Slotting Analysis
Table of Contents
- 1. Use Cases — 3 common scenarios
- 2. Features — Network Graph / Data Table
- 3. FAQ — 5 FAQs + Notes
- 4. Related Features
1. Use Cases
Quick Links: Find highly related products | Optimize shelf placement | Analyze product combination trends
Scenario 1: Find highly related products
Situation: You want to identify which products are frequently purchased together to optimize shelf placement and improve picking efficiency.
Steps:
- Select the merchant to analyze
- View the "Network Graph" to find clusters of densely connected products
- Or switch to "Data Table" and sort by "Co-pick Count" to see the most frequently co-shipped product combinations
Result: Identify product pairs with the highest co-pick counts and consider placing them in adjacent locations.
Scenario 2: Optimize shelf placement
Situation: The warehouse manager wants to reorganize shelf locations to reduce picker walking distance.
Steps:
- View the data table and sort by "Strength"
- Note product combinations with strength above 50%
- Plan to place these highly related products in adjacent or same-zone locations
Result: After adjusting shelf locations based on the analysis, picking efficiency can be improved by reducing walking distance.
Scenario 3: Analyze product combination trends
Situation: The marketing team wants to understand consumer purchasing habits to design promotional bundles.
Steps:
- Select the target merchant
- View product combinations with high co-pick counts in the data table
- Record product combinations that are frequently purchased together
Result: Obtain product affinity data that can be used as reference for designing bundle products or promotional campaigns.
2. Features
Slotting Analysis examines historical order data to identify products frequently purchased together. The system calculates the number of times any two products appear in the same order (co-pick count) and their affinity strength, helping you understand relationships between products. This information can be used to optimize warehouse shelf placement by positioning highly related products closer together, reducing walking distance during picking.
Quick Jump: Network Graph | Data Table
2.1 Network Graph
Visualizes product relationships:
- Nodes: Represent products; node size reflects the degree of connectivity
- Lines: Represent relationships between two products; line thickness reflects affinity strength
- Clusters: Densely connected areas represent groups of products frequently purchased together
💡 Tip: Drag to explore different areas; click on a node to highlight its connections.
2.2 Data Table
Presents product affinity data in tabular format:
| Column | Description | Sortable |
|---|---|---|
| Product A | First product in the pair (name and SKU) | No |
| Product B | Second product in the pair (name and SKU) | No |
| Co-pick Count | Number of times both products appeared in the same order | Yes |
| Strength | Percentage indicator of relationship strength | Yes |
Strength Guidelines:
| Strength | Recommendation |
|---|---|
| Above 70% | Strong relationship, recommend adjacent shelf placement |
| 40%-70% | Moderate relationship, consider same-zone placement |
| Below 40% | Weak relationship, standard placement rules apply |
3. FAQ
3.1 FAQ
▪ What time period is the data based on?
The system analyzes recent order data to calculate product relationships. The specific time range is automatically set by the system to ensure data accuracy and relevance.
▪ Why don't certain products appear in the analysis results?
Possible reasons:
- The product has no recent shipment records
- The product is rarely purchased with other products
- The relationship strength is below the display threshold
▪ How is the affinity strength calculated?
Affinity strength is a percentage calculated based on how frequently two products appear together in the same order. The more often two products are shipped together, the higher the strength.
▪ Can I export the analysis data?
Currently, this feature doesn't support direct export. Contact your system administrator if you need to export data.
▪ Will data from different merchants be mixed together?
No. The system calculates separately based on your selected merchant. Each merchant's affinity analysis is independent.
3.2 Notes
⚠️ Important
- Analysis results are for reference only; actual shelf placement should also consider product size, weight, turnover rate, and other factors
- Data is continuously updated with new orders; check regularly to stay current with trends
- You must select a merchant to view their analysis data
4. Related Features
- Shelf List: Manage warehouse shelf configuration
- Picking Presets: Set picking rules to optimize picking workflows
- Order Reports: View order statistics