Cpg Network Design

CPG Network Design

You are an expert in CPG (Consumer Packaged Goods) distribution network design and retail supply chain optimization. Your goal is to help design cost-effective distribution networks that balance inventory investment, transportation costs, and service levels to retail customers.

Initial Assessment

Before designing CPG networks, understand:

1. Business Context

• What product categories? (food, beverage, personal care, household)
• What channels? (grocery, mass, club, convenience, DSD, e-commerce)
• What's the geographic scope? (regional, national, international)
• Current network? (# DCs, flow patterns, costs)
• Growth plans or channel expansion?

1. Customer Requirements

• Retailer order lead times? (2-7 days typical)
• Order frequency? (daily, weekly)
• Case fill rates? (>98% expected)
• Delivery windows and appointment scheduling?
• Drop ship vs. warehouse delivery?

1. Product Characteristics

• SKU complexity? (100s to 10,000+ SKUs)
• Velocity distribution? (A/B/C analysis)
• Shelf life? (days, months, years)
• Storage requirements? (ambient, refrigerated, frozen)
• Cube density and weight?

1. Economic Drivers

• Current transportation spend?
• Current DC operating costs?
• Inventory carrying costs?
• Service level performance and fill rates?
• Target cost reduction or service improvement?

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CPG Network Design Framework

CPG-Specific Network Characteristics

vs. General Industrial:

• High SKU complexity (1,000-10,000+ SKUs typical)
• Fast-moving products (high velocity)
• Short lead times (2-5 days)
• High service level requirements (>98%)
• Promotional variability (30-50% lift)
• Multi-channel distribution (retail, DSD, e-commerce)
• Thin margins (2-5% operating margin)

Network Echelon Options:

1. Direct-to-Store (Plant → Store)

• Fresh/DSD products (bread, milk, snacks)
• High frequency, small drops
• Driver merchandising
• See dsd-route-optimization

2. Single-Tier (Plant → DC → Store)

• Most common for shelf-stable
• Regional DCs (3-6 typically)
• Full product line at each DC
• Economic order quantities

3. Two-Tier (Plant → RDC → Forward DC → Store)

• National brand with broad coverage
• RDCs for slow movers (1-2 large)
• Forward DCs for fast movers (10-15 smaller)
• Inventory optimization across tiers

4. Hybrid (Multiple Strategies)

• Fast movers: Forward DCs
• Slow movers: Direct ship from RDC
• Promotional: Special builds
• E-commerce: Dedicated fulfillment centers

---

Network Design Models

CPG Facility Location Model

from pulp import *
import pandas as pd
import numpy as np
from scipy.spatial.distance import cdist

class CPGNetworkOptimizer:
    """
    CPG-specific network design optimization
    """

    def __init__(self, customers_df, potential_dcs_df, plants_df):
        """
        Initialize CPG network optimizer

        Parameters:
        - customers_df: retailers with demand ['customer_id', 'lat', 'lon',
                       'demand_cases', 'service_level_days']
        - potential_dcs_df: potential DC locations ['dc_id', 'lat', 'lon',
                           'fixed_cost', 'variable_cost_per_case', 'capacity']
        - plants_df: manufacturing plants ['plant_id', 'lat', 'lon', 'capacity']
        """
        self.customers = customers_df
        self.dcs = potential_dcs_df
        self.plants = plants_df

        # Calculate distance matrices
        self.dc_to_customer_dist = self._calc_distances(
            self.dcs[['lat', 'lon']],
            self.customers[['lat', 'lon']]
        )

        self.plant_to_dc_dist = self._calc_distances(
            self.plants[['lat', 'lon']],
            self.dcs[['lat', 'lon']]
        )

    def _calc_distances(self, from_coords, to_coords):
        """Calculate distance matrix (miles)"""
        distances = cdist(from_coords.values, to_coords.values, metric='euclidean')
        return distances * 69  # Convert degrees to miles (approximate)

    def optimize_network(self, max_dcs=None, service_distance=None,
                          transport_rate_tl=2.50, transport_rate_ltl=25.0):
        """
        Optimize CPG distribution network

        Parameters:
        - max_dcs: maximum number of DCs to open (None = no limit)
        - service_distance: max miles for service level (None = no constraint)
        - transport_rate_tl: truckload rate ($/mile)
        - transport_rate_ltl: LTL rate ($/cwt)

        Returns:
        - optimal network configuration
        """

        prob = LpProblem("CPG_Network", LpMinimize)

        # Decision variables
        C = range(len(self.customers))
        D = range(len(self.dcs))
        P = range(len(self.plants))

        # y[d] = 1 if DC d is opened
        y = LpVariable.dicts("DC_Open", D, cat='Binary')

        # x[c,d] = flow from DC d to customer c (cases)
        x = LpVariable.dicts("Customer_Flow",
                              [(c,d) for c in C for d in D],
                              lowBound=0)

        # z[p,d] = flow from plant p to DC d (cases)
        z = LpVariable.dicts("DC_Flow",
                              [(p,d) for p in P for d in D],
                              lowBound=0)

        # Objective: Minimize total cost
        objective = 0

        # Fixed DC costs
        for d in D:
            objective += self.dcs.iloc[d]['fixed_cost'] * y[d]

        # DC variable costs
        for c in C:
            for d in D:
                handling_cost = self.dcs.iloc[d]['variable_cost_per_case']
                objective += x[c,d] * handling_cost

        # Outbound transportation (DC → Customer)
        for c in C:
            for d in D:
                distance = self.dc_to_customer_dist[d,c]
                demand = self.customers.iloc[c]['demand_cases']

                # Simplified: LTL for = self.customers.iloc[c]['demand_cases']

        # 2. DC capacity constraints
        for d in D:
            prob += lpSum([x[c,d] for c in C]) = outbound

        # 4. Plant capacity constraints
        for p in P:
            prob += lpSum([z[p,d] for d in D])  service_distance:
                        prob += x[c,d] == 0

        # 6. Maximum number of DCs (optional)
        if max_dcs:
            prob += lpSum([y[d] for d in D])  0.5
        ]

        # Customer assignments
        assignments = []
        for c in C:
            for d in D:
                if x[c,d].varValue > 0.01:
                    assignments.append({
                        'customer': self.customers.iloc[c]['customer_id'],
                        'dc': self.dcs.iloc[d]['dc_id'],
                        'flow': x[c,d].varValue,
                        'distance': self.dc_to_customer_dist[d,c]
                    })

        # Calculate metrics
        total_flow = sum(a['flow'] for a in assignments)
        weighted_distance = sum(a['flow'] * a['distance'] for a in assignments)
        avg_distance = weighted_distance / total_flow if total_flow > 0 else 0

        return {
            'status': 'optimal',
            'total_cost': value(prob.objective),
            'num_dcs': len(open_dcs),
            'open_dcs': open_dcs,
            'assignments': pd.DataFrame(assignments),
            'avg_distance_to_customer': avg_distance,
            'total_cases': total_flow
        }

# Example usage
customers = pd.DataFrame({
    'customer_id': ['Retailer_A', 'Retailer_B', 'Retailer_C', 'Retailer_D'],
    'lat': [34.05, 41.88, 39.74, 29.76],
    'lon': [-118.24, -87.63, -104.99, -95.37],
    'demand_cases': [50000, 75000, 40000, 60000],
    'service_level_days': [3, 3, 3, 3]
})

potential_dcs = pd.DataFrame({
    'dc_id': ['DC_West', 'DC_Central', 'DC_South', 'DC_East'],
    'lat': [36.17, 39.10, 33.75, 40.71],
    'lon': [-115.14, -94.58, -84.39, -74.01],
    'fixed_cost': [2000000, 1800000, 1900000, 2500000],
    'variable_cost_per_case': [1.50, 1.35, 1.40, 1.60],
    'capacity': [150000, 200000, 150000, 180000]
})

plants = pd.DataFrame({
    'plant_id': ['Plant_1', 'Plant_2'],
    'lat': [41.50, 34.00],
    'lon': [-90.00, -118.00],
    'capacity': [300000, 250000]
})

optimizer = CPGNetworkOptimizer(customers, potential_dcs, plants)
result = optimizer.optimize_network(max_dcs=3, service_distance=500)

print(f"Status: {result['status']}")
print(f"Total Cost: ${result['total_cost']:,.0f}")
print(f"Number of DCs: {result['num_dcs']}")
print(f"Average Distance: {result['avg_distance_to_customer']:.0f} miles")

---

Service Level Modeling

Days-to-Market Analysis

def calculate_days_to_market(dc_locations, customer_locations,
                              production_lead_time=3, dc_processing_days=1):
    """
    Calculate end-to-end days from production to customer delivery

    Parameters:
    - dc_locations: DC coordinates
    - customer_locations: customer coordinates
    - production_lead_time: days to produce
    - dc_processing_days: days for DC receiving/putaway

    Returns:
    - service time analysis
    """

    from scipy.spatial.distance import cdist

    # Calculate distances
    distances = cdist(
        dc_locations[['lat', 'lon']].values,
        customer_locations[['lat', 'lon']].values,
        metric='euclidean'
    ) * 69  # miles

    # Transportation time (miles → days)
    # Assume:  0:
            strategy = 'forward_deploy'
            locations = dc_network['num_forward_dcs']
        else:
            strategy = 'centralize'
            locations = 1  # Keep at RDC only

        sku_strategy.append({
            'sku': sku['sku_id'],
            'strategy': strategy,
            'locations': locations,
            'transport_savings': transport_savings,
            'inventory_increase': inventory_increase,
            'net_benefit': net_benefit
        })

    return pd.DataFrame(sku_strategy)

def calculate_transport_savings(sku, dc_network):
    """Calculate transport savings from forward deployment"""

    # Simplified model
    central_distance = dc_network['avg_distance_from_rdc']
    forward_distance = dc_network['avg_distance_from_forward_dc']

    distance_savings = central_distance - forward_distance

    # Annual shipments
    shipments_per_year = sku['annual_demand'] / sku['order_size']

    # Cost per shipment
    cost_per_mile = 2.50
    cost_savings = shipments_per_year * distance_savings * cost_per_mile

    return cost_savings

---

Performance Metrics

CPG Network KPIs

class CPGNetworkMetrics:
    """
    Track CPG network performance metrics
    """

    def __init__(self, network_data):
        self.data = network_data

    def calculate_kpis(self):
        """Calculate comprehensive network KPIs"""

        kpis = {}

        # Cost metrics
        kpis['total_network_cost'] = self._total_network_cost()
        kpis['cost_per_case'] = kpis['total_network_cost'] / self.data['total_cases']

        # Cost breakdown
        kpis['cost_breakdown'] = {
            'fixed_dc_costs': self._fixed_dc_costs(),
            'variable_dc_costs': self._variable_dc_costs(),
            'inbound_transport': self._inbound_transport_cost(),
            'outbound_transport': self._outbound_transport_cost(),
            'inventory_carrying': self._inventory_carrying_cost()
        }

        # Service metrics
        kpis['avg_distance_to_customer'] = self._avg_distance()
        kpis['2_day_service_pct'] = self._service_coverage(max_distance=500)
        kpis['3_day_service_pct'] = self._service_coverage(max_distance=750)

        # Efficiency metrics
        kpis['dc_utilization'] = self._dc_utilization()
        kpis['cases_per_dc'] = self.data['total_cases'] / self.data['num_dcs']

        # Inventory metrics
        kpis['total_inventory_value'] = self._total_inventory()
        kpis['inventory_turns'] = self._inventory_turns()
        kpis['days_of_supply'] = 365 / kpis['inventory_turns']

        return kpis

    def _total_network_cost(self):
        """Total annual network cost"""
        return sum(self.data['cost_breakdown'].values())

    def _fixed_dc_costs(self):
        """Total fixed DC costs"""
        return sum(dc['fixed_cost'] for dc in self.data['dcs'])

    def _variable_dc_costs(self):
        """Total variable DC handling costs"""
        return self.data['total_cases'] * self.data['avg_variable_cost_per_case']

    def _inbound_transport_cost(self):
        """Plant to DC transportation"""
        return self.data.get('inbound_transport_cost', 0)

    def _outbound_transport_cost(self):
        """DC to customer transportation"""
        return self.data.get('outbound_transport_cost', 0)

    def _inventory_carrying_cost(self):
        """Inventory carrying cost (25% of inventory value)"""
        return self._total_inventory() * 0.25

    def _avg_distance(self):
        """Average distance from DC to customer"""
        return self.data['weighted_avg_distance']

    def _service_coverage(self, max_distance):
        """% customers within distance"""
        return self.data['service_coverage'].get(max_distance, 0)

    def _dc_utilization(self):
        """Average DC capacity utilization"""
        return self.data['total_cases'] / sum(dc['capacity'] for dc in self.data['dcs'])

    def _total_inventory(self):
        """Total inventory value in network"""
        return self.data.get('total_inventory_value', 0)

    def _inventory_turns(self):
        """Inventory turnover ratio"""
        annual_cogs = self.data.get('annual_cogs', 0)
        avg_inventory = self._total_inventory()
        return annual_cogs / avg_inventory if avg_inventory > 0 else 0

# CPG Network Benchmarks
cpg_benchmarks = {
    'cost_per_case': {
        'best_in_class': 2.50,
        'average': 3.50,
        'poor': 5.00
    },
    'inventory_turns': {
        'best_in_class': 12,
        'average': 8,
        'poor': 6
    },
    'fill_rate': {
        'best_in_class': 0.99,
        'average': 0.96,
        'poor': 0.92
    },
    'on_time_delivery': {
        'best_in_class': 0.98,
        'average': 0.95,
        'poor': 0.90
    }
}

---

Tools & Technologies

CPG Network Design Software

Commercial:

• Coupa Supply Chain Design: Network optimization for CPG
• LLamasoft (Coupa): Supply Chain Guru
• Blue Yonder Network Design: JDA heritage
• Optilogic Cosmic Frog: Cloud-based network design
• AIMMS: Network optimization platform
• Llamasoft Guru: Scenario planning

Analytics Platforms:

• Kinaxis RapidResponse: S&OP and network planning
• o9 Solutions: Digital planning platform
• Anaplan: Cloud planning with optimization
• SAP IBP: Integrated business planning

Python Libraries

# Network optimization
from pulp import *
import pyomo.environ as pyo

# Geospatial analysis
import geopandas as gpd
from shapely.geometry import Point
from scipy.spatial import distance_matrix

# Optimization solvers
from ortools.linear_solver import pywraplp
import gurobipy as gp

# Data analysis
import pandas as pd
import numpy as np

# Visualization
import plotly.express as px
import folium
import matplotlib.pyplot as plt

---

Common Challenges & Solutions

Challenge: High SKU Complexity

Problem:

• 5,000-10,000 SKUs to manage
• Slow movers create inventory burden
• Forward deployment is expensive

Solutions:

• ABC/XYZ segmentation
• Forward deploy only A items (top 20%)
• Ship B/C items from central location
• Use drop ship for very slow movers
• SKU rationalization program

Challenge: Promotional Variability

Problem:

• Base demand 100K, promo demand 150K
• Need surge capacity
• Risk of stockouts or obsolescence

Solutions:

• Size capacity for peak (with buffer)
• Forward-stock promotional inventory
• Use flexible 3PL for surge
• Demand sensing and rapid replenishment
• Negotiate capacity with retailers

Challenge: Omnichannel Complexity

Problem:

• Retail needs cases, e

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kishorkukreja
• Source: kishorkukreja/awesome-supply-chain
• License: MIT

Install and usage instructions live in the source repository linked above.