5.2 Warehousing Systems, Storage Assignment & Order Picking Operations

Warehousing Systems, Storage Assignment & Order Picking Operations

Warehouses and distribution centers (DCs) serve as critical inventory decoupling points within modern supply chains. Rather than acting merely as passive storage sheds, high-velocity distribution facilities perform dynamic transformation functions: buffering demand volatility, consolidating freight shipments, assembling multi-item customer orders, executing light value-added assembly, and managing returns processing.

In industrial facility engineering, warehouse performance is governed by three conflicting operational goals:

1. Maximizing Cubic Space Utilization: Extracting maximum storage capacity per unit of building floor area and clear height.
2. Minimizing Material Handling Travel Time: Shortening the physical transit distance traversed by warehouse pickers, forklifts, and automated guided vehicles.
3. Maximizing Order Throughput and Responsiveness: Achieving rapid order turnaround from order drop to trailer dispatch.

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Warehouse Functional Zones & Material Flow Architecture

A comprehensive industrial warehouse is structured into seven distinct functional zones through which goods progress sequentially:

+-----------------------------------------------------------------------------------------+
|                                 TYPICAL U-SHAPED FLOW LAYOUT                            |
+-----------------------------------------------------------------------------------------+
|                                                                                         |
|  [ RECEIVING DOCKS ]                                             [ SHIPPING DOCKS ]     |
|          |                                                               ^              |
|          v                                                               |              |
|  +----------------+                                             +-----------------+     |
|  | Inbound Check  |                                             | Outbound Stage  |     |
|  | & De-Palletize |                                             | & Load Build    |     |
|  +----------------+                                             +-----------------+     |
|          |                                                               ^              |
|          v                                                               |              |
|  +----------------+      +-------------------------------+      +-----------------+     |
|  | Staging & QA   |      |      FORWARD PICKING ZONE     |      | Packaging &     |     |
|  | Inspection     | ---> |   (Flow Racks / Fast Pick)    | ---> | Sortation       |     |
|  +----------------+      +-------------------------------+      +-----------------+     |
|          |                               ^                               ^              |
|          |        Replenishment          |                               |              |
|          +-------------------------------+-------------------------------+              |
|          |                                                                              |
|          v                                                                              |
|  +-------------------------------------------------------------------------------+      |
|  |                             RESERVE BULK STORAGE                              |      |
|  |           (High-Bay Selective Racks, Double-Deep, Drive-In, AS/RS)            |      |
|  +-------------------------------------------------------------------------------+      |
+-----------------------------------------------------------------------------------------+

The Seven Core Functional Zones

1. Receiving: Inbound carriers check in, trailers are backed to dock bays, and freight is unloaded via counterbalanced lift trucks or automated conveyor un-loaders. Operations include seal verification, ASN matching, damage inspection, pallet sortation, and pallet tag printing.
2. Staging & Quality Inspection: Unit loads are held temporarily in quarantine staging lanes while quality control (QC) inspectors verify part numbers, lot codes, and sample tolerances. Cross-dock-eligible loads are identified immediately.
3. Reserve Bulk Storage: High-density, multi-tier pallet racking systems (selective pallet racks, double-deep racks, drive-in/drive-through racks, or high-bay unit-load AS/RS) storing full pallet loads. This zone buffers inventory and replenishes the forward picking area.
4. Forward Picking Area (Fast Pick): Dedicated, ergonomically optimized zones for piece and carton picking (such as carton gravity flow racks, vertical carousels, or pick-to-light shelving). Fast-moving SKUs are concentrated here to compress picker travel distances.
5. Sortation & Consolidation: In batch-picking workflows, items from multiple orders are separated and consolidated by customer order using high-speed sliding shoe sorters, tilt-tray sorters, or put-to-wall pigeonhole stations.
6. Packaging & Value-Added Services (VAS): Kitting, customized retail tagging, security labeling, protective cartonization void fill, and automated case taping.
7. Shipping: Consolidated customer cartons are palletized, stretch-wrapped, staged in designated dock doors by carrier route, and loaded onto outbound 53-foot trailers.

Macro-Flow Configurations: U-Shaped vs. Straight-Through Layouts

• U-Shaped Layout: Inbound receiving and outbound shipping docks are located along the same building wall.
  • Engineering Advantages: Shared dock apron yard space for trailers; highly flexible utilization of dock doors and lift truck operators across receiving and shipping peaks; high security with a single security gate; easy facility expansion on three remaining exterior walls.
• Straight-Through (Flow-Through) Layout: Inbound receiving is located on one exterior wall and outbound shipping is located on the opposite wall.
  • Engineering Advantages: Eliminates internal traffic bottlenecks; ideal for pure cross-docking operations and high-throughput, high-velocity facilities where inbound and outbound freight flows must never cross.

Storage Assignment Policies: Dedicated, Randomized & Class-Based

Assigning physical storage locations to inventory items (SKUs) dictates both required warehouse floor space and picker travel distance. Industrial engineers evaluate three core storage assignment policies:

+-----------------------------------------------------------------------------------------+
|                              STORAGE ASSIGNMENT POLICIES                                |
+-----------------------------------------------------------------------------------------+
|  DEDICATED STORAGE               RANDOMIZED STORAGE             CLASS-BASED (ABC)       |
|  - Each SKU has fixed slot       - Any SKU to any empty slot    - Partitioned velocity  |
|  - Sized for Individual Peaks    - Sized for Aggregate Peak     - Best of both worlds   |
|  - S = \sum Peak_i               - S = \max \sum I_i            - Zone A (fast) near I/O|
|  - Low Cube Utilization (~50%)   - High Cube Util. (80-90%)     - Zone C (slow) in back |
+-----------------------------------------------------------------------------------------+

1. Dedicated (Fixed-Location) Storage

In dedicated storage, each SKU is permanently assigned a specific, reserved set of storage slots. No other item may be stored in that slot, even when the SKU is completely stock-out.

• Space Requirement Formulation: The total storage capacity $S_{\text{dedicated}}$ must equal the sum of the peak inventory levels of all individual SKUs: $S_{\text{dedicated}} = \sum_{i=1}^N \max_t \left( I_{it} \right)$ Where $I_{it}$ is the inventory of SKU $i$ at time $t$.
• Trade-Offs: Provides high operator familiarity and straightforward inventory control without complex software. However, it results in notoriously low storage cube utilization (typically $50\%$ or less) because slots sit completely empty during off-peak seasonal cycles.

2. Randomized (Floating / Dynamic) Storage

In randomized storage, an incoming unit load is deposited into any randomly selected or algorithmically assigned empty storage slot (e.g., nearest open location). When a pallet is depleted, the slot becomes instantly available for any SKU in the catalog.

• Space Requirement Formulation: The required capacity $S_{\text{random}}$ equals the peak of the aggregate inventory across all SKUs: $S_{\text{random}} = \max_t \left( \sum_{i=1}^N I_{it} \right)$
• The Statistical Pooling Effect: Because individual SKU demand cycles are not perfectly correlated, peak inventory for one item coincides with valley inventory for another. By pooling space, randomized storage routinely achieves 20% to 40% reductions in required warehouse floor space, driving storage cube utilization up to $80\% - 90\%$.
• Implementation Requirement: Requires an automated Warehouse Management System (WMS) utilizing barcoding or RFID to track dynamic bin coordinates.

3. Class-Based Storage (ABC Velocity Zoning)

Class-based storage is an optimal engineering hybrid that captures the travel-time efficiency of dedicated storage while preserving the high cube utilization of randomized storage. Inventory is segmented into Pareto classes based on order picking frequency (velocity):

• Class A (High Velocity): Approximately $10\% - 20\%$ of SKUs accounting for $70\% - 80\%$ of all picking transactions. Allocated to premium storage slots immediately adjacent to the Input/Output (I/O) dock.
• Class B (Medium Velocity): Approximately $20\% - 30\%$ of SKUs accounting for $15\% - 20\%$ of picking transactions. Located in intermediate aisles.
• Class C (Low Velocity / Slow Movers): The remaining $50\% - 70\%$ of SKUs accounting for only $5\% - 10\%$ of picking transactions. Relegated to distant rack rows, upper rack tiers, or deep reserve storage.

Within each class zone, storage is randomized among member SKUs. This compresses total picker travel distance by up to 30% to 50% compared to purely randomized storage across the entire building footprint.

The Cube-per-Order Index (COI) Rule

When allocating dedicated or class-based storage slots to minimize total material handling travel distance, industrial engineers apply the mathematical theorem established by Heskett (1963, 1964): the Cube-per-Order Index (COI) Rule.

Definition of the Cube-per-Order Index

For any item $i$, its Cube-per-Order Index $\text{COI}_i$ is defined as the ratio of the storage space (cube or slot count) required by the item to its order activity (frequency of picking transactions):

$\text{COI}_i = \frac{S_i}{T_i}$

Where:

• $S_i$ = Storage space required by SKU $i$ (e.g., number of pallet positions, cubic feet, or bin openings).
• $T_i$ = Number of order transactions or picking trips per unit time (e.g., picks/day or orders/week).

Heskett's Optimal Slotting Theorem

To minimize the total material handling travel distance between storage locations and the Input/Output (I/O) station, SKUs must be assigned to storage locations in ascending order of their Cube-per-Order Index:

$\text{COI}_{(1)} \le \text{COI}_{(2)} \le \dots \le \text{COI}_{(N)}$

The item with the lowest $\text{COI}$ value is assigned to the storage location closest to the I/O point. As distance from the I/O point increases, items with progressively higher $\text{COI}$ values are assigned.

Engineering Insight: A common novice mistake is ranking SKUs strictly by picking activity ($T_i$) or strictly by required space ($S_i$). Heskett's theorem proves that neither alone is optimal. An item with very high picking frequency that requires a massive storage footprint (e.g., bulky packaging materials) will push all other items far down the warehouse aisles, increasing net travel distance. The optimal trade-off is governed strictly by the ratio $S_i / T_i$.

Step-by-Step Worked Numerical Example: COI Storage Slotting

Problem Statement: A warehouse engineer is slotting four products into dedicated pallet positions that run in a single line of rack faces away from the I/O shipping dock. Each pallet position adds 2 ft of one-way travel, so a trip to the midpoint of a product's block costs $4\text{ ft}$ of round-trip travel per position. The space each product occupies therefore pushes every product behind it farther from the dock. Storage requirements and daily pick trip counts are:

Step 1: Compute the Cube-per-Order Index (COI) for each SKU $\text{COI}_{\text{Alpha}} = \frac{S_{\text{Alpha}}}{T_{\text{Alpha}}} = \frac{40}{200} = 0.20\text{ pallets/(trip/day)}$ $\text{COI}_{\text{Beta}} = \frac{S_{\text{Beta}}}{T_{\text{Beta}}} = \frac{10}{100} = 0.10\text{ pallets/(trip/day)}$ $\text{COI}_{\text{Gamma}} = \frac{S_{\text{Gamma}}}{T_{\text{Gamma}}} = \frac{60}{150} = 0.40\text{ pallets/(trip/day)}$ $\text{COI}_{\text{Delta}} = \frac{S_{\text{Delta}}}{T_{\text{Delta}}} = \frac{30}{50} = 0.60\text{ pallets/(trip/day)}$

Step 2: Rank SKUs in Ascending Order of COI

1. SKU Beta: $\text{COI} = 0.10$
2. SKU Alpha: $\text{COI} = 0.20$
3. SKU Gamma: $\text{COI} = 0.40$
4. SKU Delta: $\text{COI} = 0.60$

Step 3: Assign Positions and Compute Travel

Place the blocks in COI order: Beta in positions 1–10, Alpha in 11–50, Gamma in 51–110, and Delta in 111–140. The block midpoints are 5, 30, 80, and 125 positions from the dock.

$\text{Position-trips} = (100 \times 5) + (200 \times 30) + (150 \times 80) + (50 \times 125) = 500 + 6{,}000 + 12{,}000 + 6{,}250 = 24{,}750$

At 4 ft of round-trip travel per position, daily travel is $24{,}750 \times 4 = 99{,}000\text{ ft/day}$.

Verification: Ranking by pick frequency alone puts Alpha (200 trips) first, then Gamma, Beta, and Delta. Alpha's 40 pallets now sit in front of everything else, and the midpoints become 20, 70, 105, and 125:

$(200 \times 20) + (150 \times 70) + (100 \times 105) + (50 \times 125) = 4{,}000 + 10{,}500 + 10{,}500 + 6{,}250 = 31{,}250$

That is $31{,}250 \times 4 = 125{,}000\text{ ft/day}$, 26% more travel than the COI assignment. The bulky, busy item consumed the premium space near the dock and pushed the compact Beta far back.

Storage Cube Utilization & Honeycombing Analysis

Warehouse gross square footage is a misleading indicator of storage capability. Industrial engineers focus on Storage Cube Utilization, defined as the percentage of the building's three-dimensional envelope ($L \times W \times H_{\text{clear}}$) that is occupied by actual revenue-generating inventory.

A major cause of wasted space in warehousing is Honeycombing—unoccupied storage volume that cannot be utilized due to operational, physical, or geometric accessibility constraints.

+-----------------------------------------------------------------------------------------+
|                              HONEYCOMBING LOSS MECHANICS                                |
+-----------------------------------------------------------------------------------------+
|       HORIZONTAL HONEYCOMBING                           VERTICAL HONEYCOMBING           |
|   (Partially Depleted Deep Lanes)                   (Headspace Below Rack Beams)        |
|                                                                                         |
|   +-----+ +-----+ +-----+ +-----+                   +-------------------------------+   |
|   | Pal | | Pal | | EMPTY | EMPTY |                 |======= RACK BEAM ============ |   |
|   +-----+ +-----+ +-----+ +-----+                   |  Unused Headspace (Clearance) |   |
|   <------- Lane Depth = 4 ------->                  | - - - - - - - - - - - - - - - |   |
|   (Cannot store different SKU without               | +---------------------------+ |   |
|    blocking access; 50% lane lost)                  | |        PALLET LOAD        | |   |
+-----------------------------------------------------------------------------------------+

1. Horizontal Honeycombing (Lane Depth Losses)

Horizontal honeycombing occurs in deep-lane bulk floor stacking, drive-in/drive-through racking, and push-back racking:

• Operational Cause: To maintain inventory integrity and prevent FIFO/LIFO mix-ups, warehouse operating rules mandate that only a single SKU may be stored in any given lane.
• The Depletion Problem: When pallets are picked from a 4-deep or 6-deep lane over time, the lane becomes partially empty. A lane containing only 1 remaining pallet cannot be backfilled with a different SKU without burying the original pallet and creating double-handling penalties. The empty positions in that lane represent lost capacity.
• Mathematical Model for Optimal Lane Depth ($x^*$): The trade-off balances aisle space against honeycombing. Consider an item received in lots of $Q$ unit loads that are stacked $s$ high in lanes $x$ loads deep. Each load is $w$ wide and $d$ deep, and each lane is charged half of an aisle of width $a$. Over a depletion cycle the item occupies, on average, about $\frac{1}{2}\left(\frac{Q}{s x} + 1\right)$ lanes. Each lane uses $w\left(x d + \frac{a}{2}\right)$ of floor space, so: $\text{Average Floor Area}(x) \approx \frac{w}{2}\left(\frac{Q}{s x} + 1\right)\left(x d + \frac{a}{2}\right)$ Setting the derivative with respect to $x$ equal to zero gives: $x^* = \sqrt{\frac{a \cdot Q}{2 \cdot s \cdot d}}$ Wider aisles and larger lots justify deeper lanes, while taller stacks and deeper loads favor shallower lanes. For $a = 12$ ft, $Q = 60$ loads, $s = 3$, and $d = 4$ ft, $x^* = \sqrt{720 / 24} = \sqrt{30} \approx 5.5$, so lanes about 5 or 6 loads deep minimize the average floor space.

2. Vertical Honeycombing (Headspace Losses)

Vertical honeycombing represents unutilized vertical space within the pallet rack opening:

• Operational Cause: Standard pallet racks have fixed horizontal beam elevations. If the rack opening height is set to 64 inches to accommodate the tallest anticipated load, storing a 42-inch pallet load leaves 22 inches of completely empty, unusable air space above the pallet.
• Fire Protection Restrictions (NFPA 13): Storage must maintain mandatory vertical clearance beneath fire sprinkler heads (minimum 18 inches below standard spray heads; 36 inches below ESFR heads).
• Mitigation Strategies: Modular adjustable beam levels, profiling inventory into distinct pallet height classes (e.g., 40-inch short loads vs. 60-inch tall loads), and dynamic rack height sensing in modern automated high-bay facilities.

Order Picking Methodologies & Wave Batching

Order picking accounts for 50% to 65% of total warehouse operating labor costs. In a typical manual order picking operation, picker time breakdown is:

• Traveling (walking/riding between bins): $50\% - 55\%$
• Searching and extracting items: $15\% - 20\%$
• Scanning, packaging, and paperwork: $15\% - 20\%$
• Setup, transit, and staging: $10\% - 15\%$

Because more than half of the picking cycle is consumed by physical travel, selecting the correct order picking methodology is the primary lever for industrial engineering optimization:

+-----------------------------------------------------------------------------------------+
|                              ORDER PICKING CLASSIFICATIONS                              |
+-----------------------------------------------------------------------------------------+
|  DISCRETE PICKING               ZONE PICKING                     BATCH / WAVE PICKING   |
|  - 1 picker, 1 order            - 1 picker, 1 assigned zone      - 1 picker, N orders   |
|  - Full tour of warehouse       - Pick-and-pass OR simultaneous  - Consolidate & sort   |
|  - Zero sortation needed        - Requires downstream merge      - High pick density    |
+-----------------------------------------------------------------------------------------+

1. Discrete Order Picking (Piece Picking)

A single picker traverses the entire facility to pick all line items for one customer order from start to finish.

• Pros: Simple, zero risk of mixing customer orders, no downstream sorting required, minimal integrity checking.
• Cons: Extremely long travel distances per pick line; highly inefficient for large warehouses with low pick density.

2. Zone Picking

The warehouse is divided into distinct physical zones (e.g., Zone 1: Bulk, Zone 2: Mezzanine small parts, Zone 3: Secure high-value). Pickers are permanently assigned to a specific zone and pick only items located within their territory.

• Pick-and-Pass (Sequential): An order tote moves sequentially from zone to zone via a central conveyor or hand cart. A picker completes their zone's lines and passes the tote to the next zone.
• Simultaneous (Parallel): Line items across all zones are picked at the same time. The partial orders are routed to a central Consolidation / Pack Station where they are merged before boxing.
• Pros: Shorter travel paths, pickers develop high SKU familiarity, reduces aisle congestion.
• Cons: Requires balancing workload across zones to prevent bottlenecks; parallel picking requires consolidation sortation infrastructure.

3. Batch Picking and Wave Picking

• Batch Picking: A picker gathers items for multiple orders (e.g., 10 to 30 orders) simultaneously in a single picking tour. Pickers place items into multi-compartment cart totes or pick in bulk for subsequent sortation.
• Wave Picking: Orders are released to the floor in coordinated time intervals ("waves") aligned with outbound transportation schedules (e.g., 9:00 AM FedEx Ground wave, 11:30 AM regional LTL wave), carrier departure cutoffs, or shift changes.
• Pros: Dramatically compresses travel distance per pick line by visiting an aisle once to satisfy multiple orders.
• Cons: Mandates downstream order sortation systems (e.g., automated put-to-wall systems or high-speed loop sorters).

Order Picking Routing Heuristics

Determining the shortest travel path through a warehouse rack layout is a variant of the NP-hard Traveling Salesperson Problem (TSP). While dynamic programming algorithms (such as the Ratliff and Rosenthal algorithm) can solve the TSP to mathematical optimality in simple rectangular layouts, modern warehouse management systems employ routing heuristics that are intuitive for human pickers to navigate consistently without backtracking errors:

+-----------------------------------------------------------------------------------------+
|                              ROUTING HEURISTIC PATTERNS                                 |
+-----------------------------------------------------------------------------------------+
|        S-SHAPE (Traversal)                       RETURN ROUTING                         |
|                                                                                         |
|    +--+   +--+   +--+   +--+                 +--+   +--+   +--+   +--+                  |
|    |  |   |  |   |  |   |  |                 |  |   |  |   |  |   |  |                  |
|    |  |   |  |   |  |   |  |                 |  |   |  |   |  |   |  |                  |
|    |  v   |  ^   |  v   |  ^                 |  |   |  |   |  |   |  |                  |
|    |      |  |   |      |  |                 |  v   ^  |   |      |                     |
|    +------+  +---+------+  |                 +------+  +---+------+                     |
|    <--- Traverses entire -->                 <--- Enters & returns from --->            |
|         aisles with picks                         same front cross-aisle                |
+-----------------------------------------------------------------------------------------+

1. S-Shape (Traversal) Heuristic

• Algorithm: The picker enters any aisle that contains one or more pick locations, traverses the entire length of the aisle to the opposite end, and enters the rear cross-aisle. The picker proceeds along the cross-aisle to the next aisle containing picks, entering and traversing it in the opposite direction. Aisles containing no picks are completely bypassed.
• Performance: Highly efficient when pick density is high (multiple picks per aisle), because the overhead of traversing the aisle length is amortized over many picking stops.

2. Return Heuristic

• Algorithm: The picker enters an aisle from the front cross-aisle, advances down the aisle as far as the furthest pick location, retrieves all required items along both sides, turns around, and returns to the same front cross-aisle from which they entered.
• Performance: Highly efficient when pick density is very low (e.g., 1 or 2 picks per aisle located near the front cross-aisle), avoiding unnecessary full-aisle traversal.

3. Midpoint Heuristic

• Algorithm: The warehouse is partitioned into two halves by a virtual midpoint line. The picker traverses down an aisle from the front cross-aisle up to the midpoint, picks items, and returns to the front cross-aisle. Picks in the rear half are retrieved in a separate sweep originating from the rear cross-aisle. The first and last aisles visited are traversed completely to transition between front and rear cross-aisles.
• Performance: Consistently outperforms S-shape routing when pick density is moderate (2 to 4 picks per aisle).

4. Largest Gap Heuristic

• Algorithm: The picker enters an aisle as far as the "largest gap" between two adjacent pick stops (or between a pick stop and the aisle end), picks items, and returns to the starting cross-aisle. The remaining items in the aisle are retrieved from the opposite cross-aisle. The largest unvisited gap is never crossed, eliminating wasted travel across empty rack sections.
• Performance: Yields shorter travel paths than both S-shape and Midpoint across a broad spectrum of pick densities.

Cross-Docking Operations & Logistics Integration

Cross-docking is an advanced operational logistics strategy in which inbound freight from arriving supplier trucks is unloaded, sorted, and loaded directly onto outbound customer trailers with zero intermediate storage and zero order picking.

The Economic Mechanics of Cross-Docking

In conventional warehousing, an item undergoes five discrete material handling touches: (1) Unload $\to$ (2) Put-away into bulk reserve $\to$ (3) Replenishment move to pick face $\to$ (4) Order pick extraction $\to$ (5) Outbound staging and loading. Cross-docking compresses this sequence into just two touches: Unload $\to$ Direct Load.

By bypassing storage and retrieval, cross-docking delivers immense financial benefits:

• Eliminates Warehouse Holding Costs: Products spend less than 24 hours (often under 2 hours) on the dock apron, cutting inventory carrying charges.
• Cuts Direct Labor: Eliminates the two most labor-intensive tasks: put-away and manual picking.
• Compresses Order Cycle Time: Accelerates dock-to-consumer delivery by 24 to 48 hours.

Operational Enablers for Successful Cross-Docking

Cross-docking requires flawless data integration and physical synchronization across the supply chain:

1. Advance Shipping Notices (ASN) via EDI: Inbound shipments must be preceded by electronic ASNs (EDI 856 transaction sets) providing item-level visibility before the truck physically arrives at the guard shack.
2. Standardized GS1-128 Barcoding / RFID: Inbound pallets and cartons must bear machine-readable Serial Shipping Container Codes (SSCC) that automated sorters can scan on-the-fly.
3. Tight Dock Scheduling & Inbound/Outbound Synchronization: Outbound trailers must be staged at dock doors simultaneously with arriving inbound supplier deliveries to avoid dock apron gridlock.
4. Pre-Distribution vs. Post-Distribution Cross-Docking:
   • Pre-Distribution: The vendor knows the final customer destination and pre-labels cartons before shipping. The cross-dock merely routes cartons.
   • Post-Distribution: The cross-dock facility allocates incoming bulk pallets across pending orders based on real-time demand signals upon arrival.

Cross-Dock Facility Geometric Shapes

• I-Shape (Rectangular): Doors on two opposite long walls with a sorting floor between them. Research on cross-dock design (Bartholdi and Gue, 2004) found the I shape best for smaller docks of up to roughly 150 doors.
• T-Shape and X-Shape: Adding a wing (T) or wings on two sides (X) shortens the longest internal travel paths. The same research found T shapes best at roughly 150–200 doors and X shapes best for larger LTL terminals above about 200 doors.