Layout Reflected History, Not Demand
Storage placement had accumulated over time rather than being based on current movement patterns.
LOGISTICS MVP CASE STUDY
Deciding Where Inventory Should Live Based On Data, Not Habit
A warehouse operation had grown its storage layout organically, with fast-moving items scattered far from pack stations and slow movers occupying prime shelf space. MVPHUB designed and built a greenfield MVP that recommends where inventory should be stored based on demand, movement frequency, size and picking efficiency.
♢ Greenfield Slotting MVP A slotting recommendation engine was designed and built from scratch around real movement data.
⌘ Data-Driven Slot Recommendations Storage locations are recommended based on actual demand and picking patterns, not habit.
♨ Built For Ongoing Re-Slotting The system supports recurring slotting reviews as demand patterns shift over time.
Letting Movement Data Decide Where Inventory Belongs
Warehouse layouts tend to evolve item by item, as new stock gets placed wherever there's open shelf space. Over time, that creates a layout that no longer matches how items actually move.
This platform recommends storage locations based on real demand, movement frequency, item size and picking efficiency, replacing habit-based placement with evidence.
♨IndustryWarehouse Operations
▦ProductWarehouse Slotting Optimization Platform
◈AudienceWarehouse Layout & Inventory Managers
✧DeliveryGreenfield MVP Build
The Challenge
Fast-Moving Items Sat Far From Pack Stations
High-demand items were stored in inconvenient locations, adding unnecessary travel time to picking.
Re-Slotting Decisions Were Subjective
When re-slotting did happen, it relied on manager intuition rather than consistent criteria.
What We Can Identified
A slotting optimization platform that recommends storage locations based on demand, movement frequency, item size and picking efficiency.
Movement Frequency Analysis
Item pick frequency is analyzed to identify which products should sit closest to pack stations.
Size-Aware Slot Matching
Recommendations account for item dimensions so slots are sized appropriately, not just conveniently located.
Demand-Weighted Recommendations
Slotting suggestions weigh current demand trends alongside historical movement.
Re-Slotting Impact Estimate
Before a re-slot is executed, the system estimates the expected reduction in picking travel.
Slotting Change Tracking
Every slotting change is logged, so the layout's evolution can be reviewed over time.
Periodic Review Scheduling
The system flags when it's time for a fresh slotting review based on shifting movement patterns.
How MVPHUB Deliver The Slotting Platform From Concept To MVP
1
▤ Analyze
We analyzed historical pick and movement data to understand which items moved fastest and from where.
2
◉ Model
A slotting model was built weighing movement frequency, item size and distance to pack stations.
3
♧ Design
Recommendation screens were designed to show the expected benefit of each suggested change.
4
✎ Build
Engineers built the analysis engine, recommendation logic and change tracking as one platform.
5
♢ Launch
The MVP launched generating real slotting recommendations from live movement data.
Warehouse layout should follow demand, not the other way around. Let the data recommend the slot, then let managers decide.
Engineering Behind The Slotting Recommendations
Movement Frequency Engine
Historical pick data is analyzed to rank items by how often and how recently they're picked.
Size-Constrained Matching
Slot recommendations respect the physical dimensions of both the item and the candidate location.
Impact Estimation Model
Each recommended change includes an estimate of the resulting reduction in picking travel.
Built For Recurring Reviews
The platform was designed to support periodic re-analysis as demand patterns shift.
The Outcome
Before: Disconnected And Manual
× Storage layout based on historical habit
× Fast-moving items placed far from pack stations
× Re-slotting decisions made subjectively
× No estimate of a re-slot's actual benefit
× No record of how the layout evolved
After: A Connected, Trackable Workflow
✓ Storage recommendations driven by movement data
✓ Fast movers positioned near pack stations
✓ Re-slotting decisions backed by consistent analysis
✓ Expected travel reduction shown before changes
✓ Slotting history tracked over time
A Slotting MVP Built On Real Movement Data
Greenfield slotting optimization MVP delivered
Demand-weighted slot recommendations
Re-slotting impact estimation
Slotting change history tracked
From Habit-Based Layout To Data-Driven Slotting
Let movement data decide where inventory belongs — habit is a poor substitute for evidence.
Before this engagement, the warehouse layout had grown organically with no systematic review of whether storage locations matched actual demand. The MVP recommends slotting changes based on real movement data, with the expected benefit shown up front.
THE MVPHUB PRINCIPLE
“A warehouse layout isn't fixed — it's a decision that should be revisited as often as the demand data changes. Build the system that makes revisiting it easy.
”
Is Your Warehouse Layout Based On Habit Rather Than Data?
Bring us your current storage layout and movement history, however informal the placement is today. MVPHUB can help you design and build a slotting optimization MVP grounded in real demand.
AI-accelerated. Expert-verified. Built around the outcome your first release needs to prove.
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