Risk, Sustainability, Governance and Future Directions

Introduction

Inventory and materials management is often presented as a cost, service-level and replenishment discipline. In contemporary operations, however, inventory decisions also determine an organisation’s exposure to disruption, fraud, environmental damage, regulatory sanctions and reputational loss. A low-cost supplier may create unacceptable dependency; a large safety stock may protect service but increase obsolescence and carbon emissions; an automated planning system may improve responsiveness while reproducing biased or inaccurate decisions.

Strategic inventory management therefore requires a broader question than “How much should be ordered?” The relevant question is: How can materials be made available, responsibly and economically, throughout their life cycle, despite uncertainty and changing stakeholder expectations?

This chapter examines controls for disruption, shortages, supplier failure, counterfeit materials and fraud. It also develops the relationship between inventory management and circular economy principles, waste reduction, reverse logistics, environmental, social and governance (ESG) performance, regulation, professional ethics and emerging technologies.

Learning objectives

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

1. Identify and assess major risks in inventory and materials systems.
2. Design controls for disruption, shortage, supplier failure, fraud and counterfeit materials.
3. Explain the contribution of circular economy, waste reduction and reverse logistics.
4. Integrate ESG, regulatory compliance and professional ethics into materials decisions.
5. Evaluate autonomous planning, predictive analytics and other emerging developments.
6. Recommend a balanced governance and resilience programme for an organisation.

1. The strategic risk context

1.1 Risk in materials management

Risk is the effect of uncertainty on objectives. In materials management, objectives normally include availability, quality, cost, cash efficiency, safety, compliance and sustainability. Risk may create a direct loss, such as a production stoppage, or an indirect loss, such as loss of customer trust.

Common sources include:

• demand volatility and forecast error;
• transport interruption, natural hazards and geopolitical events;
• supplier insolvency, capacity failure or poor quality;
• cyberattack and loss of transactional data;
• inaccurate stock records and weak warehouse controls;
• theft, collusion, bribery and unauthorised purchasing;
• counterfeit, substituted or adulterated materials;
• regulatory change and sustainability-related liability; and
• obsolescence, excess stock, waste and hazardous-material incidents.

Risk should not be treated only as an emergency-management issue. Decisions about supplier selection, order quantities, specifications, stock locations, information systems and contracts create the organisation’s risk profile long before a disruption occurs.

1.2 A basic risk model

A practical first assessment can combine likelihood and consequence:

where R_i is the risk score for event i, L_i is its likelihood score and C_i is its consequence score. Scores may use a defined ordinal scale, such as 1–5. The multiplication is a prioritisation device, not a probability law. It assumes that the organisation has consistently defined the scales and that users understand that a score of 15 is not necessarily three times as likely or costly as a score of 5.

When credible monetary estimates are available, expected loss is more analytically meaningful:

where EL is expected loss per planning period, p_i is the probability of event i during that period, and I_i is the financial impact if the event occurs. The probabilities should refer to the same time horizon and the impacts should be measured consistently. Expected loss supports comparison, but it can understate rare catastrophic events and cannot capture all reputational or ethical consequences.

Risk registers should record the risk owner, causes, controls, indicators, treatment plan, target date and residual risk. A risk is not controlled merely because it has been documented.

2. Resilience against disruption, shortage and supplier failure

2.1 Resilience and robustness

Robustness is the ability to continue operating under expected variation without significant adjustment. Resilience includes the ability to absorb a shock, recover performance and learn from the event. Resilience is therefore a capability, not simply a stockholding policy.

Key design choices include:

• Visibility: accurate master data, supplier information, inventory records and shipment status.
• Flexibility: alternative materials, qualified suppliers, adaptable processes and transport options.
• Redundancy: additional capacity, strategic stock or more than one source where justified.
• Collaboration: shared forecasts, joint contingency planning and early-warning communication.
• Recovery capability: documented response procedures, escalation authority and tested business-continuity plans.

Resilience has a cost. Multiple suppliers may reduce dependency but increase qualification, transaction and quality costs. Extra stock may protect production but tie up capital and become obsolete. The appropriate choice depends on material criticality, replenishment time, substitution possibilities, margin, safety implications and recovery time objectives.

2.2 Shortage controls

Shortage management begins with classification. A critical item is not necessarily the most expensive item; it may be inexpensive but essential, regulated, difficult to substitute or associated with a long qualification process. Useful dimensions include:

1. operational criticality;
2. supply lead-time and lead-time variability;
3. demand variability;
4. substitutability;
5. safety, legal and quality consequences; and
6. financial and customer-service impact.

Controls may include approved substitutes, allocation rules, reserved capacity, supplier-held stock, postponement, realistic service-level agreements and cross-functional shortage committees. Allocation must be governed by transparent principles. Giving scarce materials to the loudest internal customer encourages dysfunctional behaviour.

Where statistical assumptions are reasonable, safety stock can be estimated for variable demand during a fixed lead time:

where SS is safety stock in units, z is the standard-normal service factor, and σ_LT is the standard deviation of demand during lead time. This formula assumes that demand during lead time is approximately normally distributed, lead-time uncertainty is either negligible or already included in σ_LT, and the selected service factor is appropriate. If both demand and lead time vary independently, a common approximation is:

where L is average lead time in periods, σ_d is the standard deviation of demand per period, d̄ is average demand per period and σ_L is the standard deviation of lead time in periods. The units must be consistent. Correlation between demand and lead time, non-normal demand, intermittent demand and supplier minimum quantities may make this approximation unsuitable.

2.3 Supplier failure

Supplier risk should be assessed before contract award and monitored throughout the relationship. Due diligence may cover financial stability, capacity, quality systems, labour practices, cybersecurity, geographic exposure, dependency on sub-tier suppliers, sanctions status and environmental performance.

Controls include:

• dual or multiple sourcing for critical materials;
• approved alternative specifications and suppliers;
• supplier financial and operational monitoring;
• contractual rights to audit, obtain information and recover tooling or data;
• contingency inventory or supplier-held buffer stock;
• geographic diversification where concentration is excessive;
• supplier development and corrective-action programmes; and
• exit plans that address tooling, intellectual property, data and transition time.

Dual sourcing is not automatically safer. Two suppliers may depend on the same raw-material producer, port, region or technology. Effective diversification addresses common-mode failure rather than merely counting supplier names.

3. Fraud, counterfeit materials and integrity controls

3.1 The fraud risk in inventory systems

Fraud may involve theft, false receipts, fictitious suppliers, duplicate invoices, unauthorised substitutions, inflated quantities, collusion or manipulation of scrap records. The classic fraud triangle—pressure, opportunity and rationalisation—helps explain why controls must address both system design and organisational culture.

Important preventive controls are:

• segregation of purchasing, receiving, inventory custody and payment approval;
• role-based access and periodic review of system permissions;
• approved supplier onboarding with independent verification;
• purchase-order, receipt and invoice matching;
• cycle counting based on risk rather than convenience;
• controlled access to high-value or hazardous materials;
• serial, batch or lot traceability;
• surprise audits and investigation of unusual adjustments; and
• protected whistleblowing and non-retaliation procedures.

Segregation of duties does not mean that every activity must be performed by a different person in a small organisation. It means that no individual should control an entire transaction from authorisation through custody and payment. Where staffing makes full separation impossible, compensating controls such as independent review and management reports are required.

3.2 Counterfeit and non-conforming materials

Counterfeit materials are deliberately misrepresented in identity, origin, condition or performance. They may enter through unauthorised distributors, forged certificates, reused packaging, component substitution or inadequate inspection. The consequences can include equipment failure, safety incidents, warranty claims and legal liability.

A risk-based anti-counterfeit programme should include:

1. approved and traceable supply channels;
2. clear technical specifications and authenticity requirements;
3. supplier qualification and periodic requalification;
4. certificates of conformity verified with the issuing party;
5. inspection, testing and sampling appropriate to material criticality;
6. secure storage and chain-of-custody records;
7. quarantine and controlled disposition of suspect materials; and
8. reporting, root-cause analysis and information sharing.

Inspection alone is insufficient. A counterfeit item can pass a superficial visual inspection, and excessive testing can delay supply without addressing the source. Prevention, traceability and supplier governance must operate together.

4. Governance, compliance and professional ethics

4.1 Governance architecture

Governance defines who has authority, who is accountable, what evidence is required and how exceptions are escalated. A sound materials-governance structure normally includes:

• board or executive oversight of significant risk and sustainability exposure;
• a materials or supply-risk policy;
• clear decision rights and approval thresholds;
• documented procedures and controlled master data;
• performance and risk dashboards;
• internal audit and independent assurance; and
• corrective action with named owners and deadlines.

The three-lines model is useful: operational managers own and control risks; risk, compliance or quality functions provide guidance and monitoring; internal audit provides independent assurance. External auditors, regulators and certification bodies may provide additional assurance but do not replace management responsibility.

4.2 Regulatory compliance

Requirements differ by jurisdiction and industry, but common areas include product safety, environmental protection, hazardous-material handling, labour and modern-slavery expectations, import and export controls, sanctions, data protection, competition law, record retention and financial reporting. Organisations must identify the laws and contractual obligations that apply to their activities rather than assuming that a general certification covers everything.

A compliance control should answer four questions:

1. What requirement applies?
2. Which process and person are responsible?
3. What evidence demonstrates compliance?
4. What happens when a breach or uncertainty is identified?

Compliance is not achieved by collecting certificates without verifying their scope, currency and relevance. Records must be accurate, retrievable and protected from alteration.

4.3 Professional ethics

Ethical materials management requires impartial decisions, truthful records, respect for people, confidentiality and responsible stewardship of resources. Conflicts of interest must be declared and managed. Gifts, hospitality and supplier relationships should be governed by transparent policy. A buyer who accepts a personal benefit may compromise—and may appear to compromise—fair competition.

Ethics is especially important when commercial pressure conflicts with safety or integrity. An employee should not release unverified material merely to meet a production target. Professional judgement includes the duty to stop, escalate and document a potentially harmful decision.

5. Sustainability and the circular economy

5.1 From linear to circular systems

A linear materials model follows “take, make, use and dispose.” A circular economy seeks to reduce virgin resource extraction, retain product and material value, and prevent waste through strategies such as durability, repair, reuse, refurbishment, remanufacturing, recycling and safe recovery.

Circularity affects inventory decisions in several ways. A firm may stock repairable modules rather than complete replacement units, recover packaging, use remanufactured components or design products for disassembly. These choices alter demand patterns, quality requirements, reverse flows, storage locations and planning parameters.

Circularity does not mean that recycling is always the best option. In general, retaining a product’s function through reuse or repair may preserve more value than material recycling, while prevention is preferable to both. The technically and environmentally best option depends on energy use, transport, contamination, safety and product design.

5.2 Waste reduction

Waste includes excess purchasing, obsolete stock, damage, expired materials, unnecessary movement, over-processing, waiting and avoidable packaging. Lean methods such as value-stream mapping, standard work, 5S, root-cause analysis and continuous improvement can reduce waste, but lean inventory must not become dangerously fragile inventory.

Useful measures include:

• obsolete and excess inventory value;
• write-offs as a percentage of inventory consumed;
• packaging material per unit shipped;
• landfill diversion or recovery rate;
• inventory accuracy;
• return and repair cycle time; and
• emissions associated with storage and transport.

A reduction in warehouse stock is not automatically sustainable if it causes emergency air freight, repeated production changeovers or greater product damage.

5.3 Reverse logistics

Reverse logistics manages the flow of goods, packaging and materials from the user or downstream point back to a recovery, repair, resale, recycling or disposal point. It may be driven by returns, warranty claims, recalls, reusable containers, repair programmes or end-of-life obligations.

An effective reverse-logistics design requires:

• clear return authorisation and customer instructions;
• inspection and grading standards;
• segregation of reusable, repairable, recyclable and hazardous items;
• traceability and disposition records;
• economic evaluation of recovery alternatives; and
• feedback to product design, quality and forecasting teams.

Returns can contain valuable information about design and supplier quality. Treating them only as waste loses an opportunity for learning.

6. ESG measurement and decision-making

ESG integrates environmental, social and governance considerations into operational and investment decisions. In materials management, environmental issues include emissions, water, waste, chemicals and resource use. Social issues include worker safety, forced labour, fair treatment, community effects and supplier labour practices. Governance includes anti-bribery, accountability, data quality, auditability and control effectiveness.

Organisations should distinguish between:

• inputs, such as energy, materials and labour;
• activities, such as purchasing, transport and warehousing;
• outputs, such as emissions, waste and accidents; and
• outcomes, such as improved resource efficiency, safer work and reduced harm.

This distinction prevents a firm from claiming sustainability merely because it has introduced a policy. Measurements should have defined boundaries, time periods, units, data owners and verification methods. For example, a transport-emissions figure is meaningful only when the calculation boundary, distance, shipment weight, transport mode and emissions factor are understood.

Managers should avoid “carbon tunnel vision.” A lower-carbon supplier may have unacceptable labour practices, while a local supplier may have higher production emissions but lower transport risk and better repairability. ESG decisions require a balanced, transparent assessment rather than a single convenient indicator.

7. Technology and future directions

7.1 Predictive planning

Predictive systems use historical and external data to estimate demand, lead time, disruption probability or quality risk. Machine-learning models may identify nonlinear patterns that conventional methods miss. Their value depends on data quality, stable process definitions, appropriate validation and effective human use.

A predictive forecast should be compared with a suitable baseline, such as the existing planning method. Evaluation should use a time-based holdout rather than random splitting when the objective is future prediction. Common measures include mean absolute error:

where y_t is actual demand, ŷ_t is forecast demand and n is the number of observations. MAE is expressed in the same units as demand and gives equal weight to each absolute error. It should not be used without considering bias, service impact, intermittent demand and the cost of over- versus under-forecasting.

7.2 Autonomous planning

Autonomous or highly automated planning may adjust reorder parameters, generate purchase recommendations, allocate inventory and react to alerts with limited human intervention. The benefits can include speed, consistency and the ability to process large numbers of items.

However, automation can amplify bad master data, create correlated ordering behaviour, conceal assumptions and make accountability unclear. A responsible control framework includes:

• human approval for high-impact or irreversible decisions;
• thresholds and authority limits;
• model monitoring for drift and abnormal recommendations;
• explainable decision logs;
• scenario testing and a safe fallback process;
• cybersecurity and access control; and
• periodic review of objectives, not merely model accuracy.

The correct design is often “human-in-the-loop,” not complete replacement of professional judgement. Automation should handle scale and routine decisions while humans govern exceptions, trade-offs and ethical consequences.

7.3 Digital twins, sensors and distributed records

Internet-of-Things sensors can monitor temperature, location, condition and equipment status. Digital twins can represent assets and simulate inventory or capacity decisions. Distributed-ledger technologies may support provenance in selected applications, but they do not guarantee that the original data is truthful. A tampered or inaccurate input remains inaccurate when recorded immutably.

Technology selection should therefore begin with a business control problem. A fashionable technology without defined decision rights, data ownership and return on investment adds complexity rather than resilience.

8. Integrated implementation framework

A practical implementation sequence is:

1. Map the material system: identify flows, dependencies, critical items, suppliers, information and reverse channels.
2. Assess exposure: combine likelihood, consequence, vulnerability and existing controls.
3. Set risk appetite: define unacceptable outcomes, tolerable interruptions and required service levels.
4. Design controls: select prevention, detection, response and recovery controls.
5. Assign accountability: name owners, approvers, data stewards and escalation routes.
6. Test controls: conduct supplier audits, recall exercises, cyber simulations, stock-record checks and continuity drills.
7. Measure performance: monitor service, cost, working capital, risk, ESG and control indicators together.
8. Learn and improve: investigate incidents and near misses, update assumptions and revise specifications or network design.

A balanced dashboard might contain inventory availability, forecast bias, supplier on-time delivery, critical single-source exposure, inventory accuracy, counterfeit incidents, obsolete stock, recovery rate, emissions intensity, audit findings and overdue corrective actions. Metrics should lead to decisions; a dashboard that no one acts upon is not governance.

9. Illustrative case: Meditron Devices

Background

Meditron Devices, a fictional manufacturer of diagnostic equipment, had traditionally purchased electronic modules from one approved overseas supplier. The supplier offered low prices and consistent quality. Meditron held little safety stock because the modules were expensive and demand was stable.

Problem

A port closure and a cyber incident at the supplier stopped shipments for five weeks. Production had only six days of supply. An emergency purchase from an unfamiliar distributor contained modules with serial numbers that could not be verified. Quality staff rejected the shipment. Meditron faced delayed customer deliveries, premium transport costs and pressure from senior managers to accept the questionable material.

Analysis

The immediate problem was a shortage, but the underlying exposure was broader:

• the module was operationally critical despite its moderate purchase price;
• the supply chain had geographic and supplier concentration;
• the business-continuity plan addressed transport disruption but not cyber disruption;
• the approved-supplier process did not define an emergency qualification route;
• inventory parameters reflected average demand but not recovery time; and
• the organisation had weak traceability for emergency purchases.

Approach and solution

Meditron established a cross-functional risk team. It qualified a second supplier in another region, negotiated capacity reservations, and redesigned the module so that a technically equivalent component could be used after validation. It introduced serial-level traceability, independent certificate verification and quarantine procedures. A small strategic buffer was held at a secure regional location, with its level reviewed against recovery-time requirements rather than price alone.

The company also created an emergency procurement protocol. Emergency buying required quality and compliance approval, documented authenticity checks and executive sign-off for any deviation. Supplier cyber-risk assessments and notification duties were added to contracts. Returned modules and packaging were incorporated into a reverse-logistics programme, and obsolete electronic material was sent to an approved recovery provider.

Outcome

The programme increased qualification and holding costs, but the next disruption was absorbed without stopping production. The organisation also reduced emergency freight, improved traceability and identified design changes that enabled repair rather than full-unit replacement.

Learning points

1. Low purchase price is not the same as low total risk.
2. Criticality should include safety, substitution and recovery considerations.
3. Counterfeit controls must remain effective during emergencies.
4. Resilience requires design, contracts, data and people—not only inventory.
5. Sustainability and resilience can reinforce one another when repair, reuse and regional recovery are designed deliberately.

Challenges and limitations

Risk analysis is affected by incomplete data, uncertain probabilities and changing conditions. Quantitative scores can create false precision. Scenario planning may overlook novel shocks. Supplier audits can be manipulated or may fail to reach sub-tier suppliers. ESG data may be inconsistent across countries and organisations. Automated systems can be accurate on average but poor for rare events or new products.

These limitations call for triangulation: combine data analysis with expert judgement, supplier dialogue, stress testing, audit evidence and scenario analysis. Organisations should also recognise residual risk. No control eliminates all disruption, fraud or environmental impact; the aim is informed, proportionate and accountable management.

Chapter Summary

Strategic inventory and materials management must protect availability while managing financial, operational, ethical and environmental consequences. Risk registers, expected-loss analysis and criticality assessment help prioritise exposure, but resilience requires coordinated controls across suppliers, specifications, inventory, information systems and recovery processes. Shortage and supplier-failure controls include qualified alternatives, visibility, capacity agreements, strategic buffers and tested continuity plans.

Fraud and counterfeit-material risks require segregation of duties, traceability, supplier verification, controlled access, inspection and protected reporting channels. Governance assigns authority, accountability, evidence requirements and independent assurance. Compliance must be linked to applicable legal and contractual requirements, while professional ethics protects safety, honesty, fairness and responsible stewardship.

Circular-economy thinking extends materials management beyond acquisition and disposal. Repair, reuse, remanufacturing, recycling, waste reduction and reverse logistics can preserve value, although their actual benefits must be assessed using complete system boundaries. ESG performance should be measured with reliable definitions and balanced environmental, social and governance indicators.

Predictive analytics, autonomous planning, sensors and digital twins can improve responsiveness, but they require quality data, cybersecurity, monitoring, explainability and human accountability. The future of the profession is not the removal of judgement; it is the combination of analytical capability, ethical governance, operational knowledge and responsible technology use.

Key Terms

Risk: The effect of uncertainty on objectives.

Resilience: The ability to absorb disruption, recover performance and learn.

Robustness: The ability to perform acceptably under expected variation.

Critical item: A material whose failure or absence has significant operational, safety, legal or customer consequences.

Expected loss: Probability-weighted financial impact of uncertain events.

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

Supplier concentration: Dependence on a limited number of suppliers, regions or sources.

Counterfeit material: Material deliberately misrepresented in identity, origin, condition or performance.

Traceability: The ability to follow a material’s history, location and status.

Segregation of duties: Separation of transaction responsibilities to reduce error and fraud.

Governance: Structures and processes that direct, control and assure organisational activity.

Compliance: Conformance with applicable laws, regulations, standards and contracts.

Professional ethics: Principles guiding honest, fair, responsible and competent conduct.

Circular economy: An economic system designed to retain product and material value and reduce waste.

Reverse logistics: Management of goods and materials moving back from users or downstream points.

ESG: Environmental, social and governance considerations used in organisational decisions.

Predictive analytics: Use of data and models to estimate future outcomes or risks.

Autonomous planning: Automated adjustment or execution of planning decisions within defined controls.

Human-in-the-loop: A system in which people review, govern or intervene in automated decisions.

Residual risk: Risk remaining after controls are applied.

Review Questions

Short Answer Questions

1. Distinguish between robustness and resilience in materials management.
2. What is the purpose of a risk register?
3. Identify four controls that can reduce counterfeit-material risk.
4. Explain why recycling is not always the highest-value circular-economy option.
5. What is meant by human-in-the-loop planning?

Long Answer Questions

1. Explain how an organisation can manage supplier failure without creating excessive inventory cost.
2. Discuss the relationship between segregation of duties, traceability and fraud prevention.
3. Evaluate the role of reverse logistics in a circular materials system.
4. Explain how governance and professional ethics influence emergency procurement.
5. Assess the benefits and limitations of autonomous inventory planning.

Analytical / Application Questions

1. A critical component has one supplier, a twelve-week lead time and no approved substitute. Develop a risk-treatment plan covering prevention, detection, response and recovery.
2. A company reduces warehouse inventory by 20% but subsequently uses emergency air freight and experiences more product damage. Analyse whether the change improved sustainability.
3. Design a balanced dashboard for a materials function that must monitor service, resilience, fraud, ESG and compliance simultaneously.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes resilience from robustness?

A. Resilience concerns only inventory quantity, whereas robustness concerns only supplier price.

B. Robustness addresses expected variation, while resilience includes absorbing and recovering from disruption.

C. Robustness is a legal requirement, while resilience is an accounting measure.

D. Resilience eliminates all operational risk, while robustness measures fraud.

Correct Answer: B. Robustness addresses expected variation, while resilience includes absorbing and recovering from disruption.

Question 2

In the formula EL = ∑ p_i × I_i, what does p_i represent?

A. The inventory level for item i

B. The probability of event i in the stated planning period

C. The purchase price of item i

D. The recovery time for event i

Correct Answer: B. The probability of event i in the stated planning period

Question 3

Which control most directly reduces the opportunity for one employee to create, receive and pay for a fictitious purchase?

A. Increasing the reorder point

B. Using larger delivery vehicles

C. Segregation of duties

D. Reducing cycle-count frequency

Correct Answer: C. Segregation of duties

Question 4

Why is dual sourcing not automatically sufficient to create resilience?

A. Two suppliers always increase product defects.

B. The suppliers may share a common vulnerable sub-tier source or region.

C. Dual sourcing prevents all supplier collaboration.

D. Two suppliers eliminate the need for qualification.

Correct Answer: B. The suppliers may share a common vulnerable sub-tier source or region.

Question 5

Which option generally preserves the highest level of product value in a circular economy?

A. Disposal in landfill

B. Energy recovery from waste

C. Repair or reuse that retains the product’s function

D. Uncontrolled export of obsolete stock

Correct Answer: C. Repair or reuse that retains the product’s function

Question 6

A forecast has actual demands of 100 and 120 units and forecasts of 90 and 130 units. What is the MAE?

A. 5 units

B. 10 units

C. 15 units

D. 20 units

Correct Answer: B. 10 units

Question 7

Which statement about distributed-ledger technology is correct?

A. It guarantees that all recorded source data is truthful.

B. It removes the need for supplier qualification.

C. It can support provenance, but inaccurate input data remains inaccurate.

D. It automatically prevents counterfeit materials at the point of manufacture.

Correct Answer: C. It can support provenance, but inaccurate input data remains inaccurate.

Question 8

Which is an appropriate control for an autonomous planning system?

A. Allowing unrestricted parameter changes without logs

B. Removing all human review from high-impact decisions

C. Using decision thresholds, monitoring and a safe fallback process

D. Ignoring model drift when forecast accuracy is initially high

Correct Answer: C. Using decision thresholds, monitoring and a safe fallback process

Question 9

Which activity is most directly associated with reverse logistics?

A. Returning reusable packaging for inspection and redeployment

B. Increasing the safety stock of a new product

C. Selecting a supplier solely on purchase price

D. Issuing a sales invoice before shipment

Correct Answer: A. Returning reusable packaging for inspection and redeployment

Question 10

A risk score calculated as likelihood multiplied by consequence should be understood as:

A. A precise probability of loss

B. A prioritisation tool based on defined scoring scales

C. A guaranteed monetary value of damage

D. A replacement for all scenario analysis

Correct Answer: B. A prioritisation tool based on defined scoring scales