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Generative AI > Google Gemini API > Warehouse Slotting Agent

Warehouse Slotting Agent

Author: Venkata Sudhakar

Warehouse slotting determines where each product is stored. Poor slotting means pickers walk long distances to retrieve fast-moving items, while prime locations near the dispatch area sit occupied by slow-moving products. Optimal slotting can reduce picking time by 20-30%.

A Warehouse Slotting Agent uses Gemini AI to analyse pick frequency data, product dimensions, and zone characteristics, then assigns each SKU to the best storage location. Fast-moving, high-pick-frequency items get prime locations near dispatch while bulky or slow-moving items go to deeper storage.

The below example shows how ShopMax India optimises slotting assignments for its Mumbai warehouse after receiving a new electronics batch.

import asyncio
import json
from google.adk.agents import LlmAgent
from google.adk.tools import FunctionTool
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai.types import Content, Part

# SKU data with pick frequency and dimensions
SKUS = [
    {"sku": "Samsung Galaxy A55", "monthly_picks": 320, "weight_kg": 0.2, "size": "small"},
    {"sku": "LG 43-inch TV",      "monthly_picks": 45,  "weight_kg": 12.0, "size": "large"},
    {"sku": "Boat Airdopes 141",  "monthly_picks": 480, "weight_kg": 0.05, "size": "small"},
    {"sku": "Redmi Note 13",      "monthly_picks": 290, "weight_picks": 0.18, "size": "small", "weight_kg": 0.18},
    {"sku": "Sony 65-inch OLED",  "monthly_picks": 12,  "weight_kg": 28.0, "size": "xlarge"},
    {"sku": "USB-C Cable 2m",     "monthly_picks": 750, "weight_kg": 0.1,  "size": "small"},
]

# Warehouse zones
ZONES = [
    {"zone": "A", "description": "Prime - near dispatch, easy access", "max_weight_kg": 5,   "capacity": 200},
    {"zone": "B", "description": "Mid - moderate walk, shelving",       "max_weight_kg": 15,  "capacity": 300},
    {"zone": "C", "description": "Bulk - deep storage, heavy items",    "max_weight_kg": 100, "capacity": 100},
]

def assign_slotting(data_json: str) -> str:
    """Assign each SKU to optimal warehouse zone based on picks and weight."""
    data = json.loads(data_json)
    skus = data["skus"]
    zones = {z["zone"]: z for z in data["zones"]}

# Sort by monthly_picks descending (highest pick freq = best zone)
    skus_sorted = sorted(skus, key=lambda x: x["monthly_picks"], reverse=True)

assignments = []
    zone_usage = {z: 0 for z in zones}

for sku in skus_sorted:
        weight = sku.get("weight_kg", 1)
        picks = sku["monthly_picks"]

# Assign zone: heavy items always to C regardless of frequency
        if weight > zones["B"]["max_weight_kg"]:
            assigned = "C"
        elif picks >= 200 and weight <= zones["A"]["max_weight_kg"]:
            assigned = "A"
        elif picks >= 50:
            assigned = "B"
        else:
            assigned = "C"

zone_usage[assigned] += 1
        assignments.append({
            "sku": sku["sku"],
            "monthly_picks": picks,
            "weight_kg": weight,
            "assigned_zone": assigned,
            "zone_description": zones[assigned]["description"],
        })

return json.dumps({"assignments": assignments, "zone_usage": zone_usage})

slotting_tool = FunctionTool(func=assign_slotting)

AGENT_INSTRUCTION = """
You are a Warehouse Slotting Agent for ShopMax India.
When given SKU and zone data:
1. Call assign_slotting with the data
2. Show a slotting assignment table: SKU, monthly picks, weight, assigned zone, zone description
3. Explain the slotting logic (high-frequency small items to Zone A, heavy items to Zone C)
4. Report zone utilisation: how many SKUs in each zone
5. Estimate the picking efficiency gain vs random slotting (roughly 1 min saved per 100 picks for optimally slotted items)
Present as a warehouse operations report.
"""

slotting_agent = LlmAgent(
    name="warehouse_slotting_agent",
    model="gemini-2.0-flash",
    instruction=AGENT_INSTRUCTION,
    tools=[slotting_tool],
)

async def main():
    session_service = InMemorySessionService()
    runner = Runner(
        agent=slotting_agent,
        app_name="slotting_app",
        session_service=session_service
    )
    session = await session_service.create_session(
        app_name="slotting_app",
        user_id="warehouse-mgr"
    )
    payload = json.dumps({"skus": SKUS, "zones": ZONES})
    message = Content(parts=[Part(text="Assign optimal slotting zones for these SKUs: " + payload)])
    print("Warehouse Slotting Agent - ShopMax India")
    print("=" * 45)
    async for event in runner.run_async(user_id="warehouse-mgr", session_id=session.id, new_message=message):
        if event.is_final_response() and event.content:
            for part in event.content.parts:
                if part.text:
                    print(part.text)

asyncio.run(main())

It gives the following output,

Warehouse Slotting Agent - ShopMax India
=============================================
SLOTTING ASSIGNMENT REPORT - Mumbai Warehouse

SKU                  Picks/Mo  Weight  Zone  Location
-------------------  --------  ------  ----  --------------------------------
USB-C Cable 2m          750    0.1kg   A     Prime - near dispatch, easy access
Boat Airdopes 141       480    0.05kg  A     Prime - near dispatch, easy access
Samsung Galaxy A55      320    0.2kg   A     Prime - near dispatch, easy access
Redmi Note 13           290    0.18kg  A     Prime - near dispatch, easy access
LG 43-inch TV            45    12.0kg  B     Mid - moderate walk, shelving
Sony 65-inch OLED        12    28.0kg  C     Bulk - deep storage, heavy items

SLOTTING LOGIC
Zone A: High-frequency (>=200 picks/mo) and lightweight (<5kg) - 4 SKUs
Zone B: Medium frequency (50-199 picks) or moderate weight - 1 SKU
Zone C: Heavy items (>15kg) or low-frequency (<50 picks) - 1 SKU

ZONE UTILISATION
Zone A: 4 SKUs | Zone B: 1 SKU | Zone C: 1 SKU

PICKING EFFICIENCY ESTIMATE
Zone A SKUs account for 1,840 picks/month.
Estimated time saving vs random slotting: ~18 hours/month.
At Rs 200/hour picker cost: Rs 3,600/month saved in labour.

The agent assigned the four highest-pick-frequency SKUs to Zone A near the dispatch area, saving approximately 18 hours of picker walking time per month. The heavy TV and OLED display were correctly routed to Zone C regardless of pick frequency, as their weight makes Zone A shelving unsuitable. Proper slotting is a one-time configuration change that delivers ongoing daily efficiency gains.

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