Capacity Planning and Space Determination

Introduction

Warehouse capacity planning is the disciplined process of determining how much inventory, equipment, labour, and physical space a facility must accommodate over a defined planning horizon. Space determination translates inventory policies and operating requirements into storage positions, floor area, cubic capacity, staging space, circulation space, and expansion provisions.

The central challenge is that warehouse capacity is not equivalent to building area. A warehouse may have substantial floor area but inadequate usable capacity if clear height, rack geometry, aisle widths, fire regulations, access requirements, or staging demands are poorly considered. Conversely, a highly automated high-bay facility may store large volumes in a relatively small footprint but require substantial investment and operational controls.

Capacity decisions should therefore integrate four perspectives:

1. Inventory perspective: how much stock must be held, including cycle stock, safety stock, seasonal stock, and promotional or strategic inventory.
2. Flow perspective: how much material enters, leaves, and moves through the warehouse during peak periods.
3. Service perspective: what availability, response time, replenishment, and order-cycle commitments must be supported.
4. Strategic perspective: how demand growth, product proliferation, technology, risk, and future network changes affect capacity.

Underestimating capacity creates congestion, stockouts, unsafe handling, and service failures. Overestimating it creates unnecessary capital cost, low asset utilization, and excessive travel distances. The objective is not maximum occupancy, but economically viable capacity with sufficient operating resilience.

Learning Objectives

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

• distinguish theoretical, design, operating, and peak capacity;
• calculate storage positions from inventory and product characteristics;
• convert storage requirements into floor area and cubic capacity;
• determine aisle, staging, receiving, shipping, support, and expansion allowances;
• incorporate service-level targets, demand variability, lead-time variability, and seasonality;
• evaluate cubic utilization and practical occupancy limits;
• develop a capacity plan using scenario and sensitivity analysis; and
• recognize the operational, safety, technological, and governance implications of space decisions.

1. Capacity Concepts and Planning Boundaries

1.1 Types of capacity

Warehouse capacity should be described using precise definitions. The following distinctions are particularly important.

A warehouse should not be planned to remain completely full. High occupancy can block access locations, reduce slotting flexibility, increase replenishment difficulty, and cause inventory to spill into aisles. A practical operating occupancy target depends on the storage system and product profile. For example, a selective-pallet-rack operation may require more vacant locations than a dense drive-in system because each stock-keeping unit (SKU) needs accessible locations. Occupancy percentages should therefore be treated as design assumptions, not universal laws.

1.2 Planning horizons

Capacity plans normally use three horizons:

• Short term: daily or weekly operating control, including temporary overflow and labour scheduling.
• Medium term: one to three years, including seasonal peaks, SKU changes, and equipment additions.
• Long term: facility selection, building expansion, automation, network redesign, and lease or investment decisions.

The planning horizon determines whether the design should optimize current cost or preserve flexibility. A facility designed only for today’s average inventory may become constrained when demand grows, product dimensions change, or e-commerce order profiles increase handling intensity.

1.3 Units of analysis

Capacity can be measured in several non-equivalent units:

• pallets or pallet positions;
• cases, cartons, totes, bins, or trays;
• cubic metres of product;
• square metres of floor area;
• order lines or units processed per hour; and
• dock doors, vehicles, or staging lanes.

Storage capacity is principally a stock measure, whereas throughput capacity is a flow measure. A warehouse may have enough pallet positions but insufficient dock doors or picking labour. Conversely, it may have adequate throughput capability but insufficient reserve storage. A complete plan must examine both dimensions.

2. From Inventory Policy to Storage Positions

2.1 Inventory components

The required inventory position should be based on the maximum inventory expected during the design period, not simply average demand. A basic decomposition is:

where:

• I_design = design inventory quantity;
• I_cycle = inventory required to cover replenishment cycles;
• I_safety = protection against uncertainty;
• I_seasonal = inventory accumulated for predictable seasonal demand;
• I_promotional = additional inventory for planned campaigns; and
• I_strategic = inventory held for supply, geopolitical, or business-continuity reasons.

The terms must be defined consistently. If seasonal inventory already includes safety stock for the peak period, it must not be added twice.

For a continuously replenished item with average demand d units per day and replenishment interval T days, cycle stock is often approximated by:

This assumes relatively stable demand, regular replenishment, and a saw-tooth inventory pattern in which stock declines approximately linearly between replenishments. If replenishment quantities are fixed at Q, the equivalent average cycle stock is Q/2, provided receipts and demand are sufficiently regular.

2.2 Safety stock and service level

Safety stock protects against demand and replenishment uncertainty. For a continuous-review item with independent demand and lead-time variation, a common approximation is:

where:

• SS = safety stock in units;
• z = standard-normal service factor corresponding to the desired cycle-service probability;
• σ_LT = standard deviation of demand during lead time, in units.

If daily demand has mean d and standard deviation σ_d, lead time has mean L days and standard deviation σ_L, and demand and lead time are independent, then a commonly used approximation is:

This formula assumes that daily demand observations are independent or that autocorrelation is negligible, and that the mean and variance estimates are stable. It also uses a normal approximation, which may be unsuitable for intermittent, highly skewed, or very low-volume demand.

If both demand and lead time vary, the safety-stock calculation should reflect both sources. Using only average demand multiplied by average lead time understates uncertainty and can produce inadequate capacity.

For a periodic-review system, protection must usually cover the review period plus lead time:

where R is the review interval in days. The formula remains an approximation and should be adapted when review-period demand and lead-time uncertainty are correlated.

Service level requires careful interpretation. A cycle-service level is the probability of avoiding a stockout during a replenishment cycle; it is not the same as fill rate, which is the proportion of demand supplied immediately from stock. A high target service level increases safety stock nonlinearly because the standard-normal factor rises rapidly in the upper tail.

2.3 Converting inventory into storage positions

Inventory quantity must be converted into the physical handling unit used by the storage system. If item i requires I_i units at design peak and each pallet holds [[MATH{u_i}]] units, the number of pallet loads is:

The ceiling function is necessary because a fraction of a pallet normally still requires a physical location, although mixed-SKU or case-pick policies may alter the result.

If the system requires [[MATH{s_i}]] accessible locations per SKU or product condition, the required positions may be:

More generally, the storage-location requirement can include reserve and forward-pick locations:

The summation is over all SKUs or product classes. Special locations may be required for quarantine, returns, damaged goods, hazardous materials, temperature zones, high-value items, or quality inspection.

Worked Example 1: Storage positions

An illustrative distributor handles three product families at peak inventory. The data are:

For Product A, 4,800 ÷ 40 = 120 pallet loads. Product B requires 2,750 ÷ 50 = 55, and Product C requires 1,260 ÷ 30 = 42 because 1,260 ÷ 30 = 42 exactly. Thus the base requirement is:

Suppose 8 positions are reserved for quarantine and 5 for returns. The functional requirement is:

If management limits planned occupancy to 85 percent to preserve access and flexibility, installed capacity should be:

The 85 percent is a policy assumption. It is not a universal engineering constant and should be tested against the selected storage technology and operating experience.

3. Translating Storage Positions into Layout Area

3.1 Rack geometry and footprint

The floor area of a storage system includes more than the footprint of the load. It includes rack uprights, beams, flue spaces where applicable, structural clearances, aisle space, end-of-aisle turning areas, and access provisions.

For a simple rack module, the number of positions per aisle face can be estimated as:

where:

• L_r = usable rack run length in metres;
• p = horizontal pitch per pallet position in metres; and
• ⌊ ⌋ = floor function.

If a double-sided rack row has n_face positions per face, h storage levels, and two accessible faces, its positions are:

This expression assumes both faces are usable, all levels have the same position count, and no positions are removed for columns, sprinklers, damage protection, or irregular geometry.

The number of rows required is:

The storage block area is approximately:

where:

• w_r = rack-row depth, including load and structural clearance;
• w_a = aisle width;
• A_end = end-of-row, cross-aisle, and turning allowance; and
• all area terms are in square metres.

This is a simplified block estimate. Detailed layout design must account for column grids, fire exits, travel paths, equipment turning envelopes, pedestrian segregation, and building constraints.

3.2 Aisle allowances

Aisle width is determined by the material-handling equipment, load dimensions, turning method, rack arrangement, operating side clearances, and safety requirements. It should not be selected solely to maximize storage density.

Narrower aisles may increase pallet positions per square metre but can reduce vehicle productivity, increase collision risk, constrain equipment choice, and make emergency access more difficult. Wider aisles may reduce density while improving travel, visibility, flexibility, and safety.

Aisles generally include:

• rack aisles used by lift trucks or order-picking equipment;
• cross aisles connecting longitudinal aisles;
• main traffic aisles leading to docks and processing areas;
• pedestrian aisles and emergency egress routes; and
• service clearances around equipment, panels, and fire systems.

A useful early-stage measure is area efficiency:

This ratio is not a measure of overall warehouse performance. A layout with high storage-area efficiency may have poor travel productivity or inadequate staging. It is useful only when interpreted with throughput, safety, accessibility, and service requirements.

3.3 Cubic capacity and utilization

Cubic capacity recognizes vertical space. The gross building volume is:

where A_building is building floor area and H_clear is usable clear height. Gross volume is not fully usable because of structure, lighting, sprinklers, roof slope, fire clearances, rack beams, equipment envelopes, and product geometry.

The occupied product volume is:

where q_i is the quantity of units of SKU i, and l_i, w_i, and h_i are unit dimensions in metres. If dimensions are measured in metres, the result is cubic metres.

Cubic utilization is:

where V_usable is the volume actually available for inventory after deducting unusable height and required clearances. Product cubic utilization may be lower than rack occupancy because cartons rarely fill every part of a pallet position. Conversely, dense storage can achieve high cubic utilization while reducing accessibility.

Worked Example 2: Cubic utilization

A warehouse has 1,800 m² of floor area and 8 m of usable clear height. Gross volume is:

Suppose structural, fire, service, and operating constraints reduce usable volume to 72 percent of gross volume:

Peak inventory occupies 7,200 m³ of product volume. Therefore:

The result does not mean that 30.6 percent of the volume can automatically be filled. Some remaining space may be fragmented, inaccessible, or required for future growth and safe operation.

4. Staging, Receiving, Shipping, and Support Space

Storage is only one component of warehouse area. The facility must also absorb temporary inventory and operating processes.

4.1 Staging requirements

Staging is a flow-space requirement. It depends on throughput, dwell time, load size, dispatch waves, appointment schedules, and process reliability. A basic staging relationship is:

where:

• S = staging positions required;
• Q_p = peak throughput during the planning period, in units per hour or loads per hour;
• τ = average staging dwell time in the same time unit;
• A_load = units per staging position; and
• f_s = variability, congestion, and policy factor.

The formula follows Little’s Law, L = λW, where average work-in-process equals throughput multiplied by average time in the system. It is applicable when the process is reasonably stable and the selected time period reflects the relevant peak. For wave-based operations, appointment clustering, or highly uneven arrivals, a time-phased simulation or peak queue analysis is preferable.

Staging area is then:

where A_position is the footprint of one staging position.

Receiving and shipping staging should be analyzed separately when inbound and outbound flows have different dwell times, load dimensions, or appointment patterns. Combining them into one average can hide a severe peak imbalance.

4.2 Dock and processing areas

Receiving area may include inspection, count verification, quality release, labelling, deconsolidation, and put-away preparation. Shipping area may include picking completion, consolidation, packing, weighing, documentation, sortation, and carrier handover.

These areas are often sized by peak hourly flow rather than average daily flow. A facility processing 800 cartons per day may need capacity for 180 cartons in a concentrated two-hour dispatch wave. The relevant design rate is therefore the peak rate after considering order profiles and labour calendars.

4.3 Support and circulation areas

A complete area schedule normally includes:

Area schedules should identify whether each quantity is net usable area or gross allocated area. Mixing these bases causes systematic underestimation.

5. Peak Capacity, Variability, and Future Expansion

5.1 Average versus peak design

Capacity based on average demand is often inadequate because warehouse systems experience synchronized peaks. Demand may rise during holidays, promotions, harvest cycles, fiscal year-end, or market disruptions. Inbound supply may also arrive in batches because of supplier schedules or container economics.

A simple peak factor is:

where f_peak is derived from historical observations, forecasts, or a defined percentile rather than an arbitrary percentage. If daily demand has mean μ and standard deviation σ, a planning percentile may be approximated as:

where z_p corresponds to the selected percentile under the assumed distribution. This approach is more transparent than applying an unexplained growth factor, but it requires representative data and attention to seasonality.

5.2 Scenario analysis

A robust capacity plan should test at least three scenarios:

• Base case: most likely demand, SKU, and operating assumptions.
• Peak case: high but plausible inventory and throughput conditions.
• Stress case: supply disruption, demand surge, longer lead times, or equipment unavailability.

Each scenario should report storage positions, area, dock demand, labour, equipment, and service consequences. Sensitivity analysis is especially useful for variables such as service level, pallet cube, occupancy policy, SKU count, and peak factor.

5.3 Expansion requirements

Future capacity can be represented by a growth projection. If current design requirement is N₀, annual growth is g, and planning horizon is t years:

This assumes constant compound growth. It should not be used uncritically when growth is irregular, constrained by market saturation, or driven by discrete customer contracts.

Required expansion reserve may be estimated as:

If the result is negative, no additional positions are required under that scenario, although flexibility may still be strategically valuable. Expansion should be considered in terms of both space and interfaces. A building may have vacant floor area but insufficient electrical capacity, dock doors, fire-water supply, yard circulation, automation controls, or labour access to support expansion.

Options include:

• leaving a planned expansion zone within the building;
• designing foundations and utilities for later rack or automation installation;
• using demountable partitions and modular racking;
• securing adjacent land or a lease option;
• using overflow or third-party logistics capacity; and
• postponing automation until volume and process stability justify it.

The preferred option depends on the cost of idle capacity, the cost of future disruption, demand uncertainty, and the reversibility of investment.

6. Integrated Capacity-Planning Method

A professional capacity study can follow this sequence:

1. Define the design basis. Establish product families, planning horizon, operating days, service targets, equipment, building constraints, and peak definitions.
2. Clean and classify data. Validate SKU dimensions, weights, palletization, demand history, lead times, inventory records, and order profiles.
3. Forecast inventory and throughput. Separate average, peak, seasonal, promotional, and strategic components.
4. Calculate uncertainty allowances. Estimate safety stock and process buffers using appropriate variability models.
5. Convert inventory into handling units. Determine pallets, cases, totes, bins, forward-pick faces, quarantine, and special locations.
6. Select storage technology. Compare selective rack, double-deep, drive-in, mobile rack, shelving, mezzanines, automated storage, and floor stacking according to access and product requirements.
7. Develop area schedules. Include storage, staging, docks, processing, support, circulation, safety, and expansion.
8. Test operational feasibility. Check travel distances, replenishment workload, dock queues, equipment turns, pedestrian interactions, and emergency routes.
9. Run scenarios and sensitivity tests. Identify assumptions that most affect investment and service risk.
10. Document governance and review triggers. Specify data owners, update frequency, approval thresholds, and conditions requiring redesign.

A spreadsheet is useful for transparency, but complex operations may require discrete-event simulation, slotting optimization, digital twins, or three-dimensional building information models. Technology should improve decision quality rather than conceal weak assumptions.

7. Illustrative Integrated Case: Northstar Medical Distribution Centre

Background

The following is a fictional illustrative case. Northstar distributes medical consumables to hospitals and clinics. Demand is growing, products have different storage conditions, and service failures are costly because urgent orders support clinical operations.

The proposed facility must accommodate 6,000 pallet-equivalent units of peak reserve and forward inventory. Management requires 99 percent cycle service for critical products, separate quarantine space, temperature-controlled storage, and a three-year expansion option. Peak outbound flow is 42 pallet loads per hour for a two-hour dispatch wave. Average staging dwell time is 1.5 hours, and management applies a 1.25 staging variability factor.

Problem

An initial proposal used 6,000 pallet positions and 70 percent average occupancy but did not include quarantine, returns, peak staging, or expansion. It also treated all pallet positions as interchangeable, although critical products require dedicated accessible locations.

Analysis

Quarantine and returns require 180 positions. A 10 percent product-segregation allowance is applied to the base reserve and forward inventory, not to the quarantine positions:

At a target maximum occupancy of 85 percent:

Thus, the proposal for 6,000 installed positions would be inadequate even before future growth.

For outbound staging, peak flow is 42 pallet loads per hour and dwell time is 1.5 hours. Assuming one pallet per staging position:

The design should therefore provide at least 79 pallet staging positions, subject to a time-phased check of the two-hour dispatch wave. If each staging position occupies 1.2 m × 1.0 m, its net footprint is 1.2 m². With a factor of 1.8 for staging aisles and turning:

The area schedule must separately add receiving staging, packing, returns, temperature-controlled space, equipment parking, offices, safety routes, and expansion reserve.

Approach and solution

Northstar adopts a zoned design: high-access selective rack for critical and fast-moving products, denser reserve storage for stable palletized products, and segregated temperature-controlled and quarantine areas. The design reserves a structural bay for approximately 1,600 additional positions, but utilities and fire protection are sized for the future installation. Outbound staging is divided by dispatch wave and carrier appointment rather than treated as an undifferentiated open floor.

A monthly capacity dashboard is established with indicators for peak occupancy, blocked locations, staging dwell time, dock queue time, critical-SKU availability, and cubic utilization. A redesign trigger is set when forecast peak occupancy exceeds 82 percent for two consecutive planning cycles or when staging exceeds 90 percent of designed positions during three peak events.

Outcome

The revised plan requires more initial planning discipline and a larger installed envelope than the original proposal, but it reduces the likelihood of overflow, protects critical-product access, and avoids an expensive near-term building modification. The expansion bay also preserves strategic flexibility without requiring all future racking to be purchased immediately.

Learning points

• Inventory positions must include functional segregation and special stock.
• Occupancy policy converts functional requirements into installed capacity.
• Staging is driven by throughput and dwell time, not merely by inventory.
• Service-critical products may justify lower density and higher accessibility.
• Expansion should include utilities, interfaces, and operating processes, not only empty floor area.

8. Limitations, Governance, and Contemporary Practice

Capacity models are only as reliable as their data and assumptions. Common limitations include inaccurate master data, obsolete pallet configurations, unrepresentative demand history, ignored correlation between demand and lead time, and failure to distinguish peak hourly flow from average daily flow. Models can also understate capacity when they assume perfect cube utilization or unrestricted access.

Governance is therefore essential. Data ownership should be assigned for SKU dimensions, palletization, forecast assumptions, inventory policy, equipment constraints, and service requirements. Every model should show assumptions, units, version, source, date, and sensitivity results. Changes in product dimensions or packaging can materially alter capacity even when unit demand is unchanged.

Safety and regulatory requirements must be treated as design constraints rather than afterthoughts. Applicable building, fire, occupational safety, hazardous-material, accessibility, and environmental rules vary by jurisdiction. Rack design, load ratings, sprinkler clearances, emergency exits, pedestrian routes, battery charging, and temperature controls require competent technical review.

AI and advanced analytics can support demand forecasting, SKU clustering, slotting, anomaly detection, and scenario generation. However, AI-generated recommendations require validated data, explainable assumptions, human approval, and controls against model drift. It is inappropriate to use an opaque forecast to justify insufficient safety stock for critical medical or hazardous products. Digital twins and simulation are valuable when congestion, wave timing, automation interactions, or expansion alternatives cannot be evaluated reliably with static calculations.

Best practice is to combine analytical formulas for transparency with operational observation, engineering review, historical validation, and scenario testing. The final design should be robust, not merely optimal under one forecast.

Chapter Summary

Capacity planning converts inventory policy, variability, service objectives, and throughput requirements into physical and operational resources. Effective planning distinguishes theoretical, installed, effective, peak, and expansion capacity. Storage positions are calculated from design inventory and handling-unit characteristics, while occupancy policies convert functional requirements into installed locations.

Floor-area planning must include rack geometry, aisles, cross-aisles, turning areas, receiving, shipping, staging, processing, support, safety, and future expansion. Cubic utilization provides a useful measure of vertical-space performance but must account for inaccessible or regulated volume. Staging requirements are flow-driven and can be estimated using throughput multiplied by dwell time, with appropriate variability allowances.

Safety stock models should incorporate demand variability, lead-time variability, review periods, and service-level assumptions. Peak design should use historical or forecast-based scenarios rather than unexplained percentages. Growth projections, sensitivity analysis, governance, and expansion interfaces help ensure that capacity remains viable as conditions change. The strongest warehouse designs balance density with accessibility, productivity, safety, resilience, and total cost.

Key Terms

Capacity: The quantity or flow a warehouse can accommodate under defined conditions.

Theoretical capacity: The geometric maximum under ideal assumptions.

Effective capacity: Usable capacity after operational, safety, and accessibility constraints.

Peak capacity: Capacity required during the highest planned demand or inventory period.

Storage position: A designated physical location for a pallet, carton, tote, bin, or other handling unit.

Cycle stock: Inventory held to cover normal replenishment cycles.

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

Service level: A stated probability or performance target for satisfying demand.

Fill rate: The proportion of demand supplied immediately from available stock.

Occupancy: The proportion of installed storage positions currently or practically filled.

Cubic utilization: Product volume divided by usable warehouse volume.

Staging: Temporary placement of goods during receiving, processing, consolidation, or dispatch.

Throughput: Quantity processed or moved per unit of time.

Dwell time: Time goods remain in a process or staging area.

Forward pick: Accessible stock positioned for order picking.

Reserve storage: Bulk stock held for replenishing forward-pick locations.

Peak factor: Ratio of peak demand or flow to a reference average.

Expansion reserve: Capacity intentionally preserved or enabled for future growth.

Slotting: Assignment of products to storage and picking locations.

Design basis: Documented assumptions and constraints used for a capacity plan.

Review Questions

Short Answer Questions

1. Distinguish theoretical capacity from effective capacity.
2. Why should a warehouse not normally be designed for 100 percent occupancy?
3. Define safety stock and explain its relationship to service level.
4. What is the difference between storage capacity and throughput capacity?
5. Identify four types of non-storage space required in a warehouse.

Long Answer Questions

1. Explain how peak inventory is converted into installed storage positions, including special locations and occupancy allowances.
2. Discuss the assumptions and limitations of the safety-stock formula when both demand and lead time vary.
3. Explain how rack geometry, aisle width, cross-aisles, and turning areas affect floor-area determination.
4. Describe an integrated method for planning storage, staging, processing, support, and expansion capacity.
5. Critically evaluate the advantages and limitations of cubic utilization as a warehouse-design metric.

Analytical / Application Questions

1. A warehouse expects 3,600 units of peak inventory, with 30 units per pallet. It also needs 40 special positions and targets 80 percent maximum occupancy. Calculate the installed pallet-position requirement.
2. Peak outbound flow is 36 pallets per hour, average staging dwell time is 2 hours, and the variability factor is 1.2. Calculate required staging positions and explain what additional information is needed to convert them into area.
3. A facility has current capacity of 5,000 positions and requires 6,200 positions today. Demand is forecast to grow by 6 percent annually for three years. Calculate the projected requirement and recommend two expansion strategies.

Multiple Choice Questions with Answers

Question 1

A warehouse has 2,400 units of peak inventory and each pallet holds 40 units. How many pallet positions are required before special allowances?

A. 40

B. 60

C. 80

D. 96

Correct Answer: B. 60

Question 2

Which capacity measure accounts for safety, accessibility, and operating constraints?

A. Theoretical capacity

B. Gross capacity

C. Effective capacity

D. Nominal building volume

Correct Answer: C. Effective capacity

Question 3

If functional storage demand is 680 positions and the maximum planned occupancy is 85%, what installed capacity is required?

A. 578 positions

B. 680 positions

C. 765 positions

D. 800 positions

Correct Answer: D. 800 positions

Question 4

The basic relationship used to estimate staging work-in-process from throughput and dwell time is:

A. L = λW

B. Q = mcΔT

C. SS = zσ

D. V = AH

Correct Answer: A. L = λW

Question 5

Which item is primarily a flow-space requirement rather than a storage-space requirement?

A. Reserve pallet positions

B. Quarantine rack positions

C. Outbound dispatch staging

D. Forward-pick shelf locations

Correct Answer: C. Outbound dispatch staging

Question 6

A building has 10,000 m³ of usable volume and product occupies 7,500 m³. What is cubic utilization?

A. 25%

B. 50%

C. 75%

D. 133%

Correct Answer: C. 75%

Question 7

In the safety-stock approximation SS = zσ_LT, z represents:

A. Average lead time

B. The service-level factor

C. Daily demand

D. Pallet cube

Correct Answer: B. The service-level factor

Question 8

If current capacity is 4,000 positions, annual growth is 5%, and the horizon is two years, which expression estimates future requirement?

A. 4,000 + 0.05 × 2

B. 4,000 × 0.05²

C. 4,000 × 1.05²

D. 4,000 ÷ 1.05²

Correct Answer: C. 4,000 × 1.05²

Question 9

Why can high cubic utilization be undesirable?

A. It always reduces inventory accuracy to zero

B. It may reduce accessibility and create congestion

C. It eliminates the need for aisles

D. It guarantees excessive building height

Correct Answer: B. It may reduce accessibility and create congestion

Question 10

A peak flow is 30 pallets per hour, dwell time is 1.5 hours, and the variability factor is 1.2. What staging requirement follows the basic formula?

A. 25 positions

B. 36 positions

C. 45 positions

D. 54 positions

Correct Answer: C. 45 positions