Warehouse Slotting Efficiency Analysis Builder

Analyze warehouse slotting efficiency with SKU velocity, pick paths, cube movement, replenishment burden, congestion, safety constraints, and re-slotting recommendations.

Prompt Template

You are a warehouse operations analyst evaluating slotting efficiency and pick path performance. Build an analysis plan for:

Warehouse type: [ecommerce fulfillment, wholesale distribution, spare parts, grocery, cold storage, manufacturing support]
Data available: [SKU master, inventory locations, order lines, pick timestamps, replenishment tasks, dimensions, weight, velocity class]
Layout details: [zones, aisles, bins, pick modules, mezzanine, cold area, hazmat, dock locations]
Picking method: [batch, wave, zone, discrete, cart, pallet, goods-to-person, voice, RF scanner]
Current pain points: [long travel time, congestion, stockouts, replenishment overload, mispicks, heavy items too high, slow movers in prime slots]
Constraints: [temperature, hazmat, fragility, weight, family groups, FIFO, lot control, automation, labor rules, safety]
Measurement period: [last 4 weeks, peak season, normal month, before and after change]
Comparison needs: [zone, aisle, SKU class, order profile, shift, picker group, customer type]
Decision goal: [reduce travel, improve lines per hour, lower replenishment, rebalance zones, prepare peak season]
Data quality concerns: [missing dimensions, bad locations, moved SKUs, duplicate bins, cancelled orders, replenishment not logged]

Create:
1. Analysis plan with grain, joins, filters, and cleaning rules.
2. Core metrics for pick frequency, cube movement, travel proxy, touches, replenishment burden, and congestion.
3. SKU classification model using velocity, cube, weight, affinity, handling constraints, and variability.
4. Location quality assessment for prime slots, golden zone, slow-mover zones, and unsafe placements.
5. Pick path and co-pick affinity analysis using order-line patterns.
6. Re-slotting recommendation matrix with expected benefit, effort, risk, and owner.
7. Dashboard layout with heatmaps, Pareto charts, scatterplots, and exception tables.
8. Before-and-after experiment plan for a pilot aisle or zone.
9. Operational guardrails for safety, replenishment, inventory accuracy, and customer promise dates.
10. Executive summary template with findings, confidence, tradeoffs, and next data needs.

Do not assume the mathematically shortest pick path is best if it creates congestion, unsafe storage, or replenishment problems.

Example Output

Analysis Summary

The top 12 percent of SKUs account for 61 percent of pick lines, but 38 percent of those fast movers are outside prime pick zones. Several bulky high-velocity items also create replenishment strain because they are assigned to small forward bins.

Metrics

| Metric | Definition | Use |

|---|---|---|

| Pick frequency | Order lines per SKU per week | Identify fast movers |

| Cube movement | Units picked x item cube | Size slot capacity |

| Replenishment burden | Replen tasks per SKU per week | Avoid tiny slots for bulky movers |

| Co-pick affinity | SKUs frequently picked together | Improve adjacency |

Pilot Recommendation

Move the top 20 small fast movers from Aisle 14 to the front pick module, but leave heavy cases in lower bulk locations to avoid unsafe lifts.

Tips for Best Results

  • 💡Use both velocity and cube movement; fast tiny items and fast bulky items need different slots.
  • 💡Check replenishment burden before moving SKUs into prime locations.
  • 💡Pilot one zone first so operational disruption stays measurable.
  • 💡Pair analytics with safety and picker feedback before finalizing moves.