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monte-carlo-simulator

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Business decision framework with Monte Carlo risk analysis - instant via npx

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name: Software Investment ROI Analysis category: Technology Investment description: Comprehensive ROI analysis for software investments including productivity gains, cost savings, automation benefits, and risk-adjusted returns version: 1.0.0 tags: [software-investment, roi, productivity, automation, cost-savings, technology] parameters: - key: initialInvestment label: Software Investment ($) type: number default: 250000 min: 10000 max: 10000000 step: 10000 description: Total software purchase, implementation, and training costs - key: affectedEmployees label: Employees Affected type: number default: 50 min: 5 max: 10000 step: 5 description: Number of employees who will use or benefit from the software - key: averageEmployeeCost label: Average Employee Cost ($/year) type: number default: 120000 min: 50000 max: 300000 step: 5000 description: Fully-loaded annual cost per affected employee - key: productivityGain label: Productivity Gain (%) type: number default: 15.0 min: 2.0 max: 50.0 step: 1.0 description: Expected productivity improvement per affected employee - key: implementationMonths label: Implementation Timeline (months) type: number default: 6 min: 1 max: 24 step: 1 description: Time required for full deployment and adoption - key: adoptionRate label: User Adoption Rate (%) type: number default: 85.0 min: 50.0 max: 100.0 step: 5.0 description: Expected percentage of users who will fully adopt the software outputs: - key: annualProductivitySavings label: Annual Productivity Savings ($) description: Annual savings from improved employee productivity - key: annualMaintenanceCost label: Annual Maintenance Cost ($) description: Ongoing annual costs for software maintenance and support - key: netAnnualBenefit label: Net Annual Benefit ($) description: Annual benefits minus maintenance costs - key: simpleROI label: Simple ROI (%) description: First-year return on investment percentage - key: paybackMonths label: Payback Period (months) description: Time to recover initial investment - key: threeYearNPV label: 3-Year NPV ($) description: Net present value over three years (10% discount rate) - key: implementationRisk label: Implementation Risk Score (%) description: Risk adjustment factor based on timeline and complexity simulation: logic: | // Calculate annual productivity savings const annualEmployeeCost = averageEmployeeCost const productivitySavingPerEmployee = annualEmployeeCost * (productivityGain / 100) const totalProductivitySavings = affectedEmployees * productivitySavingPerEmployee * (adoptionRate / 100) // Add implementation risk and execution variance const implementationRisk = Math.max(0.5, 1.0 - (implementationMonths - 6) * 0.05) // Longer implementations have more risk const executionVariance = 0.7 + random() * 0.6 // ±30% execution variance const realizedSavings = totalProductivitySavings * implementationRisk * executionVariance // Calculate maintenance costs (20% of initial investment annually) const annualMaintenanceCost = initialInvestment * 0.20 const netAnnualBenefit = realizedSavings - annualMaintenanceCost // ROI calculations const simpleROI = initialInvestment > 0 ? (netAnnualBenefit / initialInvestment) * 100 : 0 const paybackMonths = netAnnualBenefit > 0 ? (initialInvestment / (netAnnualBenefit / 12)) : 999 // 3-year NPV (10% discount rate) const discountRate = 0.10 const year1NPV = netAnnualBenefit / (1 + discountRate) const year2NPV = netAnnualBenefit / Math.pow(1 + discountRate, 2) const year3NPV = netAnnualBenefit / Math.pow(1 + discountRate, 3) const threeYearNPV = year1NPV + year2NPV + year3NPV - initialInvestment return { annualProductivitySavings: Math.round(realizedSavings), annualMaintenanceCost: Math.round(annualMaintenanceCost), netAnnualBenefit: Math.round(netAnnualBenefit), simpleROI: Math.round(simpleROI * 10) / 10, paybackMonths: Math.min(Math.round(paybackMonths * 10) / 10, 99.9), threeYearNPV: Math.round(threeYearNPV), implementationRisk: Math.round(implementationRisk * 100) }