Distribution Network Design and Location Decisions

Introduction

Omnichannel distribution is not simply the addition of e-commerce to a store network. It is the design of an integrated service system in which inventory, information, orders, transport, and customer experience move across stores, distribution centers, fulfillment centers, dark stores, micro-fulfillment centers, suppliers, and pickup points. A customer may browse on a mobile device, order from a regional fulfillment center, collect the order at a store, and return it through a parcel carrier. The network must therefore be designed around total customer value and total system cost rather than around isolated channels.

A distribution network decision is strategic because facilities are expensive, difficult to relocate, and influential for many years. Location determines delivery speed, transportation exposure, labor access, inventory pooling, tax and regulatory exposure, resilience, and the feasibility of services such as same-day delivery and buy-online-pick-up-in-store (BOPIS). Poorly designed networks often produce duplicated inventory, congested stores, high split shipments, excessive last-mile cost, and inconsistent service.

This chapter develops methods for designing omnichannel networks. It explains facility roles, network structures, location and capacity models, cost-to-serve analysis, postponement and inventory pooling, scenario analysis, and managerial implementation.

Learning Objectives

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

1. Distinguish the roles of stores, warehouses, fulfillment centers, dark stores, micro-fulfillment centers, drop-ship nodes, and pickup locations.
2. Design network structures using hub-and-spoke, direct-ship, hybrid, and omnichannel configurations.
3. Apply gravity, center-of-gravity, facility-location, capacity, and cost-to-serve models.
4. Evaluate postponement, inventory pooling, and fulfillment-assignment alternatives.
5. Compare network scenarios under uncertain demand, service requirements, and disruption conditions.
6. Assess strategic, operational, ethical, technological, and regulatory implications of location decisions.

1. The Strategic Logic of an Omnichannel Network

A network converts supply into customer availability. Its performance is commonly evaluated through five dimensions:

• Service: delivery time, product availability, pickup convenience, order accuracy, and return ease.
• Cost: facility, inventory, handling, line-haul, last-mile, labor, technology, and returns costs.
• Capital: investment in buildings, automation, inventory, vehicles, and information systems.
• Resilience: ability to continue operating after demand surges, labor shortages, supplier failures, cyberattacks, or transport disruption.
• Sustainability and legitimacy: emissions, congestion, land use, labor conditions, community impact, and regulatory compliance.

These dimensions conflict. A facility close to customers may lower delivery distance but increase rent and labor costs. A centralized warehouse may reduce inventory through pooling but require longer delivery routes. A store may be a convenient pickup point but lack the space and labor required for online order assembly.

A useful decision principle is to optimize the total delivered cost at an agreed service level, not the cost of any single activity. Total cost may be represented as:

where TC is total network cost; F facility and fixed operating cost; V variable procurement or production cost; I inventory cost; H handling and processing cost; T line-haul and middle-mile transport cost; L last-mile or customer-access cost; R returns and reverse-logistics cost; and E expected disruption, penalty, or externality cost. All terms must be measured over the same time period and in the same monetary units. The expression is an accounting framework rather than a universal optimization equation; some components may be embedded in others and must not be double counted.

2. Facility Roles in the Omnichannel Network

Stores

Stores provide sales space, advice, product trial, local brand presence, returns, and increasingly fulfillment services. They can fulfill online orders from shelf inventory, serve as pickup and drop-off points, or act as local delivery dispatch sites. However, store fulfillment can create inventory inaccuracies, disrupt shoppers, increase picking labor, and remove saleable stock from shelves.

Regional distribution centers

A regional distribution center (RDC) receives large inbound shipments, consolidates inventory, and replenishes stores or other nodes. RDCs usually emphasize case handling, pallet efficiency, and predictable replenishment rather than individual-item picking. Their scale supports automation and purchasing economies but may place inventory far from final customers.

E-commerce fulfillment centers

A fulfillment center (FC) is designed for piece picking, packing, order consolidation, and parcel or courier dispatch. It may serve a broad geography and hold a large assortment. FC design depends on order-line profiles, item dimensions, picking technology, batch size, carrier collection times, and returns flows.

Dark stores

A dark store is a retail-format facility closed to ordinary shoppers and dedicated to online order fulfillment. It can use a familiar store layout and be located near demand, but its economics depend on adequate order density. When demand is low, the facility may be underutilized; when demand is high, its proximity can support rapid delivery.

Micro-fulfillment centers

A micro-fulfillment center (MFC) is a compact fulfillment operation located near dense demand, often within or adjacent to a store, urban building, or transport hub. Automation may reduce travel time and labor requirements, but installation, maintenance, software integration, fire safety, and limited assortment can be substantial constraints. MFCs are particularly relevant for high-frequency, fast-moving items.

Drop-ship nodes and suppliers

In drop-shipping, the retailer accepts the customer order while the supplier ships directly to the customer. This can expand assortment and reduce retailer inventory, but it may increase delivery variability, split shipments, packaging inconsistency, returns complexity, and loss of control over customer experience. Supplier capability and data integration are essential.

Pickup points and lockers

Pickup points, lockers, and partner locations consolidate customer collection. They can lower failed-delivery rates and last-mile cost, but require suitable opening hours, secure storage, access control, and sufficient geographic coverage.

3. Network Structures and Design Choices

Hub-and-spoke

In a hub-and-spoke network, a central or regional hub consolidates flows to spokes such as stores, pickup points, or local delivery zones. Consolidation can reduce transportation cost because high-volume trunk movements are more efficient than many small direct movements. The disadvantages are additional handling, dependence on hubs, and possible delays.

Direct-ship and drop-ship

Direct shipping sends products from a supplier or fulfillment node directly to the customer. It is attractive for bulky, slow-moving, customized, or long-tail items. It is less attractive when customers expect a single consolidated delivery or when supplier service reliability is weak.

Distributed inventory

Distributed inventory places stock in several facilities close to customers. It reduces delivery distance and can improve speed, but it fragments inventory and increases safety-stock requirements unless demand is highly correlated or replenishment is excellent.

Hybrid networks

Most mature omnichannel businesses use a hybrid design: centralized inventory for slow movers, regional inventory for predictable demand, stores or MFCs for fast movers, and suppliers for selected long-tail products. The challenge is defining allocation rules that are economically and operationally coherent.

A network should also distinguish ownership from fulfillment responsibility. A store may own inventory but not be the best node to fulfill every order. Conversely, a supplier may fulfill an order while the retailer owns the customer relationship and service obligation.

4. Location Analysis

The center-of-gravity method

The center-of-gravity method provides a preliminary geographic estimate for a facility serving geographically distributed demand. For demand points indexed by i, with coordinates (x_i,y_i) and weights w_i, the weighted location is:

Here, wᵢ may represent shipment volume, revenue, order weight, or transportation demand. Coordinates must use a meaningful distance-preserving system, and the method assumes a relatively simple transport environment. It does not account directly for roads, congestion, terrain, taxes, rent, labor, facility capacity, or service-time constraints. It is therefore a screening tool, not a final location decision.

If three demand zones have weights 100, 60, and 40 and coordinates (0,0), (10,0), and (4,8), then:

x* = (100×0 + 60×10 + 40×4) / 200 = 4.0

and

y* = (100×0 + 60×0 + 40×8) / 200 = 1.6.

The estimate is (4.0, 1.6), subject to the method's assumptions.

Weighted-distance location models

A more general single-facility model selects a location j minimizing weighted transport and fixed cost:

where Fⱼ is the fixed annual cost of candidate site j; Dᵢ is demand from zone i; and cᵢⱼ is unit cost of serving zone i from site j. If transport cost depends on distance, a simple representation is c_ij=a+b d_ij, where a is a shipment-related cost, b is cost per distance unit, and dᵢⱼ is road distance. The model assumes demand and costs are known and that one site is being evaluated. It should not be used without capacity, service, and feasibility checks.

Facility-location models

The uncapacitated facility-location model decides which candidate facilities to open and which customer zones to assign. A standard form is:

subject to:

Here, yⱼ equals 1 if facility j opens; xᵢⱼ is the amount or proportion of demand from zone i assigned to facility j; Fⱼ is fixed cost; and cᵢⱼ is assignment cost. In a binary single-source version, xᵢⱼ is also binary. The first constraint assigns all demand, and the second prevents assignment to a closed facility. This formulation assumes demand is assigned, costs are known, and service requirements are represented either in costs or additional constraints.

A capacitated version adds:

where Kⱼ is the capacity of facility j. If demand may be split, xᵢⱼ can be continuous; if each zone must be assigned wholly to one facility, binary variables are required. Real systems may need constraints for maximum delivery time, labor availability, minimum store coverage, assortment eligibility, carbon budgets, or resilience redundancy.

Covering and service models

A set-covering model minimizes facility cost while ensuring each demand zone lies within an acceptable service distance or time. A maximal-covering model maximizes covered demand subject to a limited number of facilities. These models are useful for urgent delivery, pickup access, healthcare-like service guarantees, and urban networks.

Travel time should generally replace straight-line distance when customer promises depend on roads. Time-dependent travel, delivery windows, and congestion may require routing simulation rather than a static location model.

5. Capacity, Inventory, and Pooling

Capacity planning

Capacity is not merely building floor area. It includes receiving doors, storage positions, pick stations, pack benches, sortation, labor hours, carrier collection windows, parking, and software throughput. A facility may have adequate storage but insufficient peak-hour picking capacity.

A basic utilization measure is:

where u is utilization, Q is actual throughput over a defined period, and K is practical capacity over the same period. Units must match, such as orders per hour. Practical capacity should allow for breaks, maintenance, variability, replenishment, and quality checks; using theoretical maximum capacity can produce dangerously optimistic plans.

Capacity should be tested at peak demand, not only annual average demand. A network may need temporary labor, overflow facilities, flexible carrier contracts, or dynamic order throttling during peaks.

Inventory pooling

Pooling demand in a central or regional facility can reduce aggregate safety stock when demand is not perfectly correlated. For independent demand streams with standard deviations σ₁, σ₂, ..., σ_n, the standard deviation of aggregate demand is:

If all streams have equal standard deviation σ, this becomes σ_total=σ√n. By contrast, fully separate inventories have total standard deviation exposure approximately nσ. The result illustrates risk pooling. It assumes the demand streams are independent; with correlation, the general expression is:

where ρᵢₖ is the correlation between streams i and k. Positive correlation reduces the benefit of pooling; negative correlation increases it. Safety stock also depends on the desired service level, replenishment lead time, forecast error, and review policy, so pooling is not a guarantee of a specific inventory reduction.

Postponement

Postponement delays final product configuration, packaging, labeling, or allocation until demand is more certain. Examples include applying local-language labels at a regional facility, postponing gift wrapping until order receipt, or holding common components centrally and completing variants near the customer.

Postponement can reduce obsolescence and increase assortment flexibility. It may require modular product design, late-stage equipment, reliable information, and skilled labor. It is less suitable when customers require immediate availability and final configuration is slow or expensive.

6. Cost-to-Serve and Fulfillment Assignment

Average cost can conceal economically destructive orders. Cost-to-serve assigns relevant costs to customer segments, products, channels, zones, or order types. A practical order-level expression is:

where CTSₒ is cost to serve order o; pₒ procurement or product cost; hₒ handling and fulfillment cost; tₒ middle-mile cost; lₒ last-mile or pickup cost; rₒ expected returns cost; and sₒ customer-service or exception cost. Margin analysis should compare net revenue with the costs that are truly incremental or appropriately allocated. Fixed costs should not be allocated in a way that causes managers to reject profitable incremental demand without understanding capacity constraints.

Fulfillment assignment should consider:

1. inventory availability and promised date;
2. distance and delivery cost;
3. handling complexity and labor availability;
4. probability of split shipment;
5. customer preference for delivery or pickup;
6. substitution and markdown risk;
7. facility congestion and peak capacity;
8. carbon and regulatory constraints.

The cheapest node for one order may not be the cheapest node for the network. A store-based order can avoid parcel transport but consume labor needed for shoppers. A centrally fulfilled order may cost more today but preserve store availability and customer experience.

7. Scenario Modeling and Uncertainty

Location decisions should be tested against alternative futures rather than optimized only for a single forecast. Relevant scenarios include:

• high, base, and low demand;
• rapid growth in online penetration;
• different service promises;
• labor-cost inflation;
• fuel or carrier-price changes;
• new stores or store closures;
• supplier lead-time variation;
• extreme weather or infrastructure disruption;
• regulatory restrictions on vehicles or urban facilities;
• automation investment versus manual operation.

If scenario s has probability pₛ and network cost Zₛ, expected cost is:

with ∑p_s=1. Expected cost alone is insufficient when downside risk matters. A manager may compare worst-case cost, service failure, cash investment, or a risk-adjusted objective such as E[Z]+λR, where R is a selected risk measure and λ reflects managerial risk tolerance. The risk measure must be defined clearly; it might be variance, maximum regret, disruption loss, or probability of violating a service target.

Sensitivity analysis changes one assumption at a time, such as delivery cost or demand volume. Scenario analysis changes several linked assumptions coherently. Monte Carlo simulation repeatedly samples uncertain demand, lead times, and disruptions to estimate distributions of cost and service. Optimization identifies attractive configurations; simulation tests whether those configurations remain feasible under variability.

A robust design may not be the lowest-cost design in the base case. It may retain optionality through modular expansion, multiple carriers, alternate suppliers, cross-trained labor, or a deliberately redundant facility.

8. Worked Network Example

An illustrative retailer serves three demand zones. It is considering two candidate fulfillment centers. Annual demand is 10,000, 6,000, and 4,000 orders. Candidate A has fixed annual cost of $420,000 and assignment costs of $8, $14, and $20 per order for the three zones. Candidate B has fixed annual cost of $300,000 and assignment costs of $16, $10, and $12.

If only A opens, variable assignment cost is:

(10,000×8)+(6,000×14)+(4,000×20)=244,000.

Total annual cost is therefore $664,000.

If only B opens, variable assignment cost is:

(10,000×16)+(6,000×10)+(4,000×12)=292,000.

Total annual cost is $592,000.

If both open and each zone is assigned to its lower-cost facility, Zone 1 uses A, while Zones 2 and 3 use B. Variable cost is:

(10,000×8)+(6,000×10)+(4,000×12)=200,000.

Both fixed costs total $720,000, so total cost is $920,000. On these assumptions, opening both facilities is more expensive despite lower assignment cost. The conclusion could change if both facilities provide a materially higher service level, if capacity makes single-site operation infeasible, or if disruption and customer-loss costs are included.

This example demonstrates three managerial cautions. First, a lower variable cost does not automatically justify another facility. Second, fixed costs must be evaluated over the relevant demand volume and planning horizon. Third, a cost-only model can undervalue service, resilience, and strategic option value.

9. Illustrative Case: Meridian Home and Living

Background

Meridian Home and Living is a fictional omnichannel retailer selling furniture, décor, small appliances, and seasonal products. It operates 85 stores and one national distribution center. Online orders have grown rapidly. Customers complain about delayed delivery, split shipments, and inconsistent pickup readiness. Stores hold inventory, but store associates frequently interrupt sales work to pick online orders.

Problem

Management is considering three alternatives: expand the national FC, add two regional FCs, or create a hybrid network using regional FCs for broad assortment, store pickup for selected products, supplier drop-ship for bulky items, and MFCs in two dense metropolitan areas.

The central questions are not merely “Where should facilities be located?” They are:

• Which products should be stocked at which node?
• Which customer promises justify local inventory?
• How much capacity is needed during seasonal peaks?
• How should store labor and inventory accuracy be protected?
• How much resilience is worth paying for?

Analysis

The company segmented products by demand frequency, size, margin, return rate, and service expectation. Fast-moving small items were candidates for MFCs and store pickup. Long-tail décor products were candidates for centralized inventory or drop-ship. Bulky furniture required supplier or regional fulfillment because parcel economics were unfavorable.

A center-of-gravity analysis provided an initial regional search area, but candidate sites were then evaluated using road travel time, labor availability, lease cost, carrier access, zoning, flood exposure, and expansion potential. A capacitated location model tested whether each candidate could meet peak orders per hour. Simulation then tested order surges and labor absenteeism.

The team also compared inventory pooling. High-variance seasonal products remained more centralized, while predictable fast movers were placed closer to customers. A postponement strategy delayed certain gift-packaging and promotional bundles until order receipt.

Approach and solution

Meridian selected a hybrid design. It retained the national FC for slow-moving and highly pooled inventory, added one regional FC rather than two immediately, introduced MFC capability in one metropolitan area as a staged pilot, and formalized store pickup for a limited assortment. Bulky products were assigned to qualified suppliers under stricter delivery and returns standards.

The pilot used explicit performance measures: promised-date achievement, order accuracy, picking minutes per order, store labor hours consumed, inventory record accuracy, split-order rate, return cycle time, and contribution margin after fulfillment cost. Expansion was conditional on demonstrated demand density and stable operating performance rather than on optimistic annual averages.

Outcome

The pilot reduced delivery time in the target city and improved pickup visibility. However, the MFC required better replenishment discipline than expected, and store-based fulfillment remained unsuitable for low-margin, labor-intensive orders. The company therefore narrowed the store-fulfillment assortment and invested in better inventory synchronization.

Learning points

1. Facility roles should be assigned by product and service economics, not by channel labels.
2. Local facilities require sufficient order density and operational discipline.
3. A staged pilot can preserve strategic flexibility while revealing real labor and integration costs.
4. Network design must include returns, peak capacity, and customer promises.
5. Governance rules are necessary when a facility serves several channels competing for inventory.

10. Technology, Governance, and Responsible Design

Digital twins, geographic information systems, order-management systems, warehouse-management systems, and advanced analytics can improve network design. Artificial intelligence can forecast demand, estimate travel times, identify delivery zones, and recommend fulfillment assignments. Yet algorithmic recommendations inherit data limitations. Historical sales may underrepresent underserved areas; delivery data may reflect biased service policies; and optimization may prioritize low cost over accessibility or worker safety.

Good governance requires documented assumptions, version-controlled models, explainable decision rules, data-quality checks, human review, and monitoring after implementation. Managers should distinguish forecast uncertainty from model error and should test whether recommendations create unequal service across neighborhoods.

Location decisions also raise regulatory and ethical issues. Urban facilities can increase traffic, noise, and curb competition. Employee monitoring in automated facilities can threaten dignity and privacy if performance data are used without safeguards. Flexible delivery models may shift risk to gig workers. Environmental analysis should consider building energy, packaging, delivery mileage, failed deliveries, returns, and rebound effects rather than only the number of facilities.

Data protection is relevant because omnichannel systems combine addresses, purchasing behavior, payment information, location data, and service histories. Access controls, retention policies, encryption, incident response, and lawful processing are operational necessities, not merely legal formalities.

11. Implementation Best Practices

A disciplined network-design process normally follows these stages:

1. Define the customer promise. Specify delivery time, pickup access, assortment, returns, and availability targets.
2. Segment demand. Use geography, product characteristics, order profile, margin, urgency, and volatility.
3. Map the current network. Measure flows, capacity, inventory, service failures, and cost-to-serve.
4. Generate feasible alternatives. Include existing facilities, suppliers, stores, partners, and scalable temporary capacity.
5. Model economics and service. Use location, capacity, inventory, routing, and simulation models together.
6. Stress-test scenarios. Include peaks, disruption, labor constraints, fuel costs, and demand shifts.
7. Evaluate qualitative factors. Consider talent, zoning, community impact, resilience, expansion, and governance.
8. Pilot and learn. Use stage gates and measurable success criteria.
9. Design operating rules. Define inventory ownership, allocation priorities, escalation, and exception handling.
10. Monitor and redesign. Networks are not permanent; demand, technology, regulation, and customer expectations change.

Challenges and Limitations

Models can create false precision. Inputs may be uncertain, demand may be endogenous to the service promise, and cost allocations may distort channel profitability. Average travel distance can conceal congestion and delivery-window failure. Facility capacity may be technically adequate but operationally unavailable because of labor, maintenance, or replenishment constraints. A mathematically optimal design may be difficult to implement because of leases, politics, contracts, or organizational resistance.

There is also a danger of over-localization. Adding facilities may improve speed while increasing duplicated inventory, packaging, energy use, and management complexity. Conversely, excessive centralization can make the customer promise fragile. The appropriate design is contingent on density, assortment, volatility, service value, and risk tolerance.

Chapter Summary

Omnichannel network design integrates physical facilities, inventory, information, transport, labor, customer access, and returns. Stores, RDCs, FCs, dark stores, MFCs, suppliers, and pickup points have distinct capabilities and should be assigned roles deliberately. Hub-and-spoke, direct-ship, distributed, and hybrid structures create different trade-offs between scale, speed, inventory pooling, resilience, and complexity.

Center-of-gravity analysis is a useful screening tool, while facility-location models evaluate fixed and assignment costs under explicit assumptions. Capacity must be measured at practical peak throughput. Pooling can reduce variability when demand streams are not strongly correlated, and postponement can delay costly commitments until demand is clearer. Cost-to-serve reveals the economics of individual orders and customer segments. Scenario analysis, simulation, and robust design are essential because forecasts, costs, labor, and disruptions are uncertain.

The best network is not necessarily the one with the lowest modeled cost. It is the network that delivers a defensible customer promise while balancing economics, capital, resilience, sustainability, ethics, and implementation feasibility.

Key Terms

Center of gravity: A weighted geographic estimate used to identify an initial facility search location.

Cost-to-serve: The total relevant cost of serving a customer, order, product, or segment.

Dark store: A retail-format facility dedicated to online fulfillment and closed to normal shoppers.

Drop-shipping: Fulfillment in which a supplier ships directly to the customer after the retailer receives the order.

Facility-location model: An optimization model selecting facility sites and assigning demand to them.

Fulfillment center: A facility designed primarily for processing individual customer orders.

Hub-and-spoke: A network that consolidates flows through hubs connected to spoke locations.

Inventory pooling: Combining demand or inventory across locations to reduce aggregate variability.

Micro-fulfillment center: A compact fulfillment facility located near dense customer demand.

Omnichannel distribution: Integrated management of inventory, orders, fulfillment, delivery, pickup, and returns across channels.

Postponement: Delaying final configuration, packaging, or allocation until demand information is available.

Practical capacity: Usable throughput after allowing for variability, breaks, maintenance, and operating constraints.

Pickup point: A location where customers collect orders rather than receiving home delivery.

Resilience: The ability of a network to anticipate, absorb, and recover from disruption.

Scenario analysis: Evaluation of network alternatives under coherent alternative assumptions about the future.

Set covering: A model that selects facilities to cover demand within a specified distance or time.

Store fulfillment: Picking and dispatching online orders from retail-store inventory.

Total delivered cost: The combined facility, inventory, handling, transport, last-mile, returns, and relevant external costs of serving customers.

Review Questions

Short Answer Questions

1. What distinguishes a fulfillment center from a regional distribution center?
2. Why is the center-of-gravity method considered a screening tool rather than a final location method?
3. State two advantages and two risks of micro-fulfillment centers.
4. How does inventory pooling affect safety-stock requirements?
5. Why should network capacity be evaluated using peak rather than average demand?

Long Answer Questions

1. Explain how a retailer might combine stores, regional fulfillment centers, suppliers, and pickup points in a hybrid omnichannel network.
2. Describe the assumptions and limitations of a capacitated facility-location model.
3. Discuss how postponement and inventory pooling can jointly improve omnichannel performance.
4. Explain why cost-to-serve analysis may produce different conclusions from average channel profitability.
5. Evaluate the strategic trade-offs between a centralized national fulfillment center and multiple regional facilities.

Analytical / Application Questions

1. A retailer has four demand zones and three candidate facilities with different fixed costs, capacities, and delivery costs. Design a spreadsheet or optimization approach to select facilities, assign demand, and test service constraints. State the variables, objective, and at least four constraints.
2. A business is considering an MFC for same-day delivery. Identify the demand density, order-profile, labor, technology, and risk assumptions that should be tested before investment. Propose a pilot scorecard.
3. Using the worked example, explain why opening both facilities is unattractive under the stated cost assumptions, and identify three realistic changes that could make the dual-facility design preferable.

Multiple Choice Questions with Answers

Question 1

Which facility is primarily designed for high-volume replenishment of stores rather than individual-item customer order picking?

A. Regional distribution center

B. Micro-fulfillment center

C. Pickup locker

D. Dark store

Correct Answer: A. Regional distribution center

Question 2

What is the main limitation of the center-of-gravity method?

A. It cannot use demand weights

B. It ignores practical factors such as roads, capacity, and zoning

C. It requires binary optimization variables

D. It applies only to drop-shipping networks

Correct Answer: B. It ignores practical factors such as roads, capacity, and zoning

Question 3

In a capacitated facility-location model, what does the constraint ∑_i D_i x_ij ≤ K_j y_j ensure?

A. Every facility must be opened

B. Demand is assigned only within an open facility's capacity

C. All demand must be assigned to the cheapest facility

D. Facilities must have equal capacity

Correct Answer: B. Demand is assigned only within an open facility's capacity

Question 4

If three independent demand streams each have standard deviation σ, what is the standard deviation of their aggregate demand?

A. σ

B. 2σ

C. 3σ

D. √3σ

Correct Answer: D. √3σ

Question 5

Which situation most strongly supports postponement?

A. Highly predictable demand for a standard product

B. A need to delay final configuration until demand is known

C. A product that cannot be modified after production

D. A service requiring immediate availability of every variant

Correct Answer: B. A need to delay final configuration until demand is known

Question 6

In the illustrative network example, what is the total cost when only Candidate B opens?

A. $292,000

B. $420,000

C. $592,000

D. $664,000

Correct Answer: C. $592,000

Question 7

Why might a store be a poor fulfillment node even when it is geographically close to the customer?

A. Stores can never hold inventory

B. Store fulfillment may disrupt shoppers and consume scarce labor

C. Stores cannot process returns

D. Geographic proximity always increases transport cost

Correct Answer: B. Store fulfillment may disrupt shoppers and consume scarce labor

Question 8

What does scenario analysis do?

A. Changes one input while holding all others constant

B. Evaluates coherent alternative assumptions about the future

C. Eliminates all uncertainty from a forecast

D. Measures only historical facility utilization

Correct Answer: B. Evaluates coherent alternative assumptions about the future

Question 9

Which network design is most likely to support long-tail assortment with limited retailer inventory?

A. Supplier drop-shipping

B. Store-only fulfillment

C. A closed pickup locker

D. A highly localized MFC network

Correct Answer: A. Supplier drop-shipping

Question 10

Why can a mathematically lowest-cost network be unsuitable in practice?

A. Optimization models never include costs

B. Implementation constraints, service requirements, and resilience may be omitted

C. Fixed costs are always irrelevant

D. Demand cannot be represented in models

Correct Answer: B. Implementation constraints, service requirements, and resilience may be omitted