@spaik/mcp-server-roi/dist/core/benchmarks/industry-benchmarks.js

MCP server for AI ROI prediction and tracking with Monte Carlo simulations

// Industry benchmark data
// In a production system, this would come from a database or external API
const industryBenchmarks = {
    financial_services: {
        customer_service_automation: {
            average_roi: 250,
            average_payback_months: 14,
            adoption_rate: 0.75,
            success_rate: 0.85,
            confidence_factor: 1.1,
            typical_use_cases: ['Account inquiries', 'Transaction disputes', 'Loan applications']
        },
        fraud_detection: {
            average_roi: 400,
            average_payback_months: 10,
            adoption_rate: 0.90,
            success_rate: 0.92,
            confidence_factor: 1.2,
            typical_use_cases: ['Transaction monitoring', 'Account takeover', 'Application fraud']
        },
        document_processing: {
            average_roi: 180,
            average_payback_months: 16,
            adoption_rate: 0.65,
            success_rate: 0.78,
            confidence_factor: 0.95,
            typical_use_cases: ['KYC documents', 'Loan documents', 'Compliance reports']
        }
    },
    healthcare: {
        document_processing: {
            average_roi: 220,
            average_payback_months: 18,
            adoption_rate: 0.60,
            success_rate: 0.75,
            confidence_factor: 0.9,
            typical_use_cases: ['Medical records', 'Insurance claims', 'Prior authorizations']
        },
        predictive_maintenance: {
            average_roi: 180,
            average_payback_months: 20,
            adoption_rate: 0.45,
            success_rate: 0.70,
            confidence_factor: 0.85,
            typical_use_cases: ['Medical equipment', 'HVAC systems', 'Power systems']
        },
        data_analytics: {
            average_roi: 200,
            average_payback_months: 15,
            adoption_rate: 0.70,
            success_rate: 0.80,
            confidence_factor: 1.0,
            typical_use_cases: ['Patient outcomes', 'Resource utilization', 'Cost analysis']
        }
    },
    retail: {
        customer_service_automation: {
            average_roi: 200,
            average_payback_months: 12,
            adoption_rate: 0.80,
            success_rate: 0.88,
            confidence_factor: 1.05,
            typical_use_cases: ['Order status', 'Returns processing', 'Product inquiries']
        },
        inventory_optimization: {
            average_roi: 150,
            average_payback_months: 16,
            adoption_rate: 0.70,
            success_rate: 0.82,
            confidence_factor: 1.0,
            typical_use_cases: ['Demand forecasting', 'Stock replenishment', 'Warehouse optimization']
        },
        process_automation: {
            average_roi: 175,
            average_payback_months: 14,
            adoption_rate: 0.65,
            success_rate: 0.80,
            confidence_factor: 0.95,
            typical_use_cases: ['Order processing', 'Pricing updates', 'Vendor management']
        }
    },
    manufacturing: {
        predictive_maintenance: {
            average_roi: 300,
            average_payback_months: 12,
            adoption_rate: 0.75,
            success_rate: 0.85,
            confidence_factor: 1.15,
            typical_use_cases: ['Equipment monitoring', 'Quality control', 'Downtime prevention']
        },
        inventory_optimization: {
            average_roi: 200,
            average_payback_months: 14,
            adoption_rate: 0.70,
            success_rate: 0.83,
            confidence_factor: 1.05,
            typical_use_cases: ['Supply chain', 'Raw materials', 'Finished goods']
        },
        process_automation: {
            average_roi: 250,
            average_payback_months: 15,
            adoption_rate: 0.80,
            success_rate: 0.87,
            confidence_factor: 1.1,
            typical_use_cases: ['Production planning', 'Quality inspection', 'Logistics']
        }
    }
};
// Default benchmarks for unknown combinations
const defaultBenchmark = {
    average_roi: 150,
    average_payback_months: 18,
    adoption_rate: 0.60,
    success_rate: 0.75,
    confidence_factor: 0.9,
    typical_use_cases: ['General process improvement']
};
export async function getBenchmarkData(industry, projectType) {
    // In a real implementation, this might fetch from a database or API
    // For now, return from static data
    const industryData = industryBenchmarks[industry];
    if (!industryData) {
        return defaultBenchmark;
    }
    const benchmark = industryData[projectType];
    return benchmark || defaultBenchmark;
}
export function getIndustryComparison(actualMetrics, industry, projectType) {
    const benchmark = industryBenchmarks[industry]?.[projectType] || defaultBenchmark;
    // Calculate percentiles (simplified - in reality would use distribution data)
    const roiRatio = actualMetrics.roi / benchmark.average_roi;
    const paybackRatio = benchmark.average_payback_months / actualMetrics.payback_months;
    const roiPercentile = Math.min(99, Math.max(1, 50 + (roiRatio - 1) * 50));
    const paybackPercentile = Math.min(99, Math.max(1, 50 + (paybackRatio - 1) * 50));
    let performance = 'average';
    if (roiPercentile > 75 && paybackPercentile > 75) {
        performance = 'excellent';
    }
    else if (roiPercentile > 60 || paybackPercentile > 60) {
        performance = 'above average';
    }
    else if (roiPercentile < 40 && paybackPercentile < 40) {
        performance = 'below average';
    }
    return {
        performance_vs_benchmark: performance,
        roi_percentile: Math.round(roiPercentile),
        payback_percentile: Math.round(paybackPercentile)
    };
}
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