Babysitter network-optimization-modeler

Strategic distribution network modeling skill to optimize facility locations, capacity allocation, and inventory positioning

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/business/logistics/skills/network-optimization-modeler" ~/.claude/skills/a5c-ai-babysitter-network-optimization-modeler && rm -rf "$T"

manifest: library/specializations/domains/business/logistics/skills/network-optimization-modeler/SKILL.md

Network Optimization Modeler

Overview

The Network Optimization Modeler is a strategic skill that optimizes distribution network design including facility locations, capacity allocation, and inventory positioning. It uses advanced modeling techniques to evaluate scenarios and recommend network configurations that minimize cost while meeting service requirements.

Capabilities

Facility Location Optimization: Determine optimal locations for distribution centers, fulfillment centers, and warehouses
Network Cost-to-Serve Modeling: Model total cost-to-serve including transportation, inventory, and facility costs
Capacity Planning and Allocation: Optimize capacity allocation across facilities and identify expansion needs
Scenario Analysis (Greenfield, Brownfield): Evaluate network redesign scenarios from scratch or building on existing infrastructure
Service Level Impact Assessment: Analyze the service level implications of network design decisions
Carbon Footprint Modeling: Incorporate sustainability metrics into network optimization decisions
Risk and Resilience Analysis: Evaluate network resilience to disruptions and identify vulnerability points

Tools and Libraries

Network Optimization Solvers (Llamasoft, AIMMS)
Simulation Tools
GIS Libraries
Optimization Libraries (Gurobi, CPLEX)

Used By Processes

Distribution Network Optimization
Cross-Docking Operations
Multi-Channel Fulfillment

Usage

skill: network-optimization-modeler
inputs:
  current_network:
    facilities:
      - facility_id: "DC001"
        location: "Chicago, IL"
        type: "distribution_center"
        capacity_pallets: 50000
        annual_cost: 2500000
      - facility_id: "DC002"
        location: "Dallas, TX"
        type: "distribution_center"
        capacity_pallets: 35000
        annual_cost: 1800000
  demand:
    regions:
      - region: "Northeast"
        annual_demand_pallets: 75000
        service_requirement_days: 2
      - region: "Southeast"
        annual_demand_pallets: 60000
        service_requirement_days: 2
  constraints:
    max_facilities: 5
    budget_capex: 10000000
    min_service_level_percent: 95
  scenarios:
    - name: "Add West Coast DC"
      candidate_locations: ["Los Angeles, CA", "Phoenix, AZ"]
    - name: "Expand Chicago"
      expansion_capacity: 25000
outputs:
  recommended_network:
    scenario: "Add West Coast DC"
    facilities:
      - facility_id: "DC001"
        status: "existing"
        utilization: 85
      - facility_id: "DC002"
        status: "existing"
        utilization: 78
      - facility_id: "DC003"
        location: "Los Angeles, CA"
        status: "new"
        capacity_pallets: 40000
        capex: 5000000
  metrics:
    total_annual_cost: 12500000
    cost_savings_vs_current: 1200000
    service_level_achieved: 97.5
    average_transit_days: 1.8
    carbon_reduction_percent: 12
  scenario_comparison:
    - scenario: "Current State"
      cost: 13700000
      service_level: 92.0
    - scenario: "Add West Coast DC"
      cost: 12500000
      service_level: 97.5

Integration Points

Strategic Planning Systems
Transportation Management Systems (TMS)
Warehouse Management Systems (WMS)
Financial Planning Systems
GIS/Mapping Services

Performance Metrics

Total cost-to-serve
Service level coverage
Facility utilization
Network efficiency index
Carbon footprint per unit