asset platforms that combine PLM, enterprise resource planning, customer relationship management, manufacturing execution, Internet of Things data, and maintenance-management systems. Digital work instructions, augmented-reality support, remote expert assistance, and mobile service applications can reduce errors and improve first-time fix rates. Additive manufacturing may shorten supply chains for selected low-volume parts, but qualification, material control, and configuration approval remain necessary.

Artificial intelligence is increasingly used for anomaly detection, technician assistance, demand forecasting, automated claim classification, and knowledge retrieval. Its use should be governed by explainability appropriate to the decision, model monitoring, protection against biased or incomplete data, and human review for safety-critical actions.

Good practice can be summarised in several principles:

• maintain a controlled as-maintained configuration for every significant asset;
• design service BOMs around real maintenance tasks and replaceable units;
• connect technical publications, parts, tools, skills, and safety controls to each task;
• measure reliability using valid exposure and consistent failure definitions;
• distinguish immediate containment from verified root-cause correction;
• treat warranty and service data as engineering evidence, not only as cost records;
• close the feedback loop by assigning ownership and verifying outcomes;
• protect operational, personal, and intellectual-property data;
• design for maintainability, upgradeability, recovery, and responsible retirement from the beginning.

Chapter Summary

The service lifecycle extends PLM from product delivery to operation, maintenance, modification, and retirement. Its foundation is accurate knowledge of the delivered and as-maintained configuration. Service BOMs, technical publications, asset records, warranty data, maintenance history, and spare-parts information must be integrated rather than managed as isolated documents.

Maintenance strategy should reflect failure consequences, detectability, reliability behaviour, maintainability, logistics, and lifecycle economics. Reliability, availability, warranty, and field-performance measures are meaningful only when linked to valid exposure and configuration data. Closed-loop PLM converts field experience into verified improvements in design, manufacturing, maintenance, documentation, and support. Digital twins, connected assets, analytics, and AI can strengthen this process, but they require sound data governance, cybersecurity, human judgement, and professional accountability. The strategic outcome is a product system that learns continuously from real use and delivers value throughout its complete life.

Key Terms

Service lifecycle: The stages of operation, maintenance, repair, upgrade, support, and retirement after product delivery.

Service BOM: A product structure organised for maintenance, repair, replacement, and service communication.

As-maintained configuration: The recorded configuration of an individual asset after all approved repairs, substitutions, and modifications.

Asset record: The identity, configuration, history, condition, and operational information associated with a physical product instance.

Corrective maintenance: Maintenance performed after a functional failure has occurred.

Preventive maintenance: Planned maintenance performed at defined time or usage intervals.

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

Predictive maintenance: Use of models or trends to estimate future degradation or failure risk.

Reliability: The probability that an item performs its required function for a specified period under stated conditions.

Maintainability: The probability that an item can be restored to service within a specified time and procedure.

Availability: The proportion of required time during which an asset is capable of performing its function.

MTBF: Mean time between failures for a repairable item operating between successive failures.

MTTR: Mean time to repair or restore an item after failure.

Warranty analysis: Systematic examination of claims to identify cost, failure, configuration, and causal patterns.

Failure mode: The specific manner in which a component or system fails to perform its intended function.

Reliability-centred maintenance: A method that selects maintenance tasks according to functions, failure consequences, and technically feasible actions.

Closed-loop PLM: A lifecycle system that returns operational evidence to design, manufacturing, service, and business decisions.

Digital twin: A digitally maintained representation of a physical asset or system used for monitoring, analysis, or decision support.

End-of-life management: Planning and execution of decommissioning, disposal, recovery, remanufacture, or recycling.

Review Questions

Short Answer Questions

1. Why must service PLM record the as-maintained configuration rather than only the as-designed configuration?

2. What is the principal difference between an engineering BOM and a service BOM?

3. Define reliability and maintainability.

4. Why is exposure data important in field-failure analysis?

5. What is meant by closed-loop PLM?

Long Answer Questions

1. Explain the service lifecycle and discuss how it extends conventional product lifecycle management.

2. Describe how reliability-centred maintenance supports the selection of maintenance tasks.

3. Discuss the relationship among warranty management, field-performance analysis, and engineering improvement.

4. Explain the role of spare-parts planning in achieving lifecycle value and operational availability.

5. Describe a closed-loop PLM workflow from field-data capture through verification of corrective action.

Analytical / Application Questions

1. A fleet experiences few component failures but severe downtime because replacement parts are unavailable. Analyse the problem using reliability, maintainability, availability, and logistics concepts, and recommend actions.

2. A manufacturer receives repeated warranty claims for different symptoms on products with several software and hardware revisions. Design a data and configuration-analysis approach to determine whether a systemic problem exists.

3. For a safety-critical industrial pump, compare corrective, preventive, condition-based, and predictive maintenance strategies. Recommend a combined policy and justify it using failure consequences, detectability, cost, and data requirements.

22. Multiple Choice Questions with Answers

Question 1

Which characteristic most directly distinguishes a service BOM from an engineering BOM?

A. It contains only purchased components.

B. It is organised around maintainable units, service tasks, and replacement procedures.

C. It excludes part numbers and revision information.

D. It represents only the product’s original design intent.

Correct Answer: B. It is organised around maintainable units, service tasks, and replacement procedures.

Question 2

Why is the as-maintained configuration essential in service lifecycle management?

A. It eliminates the need for engineering drawings.

B. It records the actual current configuration after repairs and modifications.

C. It guarantees that no component will fail.

D. It is used only for financial depreciation.

Correct Answer: B. It records the actual current configuration after repairs and modifications.

Question 3

A low-cost component fails without safety or major downtime consequences and can be replaced immediately. Which strategy may be most appropriate?

A. Corrective maintenance

B. Mandatory replacement after every operating cycle

C. Continuous digital-twin simulation

D. Immediate redesign of the complete product

Correct Answer: A. Corrective maintenance

Question 4

What does the simplified relationship MTBF/(MTBF + MTTR) primarily estimate?

A. Warranty cost

B. Inventory turnover

C. Steady-state availability

D. Failure severity

Correct Answer: C. Steady-state availability

Question 5

Why should warranty claims be classified by product configuration and operating exposure?

A. To avoid recording the customer’s identity

B. To distinguish genuine patterns from differences in usage and installed revisions

C. To ensure that every claim is rejected consistently

D. To replace all reliability testing with customer reports

Correct Answer: B. To distinguish genuine patterns from differences in usage and installed revisions

Question 6

Which sequence best represents a closed-loop PLM process?

A. Capture, contextualise, validate, analyse, decide, implement, verify

B. Design, advertise, sell, discard, redesign, archive, inspect

C. Purchase, store, ship, invoice, close, delete, replace

D. Predict, ignore, repair, redesign, sell, warranty, terminate

Correct Answer: A. Capture, contextualise, validate, analyse, decide, implement, verify

Question 7

In the wind-turbine case, why did correcting the service BOM matter?

A. It removed the need for condition monitoring.

B. It ensured that technicians could identify approved bearing revisions and repair kits.

C. It reduced the number of turbines in the installed base.

D. It changed the environmental conditions at turbine sites.

Correct Answer: B. It ensured that technicians could identify approved bearing revisions and repair kits.

Question 8

Which situation most clearly indicates a maintainability problem rather than only a reliability problem?

A. An asset rarely fails and is repaired quickly.

B. An asset fails frequently but replacement is immediate.

C. An asset fails infrequently, but diagnosis and restoration take a long time.

D. An asset operates continuously without intervention.

Correct Answer: C. An asset fails infrequently, but diagnosis and restoration take a long time.

Question 9

What is a major risk of deploying predictive-maintenance analytics without linking predictions to work processes?

A. The product automatically becomes more reliable.

B. Predictions may not lead to an approved task, resource, or operating decision.

C. Spare-parts demand becomes perfectly predictable.

D. Configuration records become unnecessary.

Correct Answer: B. Predictions may not lead to an approved task, resource, or operating decision.

Question 10

Which practice best supports responsible end-of-life management?

A. Discontinuing all technical records at the time of sale

B. Ignoring hazardous materials because the product is retired

C. Planning decommissioning, data erasure, material recovery, and support termination

D. Replacing every retired product with an identical new product

Correct Answer: C. Planning decommissioning, data erasure, material recovery, and support termination