9. Logistics Network Design and Capacity Decisions

Introduction and Context

Logistics network design concerns the long-term configuration of facilities, transportation links, inventory points, information flows, and outsourced logistics relationships through which products and services move from sources to customers. It is one of the most consequential decisions in supply chain management because network structure determines the cost, speed, resilience, flexibility, and service capability of the supply chain for many years.

A logistics network may include suppliers, plants, ports, cross-docks, distribution centres, fulfilment centres, retail outlets, service depots, customers, and reverse-logistics facilities. Decisions about where these facilities should be located, what activities they should perform, how much capacity they should possess, and which markets they should serve are interdependent. A low-cost location may be distant from customers; a highly responsive network may require additional facilities and inventory. Similarly, consolidating operations may create economies of scale while increasing transport distance and disruption exposure.

Network design is therefore a strategic problem rather than merely a transport-planning exercise. Transportation routing normally determines how existing facilities serve demand in the short term. Network design determines the facilities and relationships that make those routes possible in the first place. Capacity decisions add another dimension: a facility must be large enough to meet expected demand and provide operating flexibility, but excess capacity creates avoidable fixed cost.

The chapter develops analytical and managerial approaches for facility location, network configuration, flow allocation, capacity planning, distribution-structure selection, and outsourcing. It also considers uncertainty, technology, sustainability, governance, and the use of 3PL and 4PL providers.

Learning Objectives

After completing this chapter, the student should be able to:

1. Explain the strategic role and major components of logistics network design.

2. Distinguish facility location, network configuration, capacity, and flow-allocation decisions.

3. Apply centre-of-gravity and load-distance methods to preliminary location analysis.

4. Formulate and interpret basic transportation, facility-location, and mixed-integer optimisation models.

5. Evaluate distribution structures, postponement, cross-docking, and multi-echelon alternatives.

6. Use scenario and sensitivity analysis to assess uncertainty and robustness.

7. Analyse outsourcing choices and distinguish the roles of 3PL and 4PL providers.

8. Recognise practical, ethical, regulatory, technological, and sustainability considerations in network decisions.

The Strategic Architecture of a Logistics Network

A network design decision has four connected dimensions. The first is structural configuration: the number, type, ownership, and location of facilities. The second is capacity: the maximum volume, throughput, storage, labour, equipment, and service capability assigned to each facility. The third is allocation: the assignment of suppliers, plants, products, and customers to facilities and transport modes. The fourth is governance: the decision about which activities are performed internally and which are entrusted to logistics service providers.

These dimensions should be evaluated against a total-cost and service perspective. Relevant cost categories include facility acquisition or rent, construction, labour, utilities, material handling, inventory, transportation, procurement, information systems, duties and taxes, quality failures, returns, and the financial consequences of stockouts or late delivery. A decision that minimises transport cost alone may increase inventory or facility cost. A decision that minimises accounting cost may weaken resilience and produce high disruption losses.

Service requirements should be translated into measurable design parameters. Examples include order-cycle time, delivery frequency, on-time-in-full performance, product availability, temperature compliance, emergency response time, and returns-processing time. The appropriate network for spare parts is unlikely to be appropriate for low-value consumer products. Likewise, an international pharmaceutical network requires stronger traceability and regulatory controls than a network handling undifferentiated bulk commodities.

Network design is also shaped by product characteristics. High-value, low-volume products may justify decentralised inventory or rapid transport. Bulky, low-value products are often produced or stored close to demand. Products with uncertain demand benefit from postponement and flexible capacity. Perishable products require attention to lead time, shelf life, temperature, and waste. Hazardous materials impose additional restrictions on facility location, routing, insurance, and emergency planning.

Important terminology

A node is a facility or point in the network at which goods are produced, stored, consolidated, transferred, consumed, or returned. An arc is a physical or logical connection between nodes. A distribution centre stores and consolidates inventory for shipment to customers or downstream facilities. A fulfilment centre is generally designed for high-volume order processing, often including picking, packing, and parcel dispatch. A cross-dock receives inbound goods and transfers them to outbound vehicles with little or no storage.

A single-echelon network has one principal inventory or distribution level between supply and demand. A multi-echelon network contains multiple levels, such as a central distribution centre supplying regional distribution centres that serve retail stores. Flow allocation refers to the quantity assigned along particular network links. Throughput is the volume processed by a facility during a specified period. Utilisation is the proportion of available capacity being used. Network density describes the number and geographic distribution of facilities relative to the markets served.

From Strategic Questions to Analytical Models

The design process begins with a clear statement of the decision. Management should identify the planning horizon, products, customer segments, demand points, candidate facilities, transport modes, capacity choices, and constraints. A model that omits an important constraint may produce a mathematically optimal but operationally impossible recommendation.

A useful sequence is:

1. Define objectives, service requirements, and the decision horizon.

2. Assemble demand, supply, cost, lead-time, capacity, geographic, and regulatory data.

3. Generate feasible network alternatives.

4. Conduct preliminary screening using location and distance methods.

5. Optimise facility selection and product-flow allocation.

6. Test scenarios, sensitivity, resilience, and implementation feasibility.

7. Select a design and establish governance, performance measures, and review mechanisms.

The objective may be expressed as minimising total landed logistics cost, maximising contribution, or balancing cost and service. A simplified total-cost expression is:

Total cost = facility cost + transportation cost + inventory cost + handling cost + procurement cost + service-failure cost + coordination cost.

In practice, strategic models often use annualised costs. Capital expenditure may be converted into an annual equivalent using a required return and asset life. This avoids comparing a one-time construction cost with a single year's transport expense without adjustment.

Centre-of-gravity method

The centre-of-gravity method provides a rapid first approximation of a facility location. If demand points have coordinates (xᵢ, yᵢ) and weights wᵢ representing demand volume, shipment frequency, or transport cost exposure, the location is estimated as:

x* = Σ(wᵢxᵢ) / Σwᵢ

y* = Σ(wᵢyᵢ) / Σwᵢ

For example, suppose three markets have coordinates A(2,8), B(8,6), and C(6,2), with annual shipment weights of 100, 150, and 50 units. The estimated centre is x = (200+1,200+300)/300 = 5.67 and y = (800+900+100)/300 = 6.00. This point identifies a search area rather than a final site.

The method is useful because it is transparent and inexpensive. However, it assumes a relatively simple relationship between distance and cost. It may ignore road networks, terrain, congestion, land availability, labour markets, taxes, service zones, and facility capacity. A geographically central point may be inaccessible or expensive. Consequently, the centre of gravity should be followed by a feasibility screen and a more detailed model.

Load-distance analysis

Load-distance analysis compares candidate sites using the weighted distance between a facility and demand or supply points:

Load-distance score = Σ(loadᵢ × distanceᵢ).

The preferred site generally has the lowest score, subject to service and feasibility constraints. Distances may be measured as straight-line, road, travel-time, or cost distances. Road distance is more realistic for transport planning, while travel time may be more appropriate when customer responsiveness is important.

A limitation is that load-distance analysis treats the relationship between distance and cost as relatively direct. It may not capture fixed route costs, vehicle capacity, congestion, backhauls, tolls, or economies of scale. It is best viewed as a screening tool, not a substitute for a complete network model.

Transportation and facility-location models

A transportation model determines how supply should be allocated to demand at minimum variable cost when facilities are already available. Let xᵢⱼ be the quantity shipped from supply node i to demand node j, cᵢⱼ the unit shipping cost, sᵢ the supply at node i, and dⱼ the demand at node j. The basic objective is:

Minimise ΣᵢΣⱼ cᵢⱼxᵢⱼ

subject to:

Σⱼxᵢⱼ ≤ sᵢ for every supply node i;

Σᵢxᵢⱼ ≥ dⱼ for every demand node j;

xᵢⱼ ≥ 0.

A facility-location model adds a binary variable yₖ, where yₖ = 1 if candidate facility k is opened and 0 otherwise. If Fₖ is the fixed annual cost of facility k, cₖⱼ is the cost of serving demand j from facility k, and xₖⱼ is the allocated quantity, the objective may be written as:

Minimise ΣₖFₖyₖ + ΣₖΣⱼcₖⱼxₖⱼ.

Constraints link flows to facility decisions. For example, if Uₖ is facility capacity:

Σⱼxₖⱼ ≤ Uₖyₖ.

This constraint prevents flow through a facility that has not been opened. Demand constraints require each market to be served, while additional constraints may enforce maximum service time, minimum volume, product compatibility, labour availability, or a required number of facilities.

Such models are often mixed-integer linear programs because facility-open decisions are discrete while shipment quantities are continuous. Their outputs can identify facility combinations, customer assignments, flows, and total cost. Their value depends on the quality of the assumptions and data. Optimisation does not eliminate managerial judgement; it makes the implications of assumptions more explicit.

Capacity Decisions: Size, Timing, and Flexibility

Capacity is not simply the physical size of a building. It includes dock doors, storage positions, picking stations, production interfaces, vehicle access, labour, equipment, information systems, and management capability. Capacity can be measured in pallets, orders, cases, tonnes, lines picked, vehicle movements, or labour hours. The measure must reflect the actual constraint. A warehouse may have sufficient floor space but insufficient picking labour or dock capacity.

The central capacity trade-off is between under-capacity and over-capacity. Under-capacity may cause congestion, overtime, missed deliveries, lost sales, and poor working conditions. Over-capacity produces idle assets and fixed costs but may provide resilience and growth flexibility. The relevant decision is not to maximise utilisation under all circumstances. A facility operating at 98 percent of nominal capacity may perform worse than one operating at 80 percent if variability creates queues and service failures.

Capacity planning should distinguish design capacity, effective capacity, and actual output. Design capacity is the theoretical maximum. Effective capacity recognises maintenance, breaks, product mix, changeovers, labour rules, and operating schedules. Actual output is what the system achieves. Capacity cushions are deliberate reserves maintained to absorb demand peaks, disruptions, or growth.

Three broad timing approaches are common. A lead strategy adds capacity before demand is fully realised and supports service and growth, but risks excess investment. A lag strategy adds capacity only after demand is demonstrated, reducing idle assets but risking congestion and customer loss. A match strategy adds capacity in smaller increments as demand develops, often requiring modular facilities, flexible labour, or scalable technology.

Capacity decisions should also consider indivisibility and economies of scale. A large distribution centre may achieve lower unit handling cost, better automation economics, and stronger carrier consolidation. However, it may increase average delivery distance, concentration risk, and dependence on a single site. A network of smaller facilities can improve responsiveness and redundancy, but it may duplicate inventory and management overhead.

Distribution Structures and Flow Configurations

The choice of distribution structure determines how inventory and information move through the network. In direct shipment, suppliers or plants serve customers without an intermediate distribution centre. This can reduce handling and inventory pooling, but it may produce many small shipments and high transport cost. In a centralised distribution structure, one facility serves a broad market. This can pool inventory and simplify control, although delivery distances may be longer.

A regional distribution structure positions inventory closer to customers and may improve service and transport economics. It normally increases facility and safety-stock requirements. A multi-echelon design combines central and regional facilities. The central level may hold slow-moving or strategic inventory, while regional facilities hold fast-moving products. The resulting risk-pooling and service benefits must be balanced against additional handling and coordination.

Cross-docking reduces storage by transferring inbound goods directly to outbound vehicles. It is effective when demand is predictable, inbound and outbound schedules are coordinated, product labelling is accurate, and shipment quantities are suitable. It is less appropriate when products require inspection, demand is uncertain, or inbound reliability is poor.

Postponement delays final product configuration, packaging, labelling, or localisation until demand is better known. It can reduce finished-goods variety and inventory risk. For example, a generic product may be stored centrally and customised near the customer. Postponement requires modular product design, reliable information, suitable process technology, and adequate response capability.

The appropriate structure depends on customer density, product variety, demand uncertainty, order size, delivery promise, transport economics, and regulatory requirements. There is no universally superior network form.

Scenario and Sensitivity Analysis

Forecasts are uncertain, and network decisions are often difficult to reverse. Scenario analysis evaluates a proposed design under alternative assumptions rather than relying on a single forecast. Useful scenarios may include high and low demand, fuel-price changes, labour-cost inflation, supplier failure, border delays, extreme weather, changes in customer geography, and new service requirements.

Sensitivity analysis changes one or more inputs to determine which assumptions materially affect the decision. Variables commonly tested include facility fixed cost, transport rates, demand volume, service penalties, inventory carrying cost, capacity, and lead time. If a small change causes the preferred site to change, the decision is sensitive and requires further investigation. If the preferred design remains attractive over a broad range, it is more robust.

A break-even analysis can identify the demand volume at which one network alternative becomes preferable to another. If a central facility has annual fixed cost F₁ and variable cost v₁ per unit, while a regional alternative has F₂ and v₂, the break-even volume Q is:

Q = (F₂ − F₁) / (v₁ − v₂), assuming the denominator is positive.

This calculation is useful but incomplete when service, inventory, risk, and capacity differ between alternatives.

More advanced analysis may use stochastic optimisation, Monte Carlo simulation, robust optimisation, or real-options reasoning. A real-options perspective recognises that modular expansion, temporary capacity, or a lease may have value because they preserve future choices under uncertainty. The least-cost design under the expected forecast is not necessarily the best design when volatility and disruption are significant.

Outsourcing, 3PL, and 4PL Decisions

Outsourcing a logistics activity is a strategic make-or-buy decision. The relevant comparison is not only the supplier's quoted price but the total cost and capability of internal provision versus external provision. Considerations include asset investment, labour, management attention, information systems, service quality, flexibility, risk transfer, switching cost, data access, and strategic control.

A third-party logistics provider (3PL) performs logistics activities for a client. Services may include transport, warehousing, fulfilment, customs support, freight forwarding, reverse logistics, and value-added operations. A 3PL may provide assets and operational expertise, but the client usually retains significant responsibility for network strategy and coordination.

A fourth-party logistics provider (4PL) generally acts as an integrator or orchestrator of multiple logistics resources and providers. It may design the network, manage 3PL relationships, coordinate transport and warehousing, operate control towers, and provide analytical governance. The distinction is not universal in industry practice; some providers use the term 4PL for broad lead-logistics or managed-services offerings.

Outsourcing is attractive when a provider has scale, specialised knowledge, technology, geographic reach, or variable-cost capability that the organisation cannot economically build. It may be less appropriate when logistics capability is a source of competitive differentiation, customer intimacy, sensitive data, or operational learning. A hybrid model can retain strategically important activities internally while outsourcing transactional or geographically dispersed work.

A sound outsourcing process includes: defining the required outcomes; segmenting activities by strategic importance; developing a total-cost baseline; evaluating provider capabilities; conducting due diligence; designing service-level agreements; specifying performance measures and incentives; planning data integration; and establishing exit and business-continuity provisions. Contracts should address liability, subcontracting, cybersecurity, labour practices, regulatory compliance, continuity, change management, and ownership of data and improvements.

Technology and Analytical Enablement

Modern network design is supported by geographic information systems, transport-management systems, warehouse-management systems, enterprise-resource-planning systems, and control-tower platforms. GIS allows analysis of road networks, customer density, travel time, land use, and service territories. Simulation tools can represent queues, dock congestion, labour variability, and peak-season behaviour that static optimisation may overlook.

Artificial intelligence and machine learning can improve demand forecasting, estimated travel times, facility productivity analysis, and scenario generation. Digital twins can connect operational data with a virtual representation of the network and test alternative facility, capacity, and routing decisions. These tools are valuable only when data is accurate, definitions are consistent, and decision-makers understand model limitations. Automation may increase throughput and reduce error, but it can also create dependence on specialised skills, cybersecurity exposure, and high switching costs.

Illustrative Case: NorthStar Medical Supplies

Background

NorthStar Medical Supplies is an illustrative mid-sized distributor of hospital consumables. It currently operates one central warehouse and serves hospitals through parcel carriers and dedicated vehicles. Demand has expanded across three regions, while customers increasingly require next-day delivery for selected products. Some products are temperature-sensitive, and emergency orders carry high service consequences.

Situation and problem

The central warehouse is approaching its effective capacity during monthly peaks. Management is considering three alternatives: expand the existing facility, open one regional distribution centre, or outsource regional fulfilment to a 3PL. The finance team favours expansion because it has the lowest apparent fixed cost. The commercial team favours a regional facility because it would improve response time. The operations team is concerned about duplication of inventory and the risk of poor coordination.

Analysis

The design team first segmented products. Fast-moving emergency products were distinguished from slow-moving routine items, and temperature-sensitive products were analysed separately. Customer demand was mapped by postcode and converted into travel-time zones rather than straight-line distance. A centre-of-gravity calculation identified a promising area for a regional facility, but the area was rejected after screening because of limited cold-storage availability and poor access for heavy vehicles.

A mixed-integer model compared the existing network, an expanded central facility, a company-owned regional facility, and a 3PL-operated regional facility. The model included facility cost, transport, inventory, temperature-controlled handling, expected stockout cost, and capacity. Scenario analysis tested demand growth of 10 and 25 percent, a carrier-rate increase, and temporary closure of the central warehouse.

The analysis showed that expansion of the central facility was least costly under the base forecast but became capacity-constrained under high growth. The company-owned regional facility provided strong service but required substantial inventory and management capability. The 3PL alternative had a higher unit handling charge but required less capital, offered faster implementation, and performed better in the disruption scenario because inventory and fulfilment were geographically diversified.

Approach and solution

NorthStar adopted a staged design. The central facility was expanded for slow-moving and temperature-sensitive inventory. A 3PL was contracted to fulfil a defined range of fast-moving products from a regional site. Inventory ownership remained with NorthStar, while the 3PL provided fulfilment labour, systems integration, and carrier coordination. A capacity option was included in the contract to add space during seasonal peaks. The company also retained an internal network-planning function and required weekly data feeds, audit rights, contingency procedures, and a documented exit plan.

Outcome

The solution did not minimise every individual cost category. Instead, it balanced service, capital exposure, resilience, and implementation speed. The hybrid structure allowed NorthStar to learn regional demand patterns before committing to a permanent owned facility. Performance was monitored through order-cycle time, on-time-in-full delivery, inventory accuracy, temperature compliance, cost per order, and recovery time after disruption.

Learning points

The case illustrates that network design is a portfolio decision rather than a choice between ownership and outsourcing alone. Product segmentation can prevent inappropriate treatment of all inventory as identical. A location model must be supplemented by feasibility and regulatory screening. Finally, contractual flexibility may be more valuable than a lower initial unit price when demand and disruption conditions are uncertain.

Risks, Limitations, and Governance Considerations

Network studies commonly fail because demand forecasts are treated as facts, costs are omitted or double-counted, service requirements are vague, or model outputs are accepted without operational review. Other mistakes include using Euclidean distance where travel time matters, ignoring reverse logistics, assuming unlimited labour, overlooking peak capacity, and evaluating only steady-state operations.

Resilience requires attention to concentration risk, supplier and carrier dependence, cyber disruption, geopolitical restrictions, natural hazards, and infrastructure failure. Redundancy is not always efficient, but the cost of resilience should be compared with the financial and social consequences of failure. Critical products may justify dual sourcing, geographically separated facilities, safety stock, alternative transport modes, or emergency agreements.

Ethical and regulatory responsibilities include lawful working conditions, nondiscrimination, safe warehouse design, responsible subcontracting, environmental compliance, product traceability, data protection, and transparent treatment of affected communities. Facility location can influence traffic, noise, land use, emissions, and employment. Stakeholder consultation and environmental assessment may therefore be part of responsible network design.

Sustainability should be assessed across the network rather than by facility energy use alone. A larger facility may be energy-efficient per unit but generate longer delivery distances. Consolidation may reduce vehicle kilometres but increase customer lead times. Relevant measures include carbon emissions, energy intensity, packaging, empty running, modal share, waste, water use, and product returns. Life-cycle evaluation is preferable to isolated green claims.

Current Developments and Best Practice

Important developments include urban fulfilment, micro-fulfilment centres, omnichannel inventory, automated storage and retrieval, collaborative transport, control towers, and increasing use of data-driven simulation. E-commerce has encouraged networks that combine store fulfilment, central fulfilment, pickup points, and returns processing. At the same time, firms are reassessing excessive geographic concentration because of disruption and geopolitical uncertainty.

Best practice is to:

• align network design with customer promises and product economics;
• use total landed cost rather than transport cost alone;
• segment products, customers, and service requirements;
• combine optimisation with scenario, sensitivity, and feasibility analysis;
• distinguish nominal capacity from effective peak capacity;
• preserve flexibility through modular expansion, leases, or contractual options;
• include reverse logistics, sustainability, and compliance from the beginning;
• validate models with operating managers and local experts;
• establish clear ownership of data, assumptions, and decisions; and
• review the network periodically as demand, technology, risk, and regulations change.

Chapter Summary

Logistics network design determines the structure through which supply, inventory, information, and customer fulfilment are coordinated. It integrates facility location, capacity, flow allocation, distribution architecture, and governance. Centre-of-gravity and load-distance methods are useful preliminary tools, while transportation and mixed-integer facility-location models support more rigorous analysis. Capacity decisions must balance scale economies, utilisation, service, flexibility, and disruption risk. Centralised, regional, multi-echelon, direct, cross-dock, and postponement structures each offer different trade-offs.

Because forecasts and operating conditions are uncertain, scenario and sensitivity analysis are essential. Outsourcing decisions should compare total cost, capability, control, risk, technology, and strategic importance. 3PL providers typically execute logistics activities, whereas 4PL providers integrate and orchestrate multiple resources. Effective network design combines quantitative modelling with operational judgement, ethical governance, sustainability assessment, and implementation planning.

Key Terms

Network design: Strategic determination of facilities, links, capacities, flows, and governance in a logistics system.

Facility location: Selection of the geographic position of a plant, warehouse, terminal, or service facility.

Capacity: The volume or activity level a facility or process can handle during a specified period.

Flow allocation: Assignment of quantities between supply, facility, and demand nodes.

Centre of gravity: A weighted geographic estimate used to identify a preliminary facility search area.

Load-distance method: A screening technique that compares locations using weighted distance scores.

Transportation model: An optimisation model allocating supply to demand at minimum variable cost.

Mixed-integer optimisation: A model containing both continuous variables and discrete decisions such as opening a facility.

Utilisation: The proportion of available capacity being used.

Capacity cushion: Deliberate reserve capacity maintained for variability, growth, or disruption.

Cross-docking: Transfer of goods from inbound to outbound transport with minimal storage.

Postponement: Delaying final configuration or localisation until demand is more certain.

Multi-echelon network: A network containing multiple connected inventory or distribution levels.

Scenario analysis: Evaluation of a decision under alternative future conditions.

Sensitivity analysis: Examination of how changes in assumptions affect a result.

3PL: An external provider performing contracted logistics activities for a client.

4PL: An integrator that coordinates multiple logistics providers and network resources.

Total landed cost: The complete cost of making a product available, including logistics and related charges.

Risk pooling: Reduction of aggregate uncertainty by combining demand or inventory across locations.

Control tower: A technology-enabled coordination function providing network visibility and decision support.

Review Questions

Short Answer Questions

1. What is the difference between logistics network design and routine transportation routing?

2. State the formula used in the centre-of-gravity method.

3. Why can a facility with high nominal capacity still experience operational congestion?

4. What is postponement, and under what conditions can it be useful?

5. Distinguish the typical role of a 3PL from that of a 4PL.

Long Answer Questions

1. Explain the major cost, service, capacity, and risk trade-offs involved in centralised and regional distribution networks.

2. Describe how a mixed-integer facility-location model can be formulated and interpreted.

3. Discuss the strategic importance of scenario and sensitivity analysis in network design.

4. Explain the main criteria that should guide a logistics outsourcing decision.

5. Evaluate the role of technology, analytics, and digital twins in logistics network planning, including their limitations.

Analytical / Application Questions

1. Three markets have coordinates (2,8), (8,6), and (6,2), with shipment weights of 100, 150, and 50 units. Calculate the centre-of-gravity location and explain why the result should not be treated as the final site decision.

2. A company is choosing between one central facility and two regional facilities. Design a sensitivity analysis using demand growth, transport cost, inventory carrying cost, and facility capacity as variables. Explain how the results would influence the decision.

3. Using the NorthStar Medical Supplies case, recommend conditions that should be included in the 3PL contract to protect service continuity, data security, regulatory compliance, and the client's ability to exit or change the arrangement.

22. Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes logistics network design from transportation routing?

A. Network design determines long-term facilities and flows, whereas routing assigns movement through an existing network

B. Network design concerns only vehicle sequencing, whereas routing concerns facility ownership

C. Network design is always tactical, whereas routing is always strategic

D. Network design excludes capacity decisions, whereas routing includes all capacity decisions

Correct Answer: A. Network design determines long-term facilities and flows, whereas routing assigns movement through an existing network

Question 2

A firm has demand points with weights representing annual shipment volume. Which method provides a preliminary geographic location by weighting the coordinates of those points?

A. Break-even analysis

B. Centre-of-gravity method

C. Critical-path method

D. Economic order quantity method

Correct Answer: B. Centre-of-gravity method

Question 3

In a facility-location optimisation model, what is the usual purpose of a constraint such as Σj xkj ≤ Uk yk?

A. To require every facility to operate at full capacity

B. To prevent shipments through a facility that has not been opened

C. To ensure transport cost is independent of facility location

D. To convert all demand into fixed costs

Correct Answer: B. To prevent shipments through a facility that has not been opened

Question 4

Why may a facility operating at 98 percent utilisation perform worse than one operating at 80 percent utilisation?

A. High utilisation always reduces product quality by definition

B. The highly utilised facility may lack capacity cushion and suffer congestion under variability

C. Low utilisation necessarily produces higher transport cost

D. Utilisation is unrelated to throughput and service

Correct Answer: B. The highly utilised facility may lack capacity cushion and suffer congestion under variability

Question 5

Which distribution arrangement is most directly associated with transferring inbound goods to outbound vehicles with little storage?

A. Postponement

B. Cross-docking

C. Risk pooling

D. Direct sourcing

Correct Answer: B. Cross-docking

Question 6

A company delays final labelling and configuration until customer demand is known. What network principle is it applying?

A. Postponement

B. Lag capacity strategy

C. Load-distance analysis

D. Supplier dual sourcing

Correct Answer: A. Postponement

Question 7

What is the principal purpose of sensitivity analysis in a network design study?

A. To eliminate the need for demand forecasts

B. To determine how changes in assumptions affect the recommended decision

C. To guarantee that the lowest-cost site is operationally feasible

D. To calculate only the number of warehouse employees required

Correct Answer: B. To determine how changes in assumptions affect the recommended decision

Question 8

Which situation most strongly supports considering a 4PL arrangement?

A. A firm needs a single short-term trucking service for one route

B. A firm wants an integrator to coordinate several 3PLs, systems, and network activities

C. A firm wants to eliminate all performance measurement

D. A firm requires only a basic storage lease with no operations

Correct Answer: B. A firm wants an integrator to coordinate several 3PLs, systems, and network activities

Question 9

Why should a centre-of-gravity result be followed by feasibility screening?

A. The method ignores all demand weights

B. The calculated point may lack suitable roads, labour, land, capacity, or regulatory approval

C. The method can only be applied to manufacturing plants

D. The method automatically selects the most expensive location

Correct Answer: B. The calculated point may lack suitable roads, labour, land, capacity, or regulatory approval

Question 10

In the NorthStar illustrative case, why was a hybrid central-and-3PL design attractive?

A. It guaranteed that inventory would never be duplicated

B. It balanced service improvement, capital exposure, flexibility, and geographic diversification

C. It removed the need for internal network management

D. It minimised every individual cost category simultaneously

Correct Answer: B. It balanced service improvement, capital exposure, flexibility, and geographic diversification