Global Supply Chain Leadership and Future Transformation

Introduction

Global supply chains are no longer managed as linear sequences of purchasing, production, distribution and sales. They are adaptive ecosystems involving suppliers, logistics providers, technology firms, regulators, communities, customers and sometimes competitors. Their performance is shaped simultaneously by cost, service, resilience, geopolitical exposure, climate risk, data quality, technological capability and social legitimacy.

The contemporary supply-chain leader therefore performs more than an operational coordination role. The leader designs relationships across institutional and national boundaries, aligns partners around shared outcomes, governs data and automation, and mobilises transformation when established business models become inadequate. This requires both analytical discipline and the ability to work across cultures, professional communities and power differences.

This chapter examines leadership of global supply networks and evaluates major transformation themes: autonomous planning, generative artificial intelligence, servitization, additive manufacturing, climate adaptation and emerging network configurations. It concludes with an applied executive decision simulation and an illustrative case.

Learning Objectives

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

1. Explain how leadership differs from administration in global supply-chain ecosystems.
2. Diagnose cross-cultural collaboration and partner-governance problems.
3. Evaluate autonomous planning and generative AI using value, risk and control criteria.
4. Assess servitization, additive manufacturing and climate adaptation as supply-chain transformations.
5. Compare emerging global network models and select an appropriate transformation pathway.
6. Make defensible executive decisions under uncertainty using strategic, operational, ethical and financial criteria.

1. From Global Supply-Chain Management to Ecosystem Leadership

A traditional supply chain implies a relatively stable flow from upstream suppliers to a focal firm and then to customers. A global supply network is more accurately represented as a set of interdependent nodes and relationships. Materials, information, capital, services, intellectual property and risk move through the network in different directions and at different speeds.

Three changes have made leadership central.

First, interdependence has increased. A single product may depend on specialised components, cloud services, logistics capacity, certification agencies and after-sales networks spread across several countries. A local disruption can therefore create global consequences.

Second, authority is distributed. The focal firm may not control suppliers, platforms, contract manufacturers or logistics providers. Influence depends on contracts, standards, information sharing, investment, reputation and the credibility of joint commitments.

Third, objectives are plural. Supply-chain decisions must balance total cost, continuity, speed, carbon emissions, worker welfare, cybersecurity, regulatory compliance and customer value. Optimising one dimension can damage another.

Leadership can be understood as the process of creating direction, alignment and commitment among interdependent actors. Management remains essential: forecasts, inventories, schedules and contracts must be controlled. Leadership adds the ability to form a shared purpose, resolve competing interests and create conditions in which the network can adapt.

1.1 A network-leadership architecture

A useful architecture has four mutually reinforcing elements:

The architecture prevents a common error: treating technology as transformation. A planning platform cannot compensate for unclear decision rights, conflicting incentives or unreliable master data.

2. Cross-Cultural Collaboration

Culture influences how people interpret authority, disagreement, time, uncertainty, trust and obligation. National culture is only one influence; professional, organisational, regional and generational cultures also matter. Cultural frameworks can help generate questions, but they should not be used to stereotype individuals.

In a global project, one team may regard direct disagreement as evidence of honesty, while another may interpret it as disrespect. A request for immediate commitment may be understood as efficient in one context and reckless in another. Silence may indicate agreement, uncertainty, resistance or deference. Effective leaders therefore treat cultural assumptions as hypotheses to test rather than facts to impose.

2.1 Practices for collaboration

Create a shared operating language. Define terms such as “on time,” “available,” “approved,” “forecast,” “exception” and “urgent.” A shared glossary is especially important when translated language hides differences in meaning.

Make implicit rules explicit. Meeting protocols should specify who decides, how dissent is recorded, how actions are assigned and when escalation occurs.

Use psychological safety with accountability. Partners must be able to report quality, safety or compliance concerns without retaliation. Psychological safety does not mean accepting poor performance; it means separating problem disclosure from personal blame.

Combine synchronous and asynchronous communication. Written decision records reduce ambiguity across time zones, while live sessions help resolve sensitive issues and build trust.

Rotate participation. Giving different locations responsibility for agenda items reduces headquarters dominance and reveals local knowledge.

Distinguish relationship building from social conformity. Trust may develop through formal reliability, technical competence and fair dispute resolution, not only through informal social interaction.

2.2 Trust and collaboration economics

Trust lowers the perceived risk of sharing information and making relationship-specific investments. However, trust should be supported by verifiable controls. A resilient relationship combines goodwill with transparency, auditability, clear service levels and credible remedies.

A simple decision rule is to assess collaboration proposals on four dimensions: mutual value, information transparency, behavioural reliability and recoverability if the relationship fails. A partner may be attractive on cost but weak on recoverability because switching requires an 18-month qualification process. That dependence is a strategic risk even when current performance is good.

3. Partner Ecosystems and Governance

Partner ecosystems include tiered suppliers, contract manufacturers, distributors, freight forwarders, technology providers, repair partners, financiers, regulators and customers. The focal organisation must decide which activities to own, orchestrate, standardise or leave to partners.

3.1 Governance mechanisms

1. Contracts: Specify obligations, service levels, intellectual-property rights, audit rights, liability and termination conditions.
2. Relational governance: Use joint planning, steering committees, shared problem solving and senior-level trust building.
3. Technical governance: Define data schemas, cybersecurity controls, interfaces, testing and change management.
4. Performance governance: Use balanced measures rather than price alone. Measures may include quality, lead time, forecast responsiveness, emissions, recovery time and safety.
5. Escalation governance: Specify how exceptions move from operational teams to executives and how rapidly decisions must be made.

An effective governance system matches control intensity to risk. A strategic supplier of a safety-critical component requires deeper qualification, continuity planning and traceability than a low-value office-supply vendor. Excessive control can nevertheless suppress innovation and discourage capable partners.

3.2 Ecosystem mapping

Map partners according to criticality, substitutability, influence, data dependence and exposure to disruption. A small supplier may have little financial scale but very high criticality if it is the only certified source. The map should include second- and third-tier dependencies where information is available.

The leader should then identify structural vulnerabilities:

• single points of failure;
• correlated geographic exposure;
• common logistics or digital providers;
• concentrated ownership of scarce intellectual property;
• weak visibility beyond tier one; and
• incentives that encourage local optimisation at network expense.

4. Enterprise Transformation as a Leadership Challenge

Transformation changes processes, roles, technologies, measures and power relationships. It should be treated as a portfolio of interdependent changes rather than a software installation.

A practical transformation cycle is:

1. Diagnose: Establish the current operating model, pain points, capability gaps and baseline performance.
2. Frame value: Define customer, financial, resilience, sustainability and employee outcomes.
3. Design the target model: Specify processes, decision rights, data, technologies, skills and governance.
4. Experiment: Test a bounded use case with measurable success criteria.
5. Scale selectively: Expand only where benefits, controls and adoption are demonstrated.
6. Institutionalise: Embed new routines in incentives, training, budgets, audits and leadership reviews.

Transformation value should not be measured solely through labour reduction. A planning system that reduces planner effort but increases stockouts, supplier instability or compliance risk is not a successful transformation.

5. Autonomous Planning

Autonomous planning refers to systems that sense changes, generate plans, evaluate constraints and execute or recommend decisions with limited human intervention. It ranges from decision support to highly automated execution.

The maturity spectrum is useful:

Autonomy is appropriate when the decision is frequent, data is sufficiently reliable, consequences are bounded and rules are understood. Human intervention remains essential for novel disruptions, ethical trade-offs, ambiguous data and high-impact decisions.

5.1 Planning logic and constraints

A planning engine typically seeks an objective such as minimising total relevant cost while satisfying demand, capacity, inventory, service and policy constraints. A generic representation is:

Here, xᵢ is the quantity assigned to activity or source i, cᵢ is its unit cost, Iⱼ is inventory associated with location or period j, hⱼ is the applicable holding cost, Uₖ is unmet demand or another violation measure, and pₖ is its penalty. The summation indices represent all relevant activities, inventory positions and penalty categories. The units of every term must be comparable, usually currency per planning horizon. This formulation is a conceptual objective, not a universal model; actual systems may include nonlinear costs, uncertainty, emissions and service constraints.

Autonomy requires five controls: data lineage, model validation, approval thresholds, exception monitoring and rollback capability. Leaders should also monitor automation bias, in which users accept machine recommendations without adequate challenge.

6. Generative AI in Global Supply Chains

Generative AI produces text, code, summaries, scenarios or other content from patterns learned from data. In supply chains, useful applications include supplier-query drafting, contract and shipment-document summarisation, knowledge retrieval, scenario narration, exception explanation, multilingual communication and assistance with analytics code.

Generative AI should not be confused with a planning optimiser. A language model can produce a plausible explanation while lacking a valid constraint model or current data. It may also hallucinate facts, expose confidential information, reproduce bias or generate unsafe instructions.

6.1 A responsible-use framework

Before deployment, evaluate:

• Purpose: Is the use case informational, advisory or executable?
• Data: Is data accurate, current, authorised and protected?
• Evidence: Can outputs be traced to approved sources?
• Human control: Who reviews, approves and corrects outputs?
• Impact: Could an error affect safety, rights, employment, compliance or financial exposure?
• Security: Can prompts, outputs and connected tools be manipulated?
• Monitoring: Are accuracy, drift, incidents and user behaviour measured?

Low-risk uses, such as summarising a public customs notice, may be automated more readily than generating a supplier allocation that could breach sanctions or create unsafe shortages. A useful principle is human accountability, not merely human presence: a person must have the authority, competence and time to challenge the system.

7. Servitization and Outcome-Based Supply Chains

Servitization shifts value creation from selling a physical product toward selling availability, performance or an outcome. Examples include aircraft-engine hours, industrial equipment uptime, predictive-maintenance contracts and equipment-as-a-service.

The supply-chain consequences are substantial. The provider retains greater responsibility for asset reliability, spare parts, reverse logistics, remote monitoring, upgrades and end-of-life recovery. Demand for components may become less predictable because maintenance is condition-based rather than calendar-based. Product design must support diagnostics, modular repair and lifecycle economics.

A basic lifecycle-value expression is:

LCV is lifecycle value, R is revenue or contracted outcome value, C₀ is initial production and deployment cost, Cₒ is operating cost, Cₘ is maintenance cost, Cₗ is logistics and support cost, and Cᵣ is recovery or end-of-life cost. All terms must use the same currency and evaluation period. For investment decisions, these cash flows should normally be discounted; the expression is a simplified undiscounted identity for conceptual analysis.

Servitization can improve customer value and create recurring revenue, but it can also increase asset ownership, working-capital exposure and performance liability. It requires strong installed-base data and contracts that define measurement, availability, exclusions and risk sharing.

8. Additive Manufacturing and Network Redesign

Additive manufacturing builds parts layer by layer from a digital design. Its supply-chain relevance lies not only in production technology but in the possibility of moving from centralised physical inventories to distributed digital inventories.

Potential advantages include:

• economical production of low-volume or customised parts;
• reduced tooling requirements;
• lightweight geometries and part consolidation;
• local production of urgent replacement parts; and
• reduced obsolescence for long-tail items.

Constraints include material qualification, machine capacity, surface finish, certification, intellectual-property protection, repeatability, energy use and cybersecurity of design files. A printed part is not automatically equivalent to a conventionally manufactured part; process validation and traceability are essential in regulated sectors.

The strategic choice is not “centralised versus printed.” A hybrid model may retain central production for high-volume parts while using certified regional facilities for selected spares. Selection should consider demand volume, urgency, value density, qualification requirements, design suitability and total landed cost.

9. Climate Adaptation and Supply-Chain Resilience

Climate adaptation differs from mitigation. Mitigation reduces emissions; adaptation reduces vulnerability to unavoidable or expected climate impacts. Both belong in supply-chain strategy.

Climate exposure can affect facilities, transport corridors, labour productivity, water availability, energy systems, agriculture and insurance. The analysis should distinguish:

• Hazard: the physical event or trend, such as flood, heat or drought;
• Exposure: the assets, flows or communities located where the hazard occurs;
• Vulnerability: the susceptibility to damage and inability to cope; and
• Risk: the resulting possibility of loss, disruption or harm.

A qualitative risk matrix is useful for prioritisation, but climate decisions should use multiple scenarios rather than a single forecast. Adaptation options include dual sourcing, geographic diversification, elevated facilities, alternative transport routes, water recycling, heat protocols, inventory positioning, supplier financing and collaborative ecosystem planning.

Resilience is not identical to redundancy. Redundancy adds spare capacity; flexibility enables substitution; visibility supports anticipation; collaboration accelerates recovery. Each has cost and may create new environmental or social impacts.

10. Emerging Global Network Models

Several network models are becoming prominent:

No model is universally superior. Network design should match product economics, demand uncertainty, regulatory requirements, customer promise, geopolitical exposure and organisational capability.

11. Executive Decision Simulation

The following simulation is designed for classroom or executive-team use.

Situation. Meridian Mobility produces industrial battery modules in three regions. It currently relies on one qualified source for a specialised thermal-management component. A new climate assessment indicates rising flood exposure at that supplier. At the same time, customers are requesting uptime contracts, and the company is considering generative AI for service forecasting and additive manufacturing for selected replacement housings.

Decision. The executive team must choose a two-year transformation portfolio from the following options:

• qualify a second regional supplier;
• build a digital control tower;
• introduce AI-assisted service forecasting;
• establish certified additive production for selected spares; and
• redesign contracts around equipment availability.

The budget permits only three major initiatives. The team must rank options using customer value, resilience, implementation feasibility, data readiness, regulatory risk, emissions and long-term strategic fit.

Recommended process. First, map dependencies and identify the component as a single point of failure. Second, test whether the service promise increases the cost of failure and therefore changes sourcing priorities. Third, separate low-risk AI assistance from automated execution. Fourth, assess additive manufacturing through qualification and lifecycle evidence rather than novelty. Fifth, design a staged portfolio: an immediate resilience action, a data-and-governance foundation, and one revenue-model experiment.

Decision insight. A plausible outcome is to qualify the second supplier first, establish the control-tower data foundation second, and pilot AI-assisted service forecasting third. Additive manufacturing and availability contracting may follow after technical and commercial validation. The correct answer is not predetermined: students should justify trade-offs, assumptions, thresholds and stop criteria. The exercise tests whether leaders sequence transformation rather than launch disconnected technology projects.

12. Illustrative Case: Northstar Medical Devices

This is a fictional illustrative case.

Background

Northstar Medical Devices sells diagnostic equipment in 42 countries. Its products require a precision sensor from a supplier in East Asia and service technicians in regional markets. The company competes on high equipment availability, but its planning process is fragmented across spreadsheets and local enterprise systems.

Problem

A typhoon disrupted the sensor supplier for five weeks. Northstar had limited visibility beyond its direct supplier and discovered that the alternative source used the same upstream substrate producer. Simultaneously, customers demanded outcome-based service contracts. Northstar’s proposed response was to purchase an expensive global planning platform and deploy a generative AI assistant immediately.

Analysis

A cross-functional team found four interacting risks. First, the apparent dual-source strategy was not genuinely independent. Second, service contracts would increase Northstar’s responsibility for spare-part availability. Third, data definitions differed among regions, making autonomous planning unreliable. Fourth, the AI proposal lacked controls for confidential service records and source citation.

The team assessed options against continuity, customer value, implementation risk and governance readiness. It concluded that technology investment without network redesign would create false confidence.

Approach and solution

Northstar adopted a staged programme:

1. It qualified a geographically and technically independent sensor source and required upstream disclosure for critical materials.
2. It created a common data dictionary for demand, installed-base status, service level and inventory.
3. It established a partner council with procurement, engineering, service, suppliers and regional leaders.
4. It piloted a generative AI assistant that summarised approved service documents and cited its sources; it could not release purchase orders or alter customer records.
5. It tested additive manufacturing for two non-safety-critical housings, subject to engineering validation.
6. It introduced a limited availability contract with explicit measurement rules and risk-sharing provisions.

Outcome

After the pilot period, Northstar reduced response time for service-document queries and improved visibility of critical-component exposure. The alternative supplier was not used for every order because its unit price was higher, but it provided credible continuity. The additive pilot was retained for urgent, low-volume housings but rejected for the sensor component because qualification requirements were too demanding. The availability contract was expanded only after service-data quality improved.

Learning points

• Resilience depends on actual independence, not the count of contractual suppliers.
• Data governance is a prerequisite for trustworthy autonomy.
• Generative AI should begin with bounded, evidence-linked use cases.
• Servitization changes inventory, maintenance and accountability requirements.
• Transformation portfolios should sequence capability building before scale.

13. Ethical, Regulatory and Governance Considerations

Global supply-chain leadership carries responsibilities beyond shareholder return. Decisions affect workers, communities, customers, suppliers and the environment. Important concerns include forced labour, unsafe working conditions, discrimination, privacy, surveillance, corruption, sanctions, export controls, competition law and environmental justice.

AI creates additional obligations. Organisations should document intended use, training or retrieval data, model limitations, access controls, testing results, incident procedures and accountability. Automated decisions affecting workers or suppliers require special scrutiny because historical data may reproduce unequal treatment. Human review must be meaningful, and affected parties should have a route for correction or appeal where appropriate.

Climate adaptation also raises distributional questions. Relocating a facility may protect one market while transferring pollution, water demand or employment loss to another community. A responsible leader evaluates externalities and consults affected stakeholders rather than treating risk as purely financial.

14. Implementation Principles and Best Practices

1. Start with a strategic problem, not a fashionable technology.
2. Design the target operating model before selecting software.
3. Use pilots with explicit hypotheses, metrics and stop criteria.
4. Build data ownership and lineage into the operating model.
5. Balance efficiency with resilience, sustainability and safety.
6. Create decision rights that match accountability.
7. Measure partner outcomes across cost, service, risk, people and planet.
8. Develop local capability rather than imposing headquarters solutions.
9. Use scenario planning for geopolitical, climate and technology uncertainty.
10. Review models and contracts periodically because conditions and risks change.

Challenges and Limitations

Future-oriented supply-chain design involves deep uncertainty. Forecasts can be wrong, technologies can diffuse unevenly, regulations can change and partners can behave strategically. Digital twins may provide impressive visualisation without causal validity. AI systems may perform well in routine conditions and fail during unprecedented events. Regionalisation can reduce one risk while increasing cost, emissions or dependence on another scarce input. Additive manufacturing may reduce physical inventory but increase energy use or cybersecurity exposure. Servitization can strengthen customer relationships while placing excessive operational risk on the provider.

The appropriate response is not to avoid transformation but to govern it as a learning system. Leaders should state assumptions, test them, monitor leading indicators, preserve reversibility where possible and distinguish evidence from aspiration.

Chapter Summary

Global supply-chain leadership is the creation of direction, alignment, adaptation and accountability across interdependent actors. Cross-cultural effectiveness requires explicit communication, psychological safety, shared definitions and respect for local knowledge without stereotyping. Partner ecosystems need contractual, relational, technical, performance and escalation governance.

Autonomous planning can improve responsiveness when data, constraints and decision rights are reliable, but it requires guardrails and human accountability. Generative AI is valuable for bounded knowledge and communication tasks, yet it must be controlled for hallucination, confidentiality, bias and misuse. Servitization changes supply chains toward lifecycle responsibility and outcome delivery. Additive manufacturing can support customised and distributed production, but qualification and digital-IP risks remain. Climate adaptation requires assessment of hazard, exposure, vulnerability and risk, followed by a portfolio of flexibility, visibility, redundancy and collaboration.

The central managerial lesson is that transformation is an enterprise and ecosystem change, not a technology purchase. Effective leaders sequence capability building, evaluate trade-offs transparently and create resilient, ethical and evidence-based networks.

Key Terms

Autonomous planning: Planning that senses conditions, generates decisions and may execute within defined limits.

Climate adaptation: Actions that reduce vulnerability to actual or expected climate impacts.

Control tower: A coordinated visibility and exception-management capability for network decisions.

Cross-cultural collaboration: Joint work among people whose cultural assumptions and practices differ.

Digital twin: A dynamic digital representation of a physical asset, process or network.

Distributed manufacturing: Production performed close to demand through multiple qualified locations.

Ecosystem governance: Mechanisms used to coordinate and control interdependent partners.

Generative AI: AI that creates text, code, images or other outputs from learned patterns.

Human accountability: Responsibility that remains with authorised people despite machine assistance.

Partner ecosystem: The wider set of organisations contributing to value creation and delivery.

Psychological safety: A climate in which people can raise concerns without fear of interpersonal punishment.

Resilience: The ability to anticipate, absorb, adapt to and recover from disruption.

Servitization: The shift from selling products alone toward delivering services or outcomes.

Single point of failure: A node whose failure can materially disrupt the network because alternatives are inadequate.

Supply network: Interconnected organisations and flows involved in creating and delivering value.

Transformation portfolio: A coordinated set of change initiatives managed for strategic outcomes.

Trust governance: The combination of relational confidence and verifiable controls.

Visibility: Knowledge of relevant events, conditions, locations and dependencies in a network.

Review Questions

Short Answer Questions

1. How does supply-chain leadership differ from routine supply-chain management?
2. Why should cultural frameworks not be used to stereotype individuals?
3. Identify two controls required for autonomous planning.
4. What is the central difference between climate mitigation and climate adaptation?
5. Why can a contractual dual-source strategy fail to provide real resilience?

Long Answer Questions

1. Explain how contractual, relational, technical and performance governance complement one another in a global partner ecosystem.
2. Evaluate the opportunities and risks of generative AI in supply-chain operations.
3. Discuss how servitization changes inventory, maintenance, logistics and customer relationships.
4. Compare regionalised, circular and distributed-manufacturing network models.
5. Develop a transformation roadmap for a multinational manufacturer facing geopolitical and climate uncertainty.

Analytical / Application Questions

1. A firm has two suppliers, but both depend on the same upstream raw-material producer. Diagnose the resilience problem and recommend a redesigned sourcing and governance approach.
2. Design a pilot for AI-assisted exception management. Specify the use case, data requirements, human approval point, performance measures and stop criteria.
3. Using the Northstar case, decide which three initiatives should receive priority and defend your choice using customer value, resilience, feasibility, ethics and long-term strategic fit.

Multiple Choice Questions with Answers

Question 1

Which statement best describes global supply-chain leadership?

A. Maximising purchasing volume regardless of partner effects

B. Creating direction, alignment, adaptation and accountability among interdependent actors

C. Replacing all suppliers with internal operations

D. Delegating all decisions to artificial intelligence

Correct Answer: B. Creating direction, alignment, adaptation and accountability among interdependent actors

Question 2

Which practice most directly reduces ambiguity in cross-cultural global teams?

A. Assuming silence means agreement

B. Using only informal communication

C. Defining shared terms, decision rights and escalation rules

D. Allowing each location to use unrelated performance definitions

Correct Answer: C. Defining shared terms, decision rights and escalation rules

Question 3

Autonomous planning is most appropriate when decisions are frequent, data is reliable and:

A. Consequences are bounded and decision rules are understood

B. No human can review the outputs

C. The environment is completely unprecedented

D. The system has no rollback capability

Correct Answer: A. Consequences are bounded and decision rules are understood

Question 4

What is a major governance risk specific to generative AI?

A. It cannot produce language outputs

B. It always uses current and verified data

C. It may generate plausible but unsupported information

D. It eliminates the need for data governance

Correct Answer: C. It may generate plausible but unsupported information

Question 5

Servitization primarily shifts a firm toward:

A. Selling outcomes or availability over the product lifecycle

B. Eliminating all after-sales responsibilities

C. Reducing the importance of installed-base data

D. Using only calendar-based maintenance

Correct Answer: A. Selling outcomes or availability over the product lifecycle

Question 6

Which is a limitation of additive manufacturing in regulated supply chains?

A. It never permits customisation

B. Parts require no process validation

C. Qualification, repeatability and intellectual-property risks may be significant

D. It always has lower total cost than conventional production

Correct Answer: C. Qualification, repeatability and intellectual-property risks may be significant

Question 7

Climate adaptation primarily aims to:

A. Reduce vulnerability to climate impacts

B. Increase dependence on one transport corridor

C. Replace all resilience measures with inventory

D. Eliminate the need for emissions reduction

Correct Answer: A. Reduce vulnerability to climate impacts

Question 8

In the Northstar case, why was the apparent dual-source strategy inadequate?

A. The alternative supplier used the same upstream substrate producer

B. The company had too many independent sources

C. The suppliers were located in different industries

D. The alternative source had already been fully qualified

Correct Answer: A. The alternative supplier used the same upstream substrate producer

Question 9

Which principle best reflects responsible AI deployment in supply chains?

A. Automate first and establish controls later

B. Human accountability with evidence, access controls and monitoring

C. Permit models to alter records without approval

D. Use confidential data in unrestricted public tools

Correct Answer: B. Human accountability with evidence, access controls and monitoring

Question 10

Which network model emphasises reuse, repair, recovery and reverse flows?

A. Circular network

B. Single-source network

C. Purely linear network

D. Uncoordinated local network

Correct Answer: A. Circular network