Dismantling the product design and execution logic behind the WMS listing strategy

This article provides an in-depth breakdown of the product design of WMS (warehouse management system) listing strategy and the execution logic behind it. From policy configuration guides, system execution logic, to detailed explanations of preset rules and typical business scenario configuration examples, the author elaborates on how to optimize the allocation of goods storage locations in warehouse management through precise filtering conditions, flexible listing rules, and strict restrictions. The article also provides a wealth of configuration examples and best practice suggestions to help readers better understand and apply WMS listing strategies to improve warehousing efficiency and space utilization.

directory

1. overview
2. List policy configuration guide
3. The system executes the logic
4. Detailed explanation of the default listing rules
5. In-depth analysis of filter conditions and constraints
6. Example configuration for typical business scenarios

overview

WMS shelving strategy is one of the core functions of the warehouse management system, which determines which specific warehouse location the goods should be stored in after being put into storage. A well-designed listing strategy can:

• Improve operational efficiency: Place high-frequency goods in an easy-to-access location to reduce the walking distance of picking
• Optimize space utilization: Rationally allocate storage resources to avoid waste
• Ensure inventory accuracy: Reduce inventory variances through strict mixing rules
• Support business processes: differentiate the processing of different types of orders (purchases, returns, transfers, etc.)

The shelf strategy is byPolicy header informationandPolicy package detailsIt consists of two parts, the former defines the basic information and scope of application of the policy, and the latter contains specific enforcement rules and constraints.

List policy configuration guide

2.1 Configuration of policy header information

Underlying field description

Configuration points

1) Uniqueness of policy code: ensure that there is no duplication throughout the system

2) Shipper binding strategy:

• Leave blank: This is the default policy for this warehouse and applies to all shippers
• Designated shipper: Effective only for the selected shipper
• Multiple shippers: Multiple shippers can be selected at the same time

3) Warehouse-level isolation: Policies in different repositories are completely independent and can have the same policy code

2.2 Detail line configuration of the policy package

The policy package details are the core of the listing policy and define the specific execution logic. The system evaluates each row in order of priority from highest to lowest until a suitable location is found or all rules cannot be matched.

2.2.1 Core field description

2.2.2 Linkage relationship between fields

Different listing rules have different requirements for the Recommended Storage Area and Fixed Location fields:

2.3 Filter Condition Configuration

Filter criteria are adoptedSingle Choice Modeto ensure that each listing order can accurately match the corresponding processing rules.

Configurable filtering dimensions

Example of filtering logic

2.4 Constraint configuration

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The constraints are used to verify the candidate locations to ensure that the selected locations meet the business requirements. Multiple constraints can be configured at the same time.

2.4.1 Restrictions on mixing storage locations

2.4.2 Location attribute limit

2.4.3 Space constraints

The system executes the logic

3.1 Listing policy execution process

3.2 Policy matching logic

3.2.1 Policy package matching priority

The system selects the policy package in the following order of priority:

1. Exact matching: The strategy of exact matching of the shipper + warehouse
2. Warehouse default: The default policy for the warehouse (shipper field is empty)
3. System default: The system-level default policy

3.2.2 Multi-policy package matching processing mechanism

When there are multiple matching policy packages with the same priority, the system selects according to the following rules:

Fault tolerance: If all matching logic fails, the system logs the error log and marks the listing task as “pending human processing”.

3.2.3 Rule line filtering logic

For each row of rules in the policy package, the system validates the following conditions:

3.3 Constraint Condition Calibration Order

To improve system performance, the constraints are checked in the following order:

1) Quick verification (database query required)

• Location status check (availability)
• Basic attribute matching

2) Spatial verification (calculation required)

• Volume limit
• Weight Limit
• Length, width, and height restrictions

3) Inventory verification (inventory data needs to be queried)

Mixing product restrictions

Mixing batch limits

SKU location limit

4) Complex service verification (requires complex query)

Product turnover level matching

Same product requirements

Detailed explanation of the default listing rules

4.1 Overview of rule classification

The system provides 6 preset listing rules, and users are only allowed to choose one or more of these 6 preset rules, which can be roughly divided into three categories:

4.1.1 Reservoir area-oriented rules

Rule 1: Recommend a suitable storage location in the designated storage area

Rule 2: Recommend empty storage spaces

These rules allocate storage locations based on storage areas and are suitable for scenarios with clear storage area planning.

4.1.2 Fixed allocation rules

Rule 3: Recommend a fixed location

Suitable for exception handling, special commodities, or temporary storage scenarios.

4.1.3 Intelligent addressing rules

• Rule 4: Recommend the most recently listed storage location
• Rule 5: Centralized storage of the same product (priority given to occupied space)
• Rule 6: Centralized storage of the same product (empty cargo space priority)

These rules make intelligent decisions based on historical data and inventory status, optimizing storage efficiency.

4.2 Detailed analysis of each rule

4.2.1 Rule 1: Recommend suitable storage locations in the designated storage area:

• There is a clear division of warehouse area functions (e.g. picking area, storage area, return area)
• You need to store specific types of products in the designated area
• Regional management by product attributes

Execution logic:

1. Obtain all available locations in the specified storage area
2. Sort by optimization strategy (usually closest and largest remaining capacity)
3. Check the constraints one by one
4. Returns the first checked location

Configuration points:

• Recommended Library Area must be specified
• The Fixed Location field is disabled
• It is recommended to use it in accordance with space constraints

Typical configuration examples:

Filters:

Batch attribute = good product, product ABC = Class A

Shelf Listing Rules: Recommend appropriate storage locations in the specified storage area

Recommended storage area: PK-01-picking area

Constraints: Verify volume + match product turnover level

4.2.2 Rule 2: Recommended empty storage locations Applicable scenarios:

• New products need to be opened up in new storage locations
• Avoid mixing with existing inventory
• Isolated storage requirements

Execution logic:

1. Query all free locations in the system
2. If a storage area is specified, search is prioritized within that storage area
3. Sort by distance, convenience, and other factors
4. Return after checking the constraints

Configuration points:

• Optional Recommended Storage Area, leaving blank indicates full warehouse search
• The Fixed Location field is disabled
• It is usually used with the “must be empty location” constraint

4.2.3 Rule 3: Recommended Fixed Storage Locations Applicable Scenarios:

• Temporary storage of abnormal goods
• Designated storage of special goods
• Used as a back-up rule

Execution logic:

1. Returns directly to the configured fixed location
2. Verify whether the location meets the constraints
3. If the validation fails, this rule is invalid

Configuration points:

• Fixed location must be specified
• Optional “Recommended Storage Area” field (mainly used for notes)
• It is recommended to set a lower priority bottom-up rule

4.2.4 Rule 4: Recommend the most recently listed storage location applicable scenarios:

• The same goods that are continuously inbound
• Want to maintain storage continuity
• Reduce the dispersion of storage locations

Execution logic:

1. Check the listing history of the SKU
2. Get the most recently listed location
3. Check if there is still capacity left at the location
4. Validation constraints

Business Value:

• Reduce the dispersion of locations in the same SKU
• Improve picking efficiency
• Easy inventory management

4.2.5 Rule 5: Centralized storage of the same product (priority for occupying space) Applicable scenarios:

• Maximize storage density
• Fill the existing storage space first and then open a new one
• Suitable for high-frequency turnover products

Execution logic:

1. Query all locations in the warehouse where the SKU is stored
2. Sort by remaining capacity from smallest to largest (priority is given to filling nearly full slots)
3. Check the constraints one by one
4. If all occupied locations are unavailable, look for free locations

Business Value:

• Maximize location utilization
• Reduce fragmented inventory
• Facilitate inventory counting

4.2.6 Rule 6: Centralized storage of the same product (empty cargo space priority) Applicable scenarios:

• Reserve buffer space
• It is convenient for subsequent bulk picking
• Suitable for volatile commodities

Execution logic:

1. Query all locations in the warehouse where the SKU is stored
2. Sort by remaining capacity from largest to smallest (prioritize locations with ample space)
3. Validation constraints
4. If the occupied slots are not satisfied, look for free slots

Differences from Rule 5:

• Rule 5: Fill in existing → open new slots (density priority)
• Rule 6: Existing → with margin to open new positions (flexibility first)

In-depth analysis of filter conditions and constraints

5.1 Principle of filter design

5.1.1 Why single-choice mode?

In the early stages of design, the filter criteria were considered to support multiple selection mode, but in practice, multiple selection was found to bring the following problems:

1. Matching ambiguity: When multiple conditions are met at the same time, it is difficult to determine which rule to use
2. Complex logic: The combination of multiple selection conditions explodes, making it difficult to predict all possible matches
3. Difficulty in maintenance: There may be conflicts or overlaps between rules, making troubleshooting complicated

Therefore, the single choice mode ensures that each listing order can be accurately matched to a unique processing rule.

5.1.2 Business Meaning of Each Filtering Dimension Order Type Dimension:

• Procurement and warehousing: New products are warehousing, which usually requires inspection and quality inspection processes
• Sales returns: When returning goods, it is necessary to distinguish between good and defective products
• Transfer to warehousing: transfer between warehouses, clear commodity status, and relatively simple process

Batch attribute dimensions:

• Good products: goods that are qualified in quality and can be sold normally
• Defective product: defective and requires special treatment or scrapping
• Pending inspection: Products that require further quality inspection confirmation

Product Cycle Level (ABC):

• Class A (High Frequency): High sales frequency and should be stored in a location that is easy to pick
• Class B (Intermediate Frequency): Medium sales frequency, stored in the suboptimal location
• Class C (Low Frequency): Sold at a low frequency and can be stored in a distant or higher location

Packaging Level:

• Pallet: Whole pallet storage, which requires a strong load-bearing storage location
• Original box: based on the box, suitable for the box picking warehouse
• Single piece: bulk goods, suitable for piece picking warehouses

5.2 Constraint combination strategy

5.2.1 Common constraint combinations

Strict isolation combination:

✓ Do not mix products

✓ Do not mix batches

✓ Must be an empty slot

✓ Verify volume

✓ Check weight

Suitable for: valuable goods, dangerous goods, easily polluted goods

Centralized storage combinations:

✓ The same product must be available in the location

✓ Maximum number of locations that a SKU can occupy: 3

✓ Verify volume

✓ Match product turnover levels

Suitable for: high-frequency goods, bulk picking products

Quality Control Combination:

✓ Do not mix batches

✓ Verify volume

✓ Check weight

Suitable for: goods with a shelf life, goods that require batch traceability

5.2.2 The linkage mechanism between constraints and basic data of the system

The verification of constraints needs to be combined with the basic data of multiple system modules, and the judgment logic of each constraint is analyzed in detail below:

Judgment logic of mixing constraints:

Calculation logic for space constraints:

Logic for business attribute matching:

Example configuration for typical business scenarios

6.1 B2C e-commerce warehouse configuration

Scenario description

• Warehouse Type: B2C E-commerce Warehousing
• Product features: diverse SKUs, small batches, high frequency
• Business requirements: fast picking, strict quality control, exception handling

Policy configuration

Policy name: B2C e-commerce standard listing strategy

Policy code: PUTAWAY-B2C-STANDARD

Bound warehouse: East China-01 warehouse

Bind shipper: (leave blank, as default policy)

Detail line configuration:

6.2 B2B wholesale warehouse configuration

Scenario description

• Warehouse Type: B2B Wholesale Distribution
• Product characteristics: large batch, standardization, stable turnover
• Business requirements: high efficiency, large capacity, batch operation

Policy configuration

Policy name: B2B wholesale standard listing policy

Policy code: PUTAWAY-B2B-WHOLESALE

Bound warehouse: North China-02 warehouse

Bind shipper: (leave blank, as default policy)

Detail line configuration:

6.3 Configuration of cold chain warehouses

Scenario description

• Warehouse type: cold chain logistics warehousing
• Product characteristics: temperature sensitive, short shelf life, strict batch size
• Business requirements: temperature zone management, batch control, first-in, first-out

Policy configuration

Strategy name: Cold chain warehousing listing strategy

Strategy code: PUTAWAY-COLD-CHAIN

Binding warehouse: Cold Chain-01 warehouse

Binding shippers: fresh food suppliers

Detail line configuration:

6.4 Configuration Best Practices

6.4.1 Priority design principles

1. Exception handling priority: The rules for defective products and products to be inspected should be set to high priority
2. Exact match priority: Rules with more precise conditions have higher priority
3. Business Critical Priority: Rules that affect key business metrics are given higher priority
4. Bottom rule: Make sure all products have a place to go

6.4.2 Test Matrix of Test Verification Method Scenarios:

Verification steps:

1. Prepare test data to cover all possible combinations of filters
2. Execute the listing policy and record the actual matching rules
3. Compare the expected results to confirm the correctness of the configuration
4. Test boundary conditions and anomalies

6.4.3 Performance optimization suggestions

1. Rule quantity control: No more than 20 rule lines are recommended for a single policy package
2. Simplified conditions: Avoid overly complex combinations of filters
3. Index optimization: Ensure that the relevant fields of the database location query have appropriate database indexes
4. Caching mechanism: caches frequently queried database location information
5. Asynchronous processing: Asynchronous execution can be considered for constraint verification of non-critical paths

summary

WMS listing strategy is a complex and sophisticated business system that realizes intelligent decision-making on the location of goods storage through the combination of policy header information, policy package details, filter conditions and constraints.

Core Design Principles:

• Accurate matching: Ensure the certainty of rule matching through the filter criteria of radio mode
• Flexible configuration: Provides a combination of multiple listing rules and restrictions
• Performance optimization: reasonable execution sequence and algorithm design
• Business Adaptation: Support the individual needs of different types of warehouses

Application value:

• Improve the efficiency of warehousing operations
• Optimize the use of storage space
• Ensure inventory management accuracy
• Support complex business scenarios

By properly configuring shelving strategies, enterprises can maximize warehousing and operational efficiency while ensuring business compliance, which is one of the core competitiveness of modern WMS systems.