logistics-expert — Claude Code skill: what it does, how to install

---
name: logistics-expert
version: 2.0.0
description: >-
  Expert-level supply chain management, logistics optimization, warehouse systems, and
  fleet management. Use when the user mentions supply chain, warehouse, fleet, or
  optimization, or when the task involves Supply Chain Management or Technologies.
category: domains
tags: [logistics, supply-chain, warehouse, fleet, optimization]
allowed-tools:
  - Read
  - Write
  - Edit
---

# Logistics Expert

Expert guidance for supply chain management, logistics optimization, warehouse management systems, and transportation planning.

## Core Concepts

### Supply Chain Management

- Inventory management
- Demand forecasting
- Procurement and sourcing
- Warehouse management (WMS)
- Transportation management (TMS)
- Order fulfillment
- Last-mile delivery

### Optimization

- Route optimization
- Load planning
- Inventory optimization
- Network design
- Cost minimization
- Delivery scheduling

### Technologies

- RFID and barcode scanning
- GPS tracking
- IoT sensors
- Predictive analytics
- Automated warehouses
- Drone delivery

## Warehouse Management System

```python
from dataclasses import dataclass
from typing import List, Optional
from datetime import datetime
from enum import Enum

class StorageType(Enum):
    PALLET = "pallet"
    SHELF = "shelf"
    BULK = "bulk"
    COLD = "cold_storage"

@dataclass
class Location:
    location_id: str
    zone: str
    aisle: str
    rack: str
    level: int
    storage_type: StorageType
    capacity: float
    current_load: float

@dataclass
class Product:
    sku: str
    name: str
    category: str
    weight: float
    volume: float
    storage_requirements: str

@dataclass
class InventoryItem:
    item_id: str
    sku: str
    quantity: int
    location_id: str
    received_date: datetime
    expiry_date: Optional[datetime]
    batch_number: str

class WMS:
    """Warehouse Management System"""

    def __init__(self, db):
        self.db = db

    def receive_shipment(self, shipment):
        """Process incoming shipment"""
        items_received = []

        for item in shipment.items:
            # Find optimal storage location
            location = self.find_optimal_location(item)

            # Create inventory record
            inventory_item = InventoryItem(
                item_id=generate_id(),
                sku=item.sku,
                quantity=item.quantity,
                location_id=location.location_id,
                received_date=datetime.now(),
                expiry_date=item.expiry_date,
                batch_number=item.batch_number
            )

            self.db.save_inventory(inventory_item)
            self.update_location_capacity(location, item)

            items_received.append(inventory_item)

        return {
            'shipment_id': shipment.shipment_id,
            'items_received': len(items_received),
            'status': 'completed'
        }

    def find_optimal_location(self, item):
        """Find best storage location for item"""
        product = self.db.get_product(item.sku)
        available_locations = self.db.get_available_locations(
            storage_type=product.storage_requirements,
            min_capacity=product.volume * item.quantity
        )

        # Prioritize locations
        # 1. Same SKU for efficient picking
        # 2. Closest to shipping area for fast-moving items
        # 3. Maximize space utilization

        same_sku_locations = [
            loc for loc in available_locations
            if self.has_same_sku(loc, item.sku)
        ]

        if same_sku_locations:
            return same_sku_locations[0]

        # Select closest to shipping for fast-moving items
        if product.category == 'fast-moving':
            return min(available_locations, key=lambda l: l.distance_to_shipping)

        # Otherwise, optimize space utilization
        return max(available_locations, key=lambda l: l.utilization_score)

    def pick_order(self, order_id):
        """Generate picking list and route"""
        order = self.db.get_order(order_id)
        picking_list = []

        for line_item in order.line_items:
            inventory = self.db.find_inventory(
                sku=line_item.sku,
                quantity=line_item.quantity
            )

            picking_list.append({
                'sku': line_item.sku,
                'quantity': line_item.quantity,
                'location': inventory.location_id,
                'batch': inventory.batch_number
            })

        # Optimize picking route
        optimized_route = self.optimize_picking_route(picking_list)

        return {
            'order_id': order_id,
            'picking_list': optimized_route,
            'estimated_time': self.estimate_picking_time(optimized_route)
        }

    def optimize_picking_route(self, picking_list):
        """Optimize warehouse picking route"""
        # Sort by zone, aisle, rack for efficient walking path
        sorted_picks = sorted(
            picking_list,
            key=lambda x: (
                self.get_location_zone(x['location']),
                self.get_location_aisle(x['location']),
                self.get_location_rack(x['location'])
            )
        )

        return sorted_picks

    def check_stock_level(self, sku):
        """Check current stock level"""
        total_quantity = self.db.sum_quantity_by_sku(sku)
        product = self.db.get_product(sku)

        status = 'normal'
        if total_quantity <= product.reorder_point:
            status = 'reorder'
        elif total_quantity <= product.safety_stock:
            status = 'critical'

        return {
            'sku': sku,
            'quantity': total_quantity,
            'status': status,
            'reorder_point': product.reorder_point
        }
```

## Route Optimization

```python
import numpy as np
from scipy.spatial.distance import cdist
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp

class RouteOptimizer:
    """Vehicle routing optimization"""

    def optimize_delivery_routes(self, depot, deliveries, vehicles):
        """Optimize delivery routes using OR-Tools"""
        # Create distance matrix
        locations = [depot] + deliveries
        distance_matrix = self.create_distance_matrix(locations)

        # Create routing model
        manager = pywrapcp.RoutingIndexManager(
            len(distance_matrix),
            len(vehicles),
            0  # depot index
        )

        routing = pywrapcp.RoutingModel(manager)

        # Distance callback
        def distance_callback(from_index, to_index):
            from_node = manager.IndexToNode(from_index)
            to_node = manager.IndexToNode(to_index)
            return distance_matrix[from_node][to_node]

        transit_callback_index = routing.RegisterTransitCallback(distance_callback)
        routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)

        # Add capacity constraints
        def demand_callback(from_index):
            from_node = manager.IndexToNode(from_index)
            return deliveries[from_node - 1].weight if from_node > 0 else 0

        demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)

        routing.AddDimensionWithVehicleCapacity(
            demand_callback_index,
            0,  # null capacity slack
            [v.capacity for v in vehicles],
            True,  # start cumul to zero
            'Capacity'
        )

        # Solve
        search_parameters = pywrapcp.DefaultRoutingSearchParameters()
        search_parameters.first_solution_strategy = (
            routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
        )

        solution = routing.SolveWithParameters(search_parameters)

        if solution:
            return self.extract_routes(manager, routing, solution, locations)

        return None

    def create_distance_matrix(self, locations):
        """Create distance matrix from coordinates"""
        coords = np.array([(loc.lat, loc.lon) for loc in locations])
        return cdist(coords, coords, metric='euclidean')

    def extract_routes(self, manager, routing, solution, locations):
        """Extract optimized routes from solution"""
        routes = []

        for vehicle_id in range(routing.vehicles()):
            route = []
            index = routing.Start(vehicle_id)

            while not routing.IsEnd(index):
                node = manager.IndexToNode(index)
                route.append(locations[node])
                index = solution.Value(routing.NextVar(index))

            routes.append({
                'vehicle_id': vehicle_id,
                'stops': route,
                'total_distance': solution.ObjectiveValue()
            })

        return routes
```

## Fleet Management

```python
@dataclass
class Vehicle:
    vehicle_id: str
    type: str
    capacity_weight: float
    capacity_volume: float
    fuel_efficiency: float
    status: str
    current_location: dict
    maintenance_due: datetime

class FleetManagement:
    """Fleet tracking and management"""

    def __init__(self, db):
        self.db = db

    def assign_vehicle(self, delivery):
        """Assign optimal vehicle for delivery"""
        available_vehicles = self.db.get_available_vehicles(
            location=delivery.origin,
            min_capacity=delivery.total_weight
        )

        # Score vehicles based on:
        # - Distance from origin
        # - Capacity utilization
        # - Fuel efficiency
        # - Maintenance status

        best_vehicle = min(
            available_vehicles,
            key=lambda v: self.calculate_vehicle_score(v, delivery)
        )

        return best_vehicle

    def track_vehicle(self, vehicle_id):
        """Real-time vehicle tracking"""
        vehicle = self.db.get_vehicle(vehicle_id)
        current_route = self.db.get_current_route(vehicle_id)

        return {
            'vehicle_id': vehicle_id,
            'location': vehicle.current_location,
            'status': vehicle.status,
            'current_delivery': current_route.delivery_id if current_route else None,
            'eta': self.calculate_eta(vehicle, current_route) if current_route else None,
            'fuel_level': vehicle.fuel_level,
            'distance_traveled_today': vehicle.daily_distance
        }

    def schedule_maintenance(self, vehicle_id):
        """Schedule vehicle maintenance"""
        vehicle = self.db.get_vehicle(vehicle_id)
        maintenance_history = self.db.get_maintenance_history(vehicle_id)

        # Predictive maintenance based on:
        # - Mileage
        # - Hours of operation
        # - Previous issues
        # - Manufacturer schedule

        next_maintenance = self.predict_maintenance_date(
            vehicle,
            maintenance_history
        )

        return {
            'vehicle_id': vehicle_id,
            'next_maintenance': next_maintenance,
            'type': 'preventive',
            'estimated_cost': self.estimate_maintenance_cost(vehicle)
        }
```

## Inventory Forecasting

```python
from sklearn.ensemble import RandomForestRegressor
import pandas as pd

class DemandForecasting:
    """Demand forecasting and inventory optimization"""

    def forecast_demand(self, sku, forecast_period_days=30):
        """Forecast product demand"""
        # Get historical sales data
        historical_data = self.db.get_sales_history(sku, days=365)

        # Features: day of week, month, seasonality, promotions
        df = pd.DataFrame(historical_data)
        df['day_of_week'] = pd.to_datetime(df['date']).dt.dayofweek
        df['month'] = pd.to_datetime(df['date']).dt.month
        df['is_promotion'] = df['promotion_id'].notna().astype(int)

        X = df[['day_of_week', 'month', 'is_promotion']]
        y = df['quantity_sold']

        # Train model
        model = RandomForestRegressor(n_estimators=100)
        model.fit(X, y)

        # Generate forecast
        future_dates = pd.date_range(
            start=pd.Timestamp.now(),
            periods=forecast_period_days,
            freq='D'
        )

        forecast_features = pd.DataFrame({
            'day_of_week': future_dates.dayofweek,
            'month': future_dates.month,
            'is_promotion': 0  # Assume no promotions unless specified
        })

        predicted_demand = model.predict(forecast_features)

        return {
            'sku': sku,
            'forecast_period': forecast_period_days,
            'predicted_demand': predicted_demand.tolist(),
            'total_demand': sum(predicted_demand),
            'recommended_order_quantity': self.calculate_order_quantity(
                predicted_demand,
                sku
            )
        }

    def calculate_order_quantity(self, forecast, sku):
        """Calculate economic order quantity"""
        product = self.db.get_product(sku)
        total_demand = sum(forecast)

        # EOQ formula
        eoq = np.sqrt(
            (2 * total_demand * product.order_cost) /
            product.holding_cost
        )

        return int(eoq)
```

## Best Practices

- Implement real-time tracking
- Use predictive analytics
- Optimize inventory levels
- Automate warehouse operations
- Enable multi-modal transportation
- Implement quality control
- Track KPIs (on-time delivery, accuracy)
- Use lean principles
- Enable reverse logistics
- Implement safety protocols

## Anti-Patterns

❌ Manual inventory tracking
❌ No route optimization
❌ Overstocking or understocking
❌ Poor warehouse layout
❌ No real-time visibility
❌ Ignoring data analytics
❌ Inefficient picking processes

## Resources

- Council of Supply Chain Management Professionals: https://cscmp.org/
- OR-Tools: https://developers.google.com/optimization
- Warehouse Management: https://www.mhi.org/
- Transportation Management: https://www.inboundlogistics.com/