E-commerce Operations

Product listing optimization, inventory management, customer insights, and automated operations for e-commerce platforms

E-commerce Operations

This example demonstrates a comprehensive e-commerce operations management system using RCrewAI agents to handle product optimization, inventory management, customer analytics, pricing strategies, and automated operations across multiple sales channels.

Overview

Our e-commerce operations team includes:

• Product Manager - Product listing optimization and catalog management
• Inventory Specialist - Stock management and demand forecasting
• Pricing Strategist - Dynamic pricing and competitive analysis
• Customer Analytics Specialist - Customer behavior and segmentation
• Marketing Automation Expert - Campaign management and personalization
• Operations Coordinator - Cross-channel coordination and workflow optimization

Complete Implementation

require 'rcrewai'
require 'json'
require 'csv'

# Configure RCrewAI for e-commerce operations
RCrewAI.configure do |config|
  config.llm_provider = :openai
  config.temperature = 0.3  # Balanced for operational precision
end

# ===== E-COMMERCE OPERATIONS TOOLS =====

# Product Catalog Management Tool
class ProductCatalogTool < RCrewAI::Tools::Base
  def initialize(**options)
    super
    @name = 'product_catalog_manager'
    @description = 'Manage product listings, descriptions, and catalog optimization'
    @product_database = {}
    @category_mappings = {}
  end
  
  def execute(**params)
    action = params[:action]
    
    case action
    when 'optimize_listing'
      optimize_product_listing(params[:product_id], params[:optimization_data])
    when 'update_inventory'
      update_inventory_levels(params[:product_id], params[:quantity], params[:warehouse_id])
    when 'analyze_performance'
      analyze_product_performance(params[:product_id], params[:timeframe])
    when 'generate_descriptions'
      generate_product_descriptions(params[:products])
    when 'category_analysis'
      analyze_category_performance(params[:category])
    else
      "Product catalog: Unknown action #{action}"
    end
  end
  
  private
  
  def optimize_product_listing(product_id, optimization_data)
    # Simulate product listing optimization
    {
      product_id: product_id,
      original_title: "Basic Product Title",
      optimized_title: "Premium Quality [Product] - Best Value with Free Shipping",
      seo_keywords: ["premium", "best value", "free shipping", "quality"],
      description_length: 250,
      bullet_points: 5,
      optimization_score: 85,
      estimated_conversion_lift: "12-18%"
    }.to_json
  end
  
  def update_inventory_levels(product_id, quantity, warehouse_id)
    # Simulate inventory update
    {
      product_id: product_id,
      warehouse_id: warehouse_id,
      previous_quantity: 45,
      new_quantity: quantity,
      reorder_point: 20,
      status: quantity > 20 ? "in_stock" : "low_stock",
      next_reorder_date: Date.today + 7,
      supplier_info: { lead_time: 14, min_order: 100 }
    }.to_json
  end
  
  def analyze_product_performance(product_id, timeframe)
    # Simulate product performance analysis
    {
      product_id: product_id,
      timeframe: timeframe,
      total_sales: 1247,
      revenue: 24_940.00,
      conversion_rate: 3.8,
      average_rating: 4.3,
      return_rate: 2.1,
      profit_margin: 35.5,
      competitive_position: "top_quartile",
      recommendations: [
        "Increase advertising spend - high ROI",
        "Consider bundle offers",
        "Optimize for mobile conversion"
      ]
    }.to_json
  end
  
  def generate_product_descriptions(products)
    # Simulate AI-powered description generation
    {
      processed_products: products.length,
      generated_descriptions: products.length,
      seo_optimized: true,
      average_word_count: 180,
      keyword_density: "2.5%",
      readability_score: 82,
      estimated_completion_time: "#{products.length * 2} minutes"
    }.to_json
  end
end

# Inventory Management Tool
class InventoryManagementTool < RCrewAI::Tools::Base
  def initialize(**options)
    super
    @name = 'inventory_manager'
    @description = 'Manage inventory levels, demand forecasting, and supplier relationships'
    @inventory_data = {}
    @demand_forecasts = {}
  end
  
  def execute(**params)
    action = params[:action]
    
    case action
    when 'demand_forecast'
      forecast_demand(params[:product_id], params[:timeframe])
    when 'reorder_analysis'
      analyze_reorder_points(params[:category] || 'all')
    when 'supplier_optimization'
      optimize_supplier_relationships(params[:supplier_criteria])
    when 'inventory_turnover'
      calculate_inventory_turnover(params[:timeframe])
    when 'stockout_prevention'
      prevent_stockouts(params[:risk_threshold])
    else
      "Inventory management: Unknown action #{action}"
    end
  end
  
  private
  
  def forecast_demand(product_id, timeframe)
    # Simulate demand forecasting
    {
      product_id: product_id,
      forecast_period: timeframe,
      predicted_demand: 450,
      confidence_interval: "380-520 units",
      seasonal_factor: 1.15,
      trend_direction: "increasing",
      demand_drivers: [
        "Seasonal increase expected",
        "Marketing campaign impact",
        "Competitor stockout opportunity"
      ],
      recommended_stock_level: 600,
      optimal_reorder_quantity: 300
    }.to_json
  end
  
  def analyze_reorder_points(category)
    # Simulate reorder point analysis
    {
      category: category,
      total_products_analyzed: 45,
      products_below_reorder: 8,
      products_overstocked: 3,
      optimal_reorder_points: {
        "electronics" => 25,
        "clothing" => 15,
        "home_goods" => 30
      },
      total_reorder_value: 125_000.00,
      priority_reorders: [
        { product_id: "ELEC-001", urgency: "high", quantity: 150 },
        { product_id: "CLTH-045", urgency: "medium", quantity: 75 }
      ]
    }.to_json
  end
  
  def optimize_supplier_relationships(criteria)
    # Simulate supplier optimization
    {
      suppliers_evaluated: 12,
      cost_savings_identified: 15_000.00,
      lead_time_improvements: "2-3 days average",
      quality_score_increase: 8.5,
      recommended_changes: [
        "Switch primary electronics supplier for 12% cost reduction",
        "Negotiate volume discounts with textile supplier",
        "Add backup supplier for critical components"
      ],
      risk_assessment: "Low risk with diversified supplier base"
    }.to_json
  end
end

# Pricing Strategy Tool
class PricingStrategyTool < RCrewAI::Tools::Base
  def initialize(**options)
    super
    @name = 'pricing_strategist'
    @description = 'Optimize pricing strategies and competitive positioning'
  end
  
  def execute(**params)
    action = params[:action]
    
    case action
    when 'competitive_analysis'
      analyze_competitive_pricing(params[:product_category], params[:competitors])
    when 'dynamic_pricing'
      optimize_dynamic_pricing(params[:product_id], params[:market_conditions])
    when 'price_elasticity'
      calculate_price_elasticity(params[:product_id], params[:price_test_data])
    when 'promotion_strategy'
      develop_promotion_strategy(params[:campaign_goals])
    else
      "Pricing strategy: Unknown action #{action}"
    end
  end
  
  private
  
  def analyze_competitive_pricing(category, competitors)
    # Simulate competitive pricing analysis
    {
      category: category,
      competitors_analyzed: competitors&.length || 5,
      price_position: "middle_tier",
      competitive_advantage: "23% better value proposition",
      pricing_opportunities: [
        "Premium positioning available for 15% price increase",
        "Bundle pricing can improve margins by 8%",
        "Geographic pricing optimization possible"
      ],
      market_share_impact: "+2.3% with optimized pricing",
      recommended_actions: [
        "Increase prices on bestsellers by 8%",
        "Introduce tiered pricing structure",
        "Launch competitive price matching for key products"
      ]
    }.to_json
  end
  
  def optimize_dynamic_pricing(product_id, market_conditions)
    # Simulate dynamic pricing optimization
    {
      product_id: product_id,
      current_price: 49.99,
      optimal_price: 52.99,
      price_change_percentage: 6.0,
      demand_elasticity: -1.2,
      expected_volume_change: "-5%",
      expected_revenue_change: "+1%",
      profit_impact: "+8%",
      market_factors: [
        "Low competitor inventory",
        "High seasonal demand",
        "Strong product reviews"
      ],
      implementation_timeline: "Immediate - high confidence"
    }.to_json
  end
end

# ===== E-COMMERCE OPERATIONS AGENTS =====

# Product Manager
product_manager = RCrewAI::Agent.new(
  name: "product_manager",
  role: "E-commerce Product Manager",
  goal: "Optimize product listings, catalog management, and product performance across all sales channels",
  backstory: "You are an experienced e-commerce product manager with expertise in catalog optimization, SEO, and conversion optimization. You excel at maximizing product visibility and sales performance.",
  tools: [
    ProductCatalogTool.new,
    RCrewAI::Tools::WebSearch.new,
    RCrewAI::Tools::FileReader.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Inventory Specialist
inventory_specialist = RCrewAI::Agent.new(
  name: "inventory_specialist",
  role: "Inventory Management Specialist",
  goal: "Maintain optimal inventory levels, forecast demand, and optimize supplier relationships",
  backstory: "You are an inventory management expert with deep knowledge of demand forecasting, supply chain optimization, and inventory analytics. You excel at balancing stock levels with cash flow requirements.",
  tools: [
    InventoryManagementTool.new,
    ProductCatalogTool.new,
    RCrewAI::Tools::FileReader.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Pricing Strategist
pricing_strategist = RCrewAI::Agent.new(
  name: "pricing_strategist",
  role: "E-commerce Pricing Strategist",
  goal: "Develop and implement optimal pricing strategies to maximize revenue and market positioning",
  backstory: "You are a pricing strategy expert with expertise in competitive analysis, price optimization, and market positioning. You excel at balancing profitability with market competitiveness.",
  tools: [
    PricingStrategyTool.new,
    RCrewAI::Tools::WebSearch.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Customer Analytics Specialist
customer_analytics = RCrewAI::Agent.new(
  name: "customer_analytics_specialist",
  role: "Customer Analytics and Insights Specialist",
  goal: "Analyze customer behavior, segment audiences, and provide actionable insights for business growth",
  backstory: "You are a customer analytics expert with deep knowledge of customer segmentation, behavioral analysis, and predictive modeling. You excel at turning data into actionable business insights.",
  tools: [
    RCrewAI::Tools::FileReader.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Marketing Automation Expert
marketing_automation = RCrewAI::Agent.new(
  name: "marketing_automation_expert",
  role: "E-commerce Marketing Automation Specialist",
  goal: "Create and optimize automated marketing campaigns, personalization strategies, and customer journey optimization",
  backstory: "You are a marketing automation expert with expertise in email marketing, personalization, and customer journey optimization. You excel at creating automated systems that drive customer engagement and sales.",
  tools: [
    RCrewAI::Tools::FileReader.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Operations Coordinator
operations_coordinator = RCrewAI::Agent.new(
  name: "operations_coordinator",
  role: "E-commerce Operations Manager",
  goal: "Coordinate all e-commerce operations, optimize workflows, and ensure seamless execution across all channels",
  backstory: "You are an operations management expert who specializes in e-commerce workflow optimization, cross-channel coordination, and operational efficiency. You excel at creating integrated systems that drive business performance.",
  manager: true,
  allow_delegation: true,
  tools: [
    RCrewAI::Tools::FileReader.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Create e-commerce operations crew
ecommerce_crew = RCrewAI::Crew.new("ecommerce_operations_crew", process: :hierarchical)

# Add agents to crew
ecommerce_crew.add_agent(operations_coordinator)  # Manager first
ecommerce_crew.add_agent(product_manager)
ecommerce_crew.add_agent(inventory_specialist)
ecommerce_crew.add_agent(pricing_strategist)
ecommerce_crew.add_agent(customer_analytics)
ecommerce_crew.add_agent(marketing_automation)

# ===== E-COMMERCE OPERATIONS TASKS =====

# Product Optimization Task
product_optimization_task = RCrewAI::Task.new(
  name: "product_catalog_optimization",
  description: "Optimize product listings across all channels for maximum visibility and conversion. Enhance product titles, descriptions, images, and SEO optimization. Analyze product performance and identify opportunities for improvement.",
  expected_output: "Product optimization report with enhanced listings, SEO recommendations, and performance improvement strategies",
  agent: product_manager,
  async: true
)

# Inventory Management Task
inventory_management_task = RCrewAI::Task.new(
  name: "inventory_optimization",
  description: "Analyze current inventory levels, forecast demand, and optimize reorder points. Identify overstocked and understocked items, evaluate supplier performance, and develop inventory optimization strategies.",
  expected_output: "Inventory management report with demand forecasts, reorder recommendations, and supplier optimization strategies",
  agent: inventory_specialist,
  async: true
)

# Pricing Strategy Task
pricing_strategy_task = RCrewAI::Task.new(
  name: "pricing_strategy_optimization",
  description: "Develop comprehensive pricing strategies based on competitive analysis, market positioning, and profit optimization. Analyze price elasticity, identify pricing opportunities, and create dynamic pricing recommendations.",
  expected_output: "Pricing strategy document with competitive analysis, optimal pricing recommendations, and revenue impact projections",
  agent: pricing_strategist,
  context: [product_optimization_task],
  async: true
)

# Customer Analytics Task
customer_analytics_task = RCrewAI::Task.new(
  name: "customer_behavior_analysis",
  description: "Analyze customer behavior patterns, segment customer base, and identify growth opportunities. Study purchase patterns, customer lifetime value, churn indicators, and personalization opportunities.",
  expected_output: "Customer analytics report with segmentation insights, behavioral analysis, and growth opportunity recommendations",
  agent: customer_analytics,
  context: [product_optimization_task],
  async: true
)

# Marketing Automation Task
marketing_automation_task = RCrewAI::Task.new(
  name: "marketing_automation_optimization",
  description: "Design and optimize automated marketing campaigns, email sequences, and personalization strategies. Create customer journey mapping, campaign performance analysis, and conversion optimization recommendations.",
  expected_output: "Marketing automation strategy with campaign designs, personalization frameworks, and conversion optimization plans",
  agent: marketing_automation,
  context: [customer_analytics_task, pricing_strategy_task]
)

# Operations Coordination Task
operations_coordination_task = RCrewAI::Task.new(
  name: "ecommerce_operations_coordination",
  description: "Coordinate all e-commerce operations to ensure optimal performance across product management, inventory, pricing, customer analytics, and marketing automation. Identify synergies and optimize workflows.",
  expected_output: "Operations coordination report with integrated strategy recommendations, workflow optimizations, and performance metrics",
  agent: operations_coordinator,
  context: [product_optimization_task, inventory_management_task, pricing_strategy_task, customer_analytics_task, marketing_automation_task]
)

# Add tasks to crew
ecommerce_crew.add_task(product_optimization_task)
ecommerce_crew.add_task(inventory_management_task)
ecommerce_crew.add_task(pricing_strategy_task)
ecommerce_crew.add_task(customer_analytics_task)
ecommerce_crew.add_task(marketing_automation_task)
ecommerce_crew.add_task(operations_coordination_task)

# ===== E-COMMERCE BUSINESS DATA =====

business_data = {
  "store_info" => {
    "name" => "TechGear Pro",
    "category" => "Electronics & Accessories",
    "monthly_revenue" => 450_000,
    "active_products" => 1_250,
    "monthly_orders" => 3_200,
    "average_order_value" => 140.63,
    "customer_base" => 15_000
  },
  "product_categories" => {
    "smartphones" => { "products" => 85, "revenue_share" => 35.2, "margin" => 18.5 },
    "laptops" => { "products" => 45, "revenue_share" => 28.1, "margin" => 22.3 },
    "accessories" => { "products" => 320, "revenue_share" => 25.4, "margin" => 45.8 },
    "audio" => { "products" => 120, "revenue_share" => 11.3, "margin" => 35.6 }
  },
  "key_metrics" => {
    "conversion_rate" => 2.8,
    "cart_abandonment_rate" => 68.5,
    "return_rate" => 4.2,
    "customer_satisfaction" => 4.3,
    "repeat_purchase_rate" => 32.1
  },
  "operational_challenges" => [
    "Inventory management across 3 warehouses",
    "Price competition from large retailers",
    "Customer acquisition cost increasing",
    "Supply chain disruptions affecting lead times"
  ]
}

File.write("ecommerce_business_data.json", JSON.pretty_generate(business_data))

puts "🛒 E-commerce Operations System Starting"
puts "="*60
puts "Store: #{business_data['store_info']['name']}"
puts "Monthly Revenue: $#{business_data['store_info']['monthly_revenue'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}"
puts "Active Products: #{business_data['store_info']['active_products'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}"
puts "Customer Base: #{business_data['store_info']['customer_base'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}"
puts "="*60

# Sample operational data
operational_data = {
  "inventory_status" => {
    "total_sku" => 1_250,
    "low_stock_items" => 85,
    "overstock_items" => 23,
    "out_of_stock" => 12,
    "inventory_value" => 890_000,
    "turnover_rate" => 6.2
  },
  "pricing_analysis" => {
    "competitive_products" => 450,
    "price_optimizable" => 180,
    "underpriced_items" => 65,
    "overpriced_items" => 28,
    "dynamic_pricing_candidates" => 95
  },
  "customer_segments" => {
    "vip_customers" => { "count" => 450, "avg_order" => 285.50, "frequency" => 8.2 },
    "regular_customers" => { "count" => 4200, "avg_order" => 165.25, "frequency" => 3.1 },
    "new_customers" => { "count" => 2800, "avg_order" => 95.75, "frequency" => 1.2 },
    "at_risk_customers" => { "count" => 850, "avg_order" => 120.00, "frequency" => 0.8 }
  },
  "marketing_performance" => {
    "email_campaigns" => {
      "open_rate" => 24.5,
      "click_rate" => 4.2,
      "conversion_rate" => 1.8,
      "revenue_per_email" => 2.35
    },
    "abandoned_cart_recovery" => {
      "recovery_rate" => 15.8,
      "average_recovered_value" => 89.50
    }
  }
}

File.write("operational_data.json", JSON.pretty_generate(operational_data))

puts "\n📊 Operational Status Overview:"
puts "  • #{operational_data['inventory_status']['low_stock_items']} items need restocking"
puts "  • #{operational_data['pricing_analysis']['price_optimizable']} products ready for price optimization"
puts "  • #{operational_data['customer_segments']['vip_customers']['count']} VIP customers generating premium revenue"
puts "  • #{operational_data['marketing_performance']['abandoned_cart_recovery']['recovery_rate']}% cart recovery rate"

# ===== EXECUTE E-COMMERCE OPERATIONS =====

puts "\n🚀 Starting E-commerce Operations Optimization"
puts "="*60

# Execute the e-commerce crew
results = ecommerce_crew.execute

# ===== OPERATIONS RESULTS =====

puts "\n📊 E-COMMERCE OPERATIONS RESULTS"
puts "="*60

puts "Operations Success Rate: #{results[:success_rate]}%"
puts "Total Optimization Areas: #{results[:total_tasks]}"
puts "Completed Optimizations: #{results[:completed_tasks]}"
puts "Operations Status: #{results[:success_rate] >= 80 ? 'OPTIMIZED' : 'NEEDS ATTENTION'}"

operations_categories = {
  "product_catalog_optimization" => "🛍️ Product Optimization",
  "inventory_optimization" => "📦 Inventory Management",
  "pricing_strategy_optimization" => "💰 Pricing Strategy",
  "customer_behavior_analysis" => "👥 Customer Analytics",
  "marketing_automation_optimization" => "📧 Marketing Automation",
  "ecommerce_operations_coordination" => "⚙️ Operations Coordination"
}

puts "\n📋 OPERATIONS BREAKDOWN:"
puts "-"*50

results[:results].each do |ops_result|
  task_name = ops_result[:task].name
  category_name = operations_categories[task_name] || task_name
  status_emoji = ops_result[:status] == :completed ? "✅" : "❌"
  
  puts "#{status_emoji} #{category_name}"
  puts "   Specialist: #{ops_result[:assigned_agent] || ops_result[:task].agent.name}"
  puts "   Status: #{ops_result[:status]}"
  
  if ops_result[:status] == :completed
    puts "   Optimization: Successfully completed"
  else
    puts "   Issue: #{ops_result[:error]&.message}"
  end
  puts
end

# ===== SAVE E-COMMERCE DELIVERABLES =====

puts "\n💾 GENERATING E-COMMERCE OPERATIONS REPORTS"
puts "-"*50

completed_operations = results[:results].select { |r| r[:status] == :completed }

# Create e-commerce operations directory
operations_dir = "ecommerce_operations_#{Date.today.strftime('%Y%m%d')}"
Dir.mkdir(operations_dir) unless Dir.exist?(operations_dir)

completed_operations.each do |ops_result|
  task_name = ops_result[:task].name
  operations_content = ops_result[:result]
  
  filename = "#{operations_dir}/#{task_name}_report.md"
  
  formatted_report = <<~REPORT
    # #{operations_categories[task_name] || task_name.split('_').map(&:capitalize).join(' ')} Report
    
    **Operations Specialist:** #{ops_result[:assigned_agent] || ops_result[:task].agent.name}  
    **Optimization Date:** #{Time.now.strftime('%B %d, %Y')}  
    **Store:** #{business_data['store_info']['name']}
    
    ---
    
    #{operations_content}
    
    ---
    
    **Business Context:**
    - Monthly Revenue: $#{business_data['store_info']['monthly_revenue'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}
    - Active Products: #{business_data['store_info']['active_products']}
    - Customer Base: #{business_data['store_info']['customer_base']}
    - Average Order Value: $#{business_data['store_info']['average_order_value']}
    
    *Generated by RCrewAI E-commerce Operations System*
  REPORT
  
  File.write(filename, formatted_report)
  puts "  ✅ #{File.basename(filename)}"
end

# ===== E-COMMERCE DASHBOARD =====

ecommerce_dashboard = <<~DASHBOARD
  # E-commerce Operations Dashboard
  
  **Last Updated:** #{Time.now.strftime('%Y-%m-%d %H:%M:%S')}  
  **Store:** #{business_data['store_info']['name']}  
  **Operations Success Rate:** #{results[:success_rate]}%
  
  ## Business Performance Overview
  
  ### Revenue Metrics
  - **Monthly Revenue:** $#{business_data['store_info']['monthly_revenue'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}
  - **Average Order Value:** $#{business_data['store_info']['average_order_value']}
  - **Monthly Orders:** #{business_data['store_info']['monthly_orders'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}
  - **Conversion Rate:** #{business_data['key_metrics']['conversion_rate']}%
  
  ### Product Portfolio
  - **Total Active Products:** #{business_data['store_info']['active_products']}
  - **Top Category:** Smartphones (#{business_data['product_categories']['smartphones']['revenue_share']}% revenue)
  - **Highest Margin:** Accessories (#{business_data['product_categories']['accessories']['margin']}% margin)
  - **Product Performance:** #{completed_operations.any? { |o| o[:task].name.include?('product') } ? 'Optimized' : 'Needs Optimization'}
  
  ## Inventory Status
  
  ### Stock Levels
  - **Total SKUs:** #{operational_data['inventory_status']['total_sku'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}
  - **Low Stock Items:** #{operational_data['inventory_status']['low_stock_items']} (#{(operational_data['inventory_status']['low_stock_items'].to_f / operational_data['inventory_status']['total_sku'] * 100).round(1)}%)
  - **Out of Stock:** #{operational_data['inventory_status']['out_of_stock']} items
  - **Inventory Value:** $#{operational_data['inventory_status']['inventory_value'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}
  - **Turnover Rate:** #{operational_data['inventory_status']['turnover_rate']}x annually
  
  ### Inventory Health
  - **🟢 Well Stocked:** #{operational_data['inventory_status']['total_sku'] - operational_data['inventory_status']['low_stock_items'] - operational_data['inventory_status']['overstock_items'] - operational_data['inventory_status']['out_of_stock']} items
  - **🟡 Low Stock:** #{operational_data['inventory_status']['low_stock_items']} items (reorder required)
  - **🟠 Overstock:** #{operational_data['inventory_status']['overstock_items']} items (promotion candidates)
  - **🔴 Out of Stock:** #{operational_data['inventory_status']['out_of_stock']} items (immediate action)
  
  ## Customer Analytics
  
  ### Customer Segmentation
  | Segment | Count | Avg Order Value | Purchase Frequency |
  |---------|-------|-----------------|-------------------|
  | VIP | #{operational_data['customer_segments']['vip_customers']['count']} | $#{operational_data['customer_segments']['vip_customers']['avg_order']} | #{operational_data['customer_segments']['vip_customers']['frequency']}x/year |
  | Regular | #{operational_data['customer_segments']['regular_customers']['count'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse} | $#{operational_data['customer_segments']['regular_customers']['avg_order']} | #{operational_data['customer_segments']['regular_customers']['frequency']}x/year |
  | New | #{operational_data['customer_segments']['new_customers']['count'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse} | $#{operational_data['customer_segments']['new_customers']['avg_order']} | #{operational_data['customer_segments']['new_customers']['frequency']}x/year |
  | At Risk | #{operational_data['customer_segments']['at_risk_customers']['count']} | $#{operational_data['customer_segments']['at_risk_customers']['avg_order']} | #{operational_data['customer_segments']['at_risk_customers']['frequency']}x/year |
  
  ### Customer Experience Metrics
  - **Customer Satisfaction:** #{business_data['key_metrics']['customer_satisfaction']}/5.0
  - **Return Rate:** #{business_data['key_metrics']['return_rate']}%
  - **Repeat Purchase Rate:** #{business_data['key_metrics']['repeat_purchase_rate']}%
  - **Cart Abandonment:** #{business_data['key_metrics']['cart_abandonment_rate']}%
  
  ## Pricing & Competition
  
  ### Pricing Optimization Status
  - **Products Analyzed:** #{operational_data['pricing_analysis']['competitive_products']}
  - **Optimization Opportunities:** #{operational_data['pricing_analysis']['price_optimizable']} products
  - **Underpriced Items:** #{operational_data['pricing_analysis']['underpriced_items']} (revenue opportunity)
  - **Overpriced Items:** #{operational_data['pricing_analysis']['overpriced_items']} (conversion risk)
  - **Dynamic Pricing Ready:** #{operational_data['pricing_analysis']['dynamic_pricing_candidates']} products
  
  ### Revenue Impact Projections
  - **Price Optimization:** +$#{(business_data['store_info']['monthly_revenue'] * 0.08).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month potential
  - **Dynamic Pricing:** +$#{(business_data['store_info']['monthly_revenue'] * 0.05).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month estimated
  - **Bundle Strategy:** +$#{(business_data['store_info']['monthly_revenue'] * 0.12).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month projected
  
  ## Marketing Performance
  
  ### Email Marketing
  - **Open Rate:** #{operational_data['marketing_performance']['email_campaigns']['open_rate']}% (Industry avg: 21%)
  - **Click Rate:** #{operational_data['marketing_performance']['email_campaigns']['click_rate']}% (Industry avg: 2.6%)
  - **Conversion Rate:** #{operational_data['marketing_performance']['email_campaigns']['conversion_rate']}%
  - **Revenue per Email:** $#{operational_data['marketing_performance']['email_campaigns']['revenue_per_email']}
  
  ### Cart Recovery
  - **Abandonment Rate:** #{business_data['key_metrics']['cart_abandonment_rate']}%
  - **Recovery Rate:** #{operational_data['marketing_performance']['abandoned_cart_recovery']['recovery_rate']}%
  - **Avg Recovery Value:** $#{operational_data['marketing_performance']['abandoned_cart_recovery']['average_recovered_value']}
  - **Monthly Recovered Revenue:** $#{(operational_data['marketing_performance']['abandoned_cart_recovery']['recovery_rate'] / 100.0 * business_data['key_metrics']['cart_abandonment_rate'] / 100.0 * business_data['store_info']['monthly_revenue'] * 0.3).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}
  
  ## Operational Priorities
  
  ### Immediate Actions (Next 7 Days)
  - [ ] Restock #{operational_data['inventory_status']['low_stock_items']} low inventory items
  - [ ] Launch promotions for #{operational_data['inventory_status']['overstock_items']} overstock products
  - [ ] Implement pricing changes on #{operational_data['pricing_analysis']['underpriced_items']} underpriced items
  - [ ] Send re-engagement campaigns to #{operational_data['customer_segments']['at_risk_customers']['count']} at-risk customers
  
  ### Strategic Initiatives (Next 30 Days)
  - [ ] Deploy dynamic pricing for #{operational_data['pricing_analysis']['dynamic_pricing_candidates']} products
  - [ ] Launch VIP customer program enhancements
  - [ ] Optimize product listings for #{operational_data['pricing_analysis']['price_optimizable']} products
  - [ ] Implement advanced cart recovery automation
  
  ### Growth Opportunities (Next 90 Days)
  - [ ] Expand into complementary product categories
  - [ ] Implement AI-powered personalization
  - [ ] Launch affiliate and influencer programs
  - [ ] Develop mobile app for enhanced customer experience
DASHBOARD

File.write("#{operations_dir}/ecommerce_dashboard.md", ecommerce_dashboard)
puts "  ✅ ecommerce_dashboard.md"

# ===== E-COMMERCE OPERATIONS SUMMARY =====

ecommerce_summary = <<~SUMMARY
  # E-commerce Operations Executive Summary
  
  **Store:** #{business_data['store_info']['name']}  
  **Optimization Date:** #{Time.now.strftime('%B %d, %Y')}  
  **Operations Success Rate:** #{results[:success_rate]}%
  
  ## Executive Overview
  
  The comprehensive e-commerce operations optimization has been completed successfully for #{business_data['store_info']['name']}, a leading electronics and accessories retailer. Our specialized team of operations experts has delivered integrated optimization across product management, inventory control, pricing strategy, customer analytics, and marketing automation.
  
  ## Current Business Performance
  
  ### Financial Metrics
  - **Monthly Revenue:** $#{business_data['store_info']['monthly_revenue'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse} with strong growth trajectory
  - **Average Order Value:** $#{business_data['store_info']['average_order_value']} (above industry average)
  - **Customer Base:** #{business_data['store_info']['customer_base'].to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse} active customers with #{business_data['key_metrics']['repeat_purchase_rate']}% repeat rate
  - **Product Portfolio:** #{business_data['store_info']['active_products']} SKUs across 4 primary categories
  
  ### Operational Health
  - **Inventory Turnover:** #{operational_data['inventory_status']['turnover_rate']}x annually (healthy velocity)
  - **Conversion Rate:** #{business_data['key_metrics']['conversion_rate']}% (industry competitive)
  - **Customer Satisfaction:** #{business_data['key_metrics']['customer_satisfaction']}/5.0 (excellent rating)
  - **Return Rate:** #{business_data['key_metrics']['return_rate']}% (well-controlled)
  
  ## Optimization Results by Area
  
  ### ✅ Product Catalog Optimization
  - **Enhanced Listings:** Optimized product titles, descriptions, and SEO
  - **Performance Analysis:** Identified top performers and improvement opportunities
  - **Conversion Impact:** Projected 12-18% improvement in product page conversion
  - **SEO Optimization:** Improved search visibility and organic traffic potential
  
  ### ✅ Inventory Management Optimization
  - **Demand Forecasting:** Advanced predictive models for stock planning
  - **Reorder Optimization:** Streamlined reorder points and quantities
  - **Supplier Relations:** Identified cost savings and lead time improvements
  - **Stock Health:** Reduced overstock by 15% and prevented stockouts
  
  ### ✅ Pricing Strategy Enhancement
  - **Competitive Analysis:** Comprehensive market positioning assessment
  - **Dynamic Pricing:** Implemented intelligent pricing algorithms
  - **Revenue Optimization:** Projected 8% monthly revenue increase
  - **Margin Improvement:** Optimized pricing for profitability balance
  
  ### ✅ Customer Analytics & Segmentation
  - **Behavioral Analysis:** Deep insights into customer purchase patterns
  - **Segmentation Strategy:** Refined customer segments for targeted marketing
  - **Lifetime Value Optimization:** Strategies to increase customer retention
  - **Personalization Framework:** Data-driven personalization opportunities
  
  ### ✅ Marketing Automation Enhancement
  - **Campaign Optimization:** Improved email marketing performance
  - **Cart Recovery:** Enhanced abandoned cart recovery systems
  - **Customer Journey:** Optimized automation workflows
  - **Personalization:** Advanced targeting and content customization
  
  ### ✅ Operations Coordination
  - **Workflow Integration:** Streamlined cross-functional processes
  - **Performance Monitoring:** Real-time operational dashboards
  - **Strategic Alignment:** Coordinated efforts across all departments
  - **Efficiency Gains:** Optimized resource allocation and productivity
  
  ## Revenue Impact Projections
  
  ### Immediate Impact (Next 30 Days)
  - **Pricing Optimization:** +$#{(business_data['store_info']['monthly_revenue'] * 0.08).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month from pricing improvements
  - **Inventory Optimization:** +$#{(business_data['store_info']['monthly_revenue'] * 0.05).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month from reduced stockouts
  - **Cart Recovery:** +$#{(business_data['store_info']['monthly_revenue'] * 0.03).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month from improved recovery rates
  - **Total Near-term Impact:** +$#{(business_data['store_info']['monthly_revenue'] * 0.16).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month
  
  ### Medium-term Impact (Next 90 Days)
  - **Product Optimization:** +$#{(business_data['store_info']['monthly_revenue'] * 0.12).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month from conversion improvements
  - **Customer Segmentation:** +$#{(business_data['store_info']['monthly_revenue'] * 0.10).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month from targeted marketing
  - **Marketing Automation:** +$#{(business_data['store_info']['monthly_revenue'] * 0.08).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month from campaign optimization
  - **Total Medium-term Impact:** +$#{(business_data['store_info']['monthly_revenue'] * 0.30).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month additional
  
  ### Annual Revenue Projection
  - **Current Annual Revenue:** $#{(business_data['store_info']['monthly_revenue'] * 12).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}
  - **Optimized Annual Revenue:** $#{((business_data['store_info']['monthly_revenue'] * 1.46) * 12).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}
  - **Total Annual Increase:** $#{((business_data['store_info']['monthly_revenue'] * 0.46) * 12).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse} (+46% growth)
  
  ## Operational Efficiency Gains
  
  ### Process Improvements
  - **Inventory Management:** 40% reduction in manual inventory tasks
  - **Pricing Updates:** Automated pricing changes save 20 hours/week
  - **Customer Segmentation:** Real-time segmentation reduces marketing prep by 60%
  - **Reporting Automation:** Daily operational reports generated automatically
  
  ### Resource Optimization
  - **Staff Productivity:** 30% improvement in operational efficiency
  - **Inventory Costs:** 15% reduction in carrying costs through optimization
  - **Marketing ROI:** 25% improvement in marketing spend efficiency
  - **Customer Service:** 20% reduction in inventory-related inquiries
  
  ## Competitive Advantages Achieved
  
  ### Market Positioning
  - **Price Competitiveness:** Optimized pricing maintains margin while staying competitive
  - **Product Availability:** Improved inventory management reduces stockouts vs. competitors
  - **Customer Experience:** Enhanced personalization improves customer satisfaction
  - **Operational Excellence:** Streamlined operations support faster growth
  
  ### Technology Leadership
  - **Advanced Analytics:** Data-driven decision making across all operations
  - **Automation Integration:** Reduced manual processes and human error
  - **Personalization Capability:** AI-driven customer experience optimization
  - **Real-time Optimization:** Dynamic adjustments based on market conditions
  
  ## Implementation Roadmap
  
  ### Phase 1: Immediate Implementation (Weeks 1-2)
  1. **Deploy Pricing Changes:** Implement optimized pricing for identified products
  2. **Inventory Actions:** Execute reorder recommendations and promotions
  3. **Marketing Campaigns:** Launch enhanced cart recovery and segmentation
  4. **Monitoring Setup:** Activate performance tracking dashboards
  
  ### Phase 2: System Enhancement (Weeks 3-8)
  1. **Dynamic Pricing:** Roll out automated pricing algorithms
  2. **Advanced Segmentation:** Implement AI-driven customer segmentation
  3. **Product Optimization:** Deploy enhanced product listings
  4. **Automation Expansion:** Extend marketing automation capabilities
  
  ### Phase 3: Strategic Growth (Months 3-6)
  1. **Category Expansion:** Add complementary product categories
  2. **Personalization Advanced:** Implement 1:1 personalization
  3. **Mobile Optimization:** Launch mobile app and optimization
  4. **Partnership Development:** Build affiliate and influencer programs
  
  ## Risk Mitigation
  
  ### Operational Risks
  - **Supply Chain:** Diversified supplier base and buffer stock strategies
  - **Price Wars:** Intelligent pricing prevents race-to-bottom scenarios
  - **Technology Dependence:** Backup systems and manual override capabilities
  - **Customer Experience:** Quality monitoring prevents automation issues
  
  ### Market Risks
  - **Economic Downturn:** Flexible pricing and inventory strategies
  - **Competition:** Continuous monitoring and rapid response capabilities
  - **Technology Changes:** Agile architecture supports quick adaptations
  - **Regulatory Changes:** Compliance monitoring and adaptation procedures
  
  ## Success Metrics and Monitoring
  
  ### Key Performance Indicators
  - **Revenue Growth:** Target 46% annual increase
  - **Profit Margin:** Maintain 25%+ gross margin
  - **Customer Satisfaction:** Maintain 4.5+ rating
  - **Operational Efficiency:** 30%+ productivity improvement
  
  ### Monitoring Framework
  - **Daily:** Revenue, orders, inventory levels
  - **Weekly:** Pricing performance, customer metrics
  - **Monthly:** Full operational review and optimization
  - **Quarterly:** Strategic review and roadmap updates
  
  ## Conclusion
  
  The e-commerce operations optimization has positioned #{business_data['store_info']['name']} for significant growth and competitive advantage. With integrated optimization across all operational areas, the business is projected to achieve 46% revenue growth while improving operational efficiency and customer satisfaction.
  
  ### Optimization Status: COMPLETE AND EFFECTIVE
  - **All optimization areas successfully implemented**
  - **Projected ROI exceeds 300% in first year**
  - **Competitive positioning significantly strengthened**
  - **Scalable foundation established for future growth**
  
  ---
  
  **E-commerce Operations Team Performance:**
  - Product management delivered comprehensive catalog optimization
  - Inventory specialists provided advanced demand forecasting and optimization
  - Pricing strategists created intelligent pricing and competitive positioning
  - Customer analytics delivered actionable segmentation and insights
  - Marketing automation specialists optimized customer journey and campaigns
  - Operations coordination ensured integrated execution across all areas
  
  *This comprehensive e-commerce operations optimization demonstrates the power of specialized expertise working in coordination to deliver exceptional business results across all operational dimensions.*
SUMMARY

File.write("#{operations_dir}/ECOMMERCE_OPERATIONS_SUMMARY.md", ecommerce_summary)
puts "  ✅ ECOMMERCE_OPERATIONS_SUMMARY.md"

puts "\n🎉 E-COMMERCE OPERATIONS OPTIMIZATION COMPLETED!"
puts "="*70
puts "📁 Complete operations package saved to: #{operations_dir}/"
puts ""
puts "🛒 **Business Impact:**"
puts "   • #{completed_operations.length} operational areas optimized"
puts "   • $#{(business_data['store_info']['monthly_revenue'] * 0.46).round(0).to_s.reverse.gsub(/(\d{3})(?=\d)/, '\\1,').reverse}/month additional revenue projected"
puts "   • 46% annual growth potential identified"
puts "   • #{operational_data['inventory_status']['low_stock_items']} inventory issues addressed"
puts ""
puts "⚡ **Efficiency Gains:**"
puts "   • 30% improvement in operational productivity"
puts "   • 40% reduction in manual inventory management"
puts "   • 25% improvement in marketing ROI"
puts "   • 20 hours/week saved through pricing automation"
puts ""
puts "🎯 **Competitive Advantages:**"
puts "   • Dynamic pricing system deployed"
puts "   • Advanced customer segmentation implemented"
puts "   • Real-time inventory optimization active"
puts "   • Integrated marketing automation enhanced"

Key E-commerce Operations Features

1. Comprehensive Operations Management

Full spectrum e-commerce optimization across all functions:

product_manager           # Catalog and listing optimization
inventory_specialist      # Stock management and forecasting
pricing_strategist       # Competitive pricing and revenue optimization
customer_analytics       # Behavior analysis and segmentation
marketing_automation     # Campaign optimization and personalization
operations_coordinator   # Cross-functional coordination (Manager)

2. Advanced E-commerce Tools

Specialized tools for e-commerce operations:

ProductCatalogTool       # Product listing and SEO optimization
InventoryManagementTool  # Demand forecasting and stock management
PricingStrategyTool      # Dynamic pricing and competitive analysis

3. Data-Driven Decision Making

Comprehensive analytics and insights:

• Customer segmentation and behavior analysis
• Inventory forecasting and optimization
• Competitive pricing analysis
• Marketing performance tracking

4. Revenue Optimization

Multiple revenue enhancement strategies:

• Dynamic pricing optimization
• Product listing enhancement
• Customer lifetime value improvement
• Marketing automation efficiency

5. Operational Integration

Seamless coordination across all e-commerce functions:

# Integrated workflow
Product Optimization → Inventory Management → Pricing Strategy →
Customer Analytics → Marketing Automation → Operations Coordination

This e-commerce operations system provides a complete framework for optimizing online retail performance, delivering significant revenue growth while improving operational efficiency and customer satisfaction.